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Credits to - [@chargoddard](https://huggingface.co/chargoddard) for developing the framework used to merge...", "architecture": { "modality": "text->text", "input_modalities": [ "text" ], "output_modalities": [ "text" ], "tokenizer": "Llama2", "instruct_type": "airoboros" }, "top_provider": { "context_length": 6144, "max_completion_tokens": 1024, "is_moderated": false }, "per_request_limits": null, "supported_parameters": [ "frequency_penalty", "logit_bias", "logprobs", "max_tokens", "min_p", "presence_penalty", "repetition_penalty", "response_format", "seed", "stop", "temperature", "top_a", "top_k", "top_logprobs", "top_p" ] } }, { "id": "amazon/nova-2-lite-v1", "name": "Amazon: Nova 2 Lite", "provider": "openrouter", "family": "amazon", "created_at": "2025-12-02 17:31:12 UTC", "context_window": 1000000, "max_output_tokens": 65535, "knowledge_cutoff": null, "modalities": { "input": [ "text", "image", "video", "file" ], "output": [ "text" ] }, "capabilities": [ "streaming", "function_calling" ], "pricing": { "text_tokens": { "standard": { "input_per_million": 0.3, "output_per_million": 2.5 } } }, "metadata": { "description": "Nova 2 Lite is a fast, cost-effective reasoning model for everyday workloads that can process text, images, and videos to generate text. 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As of December...", "architecture": { "modality": "text+image->text", "input_modalities": [ "text", "image" ], "output_modalities": [ "text" ], "tokenizer": "Nova", "instruct_type": null }, "top_provider": { "context_length": 300000, "max_completion_tokens": 5120, "is_moderated": true }, "per_request_limits": null, "supported_parameters": [ "max_tokens", "stop", "temperature", "tools", "top_k", "top_p" ] } }, { "id": "anthracite-org/magnum-v4-72b", "name": "Magnum v4 72B", "provider": "openrouter", "family": "anthracite-org", "created_at": "2024-10-22 00:00:00 UTC", "context_window": 16384, "max_output_tokens": 2048, "knowledge_cutoff": null, "modalities": { "input": [ "text" ], "output": [ "text" ] }, "capabilities": [ "streaming", "structured_output", "predicted_outputs" ], "pricing": { "text_tokens": { "standard": { "input_per_million": 3.0, "output_per_million": 5.0 } } }, "metadata": { "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).", "architecture": { "modality": "text->text", "input_modalities": [ "text" ], "output_modalities": [ "text" ], "tokenizer": "Qwen", "instruct_type": "chatml" }, "top_provider": { "context_length": 16384, "max_completion_tokens": 2048, "is_moderated": false }, "per_request_limits": null, "supported_parameters": [ "frequency_penalty", "logit_bias", "logprobs", "max_tokens", "min_p", "presence_penalty", "repetition_penalty", "response_format", "seed", "stop", "temperature", "top_a", "top_k", "top_logprobs", "top_p" ] } }, { "id": "anthropic/claude-3-haiku", "name": "Anthropic: Claude 3 Haiku", "provider": "openrouter", "family": "anthropic", "created_at": "2024-03-13 00:00:00 UTC", "context_window": 200000, "max_output_tokens": 4096, "knowledge_cutoff": null, "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "capabilities": [ "streaming", "function_calling" ], "pricing": { "text_tokens": { "standard": { "input_per_million": 0.25, "output_per_million": 1.25, "cache_read_input_per_million": 0.03 } } }, "metadata": { "description": "Claude 3 Haiku is Anthropic's fastest and most compact model for\nnear-instant responsiveness. 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Compared to other leading proprietary...", "architecture": { "modality": "text->text", "input_modalities": [ "text" ], "output_modalities": [ "text" ], "tokenizer": "Other", "instruct_type": null }, "top_provider": { "context_length": 256000, "max_completion_tokens": 8192, "is_moderated": true }, "per_request_limits": null, "supported_parameters": [ "frequency_penalty", "max_tokens", "presence_penalty", "response_format", "seed", "stop", "structured_outputs", "temperature", "top_k", "top_p" ] } }, { "id": "cohere/command-r-08-2024", "name": "Cohere: Command R (08-2024)", "provider": "openrouter", "family": "cohere", "created_at": "2024-08-30 00:00:00 UTC", "context_window": 128000, "max_output_tokens": 4000, "knowledge_cutoff": null, "modalities": { "input": [ "text" ], "output": [ "text" ] }, "capabilities": [ "streaming", "function_calling", "structured_output" ], "pricing": { "text_tokens": { "standard": { "input_per_million": 0.15, "output_per_million": 0.6 } } }, "metadata": { "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...", "architecture": { "modality": "text->text", "input_modalities": [ "text" ], "output_modalities": [ "text" ], "tokenizer": "Cohere", "instruct_type": null }, "top_provider": { "context_length": 128000, "max_completion_tokens": 4000, "is_moderated": true }, "per_request_limits": null, "supported_parameters": [ "frequency_penalty", "max_tokens", "presence_penalty", "response_format", "seed", "stop", "structured_outputs", "temperature", "tool_choice", "tools", "top_k", "top_p" ] } }, { "id": "cohere/command-r-plus-08-2024", "name": "Cohere: Command R+ (08-2024)", "provider": "openrouter", "family": "cohere", "created_at": "2024-08-30 00:00:00 UTC", "context_window": 128000, "max_output_tokens": 4000, "knowledge_cutoff": null, "modalities": { "input": [ "text" ], "output": [ "text" ] }, "capabilities": [ "streaming", "function_calling", "structured_output" ], "pricing": { "text_tokens": { "standard": { "input_per_million": 2.5, "output_per_million": 10.0 } } }, "metadata": { "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...", "architecture": { "modality": "text->text", "input_modalities": [ "text" ], "output_modalities": [ "text" ], "tokenizer": "Cohere", "instruct_type": null }, "top_provider": { "context_length": 128000, "max_completion_tokens": 4000, "is_moderated": true }, "per_request_limits": null, "supported_parameters": [ "frequency_penalty", "max_tokens", "presence_penalty", "response_format", "seed", "stop", "structured_outputs", "temperature", "tool_choice", "tools", "top_k", "top_p" ] } }, { "id": "cohere/command-r7b-12-2024", "name": "Cohere: Command R7B (12-2024)", "provider": "openrouter", "family": "cohere", "created_at": "2024-12-14 06:35:52 UTC", "context_window": 128000, "max_output_tokens": 4000, "knowledge_cutoff": null, "modalities": { "input": [ "text" ], "output": [ "text" ] }, "capabilities": [ "streaming", "structured_output" ], "pricing": { "text_tokens": { "standard": { "input_per_million": 0.0375, "output_per_million": 0.15 } } }, "metadata": { "description": "Command R7B (12-2024) is a small, fast update of the Command R+ model, delivered in December 2024. 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This model is trained using self play with reinforcement learning...", "architecture": { "modality": "text->text", "input_modalities": [ "text" ], "output_modalities": [ "text" ], "tokenizer": "Other", "instruct_type": null }, "top_provider": { "context_length": 128000, "max_completion_tokens": null, "is_moderated": false }, "per_request_limits": null, "supported_parameters": [ "frequency_penalty", "include_reasoning", "logit_bias", "max_tokens", "min_p", "presence_penalty", "reasoning", "repetition_penalty", "response_format", "stop", "structured_outputs", "temperature", "top_k", "top_p" ] } }, { "id": "deepseek/deepseek-chat", "name": "DeepSeek: DeepSeek V3", "provider": "openrouter", "family": "deepseek", "created_at": "2024-12-26 19:28:40 UTC", "context_window": 163840, "max_output_tokens": 16384, "knowledge_cutoff": null, "modalities": { "input": [ "text" ], "output": [ "text" ] }, "capabilities": [ "streaming", "function_calling", "structured_output", "predicted_outputs" ], "pricing": { "text_tokens": { "standard": { "input_per_million": 0.32, "output_per_million": 0.8899999999999999 } } }, "metadata": { "description": "DeepSeek-V3 is the latest model from the DeepSeek team, building upon the instruction following and coding abilities of the previous versions. Pre-trained on nearly 15 trillion tokens, the reported evaluations...", "architecture": { "modality": "text->text", "input_modalities": [ "text" ], "output_modalities": [ "text" ], "tokenizer": "DeepSeek", "instruct_type": null }, "top_provider": { "context_length": 163840, "max_completion_tokens": 16384, "is_moderated": false }, "per_request_limits": null, "supported_parameters": [ "frequency_penalty", "logit_bias", "max_tokens", "min_p", "presence_penalty", "repetition_penalty", "response_format", "seed", "stop", "structured_outputs", "temperature", "tool_choice", "tools", "top_k", "top_p" ] } }, { "id": "deepseek/deepseek-chat-v3-0324", "name": "DeepSeek V3 0324", "provider": "openrouter", "family": "deepseek", "created_at": "2025-03-24 00:00:00 UTC", "context_window": 16384, "max_output_tokens": 8192, "knowledge_cutoff": null, "modalities": { "input": [ "text" ], "output": [ "text" ] }, "capabilities": [ "structured_output", "streaming", "function_calling", "predicted_outputs" ], "pricing": { "text_tokens": { "standard": { "input_per_million": 0.19999999999999998, "output_per_million": 0.77, "cache_read_input_per_million": 0.135 } } }, "metadata": { "description": "DeepSeek V3, a 685B-parameter, mixture-of-experts model, is the latest iteration of the flagship chat model family from the DeepSeek team. It succeeds the [DeepSeek V3](/deepseek/deepseek-chat-v3) model and performs really well...", "architecture": { "modality": "text->text", "input_modalities": [ "text" ], "output_modalities": [ "text" ], "tokenizer": "DeepSeek", "instruct_type": null }, "top_provider": { "context_length": 163840, "max_completion_tokens": 16384, "is_moderated": false }, "per_request_limits": null, "supported_parameters": [ "frequency_penalty", "logit_bias", "max_tokens", "min_p", "presence_penalty", "repetition_penalty", "response_format", "seed", "stop", "structured_outputs", "temperature", "tool_choice", "tools", "top_k", "top_p" ], "source": "models.dev", "provider_id": "openrouter", "open_weights": true, "attachment": false, "temperature": true, "last_updated": "2025-03-24", "cost": { "input": 0, "output": 0 }, "limit": { "context": 16384, "output": 8192 }, "knowledge": "2024-10" } }, { "id": "deepseek/deepseek-chat-v3.1", "name": "DeepSeek-V3.1", "provider": "openrouter", "family": "deepseek", "created_at": "2025-08-21 00:00:00 UTC", "context_window": 163840, "max_output_tokens": 163840, "knowledge_cutoff": null, "modalities": { "input": [ "text" ], "output": [ "text" ] }, "capabilities": [ "function_calling", "structured_output", "reasoning", "streaming", "predicted_outputs" ], "pricing": { "text_tokens": { "standard": { "input_per_million": 0.2, "output_per_million": 0.8 } } }, "metadata": { "description": "DeepSeek-V3.1 is a large hybrid reasoning model (671B parameters, 37B active) that supports both thinking and non-thinking modes via prompt templates. It extends the DeepSeek-V3 base with a two-phase long-context...", "architecture": { "modality": "text->text", "input_modalities": [ "text" ], "output_modalities": [ "text" ], "tokenizer": "DeepSeek", "instruct_type": "deepseek-v3.1" }, "top_provider": { "context_length": 32768, "max_completion_tokens": 7168, "is_moderated": false }, "per_request_limits": null, "supported_parameters": [ "frequency_penalty", "include_reasoning", "logit_bias", "logprobs", "max_tokens", "min_p", "presence_penalty", "reasoning", "repetition_penalty", "response_format", "seed", "stop", "structured_outputs", "temperature", "tool_choice", "tools", "top_k", "top_logprobs", "top_p" ], "source": "models.dev", "provider_id": "openrouter", "open_weights": true, "attachment": false, "temperature": true, "last_updated": "2025-08-21", "cost": { "input": 0.2, "output": 0.8 }, "limit": { "context": 163840, "output": 163840 }, "knowledge": "2025-07" } }, { "id": "deepseek/deepseek-r1", "name": "DeepSeek: R1", "provider": "openrouter", "family": "deepseek-thinking", "created_at": "2025-01-20 00:00:00 UTC", "context_window": 64000, "max_output_tokens": 16000, "knowledge_cutoff": null, "modalities": { "input": [ "text" ], "output": [ "text" ] }, "capabilities": [ "function_calling", "reasoning", "streaming" ], "pricing": { "text_tokens": { "standard": { "input_per_million": 0.7, "output_per_million": 2.5 } } }, "metadata": { "description": "DeepSeek R1 is here: Performance on par with [OpenAI o1](/openai/o1), but open-sourced and with fully open reasoning tokens. 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It's 671B parameters in size, with 37B active...", "architecture": { "modality": "text->text", "input_modalities": [ "text" ], "output_modalities": [ "text" ], "tokenizer": "DeepSeek", "instruct_type": "deepseek-r1" }, "top_provider": { "context_length": 163840, "max_completion_tokens": 32768, "is_moderated": false }, "per_request_limits": null, "supported_parameters": [ "frequency_penalty", "include_reasoning", "logit_bias", "max_tokens", "min_p", "presence_penalty", "reasoning", "repetition_penalty", "response_format", "seed", "stop", "structured_outputs", "temperature", "tool_choice", "tools", "top_k", "top_p" ] } }, { "id": "deepseek/deepseek-r1-distill-llama-70b", "name": "DeepSeek R1 Distill Llama 70B", "provider": "openrouter", "family": "deepseek-thinking", "created_at": "2025-01-23 00:00:00 UTC", "context_window": 8192, "max_output_tokens": 8192, "knowledge_cutoff": null, "modalities": { "input": [ "text" ], "output": [ "text" ] }, "capabilities": [ "structured_output", "reasoning", "streaming", "predicted_outputs" ], "pricing": { "text_tokens": { "standard": { "input_per_million": 0.7, "output_per_million": 0.7999999999999999 } } }, "metadata": { "description": "DeepSeek R1 Distill Llama 70B is a distilled large language model based on [Llama-3.3-70B-Instruct](/meta-llama/llama-3.3-70b-instruct), using outputs from [DeepSeek R1](/deepseek/deepseek-r1). 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It retains a 1 million token context window and scores 45.1% on hard...", "architecture": { "modality": "text+image+file->text", "input_modalities": [ "image", "text", "file" ], "output_modalities": [ "text" ], "tokenizer": "GPT", "instruct_type": null }, "top_provider": { "context_length": 1047576, "max_completion_tokens": 32768, "is_moderated": true }, "per_request_limits": null, "supported_parameters": [ "max_completion_tokens", "max_tokens", "response_format", "seed", "structured_outputs", "temperature", "tool_choice", "tools", "top_p" ], "source": "models.dev", "provider_id": "openrouter", "open_weights": false, "attachment": true, "temperature": true, "last_updated": "2025-04-14", "cost": { "input": 0.4, "output": 1.6, "cache_read": 0.1 }, "limit": { "context": 1047576, "output": 32768 }, "knowledge": "2024-04" } }, { "id": "openai/gpt-4.1-nano", "name": "OpenAI: GPT-4.1 Nano", "provider": "openrouter", "family": "openai", "created_at": "2025-04-14 17:22:49 UTC", "context_window": 1047576, "max_output_tokens": 32768, "knowledge_cutoff": null, "modalities": { "input": [ "image", "text", "file" ], "output": [ "text" ] }, "capabilities": [ "streaming", "function_calling", "structured_output" ], "pricing": { "text_tokens": { "standard": { "input_per_million": 0.09999999999999999, "output_per_million": 0.39999999999999997, "cache_read_input_per_million": 0.024999999999999998 } } }, "metadata": { "description": "For tasks that demand low latency, GPT‑4.1 nano is the fastest and cheapest model in the GPT-4.1 series. It delivers exceptional performance at a small size with its 1 million...", "architecture": { "modality": "text+image+file->text", "input_modalities": [ "image", "text", "file" ], "output_modalities": [ "text" ], "tokenizer": "GPT", "instruct_type": null }, "top_provider": { "context_length": 1047576, "max_completion_tokens": 32768, "is_moderated": true }, "per_request_limits": null, "supported_parameters": [ "max_completion_tokens", "max_tokens", "response_format", "seed", "structured_outputs", "temperature", "tool_choice", "tools", "top_p" ] } }, { "id": "openai/gpt-4o", "name": "OpenAI: GPT-4o", "provider": "openrouter", "family": "openai", "created_at": "2024-05-13 00:00:00 UTC", "context_window": 128000, "max_output_tokens": 16384, "knowledge_cutoff": null, "modalities": { "input": [ "text", "image", "file" ], "output": [ "text" ] }, "capabilities": [ "streaming", "function_calling", "structured_output" ], "pricing": { "text_tokens": { "standard": { "input_per_million": 2.5, "output_per_million": 10.0 } } }, "metadata": { "description": "GPT-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...", "architecture": { "modality": "text+image+file->text", "input_modalities": [ "text", "image", "file" ], "output_modalities": [ "text" ], "tokenizer": "GPT", "instruct_type": null }, "top_provider": { "context_length": 128000, "max_completion_tokens": 16384, "is_moderated": false }, "per_request_limits": null, "supported_parameters": [ "frequency_penalty", "logit_bias", "logprobs", "max_completion_tokens", "max_tokens", "presence_penalty", "response_format", "seed", "stop", "structured_outputs", "temperature", "tool_choice", "tools", "top_logprobs", "top_p", "web_search_options" ] } }, { "id": "openai/gpt-4o-2024-05-13", "name": "OpenAI: GPT-4o (2024-05-13)", "provider": "openrouter", "family": "openai", "created_at": "2024-05-13 00:00:00 UTC", "context_window": 128000, "max_output_tokens": 4096, "knowledge_cutoff": null, "modalities": { "input": [ "text", "image", "file" ], "output": [ "text" ] }, "capabilities": [ "streaming", "function_calling", "structured_output" ], "pricing": { "text_tokens": { "standard": { "input_per_million": 5.0, "output_per_million": 15.0 } } }, "metadata": { "description": "GPT-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...", "architecture": { "modality": "text+image+file->text", "input_modalities": [ "text", "image", "file" ], "output_modalities": [ "text" ], "tokenizer": "GPT", "instruct_type": null }, "top_provider": { "context_length": 128000, "max_completion_tokens": 4096, "is_moderated": false }, "per_request_limits": null, "supported_parameters": [ "frequency_penalty", "logit_bias", "logprobs", "max_completion_tokens", "max_tokens", "presence_penalty", "response_format", "seed", "stop", "structured_outputs", "temperature", "tool_choice", "tools", "top_logprobs", "top_p", "web_search_options" ] } }, { "id": "openai/gpt-4o-2024-08-06", "name": "OpenAI: GPT-4o (2024-08-06)", "provider": "openrouter", "family": "openai", "created_at": "2024-08-06 00:00:00 UTC", "context_window": 128000, "max_output_tokens": 16384, "knowledge_cutoff": null, "modalities": { "input": [ "text", "image", "file" ], "output": [ "text" ] }, "capabilities": [ "streaming", "function_calling", "structured_output" ], "pricing": { "text_tokens": { "standard": { "input_per_million": 2.5, "output_per_million": 10.0, "cache_read_input_per_million": 1.25 } } }, "metadata": { "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. 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This enhancement allows the model to detect nuances within audio recordings and add depth to generated user experiences. Audio outputs...", "architecture": { "modality": "text+audio->text+audio", "input_modalities": [ "audio", "text" ], "output_modalities": [ "text", "audio" ], "tokenizer": "GPT", "instruct_type": null }, "top_provider": { "context_length": 128000, "max_completion_tokens": 16384, "is_moderated": true }, "per_request_limits": null, "supported_parameters": [ "frequency_penalty", "logit_bias", "logprobs", "max_tokens", "presence_penalty", "response_format", "seed", "stop", "structured_outputs", "temperature", "tool_choice", "tools", "top_logprobs", "top_p" ] } }, { "id": "openai/gpt-4o-mini", "name": "GPT-4o-mini", "provider": "openrouter", "family": "gpt-mini", "created_at": "2024-07-18 00:00:00 UTC", "context_window": 128000, "max_output_tokens": 16384, "knowledge_cutoff": null, "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "capabilities": [ "function_calling", "structured_output", "vision", "streaming" ], "pricing": { "text_tokens": { "standard": { "input_per_million": 0.15, "output_per_million": 0.6, "cache_read_input_per_million": 0.08 } } }, "metadata": { "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. 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"created_at": "2025-02-01 11:56:14 UTC", "context_window": 131072, "max_output_tokens": 8192, "knowledge_cutoff": null, "modalities": { "input": [ "text" ], "output": [ "text" ] }, "capabilities": [ "streaming", "function_calling", "structured_output" ], "pricing": { "text_tokens": { "standard": { "input_per_million": 0.0325, "output_per_million": 0.13, "cache_read_input_per_million": 0.006500000000000001 } } }, "metadata": { "description": "Qwen-Turbo, based on Qwen2.5, is a 1M context model that provides fast speed and low cost, suitable for simple tasks.", "architecture": { "modality": "text->text", "input_modalities": [ "text" ], "output_modalities": [ "text" ], "tokenizer": "Qwen", "instruct_type": null }, "top_provider": { "context_length": 131072, "max_completion_tokens": 8192, "is_moderated": false }, "per_request_limits": null, "supported_parameters": [ "max_tokens", "presence_penalty", "response_format", "seed", "temperature", "tool_choice", "tools", "top_p" ] } }, { "id": 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Significantly upgraded for detailed recognition capabilities and text recognition abilities, supporting ultra-high pixel resolutions up to millions of pixels and extreme aspect ratios for...", "architecture": { "modality": "text+image->text", "input_modalities": [ "text", "image" ], "output_modalities": [ "text" ], "tokenizer": "Qwen", "instruct_type": null }, "top_provider": { "context_length": 131072, "max_completion_tokens": 8192, "is_moderated": false }, "per_request_limits": null, "supported_parameters": [ "max_tokens", "presence_penalty", "response_format", "seed", "temperature", "top_p" ] } }, { "id": "qwen/qwen2.5-vl-72b-instruct", "name": "Qwen2.5 VL 72B Instruct", "provider": "openrouter", "family": "qwen", "created_at": "2025-02-01 00:00:00 UTC", "context_window": 32768, "max_output_tokens": 8192, "knowledge_cutoff": null, "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "capabilities": [ "structured_output", "vision", "streaming", "predicted_outputs" ], "pricing": { "text_tokens": { "standard": { "input_per_million": 0.25, "output_per_million": 0.75 } } }, "metadata": { "description": "Qwen2.5-VL is proficient in recognizing common objects such as flowers, birds, fish, and insects. 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It supports seamless switching between a \"thinking\" mode for...", "architecture": { "modality": "text->text", "input_modalities": [ "text" ], "output_modalities": [ "text" ], "tokenizer": "Qwen3", "instruct_type": "qwen3" }, "top_provider": { "context_length": 40960, "max_completion_tokens": 40960, "is_moderated": false }, "per_request_limits": null, "supported_parameters": [ "frequency_penalty", "include_reasoning", "logit_bias", "logprobs", "max_tokens", "min_p", "presence_penalty", "reasoning", "repetition_penalty", "response_format", "seed", "stop", "structured_outputs", "temperature", "tool_choice", "tools", "top_k", "top_logprobs", "top_p" ] } }, { "id": "qwen/qwen3-235b-a22b", "name": "Qwen: Qwen3 235B A22B", "provider": "openrouter", "family": "qwen", "created_at": "2025-04-28 21:29:17 UTC", "context_window": 131072, "max_output_tokens": 8192, "knowledge_cutoff": null, "modalities": { "input": [ "text" ], "output": [ "text" ] }, "capabilities": [ "streaming", "function_calling", "structured_output" ], "pricing": { "text_tokens": { "standard": { "input_per_million": 0.45499999999999996, "output_per_million": 1.8199999999999998 } } }, "metadata": { "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...", "architecture": { "modality": "text->text", "input_modalities": [ "text" ], "output_modalities": [ "text" ], "tokenizer": "Qwen3", "instruct_type": "qwen3" }, "top_provider": { "context_length": 131072, "max_completion_tokens": 8192, "is_moderated": false }, "per_request_limits": null, "supported_parameters": [ "include_reasoning", "max_tokens", "presence_penalty", "reasoning", "response_format", "seed", "temperature", "tool_choice", "tools", "top_p" ] } }, { "id": "qwen/qwen3-235b-a22b-07-25", "name": "Qwen3 235B A22B Instruct 2507", "provider": "openrouter", "family": "qwen", "created_at": "2025-04-28 00:00:00 UTC", "context_window": 262144, "max_output_tokens": 131072, "knowledge_cutoff": null, "modalities": { "input": [ "text" ], "output": [ "text" ] }, "capabilities": [ "function_calling", "structured_output" ], "pricing": { "text_tokens": { "standard": { "input_per_million": 0.15, "output_per_million": 0.85 } } }, "metadata": { "source": "models.dev", "provider_id": "openrouter", "open_weights": true, "attachment": false, "temperature": true, "last_updated": "2025-07-21", "cost": { "input": 0.15, "output": 0.85 }, "limit": { "context": 262144, "output": 131072 }, "knowledge": "2025-04" } }, { "id": "qwen/qwen3-235b-a22b-2507", "name": "Qwen: Qwen3 235B A22B Instruct 2507", "provider": "openrouter", "family": "qwen", "created_at": "2025-07-21 17:39:15 UTC", "context_window": 262144, "max_output_tokens": 16384, "knowledge_cutoff": null, "modalities": { "input": [ "text" ], "output": [ "text" ] }, "capabilities": [ "streaming", "function_calling", "structured_output", "predicted_outputs" ], "pricing": { "text_tokens": { "standard": { "input_per_million": 0.071, "output_per_million": 0.09999999999999999 } } }, "metadata": { "description": "Qwen3-235B-A22B-Instruct-2507 is a multilingual, instruction-tuned mixture-of-experts language model based on the Qwen3-235B architecture, with 22B active parameters per forward pass. 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It activates 22B of its 235B parameters per forward pass and natively supports up to 262,144...", "architecture": { "modality": "text->text", "input_modalities": [ "text" ], "output_modalities": [ "text" ], "tokenizer": "Qwen3", "instruct_type": "qwen3" }, "top_provider": { "context_length": 131072, "max_completion_tokens": null, "is_moderated": false }, "per_request_limits": null, "supported_parameters": [ "frequency_penalty", "include_reasoning", "logit_bias", "max_tokens", "min_p", "presence_penalty", "reasoning", "repetition_penalty", "response_format", "seed", "stop", "structured_outputs", "temperature", "tool_choice", "tools", "top_k", "top_p" ], "source": "models.dev", "provider_id": "openrouter", "open_weights": true, "attachment": false, "temperature": true, "last_updated": "2025-07-25", "cost": { "input": 0.078, "output": 0.312 }, "limit": { "context": 262144, "output": 81920 }, "knowledge": "2025-04" } }, { "id": "qwen/qwen3-30b-a3b", "name": "Qwen: Qwen3 30B A3B", "provider": "openrouter", "family": "qwen", "created_at": "2025-04-28 22:16:44 UTC", "context_window": 40960, "max_output_tokens": 20000, "knowledge_cutoff": null, "modalities": { "input": [ "text" ], "output": [ "text" ] }, "capabilities": [ "streaming", "function_calling", "structured_output", "predicted_outputs" ], "pricing": { "text_tokens": { "standard": { "input_per_million": 0.09, "output_per_million": 0.44999999999999996 } } }, "metadata": { "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...", "architecture": { "modality": "text->text", "input_modalities": [ "text" ], "output_modalities": [ "text" ], "tokenizer": "Qwen3", "instruct_type": "qwen3" }, "top_provider": { "context_length": 40960, "max_completion_tokens": 20000, "is_moderated": false }, "per_request_limits": null, "supported_parameters": [ "frequency_penalty", "include_reasoning", "logit_bias", "logprobs", "max_tokens", "min_p", "presence_penalty", "reasoning", "repetition_penalty", "response_format", "seed", "stop", "structured_outputs", "temperature", "tool_choice", "tools", "top_k", "top_logprobs", "top_p" ] } }, { "id": "qwen/qwen3-30b-a3b-instruct-2507", "name": "Qwen3 30B A3B Instruct 2507", "provider": "openrouter", "family": "qwen", "created_at": "2025-07-29 00:00:00 UTC", "context_window": 262000, "max_output_tokens": 262000, "knowledge_cutoff": null, "modalities": { "input": [ "text" ], "output": [ "text" ] }, "capabilities": [ "function_calling", "structured_output", "streaming", "predicted_outputs" ], "pricing": { "text_tokens": { "standard": { "input_per_million": 0.2, "output_per_million": 0.8 } } }, "metadata": { "description": "Qwen3-30B-A3B-Instruct-2507 is a 30.5B-parameter mixture-of-experts language model from Qwen, with 3.3B active parameters per inference. It operates in non-thinking mode and is designed for high-quality instruction following, multilingual understanding, and...", "architecture": { "modality": "text->text", "input_modalities": [ "text" ], "output_modalities": [ "text" ], "tokenizer": "Qwen3", "instruct_type": null }, "top_provider": { "context_length": 262144, "max_completion_tokens": 262144, "is_moderated": false }, "per_request_limits": null, "supported_parameters": [ "frequency_penalty", "logit_bias", "max_tokens", "min_p", "presence_penalty", "repetition_penalty", "response_format", "seed", "stop", "structured_outputs", "temperature", "tool_choice", "tools", "top_k", "top_p" ], "source": "models.dev", "provider_id": "openrouter", "open_weights": true, "attachment": false, "temperature": true, "last_updated": "2025-07-29", "cost": { "input": 0.2, "output": 0.8 }, "limit": { "context": 262000, "output": 262000 }, "knowledge": "2025-04" } }, { "id": "qwen/qwen3-30b-a3b-thinking-2507", "name": "Qwen3 30B A3B Thinking 2507", "provider": "openrouter", "family": "qwen", "created_at": "2025-07-29 00:00:00 UTC", "context_window": 262000, "max_output_tokens": 262000, "knowledge_cutoff": null, "modalities": { "input": [ "text" ], "output": [ "text" ] }, "capabilities": [ "function_calling", "structured_output", "reasoning", "streaming", "predicted_outputs" ], "pricing": { "text_tokens": { "standard": { "input_per_million": 0.2, "output_per_million": 0.8 } } }, "metadata": { "description": "Qwen3-30B-A3B-Thinking-2507 is a 30B parameter Mixture-of-Experts reasoning model optimized for complex tasks requiring extended multi-step thinking. The model is designed specifically for “thinking mode,” where internal reasoning traces are separated...", "architecture": { "modality": "text->text", "input_modalities": [ "text" ], "output_modalities": [ "text" ], "tokenizer": "Qwen3", "instruct_type": null }, "top_provider": { "context_length": 131072, "max_completion_tokens": 131072, "is_moderated": false }, "per_request_limits": null, "supported_parameters": [ "frequency_penalty", "include_reasoning", "logit_bias", "max_tokens", "min_p", "presence_penalty", "reasoning", "repetition_penalty", "response_format", "seed", "stop", "structured_outputs", "temperature", "tool_choice", "tools", "top_k", "top_p" ], "source": "models.dev", "provider_id": "openrouter", "open_weights": true, "attachment": false, "temperature": true, "last_updated": "2025-07-29", "cost": { "input": 0.2, "output": 0.8 }, "limit": { "context": 262000, "output": 262000 }, "knowledge": "2025-04" } }, { "id": "qwen/qwen3-32b", "name": "Qwen: Qwen3 32B", "provider": "openrouter", "family": "qwen", "created_at": "2025-04-28 21:32:25 UTC", "context_window": 40960, "max_output_tokens": 40960, "knowledge_cutoff": null, "modalities": { "input": [ "text" ], "output": [ "text" ] }, "capabilities": [ "streaming", "function_calling", "structured_output", "predicted_outputs" ], "pricing": { "text_tokens": { "standard": { "input_per_million": 0.08, "output_per_million": 0.24, "cache_read_input_per_million": 0.04 } } }, "metadata": { "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...", "architecture": { "modality": "text->text", "input_modalities": [ "text" ], "output_modalities": [ "text" ], "tokenizer": "Qwen3", "instruct_type": "qwen3" }, "top_provider": { "context_length": 40960, "max_completion_tokens": 40960, "is_moderated": false }, "per_request_limits": null, "supported_parameters": [ "frequency_penalty", "include_reasoning", "logit_bias", "max_tokens", "min_p", "presence_penalty", "reasoning", "repetition_penalty", "response_format", "seed", "stop", "structured_outputs", "temperature", "tool_choice", "tools", "top_k", "top_p" ] } }, { "id": "qwen/qwen3-8b", "name": "Qwen: Qwen3 8B", "provider": "openrouter", "family": "qwen", "created_at": "2025-04-28 21:43:52 UTC", "context_window": 40960, "max_output_tokens": 8192, "knowledge_cutoff": null, "modalities": { "input": [ "text" ], "output": [ "text" ] }, "capabilities": [ "streaming", "function_calling", "structured_output", "predicted_outputs" ], "pricing": { "text_tokens": { "standard": { "input_per_million": 0.049999999999999996, "output_per_million": 0.39999999999999997, "cache_read_input_per_million": 0.049999999999999996 } } }, "metadata": { "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. 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It excels...", "architecture": { "modality": "text+image->text", "input_modalities": [ "text", "image" ], "output_modalities": [ "text" ], "tokenizer": "Qwen3", "instruct_type": null }, "top_provider": { "context_length": 131072, "max_completion_tokens": 32768, "is_moderated": false }, "per_request_limits": null, "supported_parameters": [ "frequency_penalty", "include_reasoning", "max_tokens", "presence_penalty", "reasoning", "repetition_penalty", "response_format", "seed", "stop", "structured_outputs", "temperature", "tool_choice", "tools", "top_k", "top_p" ] } }, { "id": "qwen/qwen3-vl-32b-instruct", "name": "Qwen: Qwen3 VL 32B Instruct", "provider": "openrouter", "family": "qwen", "created_at": "2025-10-23 14:55:32 UTC", "context_window": 131072, "max_output_tokens": 32768, "knowledge_cutoff": null, "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "capabilities": [ "streaming", "function_calling", "structured_output" ], "pricing": { "text_tokens": { "standard": { "input_per_million": 0.10400000000000001, "output_per_million": 0.41600000000000004 } } }, "metadata": { "description": "Qwen3-VL-32B-Instruct is a large-scale multimodal vision-language model designed for high-precision understanding and reasoning across text, images, and video. 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It features improved multimodal fusion with Interleaved-MRoPE for long-horizon...", "architecture": { "modality": "text+image->text", "input_modalities": [ "image", "text" ], "output_modalities": [ "text" ], "tokenizer": "Qwen3", "instruct_type": null }, "top_provider": { "context_length": 131072, "max_completion_tokens": 32768, "is_moderated": false }, "per_request_limits": null, "supported_parameters": [ "frequency_penalty", "logit_bias", "max_tokens", "min_p", "presence_penalty", "repetition_penalty", "response_format", "seed", "stop", "structured_outputs", "temperature", "tool_choice", "tools", "top_k", "top_p" ] } }, { "id": "qwen/qwen3-vl-8b-thinking", "name": "Qwen: Qwen3 VL 8B Thinking", "provider": "openrouter", "family": "qwen", "created_at": "2025-10-14 17:42:26 UTC", "context_window": 131072, "max_output_tokens": 32768, "knowledge_cutoff": null, "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "capabilities": [ "streaming", "function_calling", "structured_output" ], "pricing": { "text_tokens": { "standard": { "input_per_million": 0.117, "output_per_million": 1.365 } } }, "metadata": { "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...", "architecture": { "modality": "text+image->text", "input_modalities": [ "image", "text" ], "output_modalities": [ "text" ], "tokenizer": "Qwen3", "instruct_type": null }, "top_provider": { "context_length": 131072, "max_completion_tokens": 32768, "is_moderated": false }, "per_request_limits": null, "supported_parameters": [ "include_reasoning", "max_tokens", "presence_penalty", "reasoning", "response_format", "seed", "structured_outputs", "temperature", "tool_choice", "tools", "top_p" ] } }, { "id": "qwen/qwen3.5-122b-a10b", "name": "Qwen: Qwen3.5-122B-A10B", "provider": "openrouter", "family": "qwen", "created_at": "2026-02-25 21:09:49 UTC", "context_window": 262144, "max_output_tokens": 65536, "knowledge_cutoff": null, "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "capabilities": [ "streaming", "function_calling", "structured_output", "predicted_outputs" ], "pricing": { "text_tokens": { "standard": { "input_per_million": 0.26, "output_per_million": 2.08 } } }, "metadata": { "description": "The Qwen3.5 122B-A10B native vision-language model is built on a hybrid architecture that integrates a linear attention mechanism with a sparse mixture-of-experts model, achieving higher inference efficiency. In terms of...", "architecture": { "modality": "text+image+video->text", "input_modalities": [ "text", "image", "video" ], "output_modalities": [ "text" ], "tokenizer": "Qwen3", "instruct_type": null }, "top_provider": { "context_length": 262144, "max_completion_tokens": 65536, "is_moderated": false }, "per_request_limits": null, "supported_parameters": [ "frequency_penalty", "include_reasoning", "logit_bias", "logprobs", "max_tokens", "min_p", "presence_penalty", "reasoning", "repetition_penalty", "response_format", "seed", "stop", "structured_outputs", "temperature", "tool_choice", "tools", "top_k", "top_logprobs", "top_p" ] } }, { "id": "qwen/qwen3.5-27b", "name": "Qwen: Qwen3.5-27B", "provider": "openrouter", "family": "qwen", "created_at": "2026-02-25 21:10:10 UTC", "context_window": 262144, "max_output_tokens": 65536, "knowledge_cutoff": null, "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "capabilities": [ "streaming", "function_calling", "structured_output", "predicted_outputs" ], "pricing": { "text_tokens": { "standard": { "input_per_million": 0.195, "output_per_million": 1.56 } } }, "metadata": { "description": "The Qwen3.5 27B native vision-language Dense model incorporates a linear attention mechanism, delivering fast response times while balancing inference speed and performance. Its overall capabilities are comparable to those of...", "architecture": { "modality": "text+image+video->text", "input_modalities": [ "text", "image", "video" ], "output_modalities": [ "text" ], "tokenizer": "Qwen3", "instruct_type": null }, "top_provider": { "context_length": 262144, "max_completion_tokens": 65536, "is_moderated": false }, "per_request_limits": null, "supported_parameters": [ "frequency_penalty", "include_reasoning", "logit_bias", "logprobs", "max_tokens", "min_p", "presence_penalty", "reasoning", "repetition_penalty", "response_format", "seed", "stop", "structured_outputs", "temperature", "tool_choice", "tools", "top_k", "top_logprobs", "top_p" ] } }, { "id": "qwen/qwen3.5-35b-a3b", "name": "Qwen: Qwen3.5-35B-A3B", "provider": "openrouter", "family": "qwen", "created_at": "2026-02-25 21:10:22 UTC", "context_window": 262144, "max_output_tokens": 262144, "knowledge_cutoff": null, "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "capabilities": [ "streaming", "function_calling", "structured_output", "predicted_outputs" ], "pricing": { "text_tokens": { "standard": { "input_per_million": 0.15, "output_per_million": 1.0, "cache_read_input_per_million": 0.049999999999999996 } } }, "metadata": { "description": "The Qwen3.5 Series 35B-A3B is a native vision-language model designed with a hybrid architecture that integrates linear attention mechanisms and a sparse mixture-of-experts model, achieving higher inference efficiency. Its overall...", "architecture": { "modality": "text+image+video->text", "input_modalities": [ "text", "image", "video" ], "output_modalities": [ "text" ], "tokenizer": "Qwen3", "instruct_type": null }, "top_provider": { "context_length": 262144, "max_completion_tokens": 262144, "is_moderated": false }, "per_request_limits": null, "supported_parameters": [ "frequency_penalty", "include_reasoning", "logit_bias", "logprobs", "max_tokens", "min_p", "presence_penalty", "reasoning", "repetition_penalty", "response_format", "seed", "stop", "structured_outputs", "temperature", "tool_choice", "tools", "top_k", "top_logprobs", "top_p" ] } }, { "id": "qwen/qwen3.5-397b-a17b", "name": "Qwen3.5 397B A17B", "provider": "openrouter", "family": "qwen", "created_at": "2026-02-16 00:00:00 UTC", "context_window": 262144, "max_output_tokens": 65536, "knowledge_cutoff": null, "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "capabilities": [ "function_calling", "structured_output", "reasoning", "vision", "streaming", "predicted_outputs" ], "pricing": { "text_tokens": { "standard": { "input_per_million": 0.6, "output_per_million": 3.6 } } }, "metadata": { "description": "The Qwen3.5 series 397B-A17B native vision-language model is built on a hybrid architecture that integrates a linear attention mechanism with a sparse mixture-of-experts model, achieving higher inference efficiency. It delivers...", "architecture": { "modality": "text+image+video->text", "input_modalities": [ "text", "image", "video" ], "output_modalities": [ "text" ], "tokenizer": "Qwen3", "instruct_type": null }, "top_provider": { "context_length": 262144, "max_completion_tokens": 65536, "is_moderated": false }, "per_request_limits": null, "supported_parameters": [ "frequency_penalty", "include_reasoning", "logit_bias", "logprobs", "max_tokens", "min_p", "presence_penalty", "reasoning", "repetition_penalty", "response_format", "seed", "stop", "structured_outputs", "temperature", "tool_choice", "tools", "top_k", "top_logprobs", "top_p" ], "source": "models.dev", "provider_id": "openrouter", "open_weights": true, "attachment": true, "temperature": true, "last_updated": "2026-02-16", "cost": { "input": 0.6, "output": 3.6 }, "limit": { "context": 262144, "output": 65536 }, "knowledge": "2025-04" } }, { "id": "qwen/qwen3.5-9b", "name": "Qwen: Qwen3.5-9B", "provider": "openrouter", "family": "qwen", "created_at": "2026-03-10 14:19:56 UTC", "context_window": 262144, "max_output_tokens": null, "knowledge_cutoff": null, "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "capabilities": [ "streaming", "function_calling", "structured_output", "predicted_outputs" ], "pricing": { "text_tokens": { "standard": { "input_per_million": 0.09999999999999999, "output_per_million": 0.15 } } }, "metadata": { "description": "Qwen3.5-9B is a multimodal foundation model from the Qwen3.5 family, designed to deliver strong reasoning, coding, and visual understanding in an efficient 9B-parameter architecture. It uses a unified vision-language design...", "architecture": { "modality": "text+image+video->text", "input_modalities": [ "text", "image", "video" ], "output_modalities": [ "text" ], "tokenizer": "Qwen3", "instruct_type": null }, "top_provider": { "context_length": 262144, "max_completion_tokens": null, "is_moderated": false }, "per_request_limits": null, "supported_parameters": [ "frequency_penalty", "include_reasoning", "logit_bias", "logprobs", "max_tokens", "min_p", "presence_penalty", "reasoning", "repetition_penalty", "response_format", "stop", "structured_outputs", "temperature", "tool_choice", "tools", "top_k", "top_logprobs", "top_p" ] } }, { "id": "qwen/qwen3.5-flash-02-23", "name": "Qwen: Qwen3.5-Flash", "provider": "openrouter", "family": "qwen", "created_at": "2026-02-25 00:00:00 UTC", "context_window": 1000000, "max_output_tokens": 65536, "knowledge_cutoff": null, "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "capabilities": [ "function_calling", "structured_output", "reasoning", "vision", "streaming" ], "pricing": { "text_tokens": { "standard": { "input_per_million": 0.065, "output_per_million": 0.26 } } }, "metadata": { "description": "The Qwen3.5 native vision-language Flash models are built on a hybrid architecture that integrates a linear attention mechanism with a sparse mixture-of-experts model, achieving higher inference efficiency. Compared to the...", "architecture": { "modality": "text+image+video->text", "input_modalities": [ "text", "image", "video" ], "output_modalities": [ "text" ], "tokenizer": "Qwen3", "instruct_type": null }, "top_provider": { "context_length": 1000000, "max_completion_tokens": 65536, "is_moderated": false }, "per_request_limits": null, "supported_parameters": [ "include_reasoning", "max_tokens", "presence_penalty", "reasoning", "response_format", "seed", "structured_outputs", "temperature", "tool_choice", "tools", "top_p" ], "source": "models.dev", "provider_id": "openrouter", "open_weights": false, "attachment": true, "temperature": true, "last_updated": "2026-02-25", "cost": { "input": 0.065, "output": 0.26 }, "limit": { "context": 1000000, "output": 65536 } } }, { "id": "qwen/qwen3.5-plus-02-15", "name": "Qwen3.5 Plus 2026-02-15", "provider": "openrouter", "family": "qwen", "created_at": "2026-02-16 00:00:00 UTC", "context_window": 1000000, "max_output_tokens": 65536, "knowledge_cutoff": null, "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "capabilities": [ "function_calling", "structured_output", "reasoning", "vision", "streaming" ], "pricing": { "text_tokens": { "standard": { "input_per_million": 0.4, "output_per_million": 2.4 } } }, "metadata": { "description": "The Qwen3.5 native vision-language series Plus models are built on a hybrid architecture that integrates linear attention mechanisms with sparse mixture-of-experts models, achieving higher inference efficiency. In a variety of...", "architecture": { "modality": "text+image+video->text", "input_modalities": [ "text", "image", "video" ], "output_modalities": [ "text" ], "tokenizer": "Qwen3", "instruct_type": null }, "top_provider": { "context_length": 1000000, "max_completion_tokens": 65536, "is_moderated": false }, "per_request_limits": null, "supported_parameters": [ "include_reasoning", "max_tokens", "presence_penalty", "reasoning", "response_format", "seed", "structured_outputs", "temperature", "tool_choice", "tools", "top_p" ], "source": "models.dev", "provider_id": "openrouter", "open_weights": false, "attachment": true, "temperature": true, "last_updated": "2026-02-16", "cost": { "input": 0.4, "output": 2.4 }, "limit": { "context": 1000000, "output": 65536 }, "knowledge": "2025-04" } }, { "id": "qwen/qwen3.5-plus-20260420", "name": "Qwen: Qwen3.5 Plus 2026-04-20", "provider": "openrouter", "family": "qwen", "created_at": "2026-04-27 03:42:48 UTC", "context_window": 1000000, "max_output_tokens": 65536, "knowledge_cutoff": null, "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "capabilities": [ "streaming", "function_calling", "structured_output" ], "pricing": { "text_tokens": { "standard": { "input_per_million": 0.39999999999999997, "output_per_million": 2.4 } } }, "metadata": { "description": "Qwen3.5 Plus (April 2026) is a large-scale multimodal language model from Alibaba. It accepts text, image, and video input and produces text output, with a 1M token context window. This...", "architecture": { "modality": "text+image+video->text", "input_modalities": [ "text", "image", "video" ], "output_modalities": [ "text" ], "tokenizer": "Qwen3", "instruct_type": null }, "top_provider": { "context_length": 1000000, "max_completion_tokens": 65536, "is_moderated": false }, "per_request_limits": null, "supported_parameters": [ "include_reasoning", "max_tokens", "presence_penalty", "reasoning", "response_format", "seed", "structured_outputs", "temperature", "tool_choice", "tools", "top_p" ] } }, { "id": "qwen/qwen3.6-27b", "name": "Qwen: Qwen3.6 27B", "provider": "openrouter", "family": "qwen", "created_at": "2026-04-27 01:57:44 UTC", "context_window": 262144, "max_output_tokens": 81920, "knowledge_cutoff": null, "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "capabilities": [ "streaming", "function_calling", "structured_output", "predicted_outputs" ], "pricing": { "text_tokens": { "standard": { "input_per_million": 0.32, "output_per_million": 3.1999999999999997 } } }, "metadata": { "description": "Qwen3.6 27B is a dense 27-billion-parameter language model from the Qwen Team at Alibaba, released in April 2026. It features hybrid multimodal capabilities — accepting text, image, and video inputs...", "architecture": { "modality": "text+image+video->text", "input_modalities": [ "text", "image", "video" ], "output_modalities": [ "text" ], "tokenizer": "Qwen3", "instruct_type": null }, "top_provider": { "context_length": 262144, "max_completion_tokens": 81920, "is_moderated": false }, "per_request_limits": null, "supported_parameters": [ "frequency_penalty", "include_reasoning", "logit_bias", "logprobs", "max_tokens", "min_p", "presence_penalty", "reasoning", "repetition_penalty", "response_format", "seed", "stop", "structured_outputs", "temperature", "tool_choice", "tools", "top_k", "top_logprobs", "top_p" ] } }, { "id": "qwen/qwen3.6-35b-a3b", "name": "Qwen: Qwen3.6 35B A3B", "provider": "openrouter", "family": "qwen", "created_at": "2026-04-27 03:24:15 UTC", "context_window": 262144, "max_output_tokens": 262144, "knowledge_cutoff": null, "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "capabilities": [ "streaming", "function_calling", "structured_output", "predicted_outputs" ], "pricing": { "text_tokens": { "standard": { "input_per_million": 0.15, "output_per_million": 1.0, "cache_read_input_per_million": 0.049999999999999996 } } }, "metadata": { "description": "Qwen3.6-35B-A3B is an open-weight multimodal model from Alibaba Cloud with 35 billion total parameters and 3 billion active parameters per token. It uses a hybrid sparse mixture-of-experts architecture combining Gated...", "architecture": { "modality": "text+image+video->text", "input_modalities": [ "text", "image", "video" ], "output_modalities": [ "text" ], "tokenizer": "Qwen", "instruct_type": null }, "top_provider": { "context_length": 262144, "max_completion_tokens": 262144, "is_moderated": false }, "per_request_limits": null, "supported_parameters": [ "frequency_penalty", "include_reasoning", "logit_bias", "max_tokens", "min_p", "presence_penalty", "reasoning", "repetition_penalty", "response_format", "seed", "stop", "structured_outputs", "temperature", "tool_choice", "tools", "top_k", "top_p" ] } }, { "id": "qwen/qwen3.6-flash", "name": "Qwen: Qwen3.6 Flash", "provider": "openrouter", "family": "qwen", "created_at": "2026-04-27 03:42:42 UTC", "context_window": 1000000, "max_output_tokens": 65536, "knowledge_cutoff": null, "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "capabilities": [ "streaming", "function_calling", "structured_output" ], "pricing": { "text_tokens": { "standard": { "input_per_million": 0.25, "output_per_million": 1.5 } } }, "metadata": { "description": "Qwen3.6 Flash is a fast, efficient language model from Alibaba's Qwen 3.6 series. It supports text, image, and video input with a 1M token context window. Tiered pricing kicks in...", "architecture": { "modality": "text+image+video->text", "input_modalities": [ "text", "image", "video" ], "output_modalities": [ "text" ], "tokenizer": "Qwen3", "instruct_type": null }, "top_provider": { "context_length": 1000000, "max_completion_tokens": 65536, "is_moderated": false }, "per_request_limits": null, "supported_parameters": [ "include_reasoning", "max_tokens", "presence_penalty", "reasoning", "response_format", "seed", "structured_outputs", "temperature", "tool_choice", "tools", "top_p" ] } }, { "id": "qwen/qwen3.6-max-preview", "name": "Qwen: Qwen3.6 Max Preview", "provider": "openrouter", "family": "qwen", "created_at": "2026-04-27 03:24:02 UTC", "context_window": 262144, "max_output_tokens": 65536, "knowledge_cutoff": null, "modalities": { "input": [ "text" ], "output": [ "text" ] }, "capabilities": [ "streaming", "function_calling", "structured_output" ], "pricing": { "text_tokens": { "standard": { "input_per_million": 1.04, "output_per_million": 6.24 } } }, "metadata": { "description": "Qwen3.6-Max-Preview is a proprietary frontier model from Alibaba Cloud built on a sparse mixture-of-experts architecture with approximately 1 trillion total parameters. It is optimized for agentic coding, tool use, and...", "architecture": { "modality": "text->text", "input_modalities": [ "text" ], "output_modalities": [ "text" ], "tokenizer": "Qwen", "instruct_type": null }, "top_provider": { "context_length": 262144, "max_completion_tokens": 65536, "is_moderated": false }, "per_request_limits": null, "supported_parameters": [ "include_reasoning", "logprobs", "max_tokens", "presence_penalty", "reasoning", "response_format", "seed", "structured_outputs", "temperature", "tool_choice", "tools", "top_logprobs", "top_p" ] } }, { "id": "qwen/qwen3.6-plus", "name": "Qwen3.6 Plus", "provider": "openrouter", "family": "qwen", "created_at": "2026-04-02 00:00:00 UTC", "context_window": 1000000, "max_output_tokens": 65536, "knowledge_cutoff": null, "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "capabilities": [ "function_calling", "structured_output", "reasoning", "vision", "streaming" ], "pricing": { "text_tokens": { "standard": { "input_per_million": 0.325, "output_per_million": 1.95 } } }, "metadata": { "description": "Qwen 3.6 Plus builds on a hybrid architecture that combines efficient linear attention with sparse mixture-of-experts routing, enabling strong scalability and high-performance inference. Compared to the 3.5 series, it delivers...", "architecture": { "modality": "text+image+video->text", "input_modalities": [ "text", "image", "video" ], "output_modalities": [ "text" ], "tokenizer": "Qwen3", "instruct_type": null }, "top_provider": { "context_length": 1000000, "max_completion_tokens": 65536, "is_moderated": false }, "per_request_limits": null, "supported_parameters": [ "include_reasoning", "max_tokens", "presence_penalty", "reasoning", "response_format", "seed", "structured_outputs", "temperature", "tool_choice", "tools", "top_p" ], "source": "models.dev", "provider_id": "openrouter", "open_weights": false, "attachment": true, "temperature": true, "last_updated": "2026-04-02", "cost": { "input": 0.325, "output": 1.95 }, "limit": { "context": 1000000, "output": 65536 }, "knowledge": "2025-04" } }, { "id": "rekaai/reka-edge", "name": "Reka Edge", "provider": "openrouter", "family": "rekaai", "created_at": "2026-03-20 17:16:05 UTC", "context_window": 16384, "max_output_tokens": 16384, "knowledge_cutoff": null, "modalities": { "input": [ "image", "text", "video" ], "output": [ "text" ] }, "capabilities": [ "streaming", "function_calling" ], "pricing": { "text_tokens": { "standard": { "input_per_million": 0.09999999999999999, "output_per_million": 0.09999999999999999 } } }, "metadata": { "description": "Reka Edge is an extremely efficient 7B multimodal vision-language model that accepts image/video+text inputs and generates text outputs. This model is optimized specifically to deliver industry-leading performance in image understanding,...", "architecture": { "modality": "text+image+video->text", "input_modalities": [ "image", "text", "video" ], "output_modalities": [ "text" ], "tokenizer": "Other", "instruct_type": null }, "top_provider": { "context_length": 16384, "max_completion_tokens": 16384, "is_moderated": false }, "per_request_limits": null, "supported_parameters": [ "frequency_penalty", "max_tokens", "presence_penalty", "seed", "stop", "structured_outputs", "temperature", "tool_choice", "tools", "top_k", "top_p" ] } }, { "id": "rekaai/reka-flash-3", "name": "Reka Flash 3", "provider": "openrouter", "family": "rekaai", "created_at": "2025-03-12 20:53:33 UTC", "context_window": 65536, "max_output_tokens": 65536, "knowledge_cutoff": null, "modalities": { "input": [ "text" ], "output": [ "text" ] }, "capabilities": [ "streaming" ], "pricing": { "text_tokens": { "standard": { "input_per_million": 0.09999999999999999, "output_per_million": 0.19999999999999998 } } }, "metadata": { "description": "Reka Flash 3 is a general-purpose, instruction-tuned large language model with 21 billion parameters, developed by Reka. 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