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at","openai/gpt-5.1-codex","openai/gpt-5.1-codex-mini","google/gemini-3-pro-preview-thinking-developer","google/gemini-3-pro-preview-developer","openai/gpt-5-developer","openai/gpt-5-nano-developer","openai/gpt-4o-developer","openai/gpt-4o-mini-developer","openai/o1-developer","openai/o1-mini-developer","openai/o3-developer","openai/o3-mini-developer","openai/o4-mini-developer","openai/gpt-4.1-developer","openai/gpt-4.1-mini-developer","openai/o3-pro","openai/o4-mini","openai/o3-mini","openai/o3","openai/o1","openai/gpt-4.1","openai/gpt-4.1-mini","openai/gpt-4.1-nano","openai/gpt-4o","openai/gpt-4o-mini","anthropic/claude-opus-4.5-20251101-developer","openai/gpt-5.1-codex-developer","openai/gpt-5.1-chat-developer","openai/gpt-5.1-developer","anthropic/claude-sonnet-4-20250514","anthropic/claude-haiku-4.5-20251001","anthropic/claude-sonnet-4.5-20250929","anthropic/claude-opus-4.1-20250805","anthropic/claude-3.7-sonnet-20250219","anthropic/claude-opus-4-20250514","openai/gpt-5.1-codex-mini-developer","openai/gpt-5-pro-developer","openai/gpt-5-mini-developer","openai/gpt-5-codex-developer","anthropic/claude-opus-4-20250514-developer","openai/gpt-4.1-nano-developer","kwaipilot/kat-coder-air","kwaipilot/kat-coder-exp-72b-1010","openai/gpt-5","openai/gpt-5-chat","openai/gpt-5-codex","openai/gpt-5-mini","openai/gpt-5-nano","openai/gpt-5-pro","openai/gpt-5.1-codex-max","openai/gpt-5.2","openai/gpt-5.2-chat","google/gemini-2.5-pro","openai/gpt-5.2-developer","anthropic/claude-opus-4.5-20251101","openai/gpt-image-1-developer","google/gemini-3-flash-preview-developer","google/gemini-3-flash-preview","google/gemini-2.0-flash","google/gemini-2.0-flash-lite","deepseek-ai/deepseek-ocr","google/gemini-2.5-flash-image","google/gemini-3-pro-image-preview","xai/grok-4.1-fast-non-reasoning-developer","xai/grok-4.1-fast-reasoning-developer","xai/grok-4-fast-non-reasoning-developer","xai/grok-4-fast-reasoning-developer","xai/grok-4-0709","xai/grok-4-fast-reasoning","xai/grok-4-fast-non-reasoning","xai/grok-4.1-fast-reasoning","xai/grok-4.1-fast-non-reasoning","qwen/qwen3-max-2026-01-23","anthropic/claude-opus-4.6","google/gemini-3.1-pro-preview","anthropic/claude-sonnet-4.6","moonshotai/Kimi-K2-Instruct","google/gemini-3.1-flash-lite-preview","openai/gpt-5.2-codex","openai/gpt-5.3-codex","qwen/qwen3-32b-promote","qwen/qwen3-coder-promote","qwen/qwen3-235b-a22b-instruct-2507-promote","qwen/qwen3-next-80b-a3b-instruct-promote","qwen/qwen3-next-80b-a3b-thinking-promote","qwen/qwen3-30b-a3b-instruct-2507-promote","qwen/qwen3-vl-235b-a22b-instruct-promote","qwen/qwen3-8b-promote","qwen/qwen3-235b-a22b-thinking-2507-promote","qwen/qwen3-vl-235b-a22b-thinking-promote","qwen/qwen3-30b-a3b-thinking-2507-promote","qwen/qwen2.5-7b-instruct-promote","qwen/qwen3.5-397b-a17b-promote","qwen/qwen3-coder-next-promote","qwen/qwen3.5-122b-a10b-promote","qwen/qwen3.5-35b-a3b-promote","qwen/qwen3.5-27b-promote","qwen/qwen3-30b-a3b-promote","qwen/qwen3-max-promote","qwen/qwen3.5-plus-promote","qwen/qwen3.5-flash-promote","openai/gpt-5.4","google/gemini-3.1-flash-image-preview","alibaba/wan-2.6"],"sendClientIp":false,"pricingStrategy":"openai_chat_completions"},"provider_display_name":"AtlasCloud","provider_slug":"atlas-cloud/fp8","provider_model_id":"MiniMaxAI/MiniMax-M2","quantization":"fp8","variant":"standard","is_free":false,"can_abort":true,"max_prompt_tokens":null,"max_completion_tokens":196608,"max_tokens_per_image":null,"supported_parameters":["reasoning","include_reasoning","max_tokens","temperature","top_p","top_k","min_p","frequency_penalty","presence_penalty","repetition_penalty","seed","logit_bias","response_format","structured_outputs","tools","tool_choice"],"is_byok":false,"moderation_required":false,"data_policy":{"training":false,"trainingOpenRouter":false,"retainsPrompts":false,"canPublish":false,"privacyPolicyURL":"https://www.atlascloud.ai/privacy"},"pricing":{"prompt":"0.000000255","completion":"0.000001","input_cache_read":"0.00000003","discount":0,"display_pricing":[{"kind":"token","sku_label":"Input 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The distillation‑plus‑refinement pipeline trims compute while keeping DeepSeek‑style reasoning, so Blitz punches above its weight on MMLU, GSM‑8K and BBH compared with other mid‑size open models. With a default 128 k context window and competitive throughput, it serves as a cost‑efficient workhorse for summarization, brainstorming and light code help. Internally, Arcee uses Blitz as the default writer in Conductor pipelines when the heavier Virtuoso line is not required. Users therefore get near‑70 B quality at ~⅓ the latency and price. 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For more information, see the [Phi-4 Multimodal blog post](https://azure.microsoft.com/en-us/blog/empowering-innovation-the-next-generation-of-the-phi-family/).\n","model_version_group_id":null,"context_length":131072,"input_modalities":["text","image"],"output_modalities":["text"],"has_text_output":true,"group":"Other","instruct_type":null,"default_system":null,"default_stops":[],"hidden":false,"router":null,"warning_message":null,"promotion_message":null,"routing_error_message":null,"permaslug":"microsoft/phi-4-multimodal-instruct","supports_reasoning":false,"reasoning_config":null,"features":{},"default_parameters":{},"default_order":[],"quick_start_example_type":null,"is_trainable_text":true,"is_trainable_image":null,"knowledge_cutoff":"2024-06-30T23:59:59+00:00","endpoint":null},{"slug":"deepseek/deepseek-r1-zero","hf_slug":"deepseek-ai/DeepSeek-R1-Zero","updated_at":"2026-03-24T19:43:16.801719+00:00","created_at":"2025-03-06T21:43:54+00:00","hf_updated_at":null,"name":"DeepSeek: DeepSeek R1 Zero","short_name":"DeepSeek R1 Zero","author":"deepseek","author_display_name":"DeepSeek","description":"DeepSeek-R1-Zero is a model trained via large-scale reinforcement learning (RL) without supervised fine-tuning (SFT) as a preliminary step. 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It builds on previous iterations with improvements in world knowledge, contextual coherence, and the ability to follow user intent more effectively.\n\nThe model demonstrates enhanced performance in tasks that require open-ended thinking, problem-solving, and communication. Early testing suggests it is better at generating nuanced responses, maintaining long-context coherence, and reducing hallucinations compared to earlier versions.\n\nThis research preview is intended to help evaluate GPT-4.5’s strengths and limitations in real-world use cases as OpenAI continues to refine and develop future models. Read more at the [blog post here.](https://openai.com/index/introducing-gpt-4-5/)","model_version_group_id":null,"context_length":128000,"input_modalities":["text","image"],"output_modalities":["text"],"has_text_output":true,"group":"GPT","instruct_type":null,"default_system":null,"default_stops":[],"hidden":false,"router":null,"warning_message":null,"promotion_message":null,"routing_error_message":null,"permaslug":"openai/gpt-4.5-preview-2025-02-27","supports_reasoning":false,"reasoning_config":null,"features":{},"default_parameters":{},"default_order":[],"quick_start_example_type":null,"is_trainable_text":null,"is_trainable_image":null,"knowledge_cutoff":"2023-10-31T23:59:59+00:00","endpoint":null},{"slug":"perplexity/r1-1776","hf_slug":"perplexity-ai/r1-1776","updated_at":"2025-11-10T16:00:38.246665+00:00","created_at":"2025-02-19T22:42:09.214134+00:00","hf_updated_at":null,"name":"Perplexity: R1 1776","short_name":"R1 1776","author":"perplexity","author_display_name":"Perplexity","description":"R1 1776 is a version of DeepSeek-R1 that has been post-trained to remove censorship constraints related to topics restricted by the Chinese government. The model retains its original reasoning capabilities while providing direct responses to a wider range of queries. R1 1776 is an offline chat model that does not use the perplexity search subsystem.\n\nThe model was tested on a multilingual dataset of over 1,000 examples covering sensitive topics to measure its likelihood of refusal or overly filtered responses. [Evaluation Results](https://cdn-uploads.huggingface.co/production/uploads/675c8332d01f593dc90817f5/GiN2VqC5hawUgAGJ6oHla.png) Its performance on math and reasoning benchmarks remains similar to the base R1 model. [Reasoning Performance](https://cdn-uploads.huggingface.co/production/uploads/675c8332d01f593dc90817f5/n4Z9Byqp2S7sKUvCvI40R.png)\n\nRead more on the [Blog Post](https://perplexity.ai/hub/blog/open-sourcing-r1-1776)","model_version_group_id":null,"context_length":128000,"input_modalities":["text"],"output_modalities":["text"],"has_text_output":true,"group":"DeepSeek","instruct_type":"deepseek-r1","default_system":null,"default_stops":["<｜User｜>","<｜end▁of▁sentence｜>"],"hidden":false,"router":null,"warning_message":null,"promotion_message":null,"routing_error_message":null,"permaslug":"perplexity/r1-1776","supports_reasoning":true,"reasoning_config":{"start_token":"<think>","end_token":"</think>"},"features":{"reasoning_config":{"start_token":"<think>","end_token":"</think>"}},"default_parameters":{},"default_order":[],"quick_start_example_type":null,"is_trainable_text":null,"is_trainable_image":null,"knowledge_cutoff":null,"endpoint":null},{"slug":"cognitivecomputations/dolphin3.0-r1-mistral-24b","hf_slug":"cognitivecomputations/Dolphin3.0-R1-Mistral-24B","updated_at":"2026-03-24T19:43:16.801719+00:00","created_at":"2025-02-13T16:01:38.017763+00:00","hf_updated_at":null,"name":"Dolphin3.0 R1 Mistral 24B","short_name":"Dolphin3.0 R1 Mistral 24B","author":"cognitivecomputations","author_display_name":"Cognitive Computations","description":"Dolphin 3.0 R1 is the next generation of the Dolphin series of instruct-tuned models.  Designed to be the ultimate general purpose local model, enabling coding, math, agentic, function calling, and general use cases.\n\nThe R1 version has been trained for 3 epochs to reason using 800k reasoning traces from the Dolphin-R1 dataset.\n\nDolphin aims to be a general purpose reasoning instruct model, similar to the models behind ChatGPT, Claude, Gemini.\n\nPart of the [Dolphin 3.0 Collection](https://huggingface.co/collections/QuixiAI/dolphin-30) Curated and trained by [Eric Hartford](https://huggingface.co/ehartford), [Ben Gitter](https://huggingface.co/bigstorm), [BlouseJury](https://huggingface.co/BlouseJury) and [DphnAI](https://huggingface.co/dphn)","model_version_group_id":null,"context_length":32768,"input_modalities":["text"],"output_modalities":["text"],"has_text_output":true,"group":"Mistral","instruct_type":"deepseek-r1","default_system":null,"default_stops":["<｜User｜>","<｜end▁of▁sentence｜>"],"hidden":false,"router":null,"warning_message":null,"promotion_message":null,"routing_error_message":null,"permaslug":"cognitivecomputations/dolphin3.0-r1-mistral-24b","supports_reasoning":true,"reasoning_config":{"start_token":"<think>","end_token":"</think>","system_prompt":null},"features":{"reasoning_config":{"start_token":"<think>","end_token":"</think>","system_prompt":null},"chat_template_config":{}},"default_parameters":{"temperature":null,"top_p":null,"frequency_penalty":null},"default_order":[],"quick_start_example_type":null,"is_trainable_text":null,"is_trainable_image":null,"knowledge_cutoff":"2024-04-30T23:59:59+00:00","endpoint":null},{"slug":"cognitivecomputations/dolphin3.0-mistral-24b","hf_slug":"cognitivecomputations/Dolphin3.0-Mistral-24B","updated_at":"2026-03-24T19:43:16.801719+00:00","created_at":"2025-02-13T15:53:39.941479+00:00","hf_updated_at":null,"name":"Dolphin3.0 Mistral 24B","short_name":"Dolphin3.0 Mistral 24B","author":"cognitivecomputations","author_display_name":"Cognitive Computations","description":"Dolphin 3.0 is the next generation of the Dolphin series of instruct-tuned models.  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Users are cautioned to use this highly compliant model responsibly, as detailed in a blog post about uncensored models at [erichartford.com/uncensored-models](https://erichartford.com/uncensored-models).\n\nUsage of this model is subject to [Meta's Acceptable Use Policy](https://llama.meta.com/llama3/use-policy/).","model_version_group_id":null,"context_length":8192,"input_modalities":["text"],"output_modalities":["text"],"has_text_output":true,"group":"Llama3","instruct_type":"chatml","default_system":null,"default_stops":["<|im_start|>","<|im_end|>","<|endoftext|>"],"hidden":false,"router":null,"warning_message":null,"promotion_message":null,"routing_error_message":null,"permaslug":"cognitivecomputations/dolphin-llama-3-70b","supports_reasoning":false,"reasoning_config":null,"features":{},"default_parameters":{},"default_order":[],"quick_start_example_type":null,"is_trainable_text":null,"is_trainable_image":null,"knowledge_cutoff":"2023-12-31T23:59:59+00:00","endpoint":null},{"slug":"qwen/qwen-2-7b-instruct","hf_slug":"Qwen/Qwen2-7B-Instruct","updated_at":"2026-03-24T19:43:16.801719+00:00","created_at":"2024-07-16T00:00:00+00:00","hf_updated_at":null,"name":"Qwen 2 7B Instruct","short_name":"Qwen 2 7B Instruct","author":"qwen","author_display_name":"Qwen","description":"Qwen2 7B is a transformer-based model that excels in language understanding, multilingual capabilities, coding, mathematics, and reasoning.\n\nIt features SwiGLU activation, attention QKV bias, and group query attention. 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It combines the the best of [Meta's Llama 3 8B](https://openrouter.ai/models/meta-llama/llama-3-8b-instruct) and Nous Research's [Hermes 2 Pro](https://openrouter.ai/models/nousresearch/hermes-2-pro-llama-3-8b).\n\nHermes-2 Θ (theta) was specifically designed with a few capabilities in mind: executing function calls, generating JSON output, and most remarkably, demonstrating metacognitive abilities (contemplating the nature of thought and recognizing the diversity of cognitive processes among individuals).","model_version_group_id":null,"context_length":16384,"input_modalities":["text"],"output_modalities":["text"],"has_text_output":true,"group":"Llama3","instruct_type":"chatml","default_system":null,"default_stops":["<|im_start|>","<|im_end|>","<|endoftext|>"],"hidden":false,"router":null,"warning_message":null,"promotion_message":null,"routing_error_message":null,"permaslug":"nousresearch/hermes-2-theta-llama-3-8b","supports_reasoning":false,"reasoning_config":null,"features":{},"default_parameters":{},"default_order":[],"quick_start_example_type":null,"is_trainable_text":null,"is_trainable_image":null,"knowledge_cutoff":"2023-12-31T23:59:59+00:00","endpoint":null},{"slug":"alpindale/magnum-72b","hf_slug":"alpindale/magnum-72b-v1","updated_at":"2026-03-24T19:43:16.801719+00:00","created_at":"2024-07-11T00:00:00+00:00","hf_updated_at":null,"name":"Magnum 72B","short_name":"Magnum 72B","author":"alpindale","author_display_name":"Alpindale","description":"From the maker of [Goliath](https://openrouter.ai/models/alpindale/goliath-120b), Magnum 72B is the first in a new 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.","model_version_group_id":null,"context_length":16384,"input_modalities":["text"],"output_modalities":["text"],"has_text_output":true,"group":"Qwen","instruct_type":"chatml","default_system":null,"default_stops":["<|im_start|>","<|im_end|>","<|endoftext|>"],"hidden":false,"router":null,"warning_message":null,"promotion_message":null,"routing_error_message":null,"permaslug":"alpindale/magnum-72b","supports_reasoning":false,"reasoning_config":null,"features":{},"default_parameters":{},"default_order":[],"quick_start_example_type":null,"is_trainable_text":null,"is_trainable_image":null,"knowledge_cutoff":"2024-06-30T23:59:59+00:00","endpoint":null},{"slug":"sao10k/l3-stheno-8b","hf_slug":"Sao10K/L3-8B-Stheno-v3.3-32K","updated_at":"2026-03-24T19:43:16.801719+00:00","created_at":"2024-06-27T00:00:00+00:00","hf_updated_at":null,"name":"Sao10K: Llama 3 Stheno 8B v3.3 32K","short_name":"Llama 3 Stheno 8B v3.3 32K","author":"sao10k","author_display_name":"Sao10K","description":"Stheno 8B 32K is a creative writing/roleplay model from [Sao10k](https://ko-fi.com/sao10k). 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This large language model (LLM) is a fine-tuned version of Nemotron-4-340B-Base, designed for single and multi-turn chat use-cases with a 4,096 token context length.\n\nThe base model was pre-trained on 9 trillion tokens from diverse English texts, 50+ natural languages, and 40+ coding languages. The instruct model underwent additional alignment steps:\n\n1. Supervised Fine-tuning (SFT)\n2. Direct Preference Optimization (DPO)\n3. Reward-aware Preference Optimization (RPO)\n\nThe alignment process used approximately 20K human-annotated samples, while 98% of the data for fine-tuning was synthetically generated. Detailed information about the synthetic data generation pipeline is available in the [technical report](https://arxiv.org/html/2406.11704v1).","model_version_group_id":null,"context_length":4096,"input_modalities":["text"],"output_modalities":["text"],"has_text_output":true,"group":"Other","instruct_type":"nemotron","default_system":null,"default_stops":["<|endoftext|>","<extra_id_1>","\u0011","<extra_id_1>User"],"hidden":false,"router":null,"warning_message":null,"promotion_message":null,"routing_error_message":null,"permaslug":"nvidia/nemotron-4-340b-instruct","supports_reasoning":false,"reasoning_config":null,"features":{},"default_parameters":{},"default_order":[],"quick_start_example_type":null,"is_trainable_text":true,"is_trainable_image":null,"knowledge_cutoff":"2023-06-30T23:59:59+00:00","endpoint":null},{"slug":"anthropic/claude-3.5-sonnet-20240620","hf_slug":null,"updated_at":"2026-03-24T19:43:16.801719+00:00","created_at":"2024-06-20T00:00:00+00:00","hf_updated_at":null,"name":"Anthropic: Claude 3.5 Sonnet (2024-06-20)","short_name":"Claude 3.5 Sonnet (2024-06-20)","author":"anthropic","author_display_name":"Anthropic","description":"Claude 3.5 Sonnet delivers better-than-Opus capabilities, faster-than-Sonnet speeds, at the same Sonnet prices. 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Optimized through supervised fine-tuning and preference adjustments, it excels in tasks involving common sense, mathematics, logical reasoning, and code processing.\n\nAt time of release, Phi-3 Medium demonstrated state-of-the-art performance among lightweight models. In the MMLU-Pro eval, the model even comes close to a Llama3 70B level of performance.\n\nFor 128k context length, try [Phi-3 Medium 128K](/models/microsoft/phi-3-medium-128k-instruct).","model_version_group_id":null,"context_length":4000,"input_modalities":["text"],"output_modalities":["text"],"has_text_output":true,"group":"Other","instruct_type":"phi3","default_system":null,"default_stops":["<|end|>","<|endoftext|>"],"hidden":false,"router":null,"warning_message":null,"promotion_message":null,"routing_error_message":null,"permaslug":"microsoft/phi-3-medium-4k-instruct","supports_reasoning":false,"reasoning_config":null,"features":{},"default_parameters":{},"default_order":[],"quick_start_example_type":null,"is_trainable_text":true,"is_trainable_image":null,"knowledge_cutoff":"2024-03-31T23:59:59+00:00","endpoint":null},{"slug":"bigcode/starcoder2-15b-instruct","hf_slug":"bigcode/starcoder2-15b-instruct-v0.1","updated_at":"2026-03-24T19:43:16.801719+00:00","created_at":"2024-06-09T00:00:00+00:00","hf_updated_at":null,"name":"StarCoder2 15B Instruct","short_name":"StarCoder2 15B Instruct","author":"bigcode","author_display_name":"BigCode","description":"StarCoder2 15B Instruct excels in coding-related tasks, primarily in Python. 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This model was fine-tuned without any human annotations or distilled data from proprietary LLMs.\n\nThe base model uses [Grouped Query Attention](https://arxiv.org/abs/2305.13245) and was trained using the [Fill-in-the-Middle objective](https://arxiv.org/abs/2207.14255) objective on 4+ trillion tokens.","model_version_group_id":null,"context_length":16384,"input_modalities":["text"],"output_modalities":["text"],"has_text_output":true,"group":"Other","instruct_type":"alpaca","default_system":null,"default_stops":["###","</s>"],"hidden":false,"router":null,"warning_message":null,"promotion_message":null,"routing_error_message":null,"permaslug":"bigcode/starcoder2-15b-instruct","supports_reasoning":false,"reasoning_config":null,"features":{},"default_parameters":{},"default_order":[],"quick_start_example_type":null,"is_trainable_text":null,"is_trainable_image":null,"knowledge_cutoff":"2024-01-31T23:59:59+00:00","endpoint":null},{"slug":"cognitivecomputations/dolphin-mixtral-8x22b","hf_slug":"cognitivecomputations/dolphin-2.9.2-mixtral-8x22b","updated_at":"2026-03-24T19:43:16.801719+00:00","created_at":"2024-06-08T00:00:00+00:00","hf_updated_at":null,"name":"Dolphin 2.9.2 Mixtral 8x22B 🐬","short_name":"Dolphin 2.9.2 Mixtral 8x22B 🐬","author":"cognitivecomputations","author_display_name":"Cognitive Computations","description":"Dolphin 2.9 is designed for instruction following, conversational, and coding. 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Users are cautioned to use this highly compliant model responsibly, as detailed in a blog post about uncensored models at [erichartford.com/uncensored-models](https://erichartford.com/uncensored-models).\n\n#moe #uncensored","model_version_group_id":null,"context_length":65536,"input_modalities":["text"],"output_modalities":["text"],"has_text_output":true,"group":"Mistral","instruct_type":"chatml","default_system":null,"default_stops":["<|im_start|>","<|im_end|>","<|endoftext|>"],"hidden":false,"router":null,"warning_message":null,"promotion_message":null,"routing_error_message":null,"permaslug":"cognitivecomputations/dolphin-mixtral-8x22b","supports_reasoning":false,"reasoning_config":null,"features":{},"default_parameters":{},"default_order":[],"quick_start_example_type":null,"is_trainable_text":null,"is_trainable_image":null,"knowledge_cutoff":"2024-01-31T23:59:59+00:00","endpoint":null},{"slug":"qwen/qwen-2-72b-instruct","hf_slug":"Qwen/Qwen2-72B-Instruct","updated_at":"2026-03-24T19:43:16.801719+00:00","created_at":"2024-06-07T00:00:00+00:00","hf_updated_at":null,"name":"Qwen 2 72B Instruct","short_name":"Qwen 2 72B Instruct","author":"qwen","author_display_name":"Qwen","description":"Qwen2 72B is a transformer-based model that excels in language understanding, multilingual capabilities, coding, mathematics, and reasoning.\n\nIt features SwiGLU activation, attention QKV bias, and group query attention. It is pretrained on extensive data with supervised finetuning and direct preference optimization.\n\nFor more details, see this [blog post](https://qwenlm.github.io/blog/qwen2/) and [GitHub repo](https://github.com/QwenLM/Qwen2).\n\nUsage of this model is subject to [Tongyi Qianwen LICENSE AGREEMENT](https://huggingface.co/Qwen/Qwen1.5-110B-Chat/blob/main/LICENSE).","model_version_group_id":null,"context_length":32768,"input_modalities":["text"],"output_modalities":["text"],"has_text_output":true,"group":"Qwen","instruct_type":"chatml","default_system":null,"default_stops":["<|im_start|>","<|im_end|>","<|endoftext|>"],"hidden":false,"router":null,"warning_message":null,"promotion_message":null,"routing_error_message":null,"permaslug":"qwen/qwen-2-72b-instruct","supports_reasoning":false,"reasoning_config":null,"features":{},"default_parameters":{},"default_order":[],"quick_start_example_type":null,"is_trainable_text":true,"is_trainable_image":null,"knowledge_cutoff":"2024-06-30T23:59:59+00:00","endpoint":null},{"slug":"openchat/openchat-8b","hf_slug":"openchat/openchat-3.6-8b-20240522","updated_at":"2026-03-24T19:43:16.801719+00:00","created_at":"2024-06-01T00:00:00+00:00","hf_updated_at":null,"name":"OpenChat 3.6 8B","short_name":"OpenChat 3.6 8B","author":"openchat","author_display_name":"OpenChat","description":"OpenChat 8B is a library of open-source language models, fine-tuned with \"C-RLFT (Conditioned Reinforcement Learning Fine-Tuning)\" - a strategy inspired by offline reinforcement learning. It has been trained on mixed-quality data without preference labels.\n\nIt outperforms many similarly sized models including [Llama 3 8B Instruct](/models/meta-llama/llama-3-8b-instruct) and various fine-tuned models. It excels in general conversation, coding assistance, and mathematical reasoning.\n\n- For OpenChat fine-tuned on Mistral 7B, check out [OpenChat 7B](/models/openchat/openchat-7b).\n- For OpenChat fine-tuned on Llama 8B, check out [OpenChat 8B](/models/openchat/openchat-8b).\n\n#open-source","model_version_group_id":null,"context_length":8192,"input_modalities":["text"],"output_modalities":["text"],"has_text_output":true,"group":"Llama3","instruct_type":"openchat","default_system":null,"default_stops":["<|end_of_turn|>"],"hidden":false,"router":null,"warning_message":null,"promotion_message":null,"routing_error_message":null,"permaslug":"openchat/openchat-8b","supports_reasoning":false,"reasoning_config":null,"features":{},"default_parameters":{},"default_order":[],"quick_start_example_type":null,"is_trainable_text":null,"is_trainable_image":null,"knowledge_cutoff":"2023-12-31T23:59:59+00:00","endpoint":null},{"slug":"mistralai/mistral-7b-instruct-v0.3","hf_slug":"mistralai/Mistral-7B-Instruct-v0.3","updated_at":"2026-03-24T19:43:16.801719+00:00","created_at":"2024-05-27T00:00:00+00:00","hf_updated_at":null,"name":"Mistral: Mistral 7B Instruct v0.3","short_name":"Mistral 7B Instruct v0.3","author":"mistralai","author_display_name":"Mistral AI","description":"A high-performing, industry-standard 7.3B parameter model, with optimizations for speed and context length.\n\nAn improved version of [Mistral 7B Instruct v0.2](/models/mistralai/mistral-7b-instruct-v0.2), with the following changes:\n\n- Extended vocabulary to 32768\n- Supports v3 Tokenizer\n- Supports function calling\n\nNOTE: Support for function calling depends on the provider.","model_version_group_id":"1d07cc56-c54d-4587-b785-5093496397a4","context_length":32768,"input_modalities":["text"],"output_modalities":["text"],"has_text_output":true,"group":"Mistral","instruct_type":"mistral","default_system":null,"default_stops":["[INST]","</s>"],"hidden":false,"router":null,"warning_message":null,"promotion_message":null,"routing_error_message":null,"permaslug":"mistralai/mistral-7b-instruct-v0.3","supports_reasoning":false,"reasoning_config":null,"features":{},"default_parameters":{"temperature":0.3},"default_order":[],"quick_start_example_type":null,"is_trainable_text":true,"is_trainable_image":null,"knowledge_cutoff":"2023-09-30T23:59:59+00:00","endpoint":null},{"slug":"mistralai/mistral-7b-instruct","hf_slug":"mistralai/Mistral-7B-Instruct-v0.3","updated_at":"2026-03-24T19:43:16.801719+00:00","created_at":"2024-05-27T00:00:00+00:00","hf_updated_at":null,"name":"Mistral: Mistral 7B Instruct","short_name":"Mistral 7B Instruct","author":"mistralai","author_display_name":"Mistral AI","description":"A high-performing, industry-standard 7.3B parameter model, with optimizations for speed and context length.\n\n*Mistral 7B Instruct has multiple version variants, and this is intended to be the latest version.*","model_version_group_id":"1d07cc56-c54d-4587-b785-5093496397a4","context_length":32768,"input_modalities":["text"],"output_modalities":["text"],"has_text_output":true,"group":"Mistral","instruct_type":"mistral","default_system":null,"default_stops":["[INST]","</s>"],"hidden":false,"router":null,"warning_message":null,"promotion_message":null,"routing_error_message":null,"permaslug":"mistralai/mistral-7b-instruct","supports_reasoning":false,"reasoning_config":null,"features":{},"default_parameters":{"temperature":0.3},"default_order":[],"quick_start_example_type":null,"is_trainable_text":true,"is_trainable_image":null,"knowledge_cutoff":"2023-09-30T23:59:59+00:00","endpoint":null},{"slug":"microsoft/phi-3-mini-128k-instruct","hf_slug":"microsoft/Phi-3-mini-128k-instruct","updated_at":"2026-03-24T19:43:16.801719+00:00","created_at":"2024-05-26T00:00:00+00:00","hf_updated_at":null,"name":"Microsoft: Phi-3 Mini 128K Instruct","short_name":"Phi-3 Mini 128K Instruct","author":"microsoft","author_display_name":"Microsoft","description":"Phi-3 Mini is a powerful 3.8B parameter model designed for advanced language understanding, reasoning, and instruction following. 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In the MMLU-Pro eval, the model even comes close to a Llama3 70B level of performance.\n\nFor 4k context length, try [Phi-3 Medium 4K](/models/microsoft/phi-3-medium-4k-instruct).","model_version_group_id":null,"context_length":128000,"input_modalities":["text"],"output_modalities":["text"],"has_text_output":true,"group":"Other","instruct_type":"phi3","default_system":null,"default_stops":["<|end|>","<|endoftext|>"],"hidden":false,"router":null,"warning_message":null,"promotion_message":null,"routing_error_message":null,"permaslug":"microsoft/phi-3-medium-128k-instruct","supports_reasoning":false,"reasoning_config":null,"features":{},"default_parameters":{},"default_order":[],"quick_start_example_type":null,"is_trainable_text":true,"is_trainable_image":null,"knowledge_cutoff":"2024-03-31T23:59:59+00:00","endpoint":null},{"slug":"neversleep/llama-3-lumimaid-70b","hf_slug":"NeverSleep/Llama-3-Lumimaid-70B-v0.1","updated_at":"2026-03-24T19:43:16.801719+00:00","created_at":"2024-05-16T00:00:00+00:00","hf_updated_at":null,"name":"NeverSleep: Llama 3 Lumimaid 70B","short_name":"Llama 3 Lumimaid 70B","author":"neversleep","author_display_name":"NeverSleep","description":"The NeverSleep team is back, with a Llama 3 70B finetune trained on their curated roleplay data. 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Usage of this model is subject to [Meta's Acceptable Use Policy](https://llama.meta.com/llama3/use-policy/).","model_version_group_id":"397604e2-45fa-454e-a85d-9921f5138747","context_length":8192,"input_modalities":["text"],"output_modalities":["text"],"has_text_output":true,"group":"Llama3","instruct_type":"chatml","default_system":null,"default_stops":["<|im_start|>","<|im_end|>","<|endoftext|>"],"hidden":false,"router":null,"warning_message":null,"promotion_message":null,"routing_error_message":null,"permaslug":"meta-llama/llama-3-70b","supports_reasoning":false,"reasoning_config":null,"features":{},"default_parameters":{},"default_order":[],"quick_start_example_type":null,"is_trainable_text":true,"is_trainable_image":null,"knowledge_cutoff":"2023-12-31T23:59:59+00:00","endpoint":null},{"slug":"liuhaotian/llava-yi-34b","hf_slug":"liuhaotian/llava-v1.6-34b","updated_at":"2026-03-24T19:43:16.801719+00:00","created_at":"2024-05-11T00:00:00+00:00","hf_updated_at":null,"name":"LLaVA v1.6 34B","short_name":"LLaVA v1.6 34B","author":"liuhaotian","author_display_name":"Haotian Liu","description":"LLaVA Yi 34B is an open-source model trained by fine-tuning LLM on multimodal instruction-following data. 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Users are cautioned to use this highly compliant model responsibly, as detailed in a blog post about uncensored models at [erichartford.com/uncensored-models](https://erichartford.com/uncensored-models).\n\n#moe #uncensored","model_version_group_id":null,"context_length":32768,"input_modalities":["text"],"output_modalities":["text"],"has_text_output":true,"group":"Mistral","instruct_type":"chatml","default_system":null,"default_stops":["<|im_start|>","<|im_end|>","<|endoftext|>"],"hidden":false,"router":null,"warning_message":null,"promotion_message":null,"routing_error_message":null,"permaslug":"cognitivecomputations/dolphin-mixtral-8x7b","supports_reasoning":false,"reasoning_config":null,"features":{},"default_parameters":{},"default_order":[],"quick_start_example_type":null,"is_trainable_text":null,"is_trainable_image":null,"knowledge_cutoff":"2023-12-31T23:59:59+00:00","endpoint":null},{"slug":"rwkv/rwkv-5-world-3b","hf_slug":"RWKV/rwkv-5-world-3b","updated_at":"2026-03-24T19:43:16.801719+00:00","created_at":"2023-12-10T00:00:00+00:00","hf_updated_at":null,"name":"RWKV v5 World 3B","short_name":"RWKV v5 World 3B","author":"rwkv","author_display_name":"RWKV","description":"[RWKV](https://wiki.rwkv.com) is an RNN (recurrent neural network) with transformer-level performance. 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More details [here](https://substack.recursal.ai/p/public-rwkv-3b-model-via-openrouter).\n\n#rnn","model_version_group_id":null,"context_length":10000,"input_modalities":["text"],"output_modalities":["text"],"has_text_output":true,"group":"RWKV","instruct_type":"rwkv","default_system":null,"default_stops":["\nUser:","\n\nUser:"],"hidden":false,"router":null,"warning_message":null,"promotion_message":null,"routing_error_message":null,"permaslug":"rwkv/rwkv-5-world-3b","supports_reasoning":false,"reasoning_config":null,"features":{},"default_parameters":{},"default_order":[],"quick_start_example_type":null,"is_trainable_text":null,"is_trainable_image":null,"knowledge_cutoff":"2023-06-30T23:59:59+00:00","endpoint":null},{"slug":"recursal/rwkv-5-3b-ai-town","hf_slug":"recursal/rwkv-5-3b-ai-town","updated_at":"2026-03-24T19:43:16.801719+00:00","created_at":"2023-12-10T00:00:00+00:00","hf_updated_at":null,"name":"RWKV v5 3B AI Town","short_name":"RWKV v5 3B AI Town","author":"recursal","author_display_name":"recursal","description":"This is an [RWKV 3B model](/models/rwkv/rwkv-5-world-3b) finetuned specifically for the [AI Town](https://github.com/a16z-infra/ai-town) project.\n\n[RWKV](https://wiki.rwkv.com) is an RNN (recurrent neural network) with transformer-level performance. It aims to combine the best of RNNs and transformers - great performance, fast inference, low VRAM, fast training, \"infinite\" context length, and free sentence embedding.\n\nRWKV 3B models are provided for free, by Recursal.AI, for the beta period. More details [here](https://substack.recursal.ai/p/public-rwkv-3b-model-via-openrouter).\n\n#rnn","model_version_group_id":null,"context_length":10000,"input_modalities":["text"],"output_modalities":["text"],"has_text_output":true,"group":"RWKV","instruct_type":"rwkv","default_system":null,"default_stops":["\nUser:","\n\nUser:"],"hidden":false,"router":null,"warning_message":null,"promotion_message":null,"routing_error_message":null,"permaslug":"recursal/rwkv-5-3b-ai-town","supports_reasoning":false,"reasoning_config":null,"features":{},"default_parameters":{},"default_order":[],"quick_start_example_type":null,"is_trainable_text":null,"is_trainable_image":null,"knowledge_cutoff":"2023-06-30T23:59:59+00:00","endpoint":null},{"slug":"togethercomputer/stripedhyena-nous-7b","hf_slug":"togethercomputer/StripedHyena-Nous-7B","updated_at":"2026-03-24T19:43:16.801719+00:00","created_at":"2023-12-09T00:00:00+00:00","hf_updated_at":null,"name":"StripedHyena Nous 7B","short_name":"StripedHyena Nous 7B","author":"togethercomputer","author_display_name":"Together","description":"This is the chat model variant of the [StripedHyena series](/models?q=stripedhyena) developed by Together in collaboration with Nous Research.\n\nStripedHyena uses a new architecture that competes with traditional Transformers, particularly in long-context data processing. 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