models.json

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    "name": "AI21: Jamba Large 1.7",
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    "description": "Jamba Large 1.7 is the latest model in the Jamba open family, offering improvements in grounding, instruction-following, and overall efficiency. Built on a hybrid SSM-Transformer architecture with a 256K context window, it delivers more accurate, contextually grounded responses and better steerability than previous versions.",
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    "name": "AionLabs: Aion-1.0",
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    "name": "AionLabs: Aion-1.0-Mini",
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    "description": "Aion-1.0-Mini 32B parameter model is a distilled version of the DeepSeek-R1 model, designed for strong performance in reasoning domains such as mathematics, coding, and logic. It is a modified variant of a FuseAI model that outperforms R1-Distill-Qwen-32B and R1-Distill-Llama-70B, with benchmark results available on its [Hugging Face page](https://huggingface.co/FuseAI/FuseO1-DeepSeekR1-QwQ-SkyT1-32B-Preview), independently replicated for verification.",
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    "name": "AionLabs: Aion-2.0",
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    "description": "Aion-2.0 is a variant of DeepSeek V3.2 optimized for immersive roleplaying and storytelling. It is particularly strong at introducing tension, crises, and conflict into stories, making narratives feel more engaging. It also handles mature and darker themes with more nuance and depth.",
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    "description": "Aion-RP-Llama-3.1-8B ranks the highest in the character evaluation portion of the RPBench-Auto benchmark, a roleplaying-specific variant of Arena-Hard-Auto, where LLMs evaluate each other’s responses. It is a fine-tuned base model rather than an instruct model, designed to produce more natural and varied writing.",
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    "id": "alfredpros/codellama-7b-instruct-solidity",
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    "name": "AlfredPros: CodeLLaMa 7B Instruct Solidity",
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    "description": "A finetuned 7 billion parameters Code LLaMA - Instruct model to generate Solidity smart contract using 4-bit QLoRA finetuning provided by PEFT library.",
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    "id": "alibaba/tongyi-deepresearch-30b-a3b",
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    "name": "Tongyi DeepResearch 30B A3B",
    "created": 1758210804,
    "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.",
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    "name": "AllenAI: Olmo 2 32B Instruct",
    "created": 1741988556,
    "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. Developed by AI2, OLMo-2 32B is part of an open, research-oriented initiative, trained primarily on English-language datasets to advance the understanding and development of open-source language models.",
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    "name": "AllenAI: Olmo 3 32B Think",
    "created": 1763758276,
    "description": "Olmo 3 32B Think is a large-scale, 32-billion-parameter model purpose-built for deep reasoning, complex logic chains and advanced instruction-following scenarios. Its capacity enables strong performance on demanding evaluation tasks and highly nuanced conversational reasoning. Developed by Ai2 under the Apache 2.0 license, Olmo 3 32B Think embodies the Olmo initiative’s commitment to openness, offering full transparency across weights, code and training methodology.",
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    "description": "Olmo 3.1 32B Instruct is a large-scale, 32-billion-parameter instruction-tuned language model engineered for high-performance conversational AI, multi-turn dialogue, and practical instruction following. As part of the Olmo 3.1 family, this variant emphasizes responsiveness to complex user directions and robust chat interactions while retaining strong capabilities on reasoning and coding benchmarks. Developed by Ai2 under the Apache 2.0 license, Olmo 3.1 32B Instruct reflects the Olmo initiative’s commitment to openness and transparency.",
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    "name": "Goliath 120B",
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    "description": "A large LLM created by combining two fine-tuned Llama 70B models into one 120B model. Combines Xwin and Euryale.\n\nCredits to\n- [@chargoddard](https://huggingface.co/chargoddard) for developing the framework used to merge the model - [mergekit](https://github.com/cg123/mergekit).\n- [@Undi95](https://huggingface.co/Undi95) for helping with the merge ratios.\n\n#merge",
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    "name": "Amazon: Nova 2 Lite",
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    "name": "Amazon: Nova Micro 1.0",
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    "description": "Amazon Nova Micro 1.0 is a text-only model that delivers the lowest latency responses in the Amazon Nova family of models at a very low cost. With a context length of 128K tokens and optimized for speed and cost, Amazon Nova Micro excels at tasks such as text summarization, translation, content classification, interactive chat, and brainstorming. It has  simple mathematical reasoning and coding abilities.",
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    "description": "Transform your natural language requests into structured OpenRouter API request objects. Describe what you want to accomplish with AI models, and Body Builder will construct the appropriate API calls. Example: \"count to 10 using gemini and opus.\"\n\nThis is useful for creating multi-model requests, custom model routers, or programmatic generation of API calls from human descriptions.\n\n**BETA NOTICE**: Body Builder is in beta, and currently free. Pricing and functionality may change in the future.",
    "context_length": 128000,
    "architecture": {
      "modality": "text->text",
      "input_modalities": [
        "text"
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      "output_modalities": [
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      "tokenizer": "Router",
      "instruct_type": null
    },
    "pricing": {
      "prompt": "-1",
      "completion": "-1"
    },
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      "context_length": null,
      "max_completion_tokens": null,
      "is_moderated": false
    },
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      "temperature": null,
      "top_p": null,
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    },
    "expiration_date": null
  },
  {
    "id": "openrouter/free",
    "canonical_slug": "openrouter/free",
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    "name": "Free Models Router",
    "created": 1769917427,
    "description": "The simplest way to get free inference. openrouter/free is a router that selects free models at random from the models available on OpenRouter. The router smartly filters for models that support features needed for your request such as image understanding, tool calling, structured outputs and more. ",
    "context_length": 200000,
    "architecture": {
      "modality": "text+image->text",
      "input_modalities": [
        "text",
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      "output_modalities": [
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      "tokenizer": "Router",
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    "pricing": {
      "prompt": "0",
      "completion": "0"
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    "top_provider": {
      "context_length": null,
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      "is_moderated": false
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    "id": "perplexity/sonar",
    "canonical_slug": "perplexity/sonar",
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    "name": "Perplexity: Sonar",
    "created": 1738013808,
    "description": "Sonar is lightweight, affordable, fast, and simple to use — now featuring citations and the ability to customize sources. It is designed for companies seeking to integrate lightweight question-and-answer features optimized for speed.",
    "context_length": 127072,
    "architecture": {
      "modality": "text+image->text",
      "input_modalities": [
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      "output_modalities": [
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      "tokenizer": "Other",
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    "pricing": {
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      "web_search": "0.005"
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    "top_provider": {
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    "default_parameters": {},
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  {
    "id": "perplexity/sonar-deep-research",
    "canonical_slug": "perplexity/sonar-deep-research",
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    "name": "Perplexity: Sonar Deep Research",
    "created": 1741311246,
    "description": "Sonar Deep Research is a research-focused model designed for multi-step retrieval, synthesis, and reasoning across complex topics. It autonomously searches, reads, and evaluates sources, refining its approach as it gathers information. This enables comprehensive report generation across domains like finance, technology, health, and current events.\n\nNotes on Pricing ([Source](https://docs.perplexity.ai/guides/pricing#detailed-pricing-breakdown-for-sonar-deep-research)) \n- Input tokens comprise of Prompt tokens (user prompt) + Citation tokens (these are processed tokens from running searches)\n- Deep Research runs multiple searches to conduct exhaustive research. Searches are priced at $5/1000 searches. A request that does 30 searches will cost $0.15 in this step.\n- Reasoning is a distinct step in Deep Research since it does extensive automated reasoning through all the material it gathers during its research phase. Reasoning tokens here are a bit different than the CoTs in the answer - these are tokens that we use to reason through the research material prior to generating the outputs via the CoTs. Reasoning tokens are priced at $3/1M tokens",
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    "architecture": {
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      "input_modalities": [
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      "output_modalities": [
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      "tokenizer": "Other",
      "instruct_type": "deepseek-r1"
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      "web_search": "0.005",
      "internal_reasoning": "0.000003"
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    "top_provider": {
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      "max_completion_tokens": null,
      "is_moderated": false
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    "default_parameters": {},
    "expiration_date": null
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    "id": "perplexity/sonar-pro",
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    "name": "Perplexity: Sonar Pro",
    "created": 1741312423,
    "description": "Note: Sonar Pro pricing includes Perplexity search pricing. See [details here](https://docs.perplexity.ai/guides/pricing#detailed-pricing-breakdown-for-sonar-reasoning-pro-and-sonar-pro)\n\nFor enterprises seeking more advanced capabilities, the Sonar Pro API can handle in-depth, multi-step queries with added extensibility, like double the number of citations per search as Sonar on average. Plus, with a larger context window, it can handle longer and more nuanced searches and follow-up questions. ",
    "context_length": 200000,
    "architecture": {
      "modality": "text+image->text",
      "input_modalities": [
        "text",
        "image"
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      "output_modalities": [
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      ],
      "tokenizer": "Other",
      "instruct_type": null
    },
    "pricing": {
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    "top_provider": {
      "context_length": 200000,
      "max_completion_tokens": 8000,
      "is_moderated": false
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    "per_request_limits": null,
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    "default_parameters": {},
    "expiration_date": null
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  {
    "id": "perplexity/sonar-pro-search",
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    "hugging_face_id": "",
    "name": "Perplexity: Sonar Pro Search",
    "created": 1761854366,
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    "context_length": 200000,
    "architecture": {
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      "output_modalities": [
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      "tokenizer": "Other",
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      "web_search": "0.018"
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    "top_provider": {
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      "max_completion_tokens": 8000,
      "is_moderated": false
    },
    "per_request_limits": null,
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    ],
    "default_parameters": {
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    },
    "expiration_date": null
  },
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    "id": "perplexity/sonar-reasoning-pro",
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    "name": "Perplexity: Sonar Reasoning Pro",
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    "context_length": 128000,
    "architecture": {
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      "input_modalities": [
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      "output_modalities": [
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      "tokenizer": "Other",
      "instruct_type": "deepseek-r1"
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    "pricing": {
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    "top_provider": {
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      "is_moderated": false
    },
    "per_request_limits": null,
    "supported_parameters": [
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    ],
    "default_parameters": {},
    "expiration_date": null
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    "id": "prime-intellect/intellect-3",
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    "hugging_face_id": "PrimeIntellect/INTELLECT-3-FP8",
    "name": "Prime Intellect: INTELLECT-3",
    "created": 1764212534,
    "description": "INTELLECT-3 is a 106B-parameter Mixture-of-Experts model (12B active) post-trained from GLM-4.5-Air-Base using supervised fine-tuning (SFT) followed by large-scale reinforcement learning (RL). It offers state-of-the-art performance for its size across math, code, science, and general reasoning, consistently outperforming many larger frontier models. Designed for strong multi-step problem solving, it maintains high accuracy on structured tasks while remaining efficient at inference thanks to its MoE architecture.",
    "context_length": 131072,
    "architecture": {
      "modality": "text->text",
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      "output_modalities": [
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      "tokenizer": "Other",
      "instruct_type": null
    },
    "pricing": {
      "prompt": "0.0000002",
      "completion": "0.0000011"
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    "top_provider": {
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      "max_completion_tokens": 131072,
      "is_moderated": false
    },
    "per_request_limits": null,
    "supported_parameters": [
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      "max_tokens",
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    "default_parameters": {
      "temperature": 0.6,
      "top_p": null,
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    "expiration_date": null
  },
  {
    "id": "qwen/qwen-2.5-72b-instruct",
    "canonical_slug": "qwen/qwen-2.5-72b-instruct",
    "hugging_face_id": "Qwen/Qwen2.5-72B-Instruct",
    "name": "Qwen2.5 72B Instruct",
    "created": 1726704000,
    "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).",
    "context_length": 32768,
    "architecture": {
      "modality": "text->text",
      "input_modalities": [
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      ],
      "output_modalities": [
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      "tokenizer": "Qwen",
      "instruct_type": "chatml"
    },
    "pricing": {
      "prompt": "0.00000012",
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    },
    "top_provider": {
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      "max_completion_tokens": 16384,
      "is_moderated": false
    },
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    "supported_parameters": [
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    "default_parameters": {},
    "expiration_date": null
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  {
    "id": "qwen/qwen-2.5-7b-instruct",
    "canonical_slug": "qwen/qwen-2.5-7b-instruct",
    "hugging_face_id": "Qwen/Qwen2.5-7B-Instruct",
    "name": "Qwen: Qwen2.5 7B Instruct",
    "created": 1729036800,
    "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).",
    "context_length": 32768,
    "architecture": {
      "modality": "text->text",
      "input_modalities": [
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      "output_modalities": [
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      "tokenizer": "Qwen",
      "instruct_type": "chatml"
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    "pricing": {
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      "completion": "0.0000001"
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    "top_provider": {
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      "max_completion_tokens": 32768,
      "is_moderated": false
    },
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    "supported_parameters": [
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      "tools",
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    ],
    "default_parameters": {
      "temperature": null,
      "top_p": null,
      "frequency_penalty": null
    },
    "expiration_date": null
  },
  {
    "id": "qwen/qwen-2.5-coder-32b-instruct",
    "canonical_slug": "qwen/qwen-2.5-coder-32b-instruct",
    "hugging_face_id": "Qwen/Qwen2.5-Coder-32B-Instruct",
    "name": "Qwen2.5 Coder 32B Instruct",
    "created": 1731368400,
    "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/).",
    "context_length": 32768,
    "architecture": {
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      "input_modalities": [
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      "is_moderated": false
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    "supported_parameters": [
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    "default_parameters": {},
    "expiration_date": null
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  {
    "id": "qwen/qwen-2.5-vl-7b-instruct",
    "canonical_slug": "qwen/qwen-2-vl-7b-instruct",
    "hugging_face_id": "Qwen/Qwen2.5-VL-7B-Instruct",
    "name": "Qwen: Qwen2.5-VL 7B Instruct",
    "created": 1724803200,
    "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).",
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    "architecture": {
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      "tokenizer": "Qwen",
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    "top_provider": {
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    "supported_parameters": [
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  },
  {
    "id": "qwen/qwen-max",
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    "hugging_face_id": "",
    "name": "Qwen: Qwen-Max ",
    "created": 1738402289,
    "description": "Qwen-Max, based on Qwen2.5, provides the best inference performance among [Qwen models](/qwen), especially for complex multi-step tasks. It's a large-scale MoE model that has been pretrained on over 20 trillion tokens and further post-trained with curated Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF) methodologies. The parameter count is unknown.",
    "context_length": 32768,
    "architecture": {
      "modality": "text->text",
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      "tokenizer": "Qwen",
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    "top_provider": {
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      "max_completion_tokens": 8192,
      "is_moderated": false
    },
    "per_request_limits": null,
    "supported_parameters": [
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    ],
    "default_parameters": {},
    "expiration_date": null
  },
  {
    "id": "qwen/qwen-plus",
    "canonical_slug": "qwen/qwen-plus-2025-01-25",
    "hugging_face_id": "",
    "name": "Qwen: Qwen-Plus",
    "created": 1738409840,
    "description": "Qwen-Plus, based on the Qwen2.5 foundation model, is a 131K context model with a balanced performance, speed, and cost combination.",
    "context_length": 1000000,
    "architecture": {
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    ],
    "default_parameters": {},
    "expiration_date": null
  },
  {
    "id": "qwen/qwen-plus-2025-07-28",
    "canonical_slug": "qwen/qwen-plus-2025-07-28",
    "hugging_face_id": "",
    "name": "Qwen: Qwen Plus 0728",
    "created": 1757347599,
    "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.",
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    "architecture": {
      "modality": "text->text",
      "input_modalities": [
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      "output_modalities": [
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      "tokenizer": "Qwen3",
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    },
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