{ "openai/gpt-3.5-turbo": { "id": "openai/gpt-3.5-turbo", "canonical_slug": "openai/gpt-3.5-turbo", "hugging_face_id": null, "name": "OpenAI: GPT-3.5 Turbo", "created": 1685232000, "description": "GPT-3.5 Turbo is OpenAI's fastest model. It can understand and generate natural language or code, and is optimized for chat and traditional completion tasks.\n\nTraining data up to Sep 2021.", "context_length": 16385, "architecture": { "modality": "text->text", "input_modalities": [ "text" ], "output_modalities": [ "text" ], "tokenizer": "GPT", "instruct_type": null }, "pricing": { "prompt": "0.0000005", "completion": "0.0000015" }, "top_provider": { "context_length": 16385, "max_completion_tokens": 4096, "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" ], "default_parameters": {}, "supported_voices": null, "knowledge_cutoff": "2021-09-30", "expiration_date": null, "links": { "details": "/api/v1/models/openai/gpt-3.5-turbo/endpoints" }, "benchmarks": { "design_arena": [], "artificial_analysis": { "intelligence_index": null, "coding_index": 10.7, "agentic_index": null } }, "model_family": "openai" }, "openai/gpt-4": { "id": "openai/gpt-4", "canonical_slug": "openai/gpt-4", "hugging_face_id": null, "name": "OpenAI: GPT-4", "created": 1685232000, "description": "OpenAI's flagship model, GPT-4 is a large-scale multimodal language model capable of solving difficult problems with greater accuracy than previous models due to its broader general knowledge and advanced reasoning...", "context_length": 8191, "architecture": { "modality": "text->text", "input_modalities": [ "text" ], "output_modalities": [ "text" ], "tokenizer": "GPT", "instruct_type": null }, "pricing": { "prompt": "0.00003", "completion": "0.00006" }, "top_provider": { "context_length": 8191, "max_completion_tokens": 4096, "is_moderated": true }, "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" ], "default_parameters": {}, "supported_voices": null, "knowledge_cutoff": "2021-09-30", "expiration_date": null, "links": { "details": "/api/v1/models/openai/gpt-4/endpoints" }, "benchmarks": { "design_arena": [], "artificial_analysis": { "intelligence_index": null, "coding_index": 13.1, "agentic_index": null } }, "model_family": "openai" }, "openai/gpt-4o-mini": { "id": "openai/gpt-4o-mini", "canonical_slug": "openai/gpt-4o-mini", "hugging_face_id": null, "name": "OpenAI: GPT-4o-mini", "created": 1721260800, "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. As their most advanced small model, it is many multiples more affordable...", "context_length": 128000, "architecture": { "modality": "text+image+file->text", "input_modalities": [ "text", "image", "file" ], "output_modalities": [ "text" ], "tokenizer": "GPT", "instruct_type": null }, "pricing": { "prompt": "0.00000015", "completion": "0.0000006", "input_cache_read": "0.000000075" }, "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" ], "default_parameters": {}, "supported_voices": null, "knowledge_cutoff": "2023-10-31", "expiration_date": null, "links": { "details": "/api/v1/models/openai/gpt-4o-mini/endpoints" }, "benchmarks": { "design_arena": [], "artificial_analysis": { "intelligence_index": null, "coding_index": 11.4, "agentic_index": 1 } }, "model_family": "openai" }, "openai/gpt-oss-120b": { "id": "openai/gpt-oss-120b", "canonical_slug": "openai/gpt-oss-120b", "hugging_face_id": "openai/gpt-oss-120b", "name": "OpenAI: gpt-oss-120b", "created": 1754414231, "description": "gpt-oss-120b is an open-weight, 117B-parameter Mixture-of-Experts (MoE) language model from OpenAI designed for high-reasoning, agentic, and general-purpose production use cases. It activates 5.1B parameters per forward pass and is optimized...", "context_length": 131072, "architecture": { "modality": "text->text", "input_modalities": [ "text" ], "output_modalities": [ "text" ], "tokenizer": "GPT", "instruct_type": null }, "pricing": { "prompt": "0.00000003", "completion": "0.00000015" }, "top_provider": { "context_length": 131072, "max_completion_tokens": 131072, "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_a", "top_k", "top_logprobs", "top_p" ], "default_parameters": { "temperature": null, "top_p": null, "frequency_penalty": null }, "supported_voices": null, "knowledge_cutoff": "2024-06-30", "expiration_date": null, "links": { "details": "/api/v1/models/openai/gpt-oss-120b/endpoints" }, "benchmarks": { "design_arena": [ { "arena": "models", "category": "3d", "elo": 978, "win_rate": 29.4, "rank": 91 }, { "arena": "models", "category": "codecategories", "elo": 1012, "win_rate": 33.4, "rank": 98 }, { "arena": "models", "category": "dataviz", "elo": 1043, "win_rate": 45.1, "rank": 87 }, { "arena": "models", "category": "gamedev", "elo": 1059, "win_rate": 40.6, "rank": 85 }, { "arena": "models", "category": "uicomponent", "elo": 978, "win_rate": 35.5, "rank": 92 }, { "arena": "models", "category": "website", "elo": 1010, "win_rate": 32.5, "rank": 100 } ], "artificial_analysis": { "intelligence_index": 23.8, "coding_index": 30.4, "agentic_index": 13.2 } }, "reasoning": { "mandatory": true, "supported_efforts": [ "high", "medium", "low" ], "default_effort": "medium" }, "model_family": "openai" }, "openai/gpt-5.5": { "id": "openai/gpt-5.5", "canonical_slug": "openai/gpt-5.5-20260423", "hugging_face_id": "", "name": "OpenAI: GPT-5.5", "created": 1777051893, "description": "GPT-5.5 is OpenAI’s frontier model designed for complex professional workloads, building on GPT-5.4 with stronger reasoning, higher reliability, and improved token efficiency on hard tasks. It features a 1M+ token...", "context_length": 1050000, "architecture": { "modality": "text+image+file->text", "input_modalities": [ "file", "image", "text" ], "output_modalities": [ "text" ], "tokenizer": "GPT", "instruct_type": null }, "pricing": { "prompt": "0.000005", "completion": "0.00003", "web_search": "0.01", "input_cache_read": "0.0000005" }, "top_provider": { "context_length": 1050000, "max_completion_tokens": 128000, "is_moderated": true }, "per_request_limits": null, "supported_parameters": [ "include_reasoning", "max_completion_tokens", "max_tokens", "reasoning", "response_format", "seed", "structured_outputs", "tool_choice", "tools" ], "default_parameters": { "temperature": null, "top_p": null, "top_k": null, "frequency_penalty": null, "presence_penalty": null, "repetition_penalty": null }, "supported_voices": null, "knowledge_cutoff": "2025-12-01", "expiration_date": null, "links": { "details": "/api/v1/models/openai/gpt-5.5-20260423/endpoints" }, "benchmarks": { "design_arena": [ { "arena": "agents", "category": "agenticgamedev", "elo": 1207, "win_rate": 53, "rank": 5 }, { "arena": "agents", "category": "agentichtmlslides", "elo": 1084, "win_rate": 34.2, "rank": 9 }, { "arena": "agents", "category": "agenticslides", "elo": 1150, "win_rate": 43.5, "rank": 7 }, { "arena": "agents", "category": "agenticslides(html)", "elo": 1077, "win_rate": 33.2, "rank": 9 }, { "arena": "agents", "category": "agenticslides(python-pptx)", "elo": 1155, "win_rate": 45.2, "rank": 7 }, { "arena": "agents", "category": "androidnative", "elo": 1260, "win_rate": 55.4, "rank": 4 }, { "arena": "agents", "category": "fullstack", "elo": 1151, "win_rate": 44.5, "rank": 15 }, { "arena": "agents", "category": "godotgamedev", "elo": 1217, "win_rate": 53, "rank": 9 }, { "arena": "agents", "category": "htmlslides", "elo": 1082, "win_rate": 34.3, "rank": 11 }, { "arena": "agents", "category": "mobileapps", "elo": 1220, "win_rate": 51, "rank": 9 }, { "arena": "agents", "category": "pptxslides", "elo": 1157, "win_rate": 45.3, "rank": 7 }, { "arena": "agents", "category": "python-pptxslides", "elo": 1152, "win_rate": 43.3, "rank": 8 }, { "arena": "agents", "category": "webapps", "elo": 1182, "win_rate": 45.2, "rank": 18 }, { "arena": "models", "category": "3d", "elo": 1264, "win_rate": 53.2, "rank": 29 }, { "arena": "models", "category": "asciiart", "elo": 1316, "win_rate": 63.2, "rank": 3 }, { "arena": "models", "category": "codecategories", "elo": 1296, "win_rate": 56.7, "rank": 17 }, { "arena": "models", "category": "dataviz", "elo": 1300, "win_rate": 58.3, "rank": 9 }, { "arena": "models", "category": "gamedev", "elo": 1348, "win_rate": 62.8, "rank": 3 }, { "arena": "models", "category": "svg", "elo": 1283, "win_rate": 59.9, "rank": 6 }, { "arena": "models", "category": "uicomponent", "elo": 1298, "win_rate": 57, "rank": 16 }, { "arena": "models", "category": "website", "elo": 1290, "win_rate": 56, "rank": 19 } ], "artificial_analysis": { "intelligence_index": 54.8, "coding_index": 74.9, "agentic_index": 44.9 } }, "reasoning": { "mandatory": false, "default_enabled": true, "supported_efforts": [ "xhigh", "high", "medium", "low", "none" ], "default_effort": "medium" }, "model_family": "openai" }, "qwen/qwen-2.5-72b-instruct": { "id": "qwen/qwen-2.5-72b-instruct", "canonical_slug": "qwen/qwen-2.5-72b-instruct", "hugging_face_id": "Qwen/Qwen2.5-72B-Instruct", "name": "Qwen2.5 72B Instruct", "created": 1726704000, "description": "Qwen2.5 72B is the latest series of Qwen large language models. Qwen2.5 brings the following improvements upon Qwen2: - Significantly more knowledge and has greatly improved capabilities in coding and...", "context_length": 131072, "architecture": { "modality": "text->text", "input_modalities": [ "text" ], "output_modalities": [ "text" ], "tokenizer": "Qwen", "instruct_type": "chatml" }, "pricing": { "prompt": "0.00000036", "completion": "0.0000004" }, "top_provider": { "context_length": 32768, "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" ], "default_parameters": {}, "supported_voices": null, "knowledge_cutoff": "2024-06-30", "expiration_date": null, "links": { "details": "/api/v1/models/qwen/qwen-2.5-72b-instruct/endpoints" }, "model_family": "qwen" }, "qwen/qwen3-30b-a3b-thinking-2507": { "id": "qwen/qwen3-30b-a3b-thinking-2507", "canonical_slug": "qwen/qwen3-30b-a3b-thinking-2507", "hugging_face_id": "Qwen/Qwen3-30B-A3B-Thinking-2507", "name": "Qwen: Qwen3 30B A3B Thinking 2507", "created": 1756399192, "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...", "context_length": 131072, "architecture": { "modality": "text->text", "input_modalities": [ "text" ], "output_modalities": [ "text" ], "tokenizer": "Qwen3", "instruct_type": null }, "pricing": { "prompt": "0.00000013", "completion": "0.00000156" }, "top_provider": { "context_length": 81920, "max_completion_tokens": 32768, "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" ], "default_parameters": {}, "supported_voices": null, "knowledge_cutoff": "2025-06-30", "expiration_date": null, "links": { "details": "/api/v1/models/qwen/qwen3-30b-a3b-thinking-2507/endpoints" }, "benchmarks": { "design_arena": [ { "arena": "models", "category": "dataviz", "elo": 969, "win_rate": 33.3, "rank": 95 }, { "arena": "models", "category": "website", "elo": 973, "win_rate": 35.5, "rank": 103 } ], "artificial_analysis": { "intelligence_index": 14.4, "coding_index": 12.1, "agentic_index": 1.8 } }, "reasoning": { "mandatory": true }, "model_family": "qwen" }, "qwen/qwen3.5-flash-02-23": { "id": "qwen/qwen3.5-flash-02-23", "canonical_slug": "qwen/qwen3.5-flash-20260224", "hugging_face_id": null, "name": "Qwen: Qwen3.5-Flash", "created": 1772053776, "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...", "context_length": 1000000, "architecture": { "modality": "text+image+video->text", "input_modalities": [ "text", "image", "video" ], "output_modalities": [ "text" ], "tokenizer": "Qwen3", "instruct_type": null }, "pricing": { "prompt": "0.000000065", "completion": "0.00000026" }, "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" ], "default_parameters": { "temperature": null, "top_p": null, "frequency_penalty": null }, "supported_voices": null, "knowledge_cutoff": null, "expiration_date": null, "links": { "details": "/api/v1/models/qwen/qwen3.5-flash-20260224/endpoints" }, "reasoning": { "mandatory": false }, "model_family": "qwen" }, "qwen/qwen3.7-plus": { "id": "qwen/qwen3.7-plus", "canonical_slug": "qwen/qwen3.7-plus-20260602", "hugging_face_id": null, "name": "Qwen: Qwen3.7 Plus", "created": 1780491783, "description": "Qwen3.7-Plus is a cost-effective model in Alibaba's Qwen3.7 series. It supports text and image input with text output, building on the series' text capabilities with a comprehensive upgrade to its...", "context_length": 1000000, "architecture": { "modality": "text+image->text", "input_modalities": [ "text", "image" ], "output_modalities": [ "text" ], "tokenizer": "Qwen", "instruct_type": null }, "pricing": { "prompt": "0.00000032", "completion": "0.00000128", "input_cache_read": "0.000000064", "input_cache_write": "0.0000004" }, "top_provider": { "context_length": 1000000, "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" ], "default_parameters": {}, "supported_voices": null, "knowledge_cutoff": null, "expiration_date": null, "links": { "details": "/api/v1/models/qwen/qwen3.7-plus-20260602/endpoints" }, "benchmarks": { "design_arena": [], "artificial_analysis": { "intelligence_index": 39, "coding_index": 55.9, "agentic_index": 20.8 } }, "reasoning": { "mandatory": false, "default_enabled": true }, "model_family": "qwen" }, "deepseek/deepseek-v3.2": { "id": "deepseek/deepseek-v3.2", "canonical_slug": "deepseek/deepseek-v3.2-20251201", "hugging_face_id": "deepseek-ai/DeepSeek-V3.2", "name": "DeepSeek: DeepSeek V3.2", "created": 1764594642, "description": "DeepSeek-V3.2 is a large language model designed to harmonize high computational efficiency with strong reasoning and agentic tool-use performance. It introduces DeepSeek Sparse Attention (DSA), a fine-grained sparse attention mechanism...", "context_length": 131072, "architecture": { "modality": "text->text", "input_modalities": [ "text" ], "output_modalities": [ "text" ], "tokenizer": "DeepSeek", "instruct_type": null }, "pricing": { "prompt": "0.0000002288", "completion": "0.0000003432", "input_cache_read": "0.00000002288" }, "top_provider": { "context_length": 128000, "max_completion_tokens": 64000, "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" ], "default_parameters": { "temperature": 1, "top_p": 0.95, "top_k": null, "frequency_penalty": null, "presence_penalty": null, "repetition_penalty": null }, "supported_voices": null, "knowledge_cutoff": null, "expiration_date": null, "links": { "details": "/api/v1/models/deepseek/deepseek-v3.2-20251201/endpoints" }, "benchmarks": { "design_arena": [ { "arena": "models", "category": "3d", "elo": 1212, "win_rate": 49.9, "rank": 42 }, { "arena": "models", "category": "asciiart", "elo": 1130, "win_rate": 40.9, "rank": 43 }, { "arena": "models", "category": "codecategories", "elo": 1213, "win_rate": 49.6, "rank": 49 }, { "arena": "models", "category": "dataviz", "elo": 1202, "win_rate": 48.6, "rank": 49 }, { "arena": "models", "category": "gamedev", "elo": 1198, "win_rate": 46.7, "rank": 51 }, { "arena": "models", "category": "svg", "elo": 1089, "win_rate": 40.9, "rank": 55 }, { "arena": "models", "category": "uicomponent", "elo": 1202, "win_rate": 46.9, "rank": 48 }, { "arena": "models", "category": "website", "elo": 1216, "win_rate": 50.5, "rank": 48 } ] }, "reasoning": { "mandatory": false, "default_enabled": false }, "model_family": "deepseek" }, "deepseek/deepseek-v4-flash": { "id": "deepseek/deepseek-v4-flash", "canonical_slug": "deepseek/deepseek-v4-flash-20260423", "hugging_face_id": "deepseek-ai/DeepSeek-V4-Flash", "name": "DeepSeek: DeepSeek V4 Flash", "created": 1777000666, "description": "DeepSeek V4 Flash is an efficiency-optimized Mixture-of-Experts model from DeepSeek with 284B total parameters and 13B activated parameters, supporting a 1M-token context window. It is designed for fast inference and...", "context_length": 1048576, "architecture": { "modality": "text->text", "input_modalities": [ "text" ], "output_modalities": [ "text" ], "tokenizer": "DeepSeek", "instruct_type": null }, "pricing": { "prompt": "0.00000009", "completion": "0.00000018", "input_cache_read": "0.000000018" }, "top_provider": { "context_length": 1048576, "max_completion_tokens": 16384, "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" ], "default_parameters": { "temperature": null, "top_p": null, "top_k": null, "frequency_penalty": null, "presence_penalty": null, "repetition_penalty": null }, "supported_voices": null, "knowledge_cutoff": null, "expiration_date": null, "links": { "details": "/api/v1/models/deepseek/deepseek-v4-flash-20260423/endpoints" }, "benchmarks": { "design_arena": [ { "arena": "models", "category": "3d", "elo": 1268, "win_rate": 50, "rank": 27 }, { "arena": "models", "category": "asciiart", "elo": 1165, "win_rate": 44.4, "rank": 36 }, { "arena": "models", "category": "codecategories", "elo": 1256, "win_rate": 50.1, "rank": 31 }, { "arena": "models", "category": "dataviz", "elo": 1166, "win_rate": 41.5, "rank": 63 }, { "arena": "models", "category": "gamedev", "elo": 1263, "win_rate": 50.7, "rank": 28 }, { "arena": "models", "category": "svg", "elo": 1215, "win_rate": 49.3, "rank": 24 }, { "arena": "models", "category": "uicomponent", "elo": 1218, "win_rate": 46.2, "rank": 41 }, { "arena": "models", "category": "website", "elo": 1251, "win_rate": 50.8, "rank": 31 } ], "artificial_analysis": { "intelligence_index": 40.3, "coding_index": 56.2, "agentic_index": 31.1 } }, "reasoning": { "mandatory": false, "supported_efforts": [ "xhigh", "high" ], "default_effort": "high" }, "model_family": "deepseek" }, "deepseek/deepseek-v4-pro": { "id": "deepseek/deepseek-v4-pro", "canonical_slug": "deepseek/deepseek-v4-pro-20260423", "hugging_face_id": "deepseek-ai/DeepSeek-V4-Pro", "name": "DeepSeek: DeepSeek V4 Pro", "created": 1777000679, "description": "DeepSeek V4 Pro is a large-scale Mixture-of-Experts model from DeepSeek with 1.6T total parameters and 49B activated parameters, supporting a 1M-token context window. It is designed for advanced reasoning, coding,...", "context_length": 1048576, "architecture": { "modality": "text->text", "input_modalities": [ "text" ], "output_modalities": [ "text" ], "tokenizer": "DeepSeek", "instruct_type": null }, "pricing": { "prompt": "0.000000435", "completion": "0.00000087", "input_cache_read": "0.000000003625" }, "top_provider": { "context_length": 1048576, "max_completion_tokens": 384000, "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" ], "default_parameters": { "temperature": 1, "top_p": 1, "top_k": null, "frequency_penalty": null, "presence_penalty": null, "repetition_penalty": null }, "supported_voices": null, "knowledge_cutoff": null, "expiration_date": null, "links": { "details": "/api/v1/models/deepseek/deepseek-v4-pro-20260423/endpoints" }, "benchmarks": { "design_arena": [ { "arena": "agents", "category": "fullstack", "elo": 948, "win_rate": 22.1, "rank": 29 }, { "arena": "agents", "category": "godotgamedev", "elo": 1097, "win_rate": 34, "rank": 20 }, { "arena": "agents", "category": "webapps", "elo": 1018, "win_rate": 26.6, "rank": 25 }, { "arena": "models", "category": "3d", "elo": 1325, "win_rate": 60.5, "rank": 7 }, { "arena": "models", "category": "asciiart", "elo": 1202, "win_rate": 47.6, "rank": 22 }, { "arena": "models", "category": "codecategories", "elo": 1287, "win_rate": 54.9, "rank": 20 }, { "arena": "models", "category": "dataviz", "elo": 1221, "win_rate": 48.6, "rank": 38 }, { "arena": "models", "category": "gamedev", "elo": 1296, "win_rate": 56.1, "rank": 18 }, { "arena": "models", "category": "svg", "elo": 1193, "win_rate": 46.9, "rank": 34 }, { "arena": "models", "category": "uicomponent", "elo": 1271, "win_rate": 52, "rank": 25 }, { "arena": "models", "category": "website", "elo": 1278, "win_rate": 53.7, "rank": 23 } ], "artificial_analysis": { "intelligence_index": 44.3, "coding_index": 59.4, "agentic_index": 36.4 } }, "reasoning": { "mandatory": false, "supported_efforts": [ "xhigh", "high" ], "default_effort": "high" }, "model_family": "deepseek" }, "google/gemini-3-flash-preview": { "id": "google/gemini-3-flash-preview", "canonical_slug": "google/gemini-3-flash-preview-20251217", "hugging_face_id": "", "name": "Google: Gemini 3 Flash Preview", "created": 1765987078, "description": "Gemini 3 Flash Preview is a high speed, high value thinking model designed for agentic workflows, multi turn chat, and coding assistance. It delivers near Pro level reasoning and tool...", "context_length": 1048576, "architecture": { "modality": "text+image+file+audio+video->text", "input_modalities": [ "text", "image", "file", "audio", "video" ], "output_modalities": [ "text" ], "tokenizer": "Gemini", "instruct_type": null }, "pricing": { "prompt": "0.0000005", "completion": "0.000003", "image": "0.0000005", "audio": "0.000001", "web_search": "0.014", "internal_reasoning": "0.000003", "input_cache_read": "0.00000005", "input_cache_write": "0.00000008333333333333334" }, "top_provider": { "context_length": 1048576, "max_completion_tokens": 65535, "is_moderated": false }, "per_request_limits": null, "supported_parameters": [ "include_reasoning", "max_tokens", "reasoning", "response_format", "seed", "stop", "structured_outputs", "temperature", "tool_choice", "tools", "top_p" ], "default_parameters": { "temperature": null, "top_p": null, "top_k": null, "frequency_penalty": null, "presence_penalty": null, "repetition_penalty": null }, "supported_voices": null, "knowledge_cutoff": null, "expiration_date": null, "links": { "details": "/api/v1/models/google/gemini-3-flash-preview-20251217/endpoints" }, "benchmarks": { "design_arena": [ { "arena": "agents", "category": "agenticslides", "elo": 1073, "win_rate": 39.3, "rank": 9 }, { "arena": "agents", "category": "agenticslides(python-pptx)", "elo": 1075, "win_rate": 39.3, "rank": 9 }, { "arena": "agents", "category": "androidnative", "elo": 1059, "win_rate": 48, "rank": 24 }, { "arena": "agents", "category": "fullstack", "elo": 1128, "win_rate": 47.1, "rank": 17 }, { "arena": "agents", "category": "godotgamedev", "elo": 1218, "win_rate": 52.3, "rank": 8 }, { "arena": "agents", "category": "mobileapps", "elo": 1183, "win_rate": 49.8, "rank": 17 }, { "arena": "agents", "category": "webapps", "elo": 1186, "win_rate": 49.5, "rank": 17 }, { "arena": "models", "category": "3d", "elo": 1261, "win_rate": 62.7, "rank": 30 }, { "arena": "models", "category": "codecategories", "elo": 1238, "win_rate": 57.6, "rank": 34 }, { "arena": "models", "category": "gamedev", "elo": 1232, "win_rate": 58.3, "rank": 39 }, { "arena": "models", "category": "website", "elo": 1238, "win_rate": 57, "rank": 34 } ] }, "reasoning": { "mandatory": false, "supported_efforts": [ "high", "medium", "low", "minimal" ], "default_effort": "medium" }, "model_family": "google" }, "google/gemini-2.5-flash-lite": { "id": "google/gemini-2.5-flash-lite", "canonical_slug": "google/gemini-2.5-flash-lite", "hugging_face_id": "", "name": "Google: Gemini 2.5 Flash Lite", "created": 1753200276, "description": "Gemini 2.5 Flash-Lite is a lightweight reasoning model in the Gemini 2.5 family, optimized for ultra-low latency and cost efficiency. It offers improved throughput, faster token generation, and better performance...", "context_length": 1048576, "architecture": { "modality": "text+image+file+audio+video->text", "input_modalities": [ "text", "image", "file", "audio", "video" ], "output_modalities": [ "text" ], "tokenizer": "Gemini", "instruct_type": null }, "pricing": { "prompt": "0.0000001", "completion": "0.0000004", "image": "0.0000001", "audio": "0.0000003", "web_search": "0.014", "internal_reasoning": "0.0000004", "input_cache_read": "0.00000001", "input_cache_write": "0.00000008333333333333334" }, "top_provider": { "context_length": 1048576, "max_completion_tokens": 65535, "is_moderated": false }, "per_request_limits": null, "supported_parameters": [ "include_reasoning", "max_tokens", "reasoning", "response_format", "seed", "stop", "structured_outputs", "temperature", "tool_choice", "tools", "top_p" ], "default_parameters": { "temperature": null, "top_p": null, "frequency_penalty": null }, "supported_voices": null, "knowledge_cutoff": "2025-01-31", "expiration_date": null, "links": { "details": "/api/v1/models/google/gemini-2.5-flash-lite/endpoints" }, "reasoning": { "mandatory": false }, "model_family": "google" }, "google/gemma-4-31b-it": { "id": "google/gemma-4-31b-it", "canonical_slug": "google/gemma-4-31b-it-20260402", "hugging_face_id": "google/gemma-4-31B-it", "name": "Google: Gemma 4 31B", "created": 1775148486, "description": "Gemma 4 31B Instruct is Google DeepMind's 30.7B dense multimodal model supporting text and image input with text output. Features a 256K token context window, configurable thinking/reasoning mode, native function...", "context_length": 262144, "architecture": { "modality": "text+image+video->text", "input_modalities": [ "image", "text", "video" ], "output_modalities": [ "text" ], "tokenizer": "Gemma", "instruct_type": null }, "pricing": { "prompt": "0.00000012", "completion": "0.00000035", "input_cache_read": "0.00000009" }, "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" ], "default_parameters": { "temperature": 1, "top_p": 0.95, "top_k": 64, "frequency_penalty": null, "presence_penalty": null, "repetition_penalty": null }, "supported_voices": null, "knowledge_cutoff": null, "expiration_date": null, "links": { "details": "/api/v1/models/google/gemma-4-31b-it-20260402/endpoints" }, "benchmarks": { "design_arena": [], "artificial_analysis": { "intelligence_index": 29.4, "coding_index": 43.4, "agentic_index": 14.4 } }, "reasoning": { "mandatory": false, "default_enabled": false }, "model_family": "google" }, "google/gemini-3.5-flash": { "id": "google/gemini-3.5-flash", "canonical_slug": "google/gemini-3.5-flash-20260519", "hugging_face_id": null, "name": "Google: Gemini 3.5 Flash", "created": 1779193800, "description": "Gemini 3.5 Flash is Google's high-efficiency multimodal model, bringing near-Pro level coding and reasoning at Flash-tier cost and speed. It is highly optimized for coding proficiency and parallel agentic execution...", "context_length": 1048576, "architecture": { "modality": "text+image+file+audio+video->text", "input_modalities": [ "text", "image", "video", "file", "audio" ], "output_modalities": [ "text" ], "tokenizer": "Gemini", "instruct_type": null }, "pricing": { "prompt": "0.0000015", "completion": "0.000009", "image": "0.0000015", "audio": "0.000003", "web_search": "0.014", "internal_reasoning": "0.000009", "input_cache_read": "0.00000015", "input_cache_write": "0.00000008333333333333334" }, "top_provider": { "context_length": 1048576, "max_completion_tokens": 65536, "is_moderated": false }, "per_request_limits": null, "supported_parameters": [ "include_reasoning", "max_tokens", "reasoning", "response_format", "seed", "stop", "structured_outputs", "temperature", "tool_choice", "tools", "top_p" ], "default_parameters": { "temperature": null, "top_p": null, "top_k": null, "frequency_penalty": null, "presence_penalty": null, "repetition_penalty": null }, "supported_voices": null, "knowledge_cutoff": "2025-01-01", "expiration_date": null, "links": { "details": "/api/v1/models/google/gemini-3.5-flash-20260519/endpoints" }, "benchmarks": { "design_arena": [ { "arena": "agents", "category": "agenticgamedev", "elo": 1212, "win_rate": 54.9, "rank": 3 }, { "arena": "agents", "category": "agentichtmlslides", "elo": 1162, "win_rate": 45.8, "rank": 7 }, { "arena": "agents", "category": "agenticslides", "elo": 1244, "win_rate": 57.5, "rank": 4 }, { "arena": "agents", "category": "agenticslides(html)", "elo": 1162, "win_rate": 45.7, "rank": 7 }, { "arena": "agents", "category": "agenticslides(python-pptx)", "elo": 1242, "win_rate": 57.8, "rank": 3 }, { "arena": "agents", "category": "androidnative", "elo": 1256, "win_rate": 54.4, "rank": 5 }, { "arena": "agents", "category": "fullstack", "elo": 1264, "win_rate": 58, "rank": 6 }, { "arena": "agents", "category": "htmlslides", "elo": 1181, "win_rate": 49, "rank": 9 }, { "arena": "agents", "category": "mobileapps", "elo": 1257, "win_rate": 56.5, "rank": 3 }, { "arena": "agents", "category": "pptxslides", "elo": 1244, "win_rate": 57.7, "rank": 3 }, { "arena": "agents", "category": "python-pptxslides", "elo": 1247, "win_rate": 57.4, "rank": 4 }, { "arena": "agents", "category": "webapps", "elo": 1264, "win_rate": 54.2, "rank": 6 }, { "arena": "models", "category": "3d", "elo": 1313, "win_rate": 60.2, "rank": 14 }, { "arena": "models", "category": "asciiart", "elo": 1310, "win_rate": 62.1, "rank": 5 }, { "arena": "models", "category": "codecategories", "elo": 1306, "win_rate": 58.4, "rank": 12 }, { "arena": "models", "category": "dataviz", "elo": 1266, "win_rate": 54.9, "rank": 24 }, { "arena": "models", "category": "gamedev", "elo": 1330, "win_rate": 59.3, "rank": 9 }, { "arena": "models", "category": "svg", "elo": 1307, "win_rate": 63, "rank": 3 }, { "arena": "models", "category": "uicomponent", "elo": 1311, "win_rate": 59.4, "rank": 12 }, { "arena": "models", "category": "website", "elo": 1299, "win_rate": 57.5, "rank": 15 } ], "artificial_analysis": { "intelligence_index": 50.2, "coding_index": 70.1, "agentic_index": 37.4 } }, "reasoning": { "mandatory": true, "default_enabled": true, "supported_efforts": [ "high", "medium", "low", "minimal" ], "default_effort": "medium" }, "model_family": "google" }, "cohere/north-mini-code:free": { "id": "cohere/north-mini-code:free", "canonical_slug": "cohere/north-mini-code-20260617", "hugging_face_id": "CohereLabs/North-Mini-Code-1.0", "name": "Cohere: North Mini Code (free)", "created": 1781723748, "description": "North Mini Code is Cohere's first agentic coding model and the debut of its North family. A sparse mixture-of-experts model with 30B total parameters and 3B active, it is optimized...", "context_length": 256000, "architecture": { "modality": "text->text", "input_modalities": [ "text" ], "output_modalities": [ "text" ], "tokenizer": "Cohere", "instruct_type": null }, "pricing": { "prompt": "0", "completion": "0" }, "top_provider": { "context_length": 256000, "max_completion_tokens": 64000, "is_moderated": true }, "per_request_limits": null, "supported_parameters": [ "frequency_penalty", "include_reasoning", "max_tokens", "presence_penalty", "reasoning", "seed", "stop", "temperature", "tool_choice", "tools", "top_k", "top_p" ], "default_parameters": { "temperature": null, "top_p": null, "top_k": null, "frequency_penalty": null, "presence_penalty": null, "repetition_penalty": null }, "supported_voices": null, "knowledge_cutoff": null, "expiration_date": null, "links": { "details": "/api/v1/models/cohere/north-mini-code-20260617/endpoints" }, "reasoning": { "mandatory": false }, "model_family": "others" }, "mistralai/mistral-nemo": { "id": "mistralai/mistral-nemo", "canonical_slug": "mistralai/mistral-nemo", "hugging_face_id": "mistralai/Mistral-Nemo-Instruct-2407", "name": "Mistral: Mistral Nemo", "created": 1721347200, "description": "A 12B parameter model with a 128k token context length built by Mistral in collaboration with NVIDIA. The model is multilingual, supporting English, French, German, Spanish, Italian, Portuguese, Chinese, Japanese,...", "context_length": 131072, "architecture": { "modality": "text->text", "input_modalities": [ "text" ], "output_modalities": [ "text" ], "tokenizer": "Mistral", "instruct_type": "mistral" }, "pricing": { "prompt": "0.00000002", "completion": "0.00000003" }, "top_provider": { "context_length": 131072, "max_completion_tokens": null, "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", "structured_outputs", "temperature", "tool_choice", "tools", "top_k", "top_logprobs", "top_p" ], "default_parameters": { "temperature": 0.3 }, "supported_voices": null, "knowledge_cutoff": "2024-04-30", "expiration_date": null, "links": { "details": "/api/v1/models/mistralai/mistral-nemo/endpoints" }, "model_family": "others" }, "poolside/laguna-m.1": { "id": "poolside/laguna-m.1", "canonical_slug": "poolside/laguna-m.1-20260312", "hugging_face_id": "poolside/Laguna-M.1", "name": "Poolside: Laguna M.1", "created": 1777388504, "description": "Laguna M.1 is the flagship coding agent model from [Poolside](https://poolside.ai/), optimized for complex software engineering tasks. Designed for agentic coding workflows, it supports tool calling and reasoning, with a 256K...", "context_length": 262144, "architecture": { "modality": "text->text", "input_modalities": [ "text" ], "output_modalities": [ "text" ], "tokenizer": "Other", "instruct_type": null }, "pricing": { "prompt": "0.0000002", "completion": "0.0000004", "input_cache_read": "0.0000001" }, "top_provider": { "context_length": 262144, "max_completion_tokens": 32768, "is_moderated": false }, "per_request_limits": null, "supported_parameters": [ "include_reasoning", "max_tokens", "reasoning", "temperature", "tool_choice", "tools" ], "default_parameters": { "temperature": null, "top_p": null, "top_k": null, "frequency_penalty": null, "presence_penalty": null, "repetition_penalty": null }, "supported_voices": null, "knowledge_cutoff": null, "expiration_date": null, "links": { "details": "/api/v1/models/poolside/laguna-m.1-20260312/endpoints" }, "reasoning": { "mandatory": false, "default_enabled": true }, "model_family": "others" }, "xiaomi/mimo-v2.5": { "id": "xiaomi/mimo-v2.5", "canonical_slug": "xiaomi/mimo-v2.5-20260422", "hugging_face_id": "XiaomiMiMo/MiMo-V2.5", "name": "Xiaomi: MiMo-V2.5", "created": 1776874269, "description": "MiMo-V2.5 is a native omnimodal model by Xiaomi. It delivers Pro-level agentic performance at roughly half the inference cost, while surpassing MiMo-V2-Omni in multimodal perception across image and video understanding...", "context_length": 1048576, "architecture": { "modality": "text+image+audio+video->text", "input_modalities": [ "text", "audio", "image", "video" ], "output_modalities": [ "text" ], "tokenizer": "Other", "instruct_type": null }, "pricing": { "prompt": "0.000000105", "completion": "0.00000028" }, "top_provider": { "context_length": 32000, "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", "seed", "stop", "structured_outputs", "temperature", "tool_choice", "tools", "top_k", "top_logprobs", "top_p" ], "default_parameters": { "temperature": 1, "top_p": 0.95, "top_k": null, "frequency_penalty": null, "presence_penalty": null, "repetition_penalty": null }, "supported_voices": null, "knowledge_cutoff": null, "expiration_date": null, "links": { "details": "/api/v1/models/xiaomi/mimo-v2.5-20260422/endpoints" }, "benchmarks": { "design_arena": [ { "arena": "models", "category": "3d", "elo": 1298, "win_rate": 54.1, "rank": 19 }, { "arena": "models", "category": "asciiart", "elo": 1189, "win_rate": 47.5, "rank": 28 }, { "arena": "models", "category": "codecategories", "elo": 1303, "win_rate": 55, "rank": 15 }, { "arena": "models", "category": "dataviz", "elo": 1282, "win_rate": 54, "rank": 16 }, { "arena": "models", "category": "gamedev", "elo": 1298, "win_rate": 55.6, "rank": 17 }, { "arena": "models", "category": "svg", "elo": 1219, "win_rate": 52.3, "rank": 22 }, { "arena": "models", "category": "uicomponent", "elo": 1311, "win_rate": 56.1, "rank": 13 }, { "arena": "models", "category": "website", "elo": 1304, "win_rate": 55.2, "rank": 12 } ] }, "reasoning": { "mandatory": false }, "model_family": "others" }, "minimax/minimax-m3": { "id": "minimax/minimax-m3", "canonical_slug": "minimax/minimax-m3-20260531", "hugging_face_id": "MiniMaxAI/Minimax-M3", "name": "MiniMax: MiniMax M3", "created": 1780245374, "description": "MiniMax-M3 is a multimodal foundation model from MiniMax. It supports text, image, and video inputs with text output, a 1M-token context window, and is suited for long-horizon agentic work, coding,...", "context_length": 1048576, "architecture": { "modality": "text+image+video->text", "input_modalities": [ "text", "image", "video" ], "output_modalities": [ "text" ], "tokenizer": "Other", "instruct_type": null }, "pricing": { "prompt": "0.0000003", "completion": "0.0000012", "input_cache_read": "0.00000006" }, "top_provider": { "context_length": 524288, "max_completion_tokens": 512000, "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" ], "default_parameters": { "temperature": 1, "top_p": 0.95, "top_k": null, "frequency_penalty": null, "presence_penalty": null, "repetition_penalty": null }, "supported_voices": null, "knowledge_cutoff": null, "expiration_date": null, "links": { "details": "/api/v1/models/minimax/minimax-m3-20260531/endpoints" }, "benchmarks": { "design_arena": [ { "arena": "agents", "category": "androidnative", "elo": 1047, "win_rate": 26, "rank": 26 }, { "arena": "models", "category": "3d", "elo": 1305, "win_rate": 56.6, "rank": 17 }, { "arena": "models", "category": "asciiart", "elo": 1221, "win_rate": 49.8, "rank": 15 }, { "arena": "models", "category": "codecategories", "elo": 1306, "win_rate": 55.6, "rank": 14 }, { "arena": "models", "category": "dataviz", "elo": 1291, "win_rate": 56.7, "rank": 11 }, { "arena": "models", "category": "gamedev", "elo": 1288, "win_rate": 51.6, "rank": 20 }, { "arena": "models", "category": "svg", "elo": 1243, "win_rate": 54.3, "rank": 15 }, { "arena": "models", "category": "uicomponent", "elo": 1294, "win_rate": 54.1, "rank": 17 }, { "arena": "models", "category": "website", "elo": 1304, "win_rate": 55.6, "rank": 13 } ], "artificial_analysis": { "intelligence_index": 44.4, "coding_index": 58.6, "agentic_index": 35.4 } }, "reasoning": { "mandatory": false }, "model_family": "others" }, "z-ai/glm-5.2": { "id": "z-ai/glm-5.2", "canonical_slug": "z-ai/glm-5.2-20260616", "hugging_face_id": "zai-org/GLM-5.2", "name": "Z.ai: GLM 5.2", "created": 1781631930, "description": "GLM 5.2 is a large-scale reasoning model from Z.ai. It supports text input and output with a 1M-token context window, and is suited for long-horizon agent workflows, project-level software engineering,...", "context_length": 1048576, "architecture": { "modality": "text->text", "input_modalities": [ "text" ], "output_modalities": [ "text" ], "tokenizer": "Other", "instruct_type": null }, "pricing": { "prompt": "0.00000056", "completion": "0.00000176", "input_cache_read": "0.000000104" }, "top_provider": { "context_length": 1048576, "max_completion_tokens": 131072, "is_moderated": false }, "per_request_limits": null, "supported_parameters": [ "frequency_penalty", "include_reasoning", "logit_bias", "logprobs", "max_tokens", "min_p", "parallel_tool_calls", "presence_penalty", "reasoning", "reasoning_effort", "repetition_penalty", "response_format", "seed", "stop", "structured_outputs", "temperature", "tool_choice", "tools", "top_k", "top_logprobs", "top_p" ], "default_parameters": { "temperature": 1, "top_p": 0.95, "top_k": null, "frequency_penalty": null, "presence_penalty": null, "repetition_penalty": null }, "supported_voices": null, "knowledge_cutoff": null, "expiration_date": null, "links": { "details": "/api/v1/models/z-ai/glm-5.2-20260616/endpoints" }, "benchmarks": { "design_arena": [ { "arena": "agents", "category": "androidnative", "elo": 1252, "win_rate": 57, "rank": 6 }, { "arena": "agents", "category": "fullstack", "elo": 1293, "win_rate": 63.6, "rank": 3 }, { "arena": "agents", "category": "htmlslides", "elo": 1204, "win_rate": 52.5, "rank": 8 }, { "arena": "agents", "category": "mobileapps", "elo": 1240, "win_rate": 54, "rank": 5 }, { "arena": "agents", "category": "python-pptxslides", "elo": 1217, "win_rate": 50.3, "rank": 5 }, { "arena": "agents", "category": "webapps", "elo": 1284, "win_rate": 57.5, "rank": 3 }, { "arena": "models", "category": "3d", "elo": 1373, "win_rate": 64, "rank": 1 }, { "arena": "models", "category": "codecategories", "elo": 1360, "win_rate": 62, "rank": 1 }, { "arena": "models", "category": "dataviz", "elo": 1326, "win_rate": 59.8, "rank": 3 }, { "arena": "models", "category": "gamedev", "elo": 1357, "win_rate": 61.3, "rank": 2 }, { "arena": "models", "category": "uicomponent", "elo": 1339, "win_rate": 59.8, "rank": 5 }, { "arena": "models", "category": "website", "elo": 1356, "win_rate": 61.6, "rank": 1 } ], "artificial_analysis": { "intelligence_index": 51.1, "coding_index": 68.8, "agentic_index": 43.1 } }, "reasoning": { "mandatory": false, "default_enabled": true, "supported_efforts": [ "xhigh", "high" ], "default_effort": "high" }, "model_family": "others" } }