{"json":[{"id":"minimax/minimax-m2:free","name":"minimax/minimax-m2:free","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"minimax/minimax-m2:free","description":"MiniMax-M2 is a compact, high-efficiency large language model optimized for end-to-end coding and agentic workflows. With 10 billion activated parameters (230 billion total), it delivers near-frontier intelligence across general reasoning, tool use, and multi-step task execution while maintaining low latency and deployment efficiency.\n\nThe model excels in code generation, multi-file editing, compile-run-fix loops, and test-validated repair, showing strong results on SWE-Bench Verified, Multi-SWE-Bench, and Terminal-Bench. It also performs competitively in agentic evaluations such as BrowseComp and GAIA, effectively handling long-horizon planning, retrieval, and recovery from execution errors.\n\nBenchmarked by [Artificial Analysis](https://artificialanalysis.ai/models/minimax-m2), MiniMax-M2 ranks among the top open-source models for composite intelligence, spanning mathematics, science, and instruction-following. Its small activation footprint enables fast inference, high concurrency, and improved unit economics, making it well-suited for large-scale agents, developer assistants, and reasoning-driven applications that require responsiveness and cost efficiency.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"openrouter/andromeda-alpha","name":"openrouter/andromeda-alpha","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"openrouter/andromeda-alpha","description":"This is a small reasoning VLM trained for image understanding. It's strengths include multi-image comprehension (6+ images), especially those containing charts and text. This is a cloaked model provided to the community to gather feedback.\n\nNote: All prompts and output are logged to improve the provider’s model and its product and services. Please do not upload any personal, confidential, or otherwise sensitive information. This is a trial use only. Do not use for production or business-critical systems.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":true,"multimodalAcceptedMimetypes":["image/*"],"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"liquid/lfm2-8b-a1b","name":"liquid/lfm2-8b-a1b","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"liquid/lfm2-8b-a1b","description":"Model created via inbox interface","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"liquid/lfm-2.2-6b","name":"liquid/lfm-2.2-6b","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"liquid/lfm-2.2-6b","description":"LFM2 is a new generation of hybrid models developed by Liquid AI, specifically designed for edge AI and on-device deployment. 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Scoring >73% on SWE-bench Verified, Haiku 4.5 ranks among the world’s best coding models while maintaining exceptional responsiveness for sub-agents, parallelized execution, and scaled deployment.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":true,"multimodalAcceptedMimetypes":["image/*"],"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"qwen/qwen3-vl-8b-thinking","name":"qwen/qwen3-vl-8b-thinking","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"qwen/qwen3-vl-8b-thinking","description":"Qwen3-VL-8B-Thinking is the reasoning-optimized variant of the Qwen3-VL-8B multimodal model, designed for advanced visual and textual reasoning across complex scenes, documents, and temporal sequences. It integrates enhanced multimodal alignment and long-context processing (native 256K, expandable to 1M tokens) for tasks such as scientific visual analysis, causal inference, and mathematical reasoning over image or video inputs.\n\nCompared to the Instruct edition, the Thinking version introduces deeper visual-language fusion and deliberate reasoning pathways that improve performance on long-chain logic tasks, STEM problem-solving, and multi-step video understanding. It achieves stronger temporal grounding via Interleaved-MRoPE and timestamp-aware embeddings, while maintaining robust OCR, multilingual comprehension, and text generation on par with large text-only LLMs.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":true,"multimodalAcceptedMimetypes":["image/*"],"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"qwen/qwen3-vl-8b-instruct","name":"qwen/qwen3-vl-8b-instruct","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"qwen/qwen3-vl-8b-instruct","description":"Qwen3-VL-8B-Instruct is a multimodal vision-language model from the Qwen3-VL series, built for high-fidelity understanding and reasoning across text, images, and video. It features improved multimodal fusion with Interleaved-MRoPE for long-horizon temporal reasoning, DeepStack for fine-grained visual-text alignment, and text-timestamp alignment for precise event localization.\n\nThe model supports a native 256K-token context window, extensible to 1M tokens, and handles both static and dynamic media inputs for tasks like document parsing, visual question answering, spatial reasoning, and GUI control. It achieves text understanding comparable to leading LLMs while expanding OCR coverage to 32 languages and enhancing robustness under varied visual conditions.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":true,"multimodalAcceptedMimetypes":["image/*"],"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"openai/gpt-5-image","name":"openai/gpt-5-image","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"openai/gpt-5-image","description":"[GPT-5](https://openrouter.ai/openai/gpt-5) Image combines OpenAI's most advanced language model with state-of-the-art image generation capabilities. 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Through RLHF training, the model's general abilities have also been refined, making this release of Ring-1T more balanced in performance across various tasks.\n\nRing-1T adopts the Ling 2.0 architecture and is trained on the Ling-1T-base foundation model, which contains 1 trillion total parameters with 50 billion activated parameters, supporting a context window of up to 128K tokens.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"inclusionai/ling-1t","name":"inclusionai/ling-1t","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"inclusionai/ling-1t","description":"Ling-1T is a trillion-parameter open-weight large language model developed by inclusionAI and released under the MIT license. It represents the first flagship non-thinking model in the Ling 2.0 series, built around a sparse-activation architecture with roughly 50 billion active parameters per token. The model supports up to 128 K tokens of context and emphasizes efficient reasoning through an “Evolutionary Chain-of-Thought (Evo-CoT)” training strategy.\n\nPre-trained on more than 20 trillion reasoning-dense tokens, Ling-1T achieves strong results across code generation, mathematics, and logical reasoning benchmarks while maintaining high inference efficiency. It employs FP8 mixed-precision training, MoE routing with QK normalization, and MTP layers for compositional reasoning stability. The model also introduces LPO (Linguistics-unit Policy Optimization) for post-training alignment, enhancing sentence-level semantic control.\n\nLing-1T can perform complex text generation, multilingual reasoning, and front-end code synthesis with a focus on both functionality and aesthetics.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"openai/o3-deep-research","name":"openai/o3-deep-research","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"openai/o3-deep-research","description":"o3-deep-research is OpenAI's advanced model for deep research, designed to tackle complex, multi-step research tasks.\n\nNote: This model always uses the 'web_search' tool which adds additional cost.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":true,"multimodalAcceptedMimetypes":["image/*"],"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"openai/o4-mini-deep-research","name":"openai/o4-mini-deep-research","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"openai/o4-mini-deep-research","description":"o4-mini-deep-research is OpenAI's faster, more affordable deep research model—ideal for tackling complex, multi-step research tasks.\n\nNote: This model always uses the 'web_search' tool which adds additional cost.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":true,"multimodalAcceptedMimetypes":["image/*"],"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"nvidia/llama-3.3-nemotron-super-49b-v1.5","name":"nvidia/llama-3.3-nemotron-super-49b-v1.5","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"nvidia/llama-3.3-nemotron-super-49b-v1.5","description":"Llama-3.3-Nemotron-Super-49B-v1.5 is a 49B-parameter, English-centric reasoning/chat model derived from Meta’s Llama-3.3-70B-Instruct with a 128K context. It’s post-trained for agentic workflows (RAG, tool calling) via SFT across math, code, science, and multi-turn chat, followed by multiple RL stages; Reward-aware Preference Optimization (RPO) for alignment, RL with Verifiable Rewards (RLVR) for step-wise reasoning, and iterative DPO to refine tool-use behavior. A distillation-driven Neural Architecture Search (“Puzzle”) replaces some attention blocks and varies FFN widths to shrink memory footprint and improve throughput, enabling single-GPU (H100/H200) deployment while preserving instruction following and CoT quality.\n\nIn internal evaluations (NeMo-Skills, up to 16 runs, temp = 0.6, top_p = 0.95), the model reports strong reasoning/coding results, e.g., MATH500 pass@1 = 97.4, AIME-2024 = 87.5, AIME-2025 = 82.71, GPQA = 71.97, LiveCodeBench (24.10–25.02) = 73.58, and MMLU-Pro (CoT) = 79.53. The model targets practical inference efficiency (high tokens/s, reduced VRAM) with Transformers/vLLM support and explicit “reasoning on/off” modes (chat-first defaults, greedy recommended when disabled). Suitable for building agents, assistants, and long-context retrieval systems where balanced accuracy-to-cost and reliable tool use matter.\n","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"baidu/ernie-4.5-21b-a3b-thinking","name":"baidu/ernie-4.5-21b-a3b-thinking","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"baidu/ernie-4.5-21b-a3b-thinking","description":"ERNIE-4.5-21B-A3B-Thinking is Baidu's upgraded lightweight MoE model, refined to boost reasoning depth and quality for top-tier performance in logical puzzles, math, science, coding, text generation, and expert-level academic benchmarks.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"google/gemini-2.5-flash-image","name":"google/gemini-2.5-flash-image","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"google/gemini-2.5-flash-image","description":"Gemini 2.5 Flash Image, a.k.a. \"Nano Banana,\" is now generally available. It is a state of the art image generation model with contextual understanding. It is capable of image generation, edits, and multi-turn conversations. Aspect ratios can be controlled with the [image_config API Parameter](https://openrouter.ai/docs/features/multimodal/image-generation#image-aspect-ratio-configuration)","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":true,"multimodalAcceptedMimetypes":["image/*"],"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"qwen/qwen3-vl-30b-a3b-thinking","name":"qwen/qwen3-vl-30b-a3b-thinking","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"qwen/qwen3-vl-30b-a3b-thinking","description":"Qwen3-VL-30B-A3B-Thinking is a multimodal model that unifies strong text generation with visual understanding for images and videos. Its Thinking variant enhances reasoning in STEM, math, and complex tasks. It excels in perception of real-world/synthetic categories, 2D/3D spatial grounding, and long-form visual comprehension, achieving competitive multimodal benchmark results. For agentic use, it handles multi-image multi-turn instructions, video timeline alignments, GUI automation, and visual coding from sketches to debugged UI. Text performance matches flagship Qwen3 models, suiting document AI, OCR, UI assistance, spatial tasks, and agent research.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":true,"multimodalAcceptedMimetypes":["image/*"],"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"qwen/qwen3-vl-30b-a3b-instruct","name":"qwen/qwen3-vl-30b-a3b-instruct","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"qwen/qwen3-vl-30b-a3b-instruct","description":"Qwen3-VL-30B-A3B-Instruct is a multimodal model that unifies strong text generation with visual understanding for images and videos. Its Instruct variant optimizes instruction-following for general multimodal tasks. It excels in perception of real-world/synthetic categories, 2D/3D spatial grounding, and long-form visual comprehension, achieving competitive multimodal benchmark results. For agentic use, it handles multi-image multi-turn instructions, video timeline alignments, GUI automation, and visual coding from sketches to debugged UI. Text performance matches flagship Qwen3 models, suiting document AI, OCR, UI assistance, spatial tasks, and agent research.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":true,"multimodalAcceptedMimetypes":["image/*"],"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"openai/gpt-5-pro","name":"openai/gpt-5-pro","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"openai/gpt-5-pro","description":"GPT-5 Pro is OpenAI’s most advanced model, offering major improvements in reasoning, code quality, and user experience. It is optimized for complex tasks that require step-by-step reasoning, instruction following, and accuracy in high-stakes use cases. It supports test-time routing features and advanced prompt understanding, including user-specified intent like \"think hard about this.\" Improvements include reductions in hallucination, sycophancy, and better performance in coding, writing, and health-related tasks.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":true,"multimodalAcceptedMimetypes":["image/*"],"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"z-ai/glm-4.6","name":"z-ai/glm-4.6","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"z-ai/glm-4.6","description":"Compared with GLM-4.5, this generation brings several key improvements:\n\nLonger context window: The context window has been expanded from 128K to 200K tokens, enabling the model to handle more complex agentic tasks.\nSuperior coding performance: The model achieves higher scores on code benchmarks and demonstrates better real-world performance in applications such as Claude Code、Cline、Roo Code and Kilo Code, including improvements in generating visually polished front-end pages.\nAdvanced reasoning: GLM-4.6 shows a clear improvement in reasoning performance and supports tool use during inference, leading to stronger overall capability.\nMore capable agents: GLM-4.6 exhibits stronger performance in tool using and search-based agents, and integrates more effectively within agent frameworks.\nRefined writing: Better aligns with human preferences in style and readability, and performs more naturally in role-playing scenarios.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"z-ai/glm-4.6:exacto","name":"z-ai/glm-4.6:exacto","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"z-ai/glm-4.6:exacto","description":"Compared with GLM-4.5, this generation brings several key improvements:\n\nLonger context window: The context window has been expanded from 128K to 200K tokens, enabling the model to handle more complex agentic tasks.\nSuperior coding performance: The model achieves higher scores on code benchmarks and demonstrates better real-world performance in applications such as Claude Code、Cline、Roo Code and Kilo Code, including improvements in generating visually polished front-end pages.\nAdvanced reasoning: GLM-4.6 shows a clear improvement in reasoning performance and supports tool use during inference, leading to stronger overall capability.\nMore capable agents: GLM-4.6 exhibits stronger performance in tool using and search-based agents, and integrates more effectively within agent frameworks.\nRefined writing: Better aligns with human preferences in style and readability, and performs more naturally in role-playing scenarios.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"anthropic/claude-sonnet-4.5","name":"anthropic/claude-sonnet-4.5","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"anthropic/claude-sonnet-4.5","description":"Claude Sonnet 4.5 is Anthropic’s most advanced Sonnet model to date, optimized for real-world agents and coding workflows. It delivers state-of-the-art performance on coding benchmarks such as SWE-bench Verified, with improvements across system design, code security, and specification adherence. The model is designed for extended autonomous operation, maintaining task continuity across sessions and providing fact-based progress tracking.\n\nSonnet 4.5 also introduces stronger agentic capabilities, including improved tool orchestration, speculative parallel execution, and more efficient context and memory management. With enhanced context tracking and awareness of token usage across tool calls, it is particularly well-suited for multi-context and long-running workflows. Use cases span software engineering, cybersecurity, financial analysis, research agents, and other domains requiring sustained reasoning and tool use.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":true,"multimodalAcceptedMimetypes":["image/*"],"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"deepseek/deepseek-v3.2-exp","name":"deepseek/deepseek-v3.2-exp","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"deepseek/deepseek-v3.2-exp","description":"DeepSeek-V3.2-Exp is an experimental large language model released by DeepSeek as an intermediate step between V3.1 and future architectures. It introduces DeepSeek Sparse Attention (DSA), a fine-grained sparse attention mechanism designed to improve training and inference efficiency in long-context scenarios while maintaining output quality. Users can control the reasoning behaviour with the `reasoning` `enabled` boolean. [Learn more in our docs](https://openrouter.ai/docs/use-cases/reasoning-tokens#enable-reasoning-with-default-config)\n\nThe model was trained under conditions aligned with V3.1-Terminus to enable direct comparison. Benchmarking shows performance roughly on par with V3.1 across reasoning, coding, and agentic tool-use tasks, with minor tradeoffs and gains depending on the domain. This release focuses on validating architectural optimizations for extended context lengths rather than advancing raw task accuracy, making it primarily a research-oriented model for exploring efficient transformer designs.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"thedrummer/cydonia-24b-v4.1","name":"thedrummer/cydonia-24b-v4.1","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"thedrummer/cydonia-24b-v4.1","description":"Uncensored and creative writing model based on Mistral Small 3.2 24B with good recall, prompt adherence, and intelligence.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"relace/relace-apply-3","name":"relace/relace-apply-3","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"relace/relace-apply-3","description":"Relace Apply 3 is a specialized code-patching LLM that merges AI-suggested edits straight into your source files. It can apply updates from GPT-4o, Claude, and others into your files at 7,500 tokens/sec on average.\n\nThe model requires the prompt to be in the following format: \n{instruction}\n{initial_code}\n{edit_snippet}\n\nZero Data Retention is enabled for Relace. Learn more about this model in their [documentation](https://docs.relace.ai/api-reference/instant-apply/apply)","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"google/gemini-2.5-flash-preview-09-2025","name":"google/gemini-2.5-flash-preview-09-2025","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"google/gemini-2.5-flash-preview-09-2025","description":"Gemini 2.5 Flash Preview September 2025 Checkpoint is Google's state-of-the-art workhorse model, specifically designed for advanced reasoning, coding, mathematics, and scientific tasks. It includes built-in \"thinking\" capabilities, enabling it to provide responses with greater accuracy and nuanced context handling. \n\nAdditionally, Gemini 2.5 Flash is configurable through the \"max tokens for reasoning\" parameter, as described in the documentation (https://openrouter.ai/docs/use-cases/reasoning-tokens#max-tokens-for-reasoning).","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":true,"multimodalAcceptedMimetypes":["image/*"],"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"google/gemini-2.5-flash-lite-preview-09-2025","name":"google/gemini-2.5-flash-lite-preview-09-2025","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"google/gemini-2.5-flash-lite-preview-09-2025","description":"Gemini 2.5 Flash-Lite is a lightweight reasoning model in the Gemini 2.5 family, optimized for ultra-low latency and cost efficiency. It offers improved throughput, faster token generation, and better performance across common benchmarks compared to earlier Flash models. By default, \"thinking\" (i.e. multi-pass reasoning) is disabled to prioritize speed, but developers can enable it via the [Reasoning API parameter](https://openrouter.ai/docs/use-cases/reasoning-tokens) to selectively trade off cost for intelligence. ","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":true,"multimodalAcceptedMimetypes":["image/*"],"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"qwen/qwen3-vl-235b-a22b-thinking","name":"qwen/qwen3-vl-235b-a22b-thinking","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"qwen/qwen3-vl-235b-a22b-thinking","description":"Qwen3-VL-235B-A22B Thinking is a multimodal model that unifies strong text generation with visual understanding across images and video. The Thinking model is optimized for multimodal reasoning in STEM and math. The series emphasizes robust perception (recognition of diverse real-world and synthetic categories), spatial understanding (2D/3D grounding), and long-form visual comprehension, with competitive results on public multimodal benchmarks for both perception and reasoning.\n\nBeyond analysis, Qwen3-VL supports agentic interaction and tool use: it can follow complex instructions over multi-image, multi-turn dialogues; align text to video timelines for precise temporal queries; and operate GUI elements for automation tasks. The models also enable visual coding workflows, turning sketches or mockups into code and assisting with UI debugging, while maintaining strong text-only performance comparable to the flagship Qwen3 language models. This makes Qwen3-VL suitable for production scenarios spanning document AI, multilingual OCR, software/UI assistance, spatial/embodied tasks, and research on vision-language agents.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":true,"multimodalAcceptedMimetypes":["image/*"],"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"qwen/qwen3-vl-235b-a22b-instruct","name":"qwen/qwen3-vl-235b-a22b-instruct","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"qwen/qwen3-vl-235b-a22b-instruct","description":"Qwen3-VL-235B-A22B Instruct is an open-weight multimodal model that unifies strong text generation with visual understanding across images and video. The Instruct model targets general vision-language use (VQA, document parsing, chart/table extraction, multilingual OCR). The series emphasizes robust perception (recognition of diverse real-world and synthetic categories), spatial understanding (2D/3D grounding), and long-form visual comprehension, with competitive results on public multimodal benchmarks for both perception and reasoning.\n\nBeyond analysis, Qwen3-VL supports agentic interaction and tool use: it can follow complex instructions over multi-image, multi-turn dialogues; align text to video timelines for precise temporal queries; and operate GUI elements for automation tasks. The models also enable visual coding workflows—turning sketches or mockups into code and assisting with UI debugging—while maintaining strong text-only performance comparable to the flagship Qwen3 language models. This makes Qwen3-VL suitable for production scenarios spanning document AI, multilingual OCR, software/UI assistance, spatial/embodied tasks, and research on vision-language agents.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":true,"multimodalAcceptedMimetypes":["image/*"],"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"qwen/qwen3-max","name":"qwen/qwen3-max","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"qwen/qwen3-max","description":"Qwen3-Max is an updated release built on the Qwen3 series, offering major improvements in reasoning, instruction following, multilingual support, and long-tail knowledge coverage compared to the January 2025 version. It delivers higher accuracy in math, coding, logic, and science tasks, follows complex instructions in Chinese and English more reliably, reduces hallucinations, and produces higher-quality responses for open-ended Q&A, writing, and conversation. The model supports over 100 languages with stronger translation and commonsense reasoning, and is optimized for retrieval-augmented generation (RAG) and tool calling, though it does not include a dedicated “thinking” mode.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"qwen/qwen3-coder-plus","name":"qwen/qwen3-coder-plus","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"qwen/qwen3-coder-plus","description":"Qwen3 Coder Plus is Alibaba's proprietary version of the Open Source Qwen3 Coder 480B A35B. It is a powerful coding agent model specializing in autonomous programming via tool calling and environment interaction, combining coding proficiency with versatile general-purpose abilities.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"openai/gpt-5-codex","name":"openai/gpt-5-codex","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"openai/gpt-5-codex","description":"GPT-5-Codex is a specialized version of GPT-5 optimized for software engineering and coding workflows. It is designed for both interactive development sessions and long, independent execution of complex engineering tasks. The model supports building projects from scratch, feature development, debugging, large-scale refactoring, and code review. Compared to GPT-5, Codex is more steerable, adheres closely to developer instructions, and produces cleaner, higher-quality code outputs. Reasoning effort can be adjusted with the `reasoning.effort` parameter. Read the [docs here](https://openrouter.ai/docs/use-cases/reasoning-tokens#reasoning-effort-level)\n\nCodex integrates into developer environments including the CLI, IDE extensions, GitHub, and cloud tasks. It adapts reasoning effort dynamically—providing fast responses for small tasks while sustaining extended multi-hour runs for large projects. The model is trained to perform structured code reviews, catching critical flaws by reasoning over dependencies and validating behavior against tests. It also supports multimodal inputs such as images or screenshots for UI development and integrates tool use for search, dependency installation, and environment setup. Codex is intended specifically for agentic coding applications.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":true,"multimodalAcceptedMimetypes":["image/*"],"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"deepseek/deepseek-v3.1-terminus","name":"deepseek/deepseek-v3.1-terminus","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"deepseek/deepseek-v3.1-terminus","description":"DeepSeek-V3.1 Terminus is an update to [DeepSeek V3.1](/deepseek/deepseek-chat-v3.1) that maintains the model's original capabilities while addressing issues reported by users, including language consistency and agent capabilities, further optimizing the model's performance in coding and search agents. It is a large hybrid reasoning model (671B parameters, 37B active) that supports both thinking and non-thinking modes. It extends the DeepSeek-V3 base with a two-phase long-context training process, reaching up to 128K tokens, and uses FP8 microscaling for efficient inference. Users can control the reasoning behaviour with the `reasoning` `enabled` boolean. [Learn more in our docs](https://openrouter.ai/docs/use-cases/reasoning-tokens#enable-reasoning-with-default-config)\n\nThe model improves tool use, code generation, and reasoning efficiency, achieving performance comparable to DeepSeek-R1 on difficult benchmarks while responding more quickly. It supports structured tool calling, code agents, and search agents, making it suitable for research, coding, and agentic workflows. ","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"deepseek/deepseek-v3.1-terminus:exacto","name":"deepseek/deepseek-v3.1-terminus:exacto","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"deepseek/deepseek-v3.1-terminus:exacto","description":"DeepSeek-V3.1 Terminus is an update to [DeepSeek V3.1](/deepseek/deepseek-chat-v3.1) that maintains the model's original capabilities while addressing issues reported by users, including language consistency and agent capabilities, further optimizing the model's performance in coding and search agents. It is a large hybrid reasoning model (671B parameters, 37B active) that supports both thinking and non-thinking modes. It extends the DeepSeek-V3 base with a two-phase long-context training process, reaching up to 128K tokens, and uses FP8 microscaling for efficient inference. Users can control the reasoning behaviour with the `reasoning` `enabled` boolean. [Learn more in our docs](https://openrouter.ai/docs/use-cases/reasoning-tokens#enable-reasoning-with-default-config)\n\nThe model improves tool use, code generation, and reasoning efficiency, achieving performance comparable to DeepSeek-R1 on difficult benchmarks while responding more quickly. It supports structured tool calling, code agents, and search agents, making it suitable for research, coding, and agentic workflows. ","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"x-ai/grok-4-fast","name":"x-ai/grok-4-fast","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"x-ai/grok-4-fast","description":"Grok 4 Fast is xAI's latest multimodal model with SOTA cost-efficiency and a 2M token context window. It comes in two flavors: non-reasoning and reasoning. Read more about the model on xAI's [news post](http://x.ai/news/grok-4-fast). Reasoning can be enabled using the `reasoning` `enabled` parameter in the API. [Learn more in our docs](https://openrouter.ai/docs/use-cases/reasoning-tokens#controlling-reasoning-tokens)\n\nPrompts and completions on Grok 4 Fast Free may be used by xAI or OpenRouter to improve future models.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":true,"multimodalAcceptedMimetypes":["image/*"],"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"alibaba/tongyi-deepresearch-30b-a3b:free","name":"alibaba/tongyi-deepresearch-30b-a3b:free","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"alibaba/tongyi-deepresearch-30b-a3b:free","description":"Tongyi DeepResearch is an agentic large language model developed by Tongyi Lab, with 30 billion total parameters activating only 3 billion per token. It's optimized for long-horizon, deep information-seeking tasks and delivers state-of-the-art performance on benchmarks like Humanity's Last Exam, BrowserComp, BrowserComp-ZH, WebWalkerQA, GAIA, xbench-DeepSearch, and FRAMES. This makes it superior for complex agentic search, reasoning, and multi-step problem-solving compared to prior models.\n\nThe model includes a fully automated synthetic data pipeline for scalable pre-training, fine-tuning, and reinforcement learning. It uses large-scale continual pre-training on diverse agentic data to boost reasoning and stay fresh. It also features end-to-end on-policy RL with a customized Group Relative Policy Optimization, including token-level gradients and negative sample filtering for stable training. The model supports ReAct for core ability checks and an IterResearch-based 'Heavy' mode for max performance through test-time scaling. It's ideal for advanced research agents, tool use, and heavy inference workflows.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"alibaba/tongyi-deepresearch-30b-a3b","name":"alibaba/tongyi-deepresearch-30b-a3b","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"alibaba/tongyi-deepresearch-30b-a3b","description":"Tongyi DeepResearch is an agentic large language model developed by Tongyi Lab, with 30 billion total parameters activating only 3 billion per token. It's optimized for long-horizon, deep information-seeking tasks and delivers state-of-the-art performance on benchmarks like Humanity's Last Exam, BrowserComp, BrowserComp-ZH, WebWalkerQA, GAIA, xbench-DeepSearch, and FRAMES. This makes it superior for complex agentic search, reasoning, and multi-step problem-solving compared to prior models.\n\nThe model includes a fully automated synthetic data pipeline for scalable pre-training, fine-tuning, and reinforcement learning. It uses large-scale continual pre-training on diverse agentic data to boost reasoning and stay fresh. It also features end-to-end on-policy RL with a customized Group Relative Policy Optimization, including token-level gradients and negative sample filtering for stable training. The model supports ReAct for core ability checks and an IterResearch-based 'Heavy' mode for max performance through test-time scaling. It's ideal for advanced research agents, tool use, and heavy inference workflows.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"qwen/qwen3-coder-flash","name":"qwen/qwen3-coder-flash","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"qwen/qwen3-coder-flash","description":"Qwen3 Coder Flash is Alibaba's fast and cost efficient version of their proprietary Qwen3 Coder Plus. It is a powerful coding agent model specializing in autonomous programming via tool calling and environment interaction, combining coding proficiency with versatile general-purpose abilities.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"arcee-ai/afm-4.5b","name":"arcee-ai/afm-4.5b","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"arcee-ai/afm-4.5b","description":"AFM-4.5B is a 4.5 billion parameter instruction-tuned language model developed by Arcee AI. The model was pretrained on approximately 8 trillion tokens, including 6.5 trillion tokens of general data and 1.5 trillion tokens with an emphasis on mathematical reasoning and code generation. ","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"opengvlab/internvl3-78b","name":"opengvlab/internvl3-78b","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"opengvlab/internvl3-78b","description":"The InternVL3 series is an advanced multimodal large language model (MLLM). Compared to InternVL 2.5, InternVL3 demonstrates stronger multimodal perception and reasoning capabilities. \n\nIn addition, InternVL3 is benchmarked against the Qwen2.5 Chat models, whose pre-trained base models serve as the initialization for its language component. Benefiting from Native Multimodal Pre-Training, the InternVL3 series surpasses the Qwen2.5 series in overall text performance.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":true,"multimodalAcceptedMimetypes":["image/*"],"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"qwen/qwen3-next-80b-a3b-thinking","name":"qwen/qwen3-next-80b-a3b-thinking","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"qwen/qwen3-next-80b-a3b-thinking","description":"Qwen3-Next-80B-A3B-Thinking is a reasoning-first chat model in the Qwen3-Next line that outputs structured “thinking” traces by default. It’s designed for hard multi-step problems; math proofs, code synthesis/debugging, logic, and agentic planning, and reports strong results across knowledge, reasoning, coding, alignment, and multilingual evaluations. Compared with prior Qwen3 variants, it emphasizes stability under long chains of thought and efficient scaling during inference, and it is tuned to follow complex instructions while reducing repetitive or off-task behavior.\n\nThe model is suitable for agent frameworks and tool use (function calling), retrieval-heavy workflows, and standardized benchmarking where step-by-step solutions are required. It supports long, detailed completions and leverages throughput-oriented techniques (e.g., multi-token prediction) for faster generation. Note that it operates in thinking-only mode.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"qwen/qwen3-next-80b-a3b-instruct","name":"qwen/qwen3-next-80b-a3b-instruct","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"qwen/qwen3-next-80b-a3b-instruct","description":"Qwen3-Next-80B-A3B-Instruct is an instruction-tuned chat model in the Qwen3-Next series optimized for fast, stable responses without “thinking” traces. It targets complex tasks across reasoning, code generation, knowledge QA, and multilingual use, while remaining robust on alignment and formatting. Compared with prior Qwen3 instruct variants, it focuses on higher throughput and stability on ultra-long inputs and multi-turn dialogues, making it well-suited for RAG, tool use, and agentic workflows that require consistent final answers rather than visible chain-of-thought.\n\nThe model employs scaling-efficient training and decoding to improve parameter efficiency and inference speed, and has been validated on a broad set of public benchmarks where it reaches or approaches larger Qwen3 systems in several categories while outperforming earlier mid-sized baselines. It is best used as a general assistant, code helper, and long-context task solver in production settings where deterministic, instruction-following outputs are preferred.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"meituan/longcat-flash-chat:free","name":"meituan/longcat-flash-chat:free","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"meituan/longcat-flash-chat:free","description":"LongCat-Flash-Chat is a large-scale Mixture-of-Experts (MoE) model with 560B total parameters, of which 18.6B–31.3B (≈27B on average) are dynamically activated per input. It introduces a shortcut-connected MoE design to reduce communication overhead and achieve high throughput while maintaining training stability through advanced scaling strategies such as hyperparameter transfer, deterministic computation, and multi-stage optimization.\n\nThis release, LongCat-Flash-Chat, is a non-thinking foundation model optimized for conversational and agentic tasks. It supports long context windows up to 128K tokens and shows competitive performance across reasoning, coding, instruction following, and domain benchmarks, with particular strengths in tool use and complex multi-step interactions.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"meituan/longcat-flash-chat","name":"meituan/longcat-flash-chat","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"meituan/longcat-flash-chat","description":"LongCat-Flash-Chat is a large-scale Mixture-of-Experts (MoE) model with 560B total parameters, of which 18.6B–31.3B (≈27B on average) are dynamically activated per input. It introduces a shortcut-connected MoE design to reduce communication overhead and achieve high throughput while maintaining training stability through advanced scaling strategies such as hyperparameter transfer, deterministic computation, and multi-stage optimization.\n\nThis release, LongCat-Flash-Chat, is a non-thinking foundation model optimized for conversational and agentic tasks. It supports long context windows up to 128K tokens and shows competitive performance across reasoning, coding, instruction following, and domain benchmarks, with particular strengths in tool use and complex multi-step interactions.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"qwen/qwen-plus-2025-07-28","name":"qwen/qwen-plus-2025-07-28","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"qwen/qwen-plus-2025-07-28","description":"Qwen Plus 0728, based on the Qwen3 foundation model, is a 1 million context hybrid reasoning model with a balanced performance, speed, and cost combination.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"qwen/qwen-plus-2025-07-28:thinking","name":"qwen/qwen-plus-2025-07-28:thinking","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"qwen/qwen-plus-2025-07-28:thinking","description":"Qwen Plus 0728, based on the Qwen3 foundation model, is a 1 million context hybrid reasoning model with a balanced performance, speed, and cost combination.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"nvidia/nemotron-nano-9b-v2:free","name":"nvidia/nemotron-nano-9b-v2:free","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"nvidia/nemotron-nano-9b-v2:free","description":"NVIDIA-Nemotron-Nano-9B-v2 is a large language model (LLM) trained from scratch by NVIDIA, and designed as a unified model for both reasoning and non-reasoning tasks. It responds to user queries and tasks by first generating a reasoning trace and then concluding with a final response. \n\nThe model's reasoning capabilities can be controlled via a system prompt. If the user prefers the model to provide its final answer without intermediate reasoning traces, it can be configured to do so.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"nvidia/nemotron-nano-9b-v2","name":"nvidia/nemotron-nano-9b-v2","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"nvidia/nemotron-nano-9b-v2","description":"NVIDIA-Nemotron-Nano-9B-v2 is a large language model (LLM) trained from scratch by NVIDIA, and designed as a unified model for both reasoning and non-reasoning tasks. It responds to user queries and tasks by first generating a reasoning trace and then concluding with a final response. \n\nThe model's reasoning capabilities can be controlled via a system prompt. If the user prefers the model to provide its final answer without intermediate reasoning traces, it can be configured to do so.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"moonshotai/kimi-k2-0905","name":"moonshotai/kimi-k2-0905","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"moonshotai/kimi-k2-0905","description":"Kimi K2 0905 is the September update of [Kimi K2 0711](moonshotai/kimi-k2). It is a large-scale Mixture-of-Experts (MoE) language model developed by Moonshot AI, featuring 1 trillion total parameters with 32 billion active per forward pass. It supports long-context inference up to 256k tokens, extended from the previous 128k.\n\nThis update improves agentic coding with higher accuracy and better generalization across scaffolds, and enhances frontend coding with more aesthetic and functional outputs for web, 3D, and related tasks. Kimi K2 is optimized for agentic capabilities, including advanced tool use, reasoning, and code synthesis. It excels across coding (LiveCodeBench, SWE-bench), reasoning (ZebraLogic, GPQA), and tool-use (Tau2, AceBench) benchmarks. The model is trained with a novel stack incorporating the MuonClip optimizer for stable large-scale MoE training.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"moonshotai/kimi-k2-0905:exacto","name":"moonshotai/kimi-k2-0905:exacto","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"moonshotai/kimi-k2-0905:exacto","description":"Kimi K2 0905 is the September update of [Kimi K2 0711](moonshotai/kimi-k2). It is a large-scale Mixture-of-Experts (MoE) language model developed by Moonshot AI, featuring 1 trillion total parameters with 32 billion active per forward pass. It supports long-context inference up to 256k tokens, extended from the previous 128k.\n\nThis update improves agentic coding with higher accuracy and better generalization across scaffolds, and enhances frontend coding with more aesthetic and functional outputs for web, 3D, and related tasks. Kimi K2 is optimized for agentic capabilities, including advanced tool use, reasoning, and code synthesis. It excels across coding (LiveCodeBench, SWE-bench), reasoning (ZebraLogic, GPQA), and tool-use (Tau2, AceBench) benchmarks. The model is trained with a novel stack incorporating the MuonClip optimizer for stable large-scale MoE training.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"deepcogito/cogito-v2-preview-llama-70b","name":"deepcogito/cogito-v2-preview-llama-70b","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"deepcogito/cogito-v2-preview-llama-70b","description":"Cogito v2 70B is a dense hybrid reasoning model that combines direct answering capabilities with advanced self-reflection. Built with iterative policy improvement, it delivers strong performance across reasoning tasks while maintaining efficiency through shorter reasoning chains and improved intuition.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"deepcogito/cogito-v2-preview-llama-109b-moe","name":"deepcogito/cogito-v2-preview-llama-109b-moe","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"deepcogito/cogito-v2-preview-llama-109b-moe","description":"An instruction-tuned, hybrid-reasoning Mixture-of-Experts model built on Llama-4-Scout-17B-16E. Cogito v2 can answer directly or engage an extended “thinking” phase, with alignment guided by Iterated Distillation & Amplification (IDA). It targets coding, STEM, instruction following, and general helpfulness, with stronger multilingual, tool-calling, and reasoning performance than size-equivalent baselines. The model supports long-context use (up to 10M tokens) and standard Transformers workflows. Users can control the reasoning behaviour with the `reasoning` `enabled` boolean. 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Cogito v2 has been optimized for STEM, programming, instruction following, and tool use. It supports 128k context length and offers strong performance in both multilingual and code-heavy environments. Users can control the reasoning behaviour with the `reasoning` `enabled` boolean. [Learn more in our docs](https://openrouter.ai/docs/use-cases/reasoning-tokens#enable-reasoning-with-default-config)","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"stepfun-ai/step3","name":"stepfun-ai/step3","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"stepfun-ai/step3","description":"Step3 is a cutting-edge multimodal reasoning model—built on a Mixture-of-Experts architecture with 321B total parameters and 38B active. 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It balances state-of-the-art reasoning and multimodal performance with 8× lower cost compared to traditional large models, making it suitable for scalable deployments across professional and industrial use cases.\n\nThe model excels in domains such as coding, STEM reasoning, and enterprise adaptation. It supports hybrid, on-prem, and in-VPC deployments and is optimized for integration into custom workflows. Mistral Medium 3 offers competitive accuracy relative to larger models like Claude Sonnet 3.5/3.7, Llama 4 Maverick, and Command R+, while maintaining broad compatibility across cloud environments.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":true,"multimodalAcceptedMimetypes":["image/*"],"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"google/gemini-2.5-pro-preview-05-06","name":"google/gemini-2.5-pro-preview-05-06","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"google/gemini-2.5-pro-preview-05-06","description":"Gemini 2.5 Pro is Google’s state-of-the-art AI model designed for advanced reasoning, coding, mathematics, and scientific tasks. It employs “thinking” capabilities, enabling it to reason through responses with enhanced accuracy and nuanced context handling. Gemini 2.5 Pro achieves top-tier performance on multiple benchmarks, including first-place positioning on the LMArena leaderboard, reflecting superior human-preference alignment and complex problem-solving abilities.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":true,"multimodalAcceptedMimetypes":["image/*"],"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"arcee-ai/spotlight","name":"arcee-ai/spotlight","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"arcee-ai/spotlight","description":"Spotlight is a 7‑billion‑parameter vision‑language model derived from Qwen 2.5‑VL and fine‑tuned by Arcee AI for tight image‑text grounding tasks. It offers a 32 k‑token context window, enabling rich multimodal conversations that combine lengthy documents with one or more images. Training emphasized fast inference on consumer GPUs while retaining strong captioning, visual‐question‑answering, and diagram‑analysis accuracy. As a result, Spotlight slots neatly into agent workflows where screenshots, charts or UI mock‑ups need to be interpreted on the fly. Early benchmarks show it matching or out‑scoring larger VLMs such as LLaVA‑1.6 13 B on popular VQA and POPE alignment tests. ","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":true,"multimodalAcceptedMimetypes":["image/*"],"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"arcee-ai/maestro-reasoning","name":"arcee-ai/maestro-reasoning","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"arcee-ai/maestro-reasoning","description":"Maestro Reasoning is Arcee's flagship analysis model: a 32 B‑parameter derivative of Qwen 2.5‑32 B tuned with DPO and chain‑of‑thought RL for step‑by‑step logic. Compared to the earlier 7 B preview, the production 32 B release widens the context window to 128 k tokens and doubles pass‑rate on MATH and GSM‑8K, while also lifting code completion accuracy. Its instruction style encourages structured \"thought → answer\" traces that can be parsed or hidden according to user preference. That transparency pairs well with audit‑focused industries like finance or healthcare where seeing the reasoning path matters. In Arcee Conductor, Maestro is automatically selected for complex, multi‑constraint queries that smaller SLMs bounce. ","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"arcee-ai/virtuoso-large","name":"arcee-ai/virtuoso-large","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"arcee-ai/virtuoso-large","description":"Virtuoso‑Large is Arcee's top‑tier general‑purpose LLM at 72 B parameters, tuned to tackle cross‑domain reasoning, creative writing and enterprise QA. Unlike many 70 B peers, it retains the 128 k context inherited from Qwen 2.5, letting it ingest books, codebases or financial filings wholesale. Training blended DeepSeek R1 distillation, multi‑epoch supervised fine‑tuning and a final DPO/RLHF alignment stage, yielding strong performance on BIG‑Bench‑Hard, GSM‑8K and long‑context Needle‑In‑Haystack tests. Enterprises use Virtuoso‑Large as the \"fallback\" brain in Conductor pipelines when other SLMs flag low confidence. Despite its size, aggressive KV‑cache optimizations keep first‑token latency in the low‑second range on 8× H100 nodes, making it a practical production‑grade powerhouse.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"arcee-ai/coder-large","name":"arcee-ai/coder-large","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"arcee-ai/coder-large","description":"Coder‑Large is a 32 B‑parameter offspring of Qwen 2.5‑Instruct that has been further trained on permissively‑licensed GitHub, CodeSearchNet and synthetic bug‑fix corpora. It supports a 32k context window, enabling multi‑file refactoring or long diff review in a single call, and understands 30‑plus programming languages with special attention to TypeScript, Go and Terraform. Internal benchmarks show 5–8 pt gains over CodeLlama‑34 B‑Python on HumanEval and competitive BugFix scores thanks to a reinforcement pass that rewards compilable output. The model emits structured explanations alongside code blocks by default, making it suitable for educational tooling as well as production copilot scenarios. Cost‑wise, Together AI prices it well below proprietary incumbents, so teams can scale interactive coding without runaway spend. ","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"microsoft/phi-4-reasoning-plus","name":"microsoft/phi-4-reasoning-plus","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"microsoft/phi-4-reasoning-plus","description":"Phi-4-reasoning-plus is an enhanced 14B parameter model from Microsoft, fine-tuned from Phi-4 with additional reinforcement learning to boost accuracy on math, science, and code reasoning tasks. It uses the same dense decoder-only transformer architecture as Phi-4, but generates longer, more comprehensive outputs structured into a step-by-step reasoning trace and final answer.\n\nWhile it offers improved benchmark scores over Phi-4-reasoning across tasks like AIME, OmniMath, and HumanEvalPlus, its responses are typically ~50% longer, resulting in higher latency. Designed for English-only applications, it is well-suited for structured reasoning workflows where output quality takes priority over response speed.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"inception/mercury-coder","name":"inception/mercury-coder","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"inception/mercury-coder","description":"Mercury Coder is the first diffusion large language model (dLLM). Applying a breakthrough discrete diffusion approach, the model runs 5-10x faster than even speed optimized models like Claude 3.5 Haiku and GPT-4o Mini while matching their performance. Mercury Coder's speed means that developers can stay in the flow while coding, enjoying rapid chat-based iteration and responsive code completion suggestions. On Copilot Arena, Mercury Coder ranks 1st in speed and ties for 2nd in quality. Read more in the [blog post here](https://www.inceptionlabs.ai/blog/introducing-mercury).","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"qwen/qwen3-4b:free","name":"qwen/qwen3-4b:free","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"qwen/qwen3-4b:free","description":"Qwen3-4B is a 4 billion parameter dense language model from the Qwen3 series, designed to support both general-purpose and reasoning-intensive tasks. It introduces a dual-mode architecture—thinking and non-thinking—allowing dynamic switching between high-precision logical reasoning and efficient dialogue generation. This makes it well-suited for multi-turn chat, instruction following, and complex agent workflows.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"deepseek/deepseek-prover-v2","name":"deepseek/deepseek-prover-v2","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"deepseek/deepseek-prover-v2","description":"DeepSeek Prover V2 is a 671B parameter model, speculated to be geared towards logic and mathematics. Likely an upgrade from [DeepSeek-Prover-V1.5](https://huggingface.co/deepseek-ai/DeepSeek-Prover-V1.5-RL) Not much is known about the model yet, as DeepSeek released it on Hugging Face without an announcement or description.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"meta-llama/llama-guard-4-12b","name":"meta-llama/llama-guard-4-12b","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"meta-llama/llama-guard-4-12b","description":"Llama Guard 4 is a Llama 4 Scout-derived multimodal pretrained model, fine-tuned for content safety classification. Similar to previous versions, it can be used to classify content in both LLM inputs (prompt classification) and in LLM responses (response classification). It acts as an LLM—generating text in its output that indicates whether a given prompt or response is safe or unsafe, and if unsafe, it also lists the content categories violated.\n\nLlama Guard 4 was aligned to safeguard against the standardized MLCommons hazards taxonomy and designed to support multimodal Llama 4 capabilities. Specifically, it combines features from previous Llama Guard models, providing content moderation for English and multiple supported languages, along with enhanced capabilities to handle mixed text-and-image prompts, including multiple images. Additionally, Llama Guard 4 is integrated into the Llama Moderations API, extending robust safety classification to text and images.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":true,"multimodalAcceptedMimetypes":["image/*"],"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"qwen/qwen3-30b-a3b:free","name":"qwen/qwen3-30b-a3b:free","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"qwen/qwen3-30b-a3b:free","description":"Qwen3, the latest generation in the Qwen large language model series, features both dense and mixture-of-experts (MoE) architectures to excel in reasoning, multilingual support, and advanced agent tasks. Its unique ability to switch seamlessly between a thinking mode for complex reasoning and a non-thinking mode for efficient dialogue ensures versatile, high-quality performance.\n\nSignificantly outperforming prior models like QwQ and Qwen2.5, Qwen3 delivers superior mathematics, coding, commonsense reasoning, creative writing, and interactive dialogue capabilities. The Qwen3-30B-A3B variant includes 30.5 billion parameters (3.3 billion activated), 48 layers, 128 experts (8 activated per task), and supports up to 131K token contexts with YaRN, setting a new standard among open-source models.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"qwen/qwen3-30b-a3b","name":"qwen/qwen3-30b-a3b","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"qwen/qwen3-30b-a3b","description":"Qwen3, the latest generation in the Qwen large language model series, features both dense and mixture-of-experts (MoE) architectures to excel in reasoning, multilingual support, and advanced agent tasks. Its unique ability to switch seamlessly between a thinking mode for complex reasoning and a non-thinking mode for efficient dialogue ensures versatile, high-quality performance.\n\nSignificantly outperforming prior models like QwQ and Qwen2.5, Qwen3 delivers superior mathematics, coding, commonsense reasoning, creative writing, and interactive dialogue capabilities. The Qwen3-30B-A3B variant includes 30.5 billion parameters (3.3 billion activated), 48 layers, 128 experts (8 activated per task), and supports up to 131K token contexts with YaRN, setting a new standard among open-source models.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"qwen/qwen3-8b:free","name":"qwen/qwen3-8b:free","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"qwen/qwen3-8b:free","description":"Qwen3-8B is a dense 8.2B parameter causal language model from the Qwen3 series, designed for both reasoning-heavy tasks and efficient dialogue. It supports seamless switching between \"thinking\" mode for math, coding, and logical inference, and \"non-thinking\" mode for general conversation. The model is fine-tuned for instruction-following, agent integration, creative writing, and multilingual use across 100+ languages and dialects. It natively supports a 32K token context window and can extend to 131K tokens with YaRN scaling.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"qwen/qwen3-8b","name":"qwen/qwen3-8b","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"qwen/qwen3-8b","description":"Qwen3-8B is a dense 8.2B parameter causal language model from the Qwen3 series, designed for both reasoning-heavy tasks and efficient dialogue. It supports seamless switching between \"thinking\" mode for math, coding, and logical inference, and \"non-thinking\" mode for general conversation. The model is fine-tuned for instruction-following, agent integration, creative writing, and multilingual use across 100+ languages and dialects. It natively supports a 32K token context window and can extend to 131K tokens with YaRN scaling.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"qwen/qwen3-14b:free","name":"qwen/qwen3-14b:free","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"qwen/qwen3-14b:free","description":"Qwen3-14B is a dense 14.8B parameter causal language model from the Qwen3 series, designed for both complex reasoning and efficient dialogue. It supports seamless switching between a \"thinking\" mode for tasks like math, programming, and logical inference, and a \"non-thinking\" mode for general-purpose conversation. The model is fine-tuned for instruction-following, agent tool use, creative writing, and multilingual tasks across 100+ languages and dialects. It natively handles 32K token contexts and can extend to 131K tokens using YaRN-based scaling.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"qwen/qwen3-14b","name":"qwen/qwen3-14b","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"qwen/qwen3-14b","description":"Qwen3-14B is a dense 14.8B parameter causal language model from the Qwen3 series, designed for both complex reasoning and efficient dialogue. It supports seamless switching between a \"thinking\" mode for tasks like math, programming, and logical inference, and a \"non-thinking\" mode for general-purpose conversation. The model is fine-tuned for instruction-following, agent tool use, creative writing, and multilingual tasks across 100+ languages and dialects. It natively handles 32K token contexts and can extend to 131K tokens using YaRN-based scaling.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"qwen/qwen3-32b","name":"qwen/qwen3-32b","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"qwen/qwen3-32b","description":"Qwen3-32B is a dense 32.8B parameter causal language model from the Qwen3 series, optimized for both complex reasoning and efficient dialogue. It supports seamless switching between a \"thinking\" mode for tasks like math, coding, and logical inference, and a \"non-thinking\" mode for faster, general-purpose conversation. The model demonstrates strong performance in instruction-following, agent tool use, creative writing, and multilingual tasks across 100+ languages and dialects. It natively handles 32K token contexts and can extend to 131K tokens using YaRN-based scaling. ","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"qwen/qwen3-235b-a22b:free","name":"qwen/qwen3-235b-a22b:free","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"qwen/qwen3-235b-a22b:free","description":"Qwen3-235B-A22B is a 235B parameter mixture-of-experts (MoE) model developed by Qwen, activating 22B parameters per forward pass. It supports seamless switching between a \"thinking\" mode for complex reasoning, math, and code tasks, and a \"non-thinking\" mode for general conversational efficiency. The model demonstrates strong reasoning ability, multilingual support (100+ languages and dialects), advanced instruction-following, and agent tool-calling capabilities. It natively handles a 32K token context window and extends up to 131K tokens using YaRN-based scaling.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"qwen/qwen3-235b-a22b","name":"qwen/qwen3-235b-a22b","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"qwen/qwen3-235b-a22b","description":"Qwen3-235B-A22B is a 235B parameter mixture-of-experts (MoE) model developed by Qwen, activating 22B parameters per forward pass. It supports seamless switching between a \"thinking\" mode for complex reasoning, math, and code tasks, and a \"non-thinking\" mode for general conversational efficiency. The model demonstrates strong reasoning ability, multilingual support (100+ languages and dialects), advanced instruction-following, and agent tool-calling capabilities. It natively handles a 32K token context window and extends up to 131K tokens using YaRN-based scaling.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"tngtech/deepseek-r1t-chimera:free","name":"tngtech/deepseek-r1t-chimera:free","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"tngtech/deepseek-r1t-chimera:free","description":"DeepSeek-R1T-Chimera is created by merging DeepSeek-R1 and DeepSeek-V3 (0324), combining the reasoning capabilities of R1 with the token efficiency improvements of V3. It is based on a DeepSeek-MoE Transformer architecture and is optimized for general text generation tasks.\n\nThe model merges pretrained weights from both source models to balance performance across reasoning, efficiency, and instruction-following tasks. It is released under the MIT license and intended for research and commercial use.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"tngtech/deepseek-r1t-chimera","name":"tngtech/deepseek-r1t-chimera","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"tngtech/deepseek-r1t-chimera","description":"DeepSeek-R1T-Chimera is created by merging DeepSeek-R1 and DeepSeek-V3 (0324), combining the reasoning capabilities of R1 with the token efficiency improvements of V3. It is based on a DeepSeek-MoE Transformer architecture and is optimized for general text generation tasks.\n\nThe model merges pretrained weights from both source models to balance performance across reasoning, efficiency, and instruction-following tasks. It is released under the MIT license and intended for research and commercial use.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"microsoft/mai-ds-r1:free","name":"microsoft/mai-ds-r1:free","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"microsoft/mai-ds-r1:free","description":"MAI-DS-R1 is a post-trained variant of DeepSeek-R1 developed by the Microsoft AI team to improve the model’s responsiveness on previously blocked topics while enhancing its safety profile. Built on top of DeepSeek-R1’s reasoning foundation, it integrates 110k examples from the Tulu-3 SFT dataset and 350k internally curated multilingual safety-alignment samples. The model retains strong reasoning, coding, and problem-solving capabilities, while unblocking a wide range of prompts previously restricted in R1.\n\nMAI-DS-R1 demonstrates improved performance on harm mitigation benchmarks and maintains competitive results across general reasoning tasks. It surpasses R1-1776 in satisfaction metrics for blocked queries and reduces leakage in harmful content categories. The model is based on a transformer MoE architecture and is suitable for general-purpose use cases, excluding high-stakes domains such as legal, medical, or autonomous systems.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"microsoft/mai-ds-r1","name":"microsoft/mai-ds-r1","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"microsoft/mai-ds-r1","description":"MAI-DS-R1 is a post-trained variant of DeepSeek-R1 developed by the Microsoft AI team to improve the model’s responsiveness on previously blocked topics while enhancing its safety profile. Built on top of DeepSeek-R1’s reasoning foundation, it integrates 110k examples from the Tulu-3 SFT dataset and 350k internally curated multilingual safety-alignment samples. The model retains strong reasoning, coding, and problem-solving capabilities, while unblocking a wide range of prompts previously restricted in R1.\n\nMAI-DS-R1 demonstrates improved performance on harm mitigation benchmarks and maintains competitive results across general reasoning tasks. It surpasses R1-1776 in satisfaction metrics for blocked queries and reduces leakage in harmful content categories. The model is based on a transformer MoE architecture and is suitable for general-purpose use cases, excluding high-stakes domains such as legal, medical, or autonomous systems.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"thudm/glm-z1-32b","name":"thudm/glm-z1-32b","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"thudm/glm-z1-32b","description":"GLM-Z1-32B-0414 is an enhanced reasoning variant of GLM-4-32B, built for deep mathematical, logical, and code-oriented problem solving. It applies extended reinforcement learning—both task-specific and general pairwise preference-based—to improve performance on complex multi-step tasks. Compared to the base GLM-4-32B model, Z1 significantly boosts capabilities in structured reasoning and formal domains.\n\nThe model supports enforced “thinking” steps via prompt engineering and offers improved coherence for long-form outputs. It’s optimized for use in agentic workflows, and includes support for long context (via YaRN), JSON tool calling, and fine-grained sampling configuration for stable inference. Ideal for use cases requiring deliberate, multi-step reasoning or formal derivations.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"openai/o4-mini-high","name":"openai/o4-mini-high","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"openai/o4-mini-high","description":"OpenAI o4-mini-high is the same model as [o4-mini](/openai/o4-mini) with reasoning_effort set to high. \n\nOpenAI o4-mini is a compact reasoning model in the o-series, optimized for fast, cost-efficient performance while retaining strong multimodal and agentic capabilities. It supports tool use and demonstrates competitive reasoning and coding performance across benchmarks like AIME (99.5% with Python) and SWE-bench, outperforming its predecessor o3-mini and even approaching o3 in some domains.\n\nDespite its smaller size, o4-mini exhibits high accuracy in STEM tasks, visual problem solving (e.g., MathVista, MMMU), and code editing. It is especially well-suited for high-throughput scenarios where latency or cost is critical. Thanks to its efficient architecture and refined reinforcement learning training, o4-mini can chain tools, generate structured outputs, and solve multi-step tasks with minimal delay—often in under a minute.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":true,"multimodalAcceptedMimetypes":["image/*"],"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"openai/o3","name":"openai/o3","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"openai/o3","description":"o3 is a well-rounded and powerful model across domains. It sets a new standard for math, science, coding, and visual reasoning tasks. It also excels at technical writing and instruction-following. Use it to think through multi-step problems that involve analysis across text, code, and images. ","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":true,"multimodalAcceptedMimetypes":["image/*"],"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"openai/o4-mini","name":"openai/o4-mini","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"openai/o4-mini","description":"OpenAI o4-mini is a compact reasoning model in the o-series, optimized for fast, cost-efficient performance while retaining strong multimodal and agentic capabilities. It supports tool use and demonstrates competitive reasoning and coding performance across benchmarks like AIME (99.5% with Python) and SWE-bench, outperforming its predecessor o3-mini and even approaching o3 in some domains.\n\nDespite its smaller size, o4-mini exhibits high accuracy in STEM tasks, visual problem solving (e.g., MathVista, MMMU), and code editing. It is especially well-suited for high-throughput scenarios where latency or cost is critical. Thanks to its efficient architecture and refined reinforcement learning training, o4-mini can chain tools, generate structured outputs, and solve multi-step tasks with minimal delay—often in under a minute.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":true,"multimodalAcceptedMimetypes":["image/*"],"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"shisa-ai/shisa-v2-llama3.3-70b:free","name":"shisa-ai/shisa-v2-llama3.3-70b:free","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"shisa-ai/shisa-v2-llama3.3-70b:free","description":"Shisa V2 Llama 3.3 70B is a bilingual Japanese-English chat model fine-tuned by Shisa.AI on Meta’s Llama-3.3-70B-Instruct base. It prioritizes Japanese language performance while retaining strong English capabilities. The model was optimized entirely through post-training, using a refined mix of supervised fine-tuning (SFT) and DPO datasets including regenerated ShareGPT-style data, translation tasks, roleplaying conversations, and instruction-following prompts. Unlike earlier Shisa releases, this version avoids tokenizer modifications or extended pretraining.\n\nShisa V2 70B achieves leading Japanese task performance across a wide range of custom and public benchmarks, including JA MT Bench, ELYZA 100, and Rakuda. It supports a 128K token context length and integrates smoothly with inference frameworks like vLLM and SGLang. While it inherits safety characteristics from its base model, no additional alignment was applied. The model is intended for high-performance bilingual chat, instruction following, and translation tasks across JA/EN.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"shisa-ai/shisa-v2-llama3.3-70b","name":"shisa-ai/shisa-v2-llama3.3-70b","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"shisa-ai/shisa-v2-llama3.3-70b","description":"Shisa V2 Llama 3.3 70B is a bilingual Japanese-English chat model fine-tuned by Shisa.AI on Meta’s Llama-3.3-70B-Instruct base. It prioritizes Japanese language performance while retaining strong English capabilities. 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It is optimized for creative roleplay and dialogue generation, with an emphasis on minimizing cross-context repetition while preserving stylistic diversity.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"arliai/qwq-32b-arliai-rpr-v1","name":"arliai/qwq-32b-arliai-rpr-v1","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"arliai/qwq-32b-arliai-rpr-v1","description":"QwQ-32B-ArliAI-RpR-v1 is a 32B parameter model fine-tuned from Qwen/QwQ-32B using a curated creative writing and roleplay dataset originally developed for the RPMax series. It is designed to maintain coherence and reasoning across long multi-turn conversations by introducing explicit reasoning steps per dialogue turn, generated and refined using the base model itself.\n\nThe model was trained using RS-QLORA+ on 8K sequence lengths and supports up to 128K context windows (with practical performance around 32K). It is optimized for creative roleplay and dialogue generation, with an emphasis on minimizing cross-context repetition while preserving stylistic diversity.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"agentica-org/deepcoder-14b-preview:free","name":"agentica-org/deepcoder-14b-preview:free","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"agentica-org/deepcoder-14b-preview:free","description":"DeepCoder-14B-Preview is a 14B parameter code generation model fine-tuned from DeepSeek-R1-Distill-Qwen-14B using reinforcement learning with GRPO+ and iterative context lengthening. It is optimized for long-context program synthesis and achieves strong performance across coding benchmarks, including 60.6% on LiveCodeBench v5, competitive with models like o3-Mini","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"agentica-org/deepcoder-14b-preview","name":"agentica-org/deepcoder-14b-preview","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"agentica-org/deepcoder-14b-preview","description":"DeepCoder-14B-Preview is a 14B parameter code generation model fine-tuned from DeepSeek-R1-Distill-Qwen-14B using reinforcement learning with GRPO+ and iterative context lengthening. It is optimized for long-context program synthesis and achieves strong performance across coding benchmarks, including 60.6% on LiveCodeBench v5, competitive with models like o3-Mini","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"x-ai/grok-3-mini-beta","name":"x-ai/grok-3-mini-beta","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"x-ai/grok-3-mini-beta","description":"Grok 3 Mini is a lightweight, smaller thinking model. Unlike traditional models that generate answers immediately, Grok 3 Mini thinks before responding. It’s ideal for reasoning-heavy tasks that don’t demand extensive domain knowledge, and shines in math-specific and quantitative use cases, such as solving challenging puzzles or math problems.\n\nTransparent \"thinking\" traces accessible. Defaults to low reasoning, can boost with setting `reasoning: { effort: \"high\" }`\n\nNote: That there are two xAI endpoints for this model. By default when using this model we will always route you to the base endpoint. If you want the fast endpoint you can add `provider: { sort: throughput}`, to sort by throughput instead. \n","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"x-ai/grok-3-beta","name":"x-ai/grok-3-beta","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"x-ai/grok-3-beta","description":"Grok 3 is the latest model from xAI. It's their flagship model that excels at enterprise use cases like data extraction, coding, and text summarization. Possesses deep domain knowledge in finance, healthcare, law, and science.\n\nExcels in structured tasks and benchmarks like GPQA, LCB, and MMLU-Pro where it outperforms Grok 3 Mini even on high thinking. \n\nNote: That there are two xAI endpoints for this model. By default when using this model we will always route you to the base endpoint. If you want the fast endpoint you can add `provider: { sort: throughput}`, to sort by throughput instead. \n","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"nvidia/llama-3.1-nemotron-ultra-253b-v1","name":"nvidia/llama-3.1-nemotron-ultra-253b-v1","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"nvidia/llama-3.1-nemotron-ultra-253b-v1","description":"Llama-3.1-Nemotron-Ultra-253B-v1 is a large language model (LLM) optimized for advanced reasoning, human-interactive chat, retrieval-augmented generation (RAG), and tool-calling tasks. Derived from Meta’s Llama-3.1-405B-Instruct, it has been significantly customized using Neural Architecture Search (NAS), resulting in enhanced efficiency, reduced memory usage, and improved inference latency. The model supports a context length of up to 128K tokens and can operate efficiently on an 8x NVIDIA H100 node.\n\nNote: you must include `detailed thinking on` in the system prompt to enable reasoning. Please see [Usage Recommendations](https://huggingface.co/nvidia/Llama-3_1-Nemotron-Ultra-253B-v1#quick-start-and-usage-recommendations) for more.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"meta-llama/llama-4-maverick:free","name":"meta-llama/llama-4-maverick:free","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"meta-llama/llama-4-maverick:free","description":"Llama 4 Maverick 17B Instruct (128E) is a high-capacity multimodal language model from Meta, built on a mixture-of-experts (MoE) architecture with 128 experts and 17 billion active parameters per forward pass (400B total). It supports multilingual text and image input, and produces multilingual text and code output across 12 supported languages. 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Released on April 5, 2025 under the Llama 4 Community License, Maverick is suited for research and commercial applications requiring advanced multimodal understanding and high model throughput.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":true,"multimodalAcceptedMimetypes":["image/*"],"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"meta-llama/llama-4-scout:free","name":"meta-llama/llama-4-scout:free","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"meta-llama/llama-4-scout:free","description":"Llama 4 Scout 17B Instruct (16E) is a mixture-of-experts (MoE) language model developed by Meta, activating 17 billion parameters out of a total of 109B. It supports native multimodal input (text and image) and multilingual output (text and code) across 12 supported languages. Designed for assistant-style interaction and visual reasoning, Scout uses 16 experts per forward pass and features a context length of 10 million tokens, with a training corpus of ~40 trillion tokens.\n\nBuilt for high efficiency and local or commercial deployment, Llama 4 Scout incorporates early fusion for seamless modality integration. It is instruction-tuned for use in multilingual chat, captioning, and image understanding tasks. 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Designed for assistant-style interaction and visual reasoning, Scout uses 16 experts per forward pass and features a context length of 10 million tokens, with a training corpus of ~40 trillion tokens.\n\nBuilt for high efficiency and local or commercial deployment, Llama 4 Scout incorporates early fusion for seamless modality integration. It is instruction-tuned for use in multilingual chat, captioning, and image understanding tasks. 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Equipped with an extensive 128k token context window and optimized for efficient local inference, it supports use cases such as conversational agents, function calling, long-document comprehension, and privacy-sensitive deployments. The updated version is [Mistral Small 3.2](mistralai/mistral-small-3.2-24b-instruct)","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":true,"multimodalAcceptedMimetypes":["image/*"],"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"mistralai/mistral-small-3.1-24b-instruct","name":"mistralai/mistral-small-3.1-24b-instruct","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"mistralai/mistral-small-3.1-24b-instruct","description":"Mistral Small 3.1 24B Instruct is an upgraded variant of Mistral Small 3 (2501), featuring 24 billion parameters with advanced multimodal capabilities. 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The updated version is [Mistral Small 3.2](mistralai/mistral-small-3.2-24b-instruct)","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":true,"multimodalAcceptedMimetypes":["image/*"],"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"allenai/olmo-2-0325-32b-instruct","name":"allenai/olmo-2-0325-32b-instruct","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"allenai/olmo-2-0325-32b-instruct","description":"OLMo-2 32B Instruct is a supervised instruction-finetuned variant of the OLMo-2 32B March 2025 base model. It excels in complex reasoning and instruction-following tasks across diverse benchmarks such as GSM8K, MATH, IFEval, and general NLP evaluation. 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It's a proprietary weights-available model and excels at reasoning, code, JSON, chat, and more. Read the launch announcement [here](https://mistral.ai/news/mistral-large-2407/).\n\nIt supports dozens of languages including French, German, Spanish, Italian, Portuguese, Arabic, Hindi, Russian, Chinese, Japanese, and Korean, along with 80+ coding languages including Python, Java, C, C++, JavaScript, and Bash. Its long context window allows precise information recall from large documents.\n","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"mistralai/pixtral-large-2411","name":"mistralai/pixtral-large-2411","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"mistralai/pixtral-large-2411","description":"Pixtral Large is a 124B parameter, open-weight, multimodal model built on top of [Mistral Large 2](/mistralai/mistral-large-2411). The model is able to understand documents, charts and natural images.\n\nThe model is available under the Mistral Research License (MRL) for research and educational use, and the Mistral Commercial License for experimentation, testing, and production for commercial purposes.\n\n","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":true,"multimodalAcceptedMimetypes":["image/*"],"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"qwen/qwen-2.5-coder-32b-instruct:free","name":"qwen/qwen-2.5-coder-32b-instruct:free","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"qwen/qwen-2.5-coder-32b-instruct:free","description":"Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen). Qwen2.5-Coder brings the following improvements upon CodeQwen1.5:\n\n- Significantly improvements in **code generation**, **code reasoning** and **code fixing**. \n- A more comprehensive foundation for real-world applications such as **Code Agents**. Not only enhancing coding capabilities but also maintaining its strengths in mathematics and general competencies.\n\nTo read more about its evaluation results, check out [Qwen 2.5 Coder's blog](https://qwenlm.github.io/blog/qwen2.5-coder-family/).","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"qwen/qwen-2.5-coder-32b-instruct","name":"qwen/qwen-2.5-coder-32b-instruct","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"qwen/qwen-2.5-coder-32b-instruct","description":"Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen). Qwen2.5-Coder brings the following improvements upon CodeQwen1.5:\n\n- Significantly improvements in **code generation**, **code reasoning** and **code fixing**. \n- A more comprehensive foundation for real-world applications such as **Code Agents**. Not only enhancing coding capabilities but also maintaining its strengths in mathematics and general competencies.\n\nTo read more about its evaluation results, check out [Qwen 2.5 Coder's blog](https://qwenlm.github.io/blog/qwen2.5-coder-family/).","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"raifle/sorcererlm-8x22b","name":"raifle/sorcererlm-8x22b","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"raifle/sorcererlm-8x22b","description":"SorcererLM is an advanced RP and storytelling model, built as a Low-rank 16-bit LoRA fine-tuned on [WizardLM-2 8x22B](/microsoft/wizardlm-2-8x22b).\n\n- Advanced reasoning and emotional intelligence for engaging and immersive interactions\n- Vivid writing capabilities enriched with spatial and contextual awareness\n- Enhanced narrative depth, promoting creative and dynamic storytelling","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"thedrummer/unslopnemo-12b","name":"thedrummer/unslopnemo-12b","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"thedrummer/unslopnemo-12b","description":"UnslopNemo v4.1 is the latest addition from the creator of Rocinante, designed for adventure writing and role-play scenarios.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"anthropic/claude-3.5-haiku","name":"anthropic/claude-3.5-haiku","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"anthropic/claude-3.5-haiku","description":"Claude 3.5 Haiku features offers enhanced capabilities in speed, coding accuracy, and tool use. Engineered to excel in real-time applications, it delivers quick response times that are essential for dynamic tasks such as chat interactions and immediate coding suggestions.\n\nThis makes it highly suitable for environments that demand both speed and precision, such as software development, customer service bots, and data management systems.\n\nThis model is currently pointing to [Claude 3.5 Haiku (2024-10-22)](/anthropic/claude-3-5-haiku-20241022).","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":true,"multimodalAcceptedMimetypes":["image/*"],"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"anthropic/claude-3.5-haiku-20241022","name":"anthropic/claude-3.5-haiku-20241022","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"anthropic/claude-3.5-haiku-20241022","description":"Claude 3.5 Haiku features enhancements across all skill sets including coding, tool use, and reasoning. As the fastest model in the Anthropic lineup, it offers rapid response times suitable for applications that require high interactivity and low latency, such as user-facing chatbots and on-the-fly code completions. It also excels in specialized tasks like data extraction and real-time content moderation, making it a versatile tool for a broad range of industries.\n\nIt does not support image inputs.\n\nSee the launch announcement and benchmark results [here](https://www.anthropic.com/news/3-5-models-and-computer-use)","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":true,"multimodalAcceptedMimetypes":["image/*"],"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"anthracite-org/magnum-v4-72b","name":"anthracite-org/magnum-v4-72b","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"anthracite-org/magnum-v4-72b","description":"This is a series of models designed to replicate the prose quality of the Claude 3 models, specifically Sonnet(https://openrouter.ai/anthropic/claude-3.5-sonnet) and Opus(https://openrouter.ai/anthropic/claude-3-opus).\n\nThe model is fine-tuned on top of [Qwen2.5 72B](https://openrouter.ai/qwen/qwen-2.5-72b-instruct).","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"anthropic/claude-3.5-sonnet","name":"anthropic/claude-3.5-sonnet","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"anthropic/claude-3.5-sonnet","description":"New Claude 3.5 Sonnet delivers better-than-Opus capabilities, faster-than-Sonnet speeds, at the same Sonnet prices. Sonnet is particularly good at:\n\n- Coding: Scores ~49% on SWE-Bench Verified, higher than the last best score, and without any fancy prompt scaffolding\n- Data science: Augments human data science expertise; navigates unstructured data while using multiple tools for insights\n- Visual processing: excelling at interpreting charts, graphs, and images, accurately transcribing text to derive insights beyond just the text alone\n- Agentic tasks: exceptional tool use, making it great at agentic tasks (i.e. complex, multi-step problem solving tasks that require engaging with other systems)\n\n#multimodal","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":true,"multimodalAcceptedMimetypes":["image/*"],"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"mistralai/ministral-3b","name":"mistralai/ministral-3b","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"mistralai/ministral-3b","description":"Ministral 3B is a 3B parameter model optimized for on-device and edge computing. It excels in knowledge, commonsense reasoning, and function-calling, outperforming larger models like Mistral 7B on most benchmarks. Supporting up to 128k context length, it’s ideal for orchestrating agentic workflows and specialist tasks with efficient inference.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"mistralai/ministral-8b","name":"mistralai/ministral-8b","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"mistralai/ministral-8b","description":"Ministral 8B is an 8B parameter model featuring a unique interleaved sliding-window attention pattern for faster, memory-efficient inference. Designed for edge use cases, it supports up to 128k context length and excels in knowledge and reasoning tasks. It outperforms peers in the sub-10B category, making it perfect for low-latency, privacy-first applications.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"qwen/qwen-2.5-7b-instruct","name":"qwen/qwen-2.5-7b-instruct","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"qwen/qwen-2.5-7b-instruct","description":"Qwen2.5 7B is the latest series of Qwen large language models. Qwen2.5 brings the following improvements upon Qwen2:\n\n- Significantly more knowledge and has greatly improved capabilities in coding and mathematics, thanks to our specialized expert models in these domains.\n\n- Significant improvements in instruction following, generating long texts (over 8K tokens), understanding structured data (e.g, tables), and generating structured outputs especially JSON. More resilient to the diversity of system prompts, enhancing role-play implementation and condition-setting for chatbots.\n\n- Long-context Support up to 128K tokens and can generate up to 8K tokens.\n\n- Multilingual support for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more.\n\nUsage of this model is subject to [Tongyi Qianwen LICENSE AGREEMENT](https://huggingface.co/Qwen/Qwen1.5-110B-Chat/blob/main/LICENSE).","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"nvidia/llama-3.1-nemotron-70b-instruct","name":"nvidia/llama-3.1-nemotron-70b-instruct","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"nvidia/llama-3.1-nemotron-70b-instruct","description":"NVIDIA's Llama 3.1 Nemotron 70B is a language model designed for generating precise and useful responses. Leveraging [Llama 3.1 70B](/models/meta-llama/llama-3.1-70b-instruct) architecture and Reinforcement Learning from Human Feedback (RLHF), it excels in automatic alignment benchmarks. This model is tailored for applications requiring high accuracy in helpfulness and response generation, suitable for diverse user queries across multiple domains.\n\nUsage of this model is subject to [Meta's Acceptable Use Policy](https://www.llama.com/llama3/use-policy/).","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"inflection/inflection-3-pi","name":"inflection/inflection-3-pi","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"inflection/inflection-3-pi","description":"Inflection 3 Pi powers Inflection's [Pi](https://pi.ai) chatbot, including backstory, emotional intelligence, productivity, and safety. It has access to recent news, and excels in scenarios like customer support and roleplay.\n\nPi has been trained to mirror your tone and style, if you use more emojis, so will Pi! Try experimenting with various prompts and conversation styles.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"inflection/inflection-3-productivity","name":"inflection/inflection-3-productivity","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"inflection/inflection-3-productivity","description":"Inflection 3 Productivity is optimized for following instructions. It is better for tasks requiring JSON output or precise adherence to provided guidelines. It has access to recent news.\n\nFor emotional intelligence similar to Pi, see [Inflect 3 Pi](/inflection/inflection-3-pi)\n\nSee [Inflection's announcement](https://inflection.ai/blog/enterprise) for more details.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"anthracite-org/magnum-v2-72b","name":"anthracite-org/magnum-v2-72b","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"anthracite-org/magnum-v2-72b","description":"From the maker of [Goliath](https://openrouter.ai/models/alpindale/goliath-120b), Magnum 72B is the seventh in a family of models designed to achieve the prose quality of the Claude 3 models, notably Opus & Sonnet.\n\nThe model is based on [Qwen2 72B](https://openrouter.ai/models/qwen/qwen-2-72b-instruct) and trained with 55 million tokens of highly curated roleplay (RP) data.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"thedrummer/rocinante-12b","name":"thedrummer/rocinante-12b","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"thedrummer/rocinante-12b","description":"Rocinante 12B is designed for engaging storytelling and rich prose.\n\nEarly testers have reported:\n- Expanded vocabulary with unique and expressive word choices\n- Enhanced creativity for vivid narratives\n- Adventure-filled and captivating stories","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"meta-llama/llama-3.2-90b-vision-instruct","name":"meta-llama/llama-3.2-90b-vision-instruct","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"meta-llama/llama-3.2-90b-vision-instruct","description":"The Llama 90B Vision model is a top-tier, 90-billion-parameter multimodal model designed for the most challenging visual reasoning and language tasks. It offers unparalleled accuracy in image captioning, visual question answering, and advanced image-text comprehension. Pre-trained on vast multimodal datasets and fine-tuned with human feedback, the Llama 90B Vision is engineered to handle the most demanding image-based AI tasks.\n\nThis model is perfect for industries requiring cutting-edge multimodal AI capabilities, particularly those dealing with complex, real-time visual and textual analysis.\n\nClick here for the [original model card](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/MODEL_CARD_VISION.md).\n\nUsage of this model is subject to [Meta's Acceptable Use Policy](https://www.llama.com/llama3/use-policy/).","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":true,"multimodalAcceptedMimetypes":["image/*"],"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"meta-llama/llama-3.2-3b-instruct:free","name":"meta-llama/llama-3.2-3b-instruct:free","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"meta-llama/llama-3.2-3b-instruct:free","description":"Llama 3.2 3B is a 3-billion-parameter multilingual large language model, optimized for advanced natural language processing tasks like dialogue generation, reasoning, and summarization. Designed with the latest transformer architecture, it supports eight languages, including English, Spanish, and Hindi, and is adaptable for additional languages.\n\nTrained on 9 trillion tokens, the Llama 3.2 3B model excels in instruction-following, complex reasoning, and tool use. Its balanced performance makes it ideal for applications needing accuracy and efficiency in text generation across multilingual settings.\n\nClick here for the [original model card](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/MODEL_CARD.md).\n\nUsage of this model is subject to [Meta's Acceptable Use Policy](https://www.llama.com/llama3/use-policy/).","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"meta-llama/llama-3.2-3b-instruct","name":"meta-llama/llama-3.2-3b-instruct","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"meta-llama/llama-3.2-3b-instruct","description":"Llama 3.2 3B is a 3-billion-parameter multilingual large language model, optimized for advanced natural language processing tasks like dialogue generation, reasoning, and summarization. Designed with the latest transformer architecture, it supports eight languages, including English, Spanish, and Hindi, and is adaptable for additional languages.\n\nTrained on 9 trillion tokens, the Llama 3.2 3B model excels in instruction-following, complex reasoning, and tool use. Its balanced performance makes it ideal for applications needing accuracy and efficiency in text generation across multilingual settings.\n\nClick here for the [original model card](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/MODEL_CARD.md).\n\nUsage of this model is subject to [Meta's Acceptable Use Policy](https://www.llama.com/llama3/use-policy/).","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"meta-llama/llama-3.2-1b-instruct","name":"meta-llama/llama-3.2-1b-instruct","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"meta-llama/llama-3.2-1b-instruct","description":"Llama 3.2 1B is a 1-billion-parameter language model focused on efficiently performing natural language tasks, such as summarization, dialogue, and multilingual text analysis. Its smaller size allows it to operate efficiently in low-resource environments while maintaining strong task performance.\n\nSupporting eight core languages and fine-tunable for more, Llama 1.3B is ideal for businesses or developers seeking lightweight yet powerful AI solutions that can operate in diverse multilingual settings without the high computational demand of larger models.\n\nClick here for the [original model card](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/MODEL_CARD.md).\n\nUsage of this model is subject to [Meta's Acceptable Use Policy](https://www.llama.com/llama3/use-policy/).","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"meta-llama/llama-3.2-11b-vision-instruct","name":"meta-llama/llama-3.2-11b-vision-instruct","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"meta-llama/llama-3.2-11b-vision-instruct","description":"Llama 3.2 11B Vision is a multimodal model with 11 billion parameters, designed to handle tasks combining visual and textual data. It excels in tasks such as image captioning and visual question answering, bridging the gap between language generation and visual reasoning. Pre-trained on a massive dataset of image-text pairs, it performs well in complex, high-accuracy image analysis.\n\nIts ability to integrate visual understanding with language processing makes it an ideal solution for industries requiring comprehensive visual-linguistic AI applications, such as content creation, AI-driven customer service, and research.\n\nClick here for the [original model card](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/MODEL_CARD_VISION.md).\n\nUsage of this model is subject to [Meta's Acceptable Use Policy](https://www.llama.com/llama3/use-policy/).","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":true,"multimodalAcceptedMimetypes":["image/*"],"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"qwen/qwen-2.5-72b-instruct:free","name":"qwen/qwen-2.5-72b-instruct:free","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"qwen/qwen-2.5-72b-instruct:free","description":"Qwen2.5 72B is the latest series of Qwen large language models. Qwen2.5 brings the following improvements upon Qwen2:\n\n- Significantly more knowledge and has greatly improved capabilities in coding and mathematics, thanks to our specialized expert models in these domains.\n\n- Significant improvements in instruction following, generating long texts (over 8K tokens), understanding structured data (e.g, tables), and generating structured outputs especially JSON. More resilient to the diversity of system prompts, enhancing role-play implementation and condition-setting for chatbots.\n\n- Long-context Support up to 128K tokens and can generate up to 8K tokens.\n\n- Multilingual support for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more.\n\nUsage of this model is subject to [Tongyi Qianwen LICENSE AGREEMENT](https://huggingface.co/Qwen/Qwen1.5-110B-Chat/blob/main/LICENSE).","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"qwen/qwen-2.5-72b-instruct","name":"qwen/qwen-2.5-72b-instruct","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"qwen/qwen-2.5-72b-instruct","description":"Qwen2.5 72B is the latest series of Qwen large language models. Qwen2.5 brings the following improvements upon Qwen2:\n\n- Significantly more knowledge and has greatly improved capabilities in coding and mathematics, thanks to our specialized expert models in these domains.\n\n- Significant improvements in instruction following, generating long texts (over 8K tokens), understanding structured data (e.g, tables), and generating structured outputs especially JSON. More resilient to the diversity of system prompts, enhancing role-play implementation and condition-setting for chatbots.\n\n- Long-context Support up to 128K tokens and can generate up to 8K tokens.\n\n- Multilingual support for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more.\n\nUsage of this model is subject to [Tongyi Qianwen LICENSE AGREEMENT](https://huggingface.co/Qwen/Qwen1.5-110B-Chat/blob/main/LICENSE).","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"neversleep/llama-3.1-lumimaid-8b","name":"neversleep/llama-3.1-lumimaid-8b","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"neversleep/llama-3.1-lumimaid-8b","description":"Lumimaid v0.2 8B is a finetune of [Llama 3.1 8B](/models/meta-llama/llama-3.1-8b-instruct) with a \"HUGE step up dataset wise\" compared to Lumimaid v0.1. Sloppy chats output were purged.\n\nUsage of this model is subject to [Meta's Acceptable Use Policy](https://llama.meta.com/llama3/use-policy/).","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"openai/o1-mini-2024-09-12","name":"openai/o1-mini-2024-09-12","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"openai/o1-mini-2024-09-12","description":"The latest and strongest model family from OpenAI, o1 is designed to spend more time thinking before responding.\n\nThe o1 models are optimized for math, science, programming, and other STEM-related tasks. They consistently exhibit PhD-level accuracy on benchmarks in physics, chemistry, and biology. Learn more in the [launch announcement](https://openai.com/o1).\n\nNote: This model is currently experimental and not suitable for production use-cases, and may be heavily rate-limited.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"openai/o1-mini","name":"openai/o1-mini","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"openai/o1-mini","description":"The latest and strongest model family from OpenAI, o1 is designed to spend more time thinking before responding.\n\nThe o1 models are optimized for math, science, programming, and other STEM-related tasks. They consistently exhibit PhD-level accuracy on benchmarks in physics, chemistry, and biology. Learn more in the [launch announcement](https://openai.com/o1).\n\nNote: This model is currently experimental and not suitable for production use-cases, and may be heavily rate-limited.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"mistralai/pixtral-12b","name":"mistralai/pixtral-12b","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"mistralai/pixtral-12b","description":"The first multi-modal, text+image-to-text model from Mistral AI. Its weights were launched via torrent: https://x.com/mistralai/status/1833758285167722836.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":true,"multimodalAcceptedMimetypes":["image/*"],"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"cohere/command-r-08-2024","name":"cohere/command-r-08-2024","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"cohere/command-r-08-2024","description":"command-r-08-2024 is an update of the [Command R](/models/cohere/command-r) with improved performance for multilingual retrieval-augmented generation (RAG) and tool use. More broadly, it is better at math, code and reasoning and is competitive with the previous version of the larger Command R+ model.\n\nRead the launch post [here](https://docs.cohere.com/changelog/command-gets-refreshed).\n\nUse of this model is subject to Cohere's [Usage Policy](https://docs.cohere.com/docs/usage-policy) and [SaaS Agreement](https://cohere.com/saas-agreement).","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"cohere/command-r-plus-08-2024","name":"cohere/command-r-plus-08-2024","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"cohere/command-r-plus-08-2024","description":"command-r-plus-08-2024 is an update of the [Command R+](/models/cohere/command-r-plus) with roughly 50% higher throughput and 25% lower latencies as compared to the previous Command R+ version, while keeping the hardware footprint the same.\n\nRead the launch post [here](https://docs.cohere.com/changelog/command-gets-refreshed).\n\nUse of this model is subject to Cohere's [Usage Policy](https://docs.cohere.com/docs/usage-policy) and [SaaS Agreement](https://cohere.com/saas-agreement).","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"qwen/qwen-2.5-vl-7b-instruct","name":"qwen/qwen-2.5-vl-7b-instruct","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"qwen/qwen-2.5-vl-7b-instruct","description":"Qwen2.5 VL 7B is a multimodal LLM from the Qwen Team with the following key enhancements:\n\n- SoTA understanding of images of various resolution & ratio: Qwen2.5-VL achieves state-of-the-art performance on visual understanding benchmarks, including MathVista, DocVQA, RealWorldQA, MTVQA, etc.\n\n- Understanding videos of 20min+: Qwen2.5-VL can understand videos over 20 minutes for high-quality video-based question answering, dialog, content creation, etc.\n\n- Agent that can operate your mobiles, robots, etc.: with the abilities of complex reasoning and decision making, Qwen2.5-VL can be integrated with devices like mobile phones, robots, etc., for automatic operation based on visual environment and text instructions.\n\n- Multilingual Support: to serve global users, besides English and Chinese, Qwen2.5-VL now supports the understanding of texts in different languages inside images, including most European languages, Japanese, Korean, Arabic, Vietnamese, etc.\n\nFor more details, see this [blog post](https://qwenlm.github.io/blog/qwen2-vl/) and [GitHub repo](https://github.com/QwenLM/Qwen2-VL).\n\nUsage of this model is subject to [Tongyi Qianwen LICENSE AGREEMENT](https://huggingface.co/Qwen/Qwen1.5-110B-Chat/blob/main/LICENSE).","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":true,"multimodalAcceptedMimetypes":["image/*"],"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"sao10k/l3.1-euryale-70b","name":"sao10k/l3.1-euryale-70b","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"sao10k/l3.1-euryale-70b","description":"Euryale L3.1 70B v2.2 is a model focused on creative roleplay from [Sao10k](https://ko-fi.com/sao10k). It is the successor of [Euryale L3 70B v2.1](/models/sao10k/l3-euryale-70b).","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"microsoft/phi-3.5-mini-128k-instruct","name":"microsoft/phi-3.5-mini-128k-instruct","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"microsoft/phi-3.5-mini-128k-instruct","description":"Phi-3.5 models are lightweight, state-of-the-art open models. These models were trained with Phi-3 datasets that include both synthetic data and the filtered, publicly available websites data, with a focus on high quality and reasoning-dense properties. Phi-3.5 Mini uses 3.8B parameters, and is a dense decoder-only transformer model using the same tokenizer as [Phi-3 Mini](/models/microsoft/phi-3-mini-128k-instruct).\n\nThe models underwent a rigorous enhancement process, incorporating both supervised fine-tuning, proximal policy optimization, and direct preference optimization to ensure precise instruction adherence and robust safety measures. When assessed against benchmarks that test common sense, language understanding, math, code, long context and logical reasoning, Phi-3.5 models showcased robust and state-of-the-art performance among models with less than 13 billion parameters.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"nousresearch/hermes-3-llama-3.1-70b","name":"nousresearch/hermes-3-llama-3.1-70b","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"nousresearch/hermes-3-llama-3.1-70b","description":"Hermes 3 is a generalist language model with many improvements over [Hermes 2](/models/nousresearch/nous-hermes-2-mistral-7b-dpo), including advanced agentic capabilities, much better roleplaying, reasoning, multi-turn conversation, long context coherence, and improvements across the board.\n\nHermes 3 70B is a competitive, if not superior finetune of the [Llama-3.1 70B foundation model](/models/meta-llama/llama-3.1-70b-instruct), focused on aligning LLMs to the user, with powerful steering capabilities and control given to the end user.\n\nThe Hermes 3 series builds and expands on the Hermes 2 set of capabilities, including more powerful and reliable function calling and structured output capabilities, generalist assistant capabilities, and improved code generation skills.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"nousresearch/hermes-3-llama-3.1-405b:free","name":"nousresearch/hermes-3-llama-3.1-405b:free","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"nousresearch/hermes-3-llama-3.1-405b:free","description":"Hermes 3 is a generalist language model with many improvements over Hermes 2, including advanced agentic capabilities, much better roleplaying, reasoning, multi-turn conversation, long context coherence, and improvements across the board.\n\nHermes 3 405B is a frontier-level, full-parameter finetune of the Llama-3.1 405B foundation model, focused on aligning LLMs to the user, with powerful steering capabilities and control given to the end user.\n\nThe Hermes 3 series builds and expands on the Hermes 2 set of capabilities, including more powerful and reliable function calling and structured output capabilities, generalist assistant capabilities, and improved code generation skills.\n\nHermes 3 is competitive, if not superior, to Llama-3.1 Instruct models at general capabilities, with varying strengths and weaknesses attributable between the two.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"nousresearch/hermes-3-llama-3.1-405b","name":"nousresearch/hermes-3-llama-3.1-405b","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"nousresearch/hermes-3-llama-3.1-405b","description":"Hermes 3 is a generalist language model with many improvements over Hermes 2, including advanced agentic capabilities, much better roleplaying, reasoning, multi-turn conversation, long context coherence, and improvements across the board.\n\nHermes 3 405B is a frontier-level, full-parameter finetune of the Llama-3.1 405B foundation model, focused on aligning LLMs to the user, with powerful steering capabilities and control given to the end user.\n\nThe Hermes 3 series builds and expands on the Hermes 2 set of capabilities, including more powerful and reliable function calling and structured output capabilities, generalist assistant capabilities, and improved code generation skills.\n\nHermes 3 is competitive, if not superior, to Llama-3.1 Instruct models at general capabilities, with varying strengths and weaknesses attributable between the two.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"openai/chatgpt-4o-latest","name":"openai/chatgpt-4o-latest","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"openai/chatgpt-4o-latest","description":"OpenAI ChatGPT 4o is continually updated by OpenAI to point to the current version of GPT-4o used by ChatGPT. It therefore differs slightly from the API version of [GPT-4o](/models/openai/gpt-4o) in that it has additional RLHF. It is intended for research and evaluation.\n\nOpenAI notes that this model is not suited for production use-cases as it may be removed or redirected to another model in the future.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":true,"multimodalAcceptedMimetypes":["image/*"],"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"sao10k/l3-lunaris-8b","name":"sao10k/l3-lunaris-8b","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"sao10k/l3-lunaris-8b","description":"Lunaris 8B is a versatile generalist and roleplaying model based on Llama 3. It's a strategic merge of multiple models, designed to balance creativity with improved logic and general knowledge.\n\nCreated by [Sao10k](https://huggingface.co/Sao10k), this model aims to offer an improved experience over Stheno v3.2, with enhanced creativity and logical reasoning.\n\nFor best results, use with Llama 3 Instruct context template, temperature 1.4, and min_p 0.1.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"openai/gpt-4o-2024-08-06","name":"openai/gpt-4o-2024-08-06","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"openai/gpt-4o-2024-08-06","description":"The 2024-08-06 version of GPT-4o offers improved performance in structured outputs, with the ability to supply a JSON schema in the respone_format. Read more [here](https://openai.com/index/introducing-structured-outputs-in-the-api/).\n\nGPT-4o (\"o\" for \"omni\") is OpenAI's latest AI model, supporting both text and image inputs with text outputs. It maintains the intelligence level of [GPT-4 Turbo](/models/openai/gpt-4-turbo) while being twice as fast and 50% more cost-effective. GPT-4o also offers improved performance in processing non-English languages and enhanced visual capabilities.\n\nFor benchmarking against other models, it was briefly called [\"im-also-a-good-gpt2-chatbot\"](https://twitter.com/LiamFedus/status/1790064963966370209)","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":true,"multimodalAcceptedMimetypes":["image/*"],"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"meta-llama/llama-3.1-405b","name":"meta-llama/llama-3.1-405b","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"meta-llama/llama-3.1-405b","description":"Meta's latest class of model (Llama 3.1) launched with a variety of sizes & flavors. This is the base 405B pre-trained version.\n\nIt has demonstrated strong performance compared to leading closed-source models in human evaluations.\n\nTo read more about the model release, [click here](https://ai.meta.com/blog/meta-llama-3/). Usage of this model is subject to [Meta's Acceptable Use Policy](https://llama.meta.com/llama3/use-policy/).","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"meta-llama/llama-3.1-8b-instruct","name":"meta-llama/llama-3.1-8b-instruct","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"meta-llama/llama-3.1-8b-instruct","description":"Meta's latest class of model (Llama 3.1) launched with a variety of sizes & flavors. This 8B instruct-tuned version is fast and efficient.\n\nIt has demonstrated strong performance compared to leading closed-source models in human evaluations.\n\nTo read more about the model release, [click here](https://ai.meta.com/blog/meta-llama-3-1/). Usage of this model is subject to [Meta's Acceptable Use Policy](https://llama.meta.com/llama3/use-policy/).","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"meta-llama/llama-3.1-70b-instruct","name":"meta-llama/llama-3.1-70b-instruct","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"meta-llama/llama-3.1-70b-instruct","description":"Meta's latest class of model (Llama 3.1) launched with a variety of sizes & flavors. This 70B instruct-tuned version is optimized for high quality dialogue usecases.\n\nIt has demonstrated strong performance compared to leading closed-source models in human evaluations.\n\nTo read more about the model release, [click here](https://ai.meta.com/blog/meta-llama-3-1/). Usage of this model is subject to [Meta's Acceptable Use Policy](https://llama.meta.com/llama3/use-policy/).","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"meta-llama/llama-3.1-405b-instruct","name":"meta-llama/llama-3.1-405b-instruct","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"meta-llama/llama-3.1-405b-instruct","description":"The highly anticipated 400B class of Llama3 is here! Clocking in at 128k context with impressive eval scores, the Meta AI team continues to push the frontier of open-source LLMs.\n\nMeta's latest class of model (Llama 3.1) launched with a variety of sizes & flavors. This 405B instruct-tuned version is optimized for high quality dialogue usecases.\n\nIt has demonstrated strong performance compared to leading closed-source models including GPT-4o and Claude 3.5 Sonnet in evaluations.\n\nTo read more about the model release, [click here](https://ai.meta.com/blog/meta-llama-3-1/). Usage of this model is subject to [Meta's Acceptable Use Policy](https://llama.meta.com/llama3/use-policy/).","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"mistralai/mistral-nemo:free","name":"mistralai/mistral-nemo:free","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"mistralai/mistral-nemo:free","description":"A 12B parameter model with a 128k token context length built by Mistral in collaboration with NVIDIA.\n\nThe model is multilingual, supporting English, French, German, Spanish, Italian, Portuguese, Chinese, Japanese, Korean, Arabic, and Hindi.\n\nIt supports function calling and is released under the Apache 2.0 license.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"mistralai/mistral-nemo","name":"mistralai/mistral-nemo","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"mistralai/mistral-nemo","description":"A 12B parameter model with a 128k token context length built by Mistral in collaboration with NVIDIA.\n\nThe model is multilingual, supporting English, French, German, Spanish, Italian, Portuguese, Chinese, Japanese, Korean, Arabic, and Hindi.\n\nIt supports function calling and is released under the Apache 2.0 license.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"openai/gpt-4o-mini-2024-07-18","name":"openai/gpt-4o-mini-2024-07-18","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"openai/gpt-4o-mini-2024-07-18","description":"GPT-4o mini is OpenAI's newest model after [GPT-4 Omni](/models/openai/gpt-4o), supporting both text and image inputs with text outputs.\n\nAs their most advanced small model, it is many multiples more affordable than other recent frontier models, and more than 60% cheaper than [GPT-3.5 Turbo](/models/openai/gpt-3.5-turbo). It maintains SOTA intelligence, while being significantly more cost-effective.\n\nGPT-4o mini achieves an 82% score on MMLU and presently ranks higher than GPT-4 on chat preferences [common leaderboards](https://arena.lmsys.org/).\n\nCheck out the [launch announcement](https://openai.com/index/gpt-4o-mini-advancing-cost-efficient-intelligence/) to learn more.\n\n#multimodal","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":true,"multimodalAcceptedMimetypes":["image/*"],"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"openai/gpt-4o-mini","name":"openai/gpt-4o-mini","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"openai/gpt-4o-mini","description":"GPT-4o mini is OpenAI's newest model after [GPT-4 Omni](/models/openai/gpt-4o), supporting both text and image inputs with text outputs.\n\nAs their most advanced small model, it is many multiples more affordable than other recent frontier models, and more than 60% cheaper than [GPT-3.5 Turbo](/models/openai/gpt-3.5-turbo). It maintains SOTA intelligence, while being significantly more cost-effective.\n\nGPT-4o mini achieves an 82% score on MMLU and presently ranks higher than GPT-4 on chat preferences [common leaderboards](https://arena.lmsys.org/).\n\nCheck out the [launch announcement](https://openai.com/index/gpt-4o-mini-advancing-cost-efficient-intelligence/) to learn more.\n\n#multimodal","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":true,"multimodalAcceptedMimetypes":["image/*"],"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"google/gemma-2-27b-it","name":"google/gemma-2-27b-it","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"google/gemma-2-27b-it","description":"Gemma 2 27B by Google is an open model built from the same research and technology used to create the [Gemini models](/models?q=gemini).\n\nGemma models are well-suited for a variety of text generation tasks, including question answering, summarization, and reasoning.\n\nSee the [launch announcement](https://blog.google/technology/developers/google-gemma-2/) for more details. Usage of Gemma is subject to Google's [Gemma Terms of Use](https://ai.google.dev/gemma/terms).","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"google/gemma-2-9b-it:free","name":"google/gemma-2-9b-it:free","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"google/gemma-2-9b-it:free","description":"Gemma 2 9B by Google is an advanced, open-source language model that sets a new standard for efficiency and performance in its size class.\n\nDesigned for a wide variety of tasks, it empowers developers and researchers to build innovative applications, while maintaining accessibility, safety, and cost-effectiveness.\n\nSee the [launch announcement](https://blog.google/technology/developers/google-gemma-2/) for more details. Usage of Gemma is subject to Google's [Gemma Terms of Use](https://ai.google.dev/gemma/terms).","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"google/gemma-2-9b-it","name":"google/gemma-2-9b-it","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"google/gemma-2-9b-it","description":"Gemma 2 9B by Google is an advanced, open-source language model that sets a new standard for efficiency and performance in its size class.\n\nDesigned for a wide variety of tasks, it empowers developers and researchers to build innovative applications, while maintaining accessibility, safety, and cost-effectiveness.\n\nSee the [launch announcement](https://blog.google/technology/developers/google-gemma-2/) for more details. Usage of Gemma is subject to Google's [Gemma Terms of Use](https://ai.google.dev/gemma/terms).","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"anthropic/claude-3.5-sonnet-20240620","name":"anthropic/claude-3.5-sonnet-20240620","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"anthropic/claude-3.5-sonnet-20240620","description":"Claude 3.5 Sonnet delivers better-than-Opus capabilities, faster-than-Sonnet speeds, at the same Sonnet prices. 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Just like is predecessor, [LlamaGuard 1](https://huggingface.co/meta-llama/LlamaGuard-7b), it can do both prompt and response classification.\n\nLlamaGuard 2 acts as a normal LLM would, generating text that indicates whether the given input/output is safe/unsafe. If deemed unsafe, it will also share the content categories violated.\n\nFor best results, please use raw prompt input or the `/completions` endpoint, instead of the chat API.\n\nIt has demonstrated strong performance compared to leading closed-source models in human evaluations.\n\nTo read more about the model release, [click here](https://ai.meta.com/blog/meta-llama-3/). Usage of this model is subject to [Meta's Acceptable Use Policy](https://llama.meta.com/llama3/use-policy/).","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"meta-llama/llama-3-70b-instruct","name":"meta-llama/llama-3-70b-instruct","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"meta-llama/llama-3-70b-instruct","description":"Meta's latest class of model (Llama 3) launched with a variety of sizes & flavors. This 70B instruct-tuned version was optimized for high quality dialogue usecases.\n\nIt has demonstrated strong performance compared to leading closed-source models in human evaluations.\n\nTo read more about the model release, [click here](https://ai.meta.com/blog/meta-llama-3/). Usage of this model is subject to [Meta's Acceptable Use Policy](https://llama.meta.com/llama3/use-policy/).","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"meta-llama/llama-3-8b-instruct","name":"meta-llama/llama-3-8b-instruct","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"meta-llama/llama-3-8b-instruct","description":"Meta's latest class of model (Llama 3) launched with a variety of sizes & flavors. This 8B instruct-tuned version was optimized for high quality dialogue usecases.\n\nIt has demonstrated strong performance compared to leading closed-source models in human evaluations.\n\nTo read more about the model release, [click here](https://ai.meta.com/blog/meta-llama-3/). Usage of this model is subject to [Meta's Acceptable Use Policy](https://llama.meta.com/llama3/use-policy/).","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"mistralai/mixtral-8x22b-instruct","name":"mistralai/mixtral-8x22b-instruct","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"mistralai/mixtral-8x22b-instruct","description":"Mistral's official instruct fine-tuned version of [Mixtral 8x22B](/models/mistralai/mixtral-8x22b). It uses 39B active parameters out of 141B, offering unparalleled cost efficiency for its size. 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Read the launch announcement [here](https://mistral.ai/news/mistral-large-2407/).\n\nIt supports dozens of languages including French, German, Spanish, Italian, Portuguese, Arabic, Hindi, Russian, Chinese, Japanese, and Korean, along with 80+ coding languages including Python, Java, C, C++, JavaScript, and Bash. Its long context window allows precise information recall from large documents.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"openai/gpt-3.5-turbo-0613","name":"openai/gpt-3.5-turbo-0613","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"openai/gpt-3.5-turbo-0613","description":"GPT-3.5 Turbo is OpenAI's fastest model. 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It's best used for large batch processing tasks where cost is a significant factor but reasoning capabilities are not crucial.","logoUrl":null,"providers":null,"promptExamples":null,"parameters":{"stop_sequences":null},"preprompt":null,"multimodal":false,"multimodalAcceptedMimetypes":null,"unlisted":false,"hasInferenceAPI":false,"isRouter":false},{"id":"mistralai/mistral-small","name":"mistralai/mistral-small","websiteUrl":null,"modelUrl":null,"datasetName":null,"datasetUrl":null,"displayName":"mistralai/mistral-small","description":"With 22 billion parameters, Mistral Small v24.09 offers a convenient mid-point between (Mistral NeMo 12B)[/mistralai/mistral-nemo] and (Mistral Large 2)[/mistralai/mistral-large], providing a cost-effective solution that can be deployed across various platforms and environments. 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