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This makes Qwen3-VL suitable for production scenarios spanning document AI, multilingual OCR, software/UI assistance, spatial/embodied tasks, and research on vision-language agents.","80","2026-02-24T09:53:04.210Z","2026-02-24T10:19:46.338Z","2026-02-24T10:19:46.385Z",{"id":736,"documentId":737,"name":738,"alternativeText":706,"caption":706,"width":171,"height":171,"formats":13,"hash":739,"ext":334,"mime":335,"size":740,"url":741,"previewUrl":13,"provider":62,"provider_metadata":13,"createdAt":742,"updatedAt":743,"publishedAt":744,"focalPoint":13},2950,"wtpisj2s84fkz665c7kbps1x","llm-qwen.svg","llm_qwen_778ce81c01",4.52,"https://n8niostorageaccount.blob.core.windows.net/n8nio-strapi-blobs-prod/assets/llm_qwen_778ce81c01.svg","2026-02-12T14:36:53.689Z","2026-02-12T14:36:54.404Z","2026-02-12T14:36:53.690Z",{"id":746,"prompt":713,"completion":747,"request":267,"image":267,"web_search":267,"internal_reasoning":267,"input_cache_read":13,"input_cache_write":13},207,0.0000012,{"overallScore":749,"results":750,"ranking":755,"model":756},84,{"cost":751,"logic":752,"speed":753,"overall":749,"scoring":522,"toolUse":631,"hallucination":752,"classification":595,"structuredOutput":754},97,89,75,92,{"cost":129,"logic":108,"speed":232,"overall":108,"scoring":189,"toolUse":108,"hallucination":140,"classification":101,"structuredOutput":118},{"id":757,"documentId":758,"active":8,"modelId":759,"openRouterModelId":760,"modelName":761,"company":691,"contextLength":692,"outputCapabilities":762,"inputCapabilities":763,"description":764,"maxCompletionTokens":765,"slug":766,"officialNodeType":13,"officialNodeParams":13,"createdAt":767,"updatedAt":768,"publishedAt":769,"companyLogoMedia":770,"pricing":771},183,"i9esidpk7opkzw701ubwidcz","grok-4.1-fast","x-ai/grok-4.1-fast","Grok 4.1 Fast",[694],[694,696],"Grok 4.1 Fast is xAI's best agentic tool calling model that shines in real-world use cases like customer support and deep research. 2M context window.\n\nReasoning can be enabled/disabled using the `reasoning` `enabled` parameter in the API. 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It delivers higher accuracy in math, coding, logic, and science tasks, follows complex instructions in Chinese and English more reliably, reduces hallucinations, and produces higher-quality responses for open-ended Q&A, writing, and conversation. 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Fast, smart, and great for logic-based tasks that do not require deep domain knowledge. The raw thinking traces are accessible.","36","2026-02-24T09:27:44.999Z","2026-02-24T10:09:25.136Z","2026-02-24T10:09:25.175Z",{"id":703,"documentId":704,"name":705,"alternativeText":706,"caption":706,"width":171,"height":171,"formats":13,"hash":707,"ext":334,"mime":335,"size":708,"url":709,"previewUrl":13,"provider":62,"provider_metadata":13,"createdAt":710,"updatedAt":711,"publishedAt":710,"focalPoint":13},{"id":931,"prompt":932,"completion":714,"request":267,"image":267,"web_search":267,"internal_reasoning":267,"input_cache_read":267,"input_cache_write":13},177,3e-7,{"overallScore":912,"results":934,"ranking":935,"model":936},{"cost":639,"logic":752,"speed":682,"overall":912,"scoring":199,"toolUse":654,"hallucination":684,"classification":546,"structuredOutput":684},{"cost":212,"logic":108,"speed":189,"overall":148,"scoring":232,"toolUse":118,"hallucination":101,"classification":118,"structuredOutput":101},{"id":937,"documentId":938,"active":8,"modelId":939,"openRouterModelId":940,"modelName":941,"company":836,"contextLength":942,"outputCapabilities":943,"inputCapabilities":944,"description":945,"maxCompletionTokens":946,"slug":939,"officialNodeType":13,"officialNodeParams":13,"createdAt":947,"updatedAt":948,"publishedAt":949,"companyLogoMedia":950,"pricing":951},239,"ln0jgsqcbrp9r1bk68ksvw4p","claude-sonnet-4.5","anthropic/claude-sonnet-4.5","Claude Sonnet 4.5","1000000",[694],[694,696,786],"Claude Sonnet 4.5 is Anthropic’s most advanced Sonnet model to date, optimized for real-world agents and coding workflows. It delivers state-of-the-art performance on coding benchmarks such as SWE-bench Verified, with improvements across system design, code security, and specification adherence. The model is designed for extended autonomous operation, maintaining task continuity across sessions and providing fact-based progress tracking.\n\nSonnet 4.5 also introduces stronger agentic capabilities, including improved tool orchestration, speculative parallel execution, and more efficient context and memory management. With enhanced context tracking and awareness of token usage across tool calls, it is particularly well-suited for multi-context and long-running workflows. Use cases span software engineering, cybersecurity, financial analysis, research agents, and other domains requiring sustained reasoning and tool use.","9","2026-02-18T11:44:20.003Z","2026-02-24T13:35:53.867Z","2026-02-24T13:35:53.923Z",{"id":846,"documentId":847,"name":848,"alternativeText":706,"caption":706,"width":171,"height":171,"formats":13,"hash":849,"ext":334,"mime":335,"size":850,"url":851,"previewUrl":13,"provider":62,"provider_metadata":13,"createdAt":852,"updatedAt":853,"publishedAt":854,"focalPoint":13},{"id":952,"prompt":953,"completion":954,"request":267,"image":267,"web_search":267,"internal_reasoning":267,"input_cache_read":267,"input_cache_write":267},236,0.000003,0.000015,{"overallScore":912,"results":956,"ranking":958,"model":959},{"cost":180,"logic":807,"speed":957,"overall":912,"scoring":522,"toolUse":828,"hallucination":751,"classification":595,"structuredOutput":754},67,{"cost":232,"logic":129,"speed":495,"overall":148,"scoring":189,"toolUse":101,"hallucination":118,"classification":101,"structuredOutput":118},{"id":522,"documentId":960,"active":8,"modelId":961,"openRouterModelId":962,"modelName":963,"company":964,"contextLength":965,"outputCapabilities":966,"inputCapabilities":967,"description":970,"maxCompletionTokens":971,"slug":961,"officialNodeType":13,"officialNodeParams":13,"createdAt":972,"updatedAt":973,"publishedAt":974,"companyLogoMedia":975,"pricing":985},"mjuyiklkb9tergndhatuiue9","gemini-3-pro-preview","google/gemini-3-pro-preview","Gemini 3 Pro Preview","Google","1048576",[694],[694,696,786,968,969],"audio","video","Gemini 3 Pro is Google’s flagship frontier model for high-precision multimodal reasoning, combining strong performance across text, image, video, audio, and code with a 1M-token context window. Reasoning Details must be preserved when using multi-turn tool calling, see our docs here: https://openrouter.ai/docs/use-cases/reasoning-tokens#preserving-reasoning-blocks. It delivers state-of-the-art benchmark results in general reasoning, STEM problem solving, factual QA, and multimodal understanding, including leading scores on LMArena, GPQA Diamond, MathArena Apex, MMMU-Pro, and Video-MMMU. Interactions emphasize depth and interpretability: the model is designed to infer intent with minimal prompting and produce direct, insight-focused responses.\n\nBuilt for advanced development and agentic workflows, Gemini 3 Pro provides robust tool-calling, long-horizon planning stability, and strong zero-shot generation for complex UI, visualization, and coding tasks. It excels at agentic coding (SWE-Bench Verified, Terminal-Bench 2.0), multimodal analysis, and structured long-form tasks such as research synthesis, planning, and interactive learning experiences. Suitable applications include autonomous agents, coding assistants, multimodal analytics, scientific reasoning, and high-context information processing.","29","2026-02-24T08:38:22.139Z","2026-02-24T08:38:23.195Z","2026-02-24T08:38:23.231Z",{"id":976,"documentId":977,"name":978,"alternativeText":706,"caption":706,"width":658,"height":658,"formats":13,"hash":979,"ext":334,"mime":335,"size":980,"url":981,"previewUrl":13,"provider":62,"provider_metadata":13,"createdAt":982,"updatedAt":983,"publishedAt":984,"focalPoint":13},2933,"ilkiegnn86xi4pa2hvavx2ih","llm-gemini.svg","llm_gemini_0009b99ce9",11.6,"https://n8niostorageaccount.blob.core.windows.net/n8nio-strapi-blobs-prod/assets/llm_gemini_0009b99ce9.svg","2026-02-12T14:35:35.562Z","2026-02-12T14:35:36.355Z","2026-02-12T14:35:35.563Z",{"id":506,"prompt":986,"completion":987,"request":267,"image":715,"web_search":267,"internal_reasoning":267,"input_cache_read":267,"input_cache_write":267},0.000002,0.000012,{"overallScore":828,"results":989,"ranking":991,"model":992},{"cost":751,"logic":828,"speed":990,"overall":828,"scoring":591,"toolUse":514,"hallucination":826,"classification":546,"structuredOutput":957},95,{"cost":129,"logic":153,"speed":140,"overall":153,"scoring":158,"toolUse":148,"hallucination":153,"classification":118,"structuredOutput":140},{"id":993,"documentId":994,"active":8,"modelId":995,"openRouterModelId":996,"modelName":997,"company":726,"contextLength":783,"outputCapabilities":998,"inputCapabilities":999,"description":1000,"maxCompletionTokens":1001,"slug":995,"officialNodeType":13,"officialNodeParams":13,"createdAt":1002,"updatedAt":1003,"publishedAt":1004,"companyLogoMedia":1005,"pricing":1006},208,"i1vum2j9ezhvbu8xm196f2br","qwen3-coder-flash","qwen/qwen3-coder-flash","Qwen3 Coder Flash",[694],[694],"Qwen3 Coder Flash is Alibaba's fast and cost efficient version of their proprietary Qwen3 Coder Plus. It is a powerful coding agent model specializing in autonomous programming via tool calling and environment interaction, combining coding proficiency with versatile general-purpose abilities.","86","2026-02-24T09:51:34.556Z","2026-02-24T10:19:13.177Z","2026-02-24T10:19:13.235Z",{"id":736,"documentId":737,"name":738,"alternativeText":706,"caption":706,"width":171,"height":171,"formats":13,"hash":739,"ext":334,"mime":335,"size":740,"url":741,"previewUrl":13,"provider":62,"provider_metadata":13,"createdAt":742,"updatedAt":743,"publishedAt":744,"focalPoint":13},{"id":1007,"prompt":932,"completion":1008,"request":267,"image":267,"web_search":267,"internal_reasoning":267,"input_cache_read":267,"input_cache_write":13},204,0.0000015,{"overallScore":1010,"results":1011,"ranking":1012,"model":1013},76,{"cost":680,"logic":826,"speed":807,"overall":1010,"scoring":246,"toolUse":514,"hallucination":611,"classification":546,"structuredOutput":753},{"cost":108,"logic":148,"speed":175,"overall":158,"scoring":108,"toolUse":148,"hallucination":199,"classification":118,"structuredOutput":129},{"id":1014,"documentId":1015,"active":8,"modelId":1016,"openRouterModelId":1017,"modelName":1018,"company":1019,"contextLength":727,"outputCapabilities":1020,"inputCapabilities":1021,"description":1022,"maxCompletionTokens":1023,"slug":1024,"officialNodeType":13,"officialNodeParams":13,"createdAt":1025,"updatedAt":1026,"publishedAt":1027,"companyLogoMedia":1028,"pricing":1037},213,"yn1slrmzzaml02yjrt5lr78l","seed-1.6-flash","bytedance-seed/seed-1.6-flash","Seed 1.6 Flash","ByteDance Seed",[694],[696,694,969],"Seed 1.6 Flash is an ultra-fast multimodal deep thinking model by ByteDance Seed, supporting both text and visual understanding. It features a 256k context window and can generate outputs of up to 16k tokens.","53","seed-1-6-flash","2026-02-24T09:53:49.602Z","2026-02-24T10:20:20.489Z","2026-02-24T10:20:20.534Z",{"id":1029,"documentId":1030,"name":1031,"alternativeText":706,"caption":706,"width":171,"height":171,"formats":13,"hash":1032,"ext":334,"mime":335,"size":1033,"url":1034,"previewUrl":13,"provider":62,"provider_metadata":13,"createdAt":1035,"updatedAt":1036,"publishedAt":1035,"focalPoint":13},2931,"saf2kleux0jaa6fp4t3tcxmx","llm-bytedance.svg","llm_bytedance_270930eef9",1.16,"https://n8niostorageaccount.blob.core.windows.net/n8nio-strapi-blobs-prod/assets/llm_bytedance_270930eef9.svg","2026-02-12T14:35:27.678Z","2026-02-12T14:35:28.433Z",{"id":1038,"prompt":1039,"completion":932,"request":267,"image":267,"web_search":267,"internal_reasoning":267,"input_cache_read":13,"input_cache_write":13},209,7.5e-8,{"overallScore":753,"results":1041,"ranking":1044,"model":1045},{"cost":718,"logic":1042,"speed":883,"overall":753,"scoring":522,"toolUse":599,"hallucination":1043,"classification":546,"structuredOutput":957},73,94,{"cost":140,"logic":162,"speed":254,"overall":162,"scoring":189,"toolUse":129,"hallucination":108,"classification":118,"structuredOutput":140},{"id":534,"documentId":1046,"active":8,"modelId":1047,"openRouterModelId":1048,"modelName":1049,"company":1050,"contextLength":1051,"outputCapabilities":1052,"inputCapabilities":1053,"description":1054,"maxCompletionTokens":1055,"slug":1056,"officialNodeType":13,"officialNodeParams":13,"createdAt":1057,"updatedAt":1058,"publishedAt":1059,"companyLogoMedia":1060,"pricing":1069},"f01cvutxvin3dqb8mxz99j1x","glm-4.6","z-ai/glm-4.6","GLM 4.6","Z.AI","202752",[694],[694],"Compared with GLM-4.5, this generation brings several key improvements:\n\nLonger context window: The context window has been expanded from 128K to 200K tokens, enabling the model to handle more complex agentic tasks.\nSuperior coding performance: The model achieves higher scores on code benchmarks and demonstrates better real-world performance in applications such as Claude Code、Cline、Roo Code and Kilo Code, including improvements in generating visually polished front-end pages.\nAdvanced reasoning: GLM-4.6 shows a clear improvement in reasoning performance and supports tool use during inference, leading to stronger overall capability.\nMore capable agents: GLM-4.6 exhibits stronger performance in tool using and search-based agents, and integrates more effectively within agent frameworks.\nRefined writing: Better aligns with human preferences in style and readability, and performs more naturally in role-playing scenarios.","77","glm-4-6","2026-02-24T08:39:27.635Z","2026-02-24T08:39:28.782Z","2026-02-24T08:39:28.819Z",{"id":1061,"documentId":1062,"name":1063,"alternativeText":706,"caption":706,"width":171,"height":171,"formats":13,"hash":1064,"ext":334,"mime":335,"size":1065,"url":1066,"previewUrl":13,"provider":62,"provider_metadata":13,"createdAt":1067,"updatedAt":1068,"publishedAt":1067,"focalPoint":13},2955,"epr42vd1uubuertaspm7kj24","llm-zai.svg","llm_zai_7529399a7c",0.3,"https://n8niostorageaccount.blob.core.windows.net/n8nio-strapi-blobs-prod/assets/llm_zai_7529399a7c.svg","2026-02-12T14:37:14.225Z","2026-02-12T14:37:15.393Z",{"id":514,"prompt":1070,"completion":1008,"request":267,"image":267,"web_search":267,"internal_reasoning":267,"input_cache_read":267,"input_cache_write":267},3.5e-7,{"overallScore":1072,"results":1073,"ranking":1074,"model":1075},74,{"cost":751,"logic":774,"speed":718,"overall":1072,"scoring":212,"toolUse":631,"hallucination":681,"classification":217,"structuredOutput":683},{"cost":129,"logic":87,"speed":129,"overall":166,"scoring":227,"toolUse":108,"hallucination":129,"classification":129,"structuredOutput":108},{"id":514,"documentId":1076,"active":8,"modelId":1077,"openRouterModelId":1078,"modelName":1079,"company":964,"contextLength":965,"outputCapabilities":1080,"inputCapabilities":1081,"description":1082,"maxCompletionTokens":1083,"slug":1077,"officialNodeType":13,"officialNodeParams":13,"createdAt":1084,"updatedAt":1085,"publishedAt":1086,"companyLogoMedia":1087,"pricing":1088},"kq5akhfdmcib4jmdm2hethg3","gemini-3-flash-preview","google/gemini-3-flash-preview","Gemini 3 Flash Preview",[694],[694,696,786,968,969],"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 use performance with substantially lower latency than larger Gemini variants, making it well suited for interactive development, long running agent loops, and collaborative coding tasks. Compared to Gemini 2.5 Flash, it provides broad quality improvements across reasoning, multimodal understanding, and reliability.\n\nThe model supports a 1M token context window and multimodal inputs including text, images, audio, video, and PDFs, with text output. It includes configurable reasoning via thinking levels (minimal, low, medium, high), structured output, tool use, and automatic context caching. Gemini 3 Flash Preview is optimized for users who want strong reasoning and agentic behavior without the cost or latency of full scale frontier models.","59","2026-02-24T08:34:56.297Z","2026-02-24T08:34:57.935Z","2026-02-24T08:34:57.976Z",{"id":976,"documentId":977,"name":978,"alternativeText":706,"caption":706,"width":658,"height":658,"formats":13,"hash":979,"ext":334,"mime":335,"size":980,"url":981,"previewUrl":13,"provider":62,"provider_metadata":13,"createdAt":982,"updatedAt":983,"publishedAt":984,"focalPoint":13},{"id":180,"prompt":714,"completion":953,"request":267,"image":267,"web_search":267,"internal_reasoning":267,"input_cache_read":267,"input_cache_write":267},{"overallScore":1072,"results":1090,"ranking":1091,"model":1092},{"cost":859,"logic":807,"speed":683,"overall":1072,"scoring":522,"toolUse":546,"hallucination":683,"classification":502,"structuredOutput":754},{"cost":194,"logic":129,"speed":204,"overall":166,"scoring":189,"toolUse":87,"hallucination":148,"classification":108,"structuredOutput":118},{"id":561,"documentId":1093,"active":8,"modelId":1094,"openRouterModelId":1095,"modelName":1096,"company":782,"contextLength":814,"outputCapabilities":1097,"inputCapabilities":1098,"description":1099,"maxCompletionTokens":1100,"slug":1101,"officialNodeType":13,"officialNodeParams":13,"createdAt":1102,"updatedAt":1103,"publishedAt":1104,"companyLogoMedia":1105,"pricing":1106},"fvrmopzlwghijdehpnaghq26","gpt-5.1","openai/gpt-5.1","GPT-5.1",[694],[694,696,786],"GPT-5.1 is the latest frontier-grade model in the GPT-5 series, offering stronger general-purpose reasoning, improved instruction adherence, and a more natural conversational style compared to GPT-5. It uses adaptive reasoning to allocate computation dynamically, responding quickly to simple queries while spending more depth on complex tasks. The model produces clearer, more grounded explanations with reduced jargon, making it easier to follow even on technical or multi-step problems.\n\nBuilt for broad task coverage, GPT-5.1 delivers consistent gains across math, coding, and structured analysis workloads, with more coherent long-form answers and improved tool-use reliability. It also features refined conversational alignment, enabling warmer, more intuitive responses without compromising precision. GPT-5.1 serves as the primary full-capability successor to GPT-5","70","gpt-5-1","2026-02-24T08:42:48.337Z","2026-02-24T08:43:05.292Z","2026-02-24T08:43:05.334Z",{"id":793,"documentId":794,"name":795,"alternativeText":706,"caption":706,"width":171,"height":171,"formats":13,"hash":796,"ext":334,"mime":335,"size":797,"url":798,"previewUrl":13,"provider":62,"provider_metadata":13,"createdAt":799,"updatedAt":800,"publishedAt":799,"focalPoint":13},{"id":546,"prompt":802,"completion":803,"request":267,"image":267,"web_search":715,"internal_reasoning":267,"input_cache_read":267,"input_cache_write":267},{"overallScore":1042,"results":1108,"ranking":1110,"model":1111},{"cost":323,"logic":1109,"speed":684,"overall":1042,"scoring":607,"toolUse":250,"hallucination":502,"classification":595,"structuredOutput":753},66,{"cost":118,"logic":175,"speed":101,"overall":171,"scoring":148,"toolUse":153,"hallucination":232,"classification":101,"structuredOutput":129},{"id":1112,"documentId":1113,"active":8,"modelId":1114,"openRouterModelId":1115,"modelName":1116,"company":1117,"contextLength":783,"outputCapabilities":1118,"inputCapabilities":1119,"description":1120,"maxCompletionTokens":1121,"slug":1114,"officialNodeType":13,"officialNodeParams":13,"createdAt":1122,"updatedAt":1123,"publishedAt":1124,"companyLogoMedia":1125,"pricing":1134},195,"d6utdase2iddl1n91765wojj","mercury","inception/mercury","Mercury","Inception",[694],[694],"Mercury is the first diffusion large language model (dLLM). Applying a breakthrough discrete diffusion approach, the model runs 5-10x faster than even speed optimized models like GPT-4.1 Nano and Claude 3.5 Haiku while matching their performance. Mercury's speed enables developers to provide responsive user experiences, including with voice agents, search interfaces, and chatbots. Read more in the [blog post]\n(https://www.inceptionlabs.ai/blog/introducing-mercury) here. ","83","2026-02-24T09:36:57.450Z","2026-02-24T10:16:13.020Z","2026-02-24T10:16:13.069Z",{"id":1126,"documentId":1127,"name":1128,"alternativeText":706,"caption":706,"width":171,"height":171,"formats":13,"hash":1129,"ext":334,"mime":335,"size":1130,"url":1131,"previewUrl":13,"provider":62,"provider_metadata":13,"createdAt":1132,"updatedAt":1133,"publishedAt":1132,"focalPoint":13},2938,"mqiohth7ihqs0qgl59sekbif","llm-inception.svg","llm_inception_a3c38c37ee",0.44,"https://n8niostorageaccount.blob.core.windows.net/n8nio-strapi-blobs-prod/assets/llm_inception_a3c38c37ee.svg","2026-02-12T14:35:53.337Z","2026-02-12T14:35:54.908Z",{"id":1135,"prompt":1136,"completion":856,"request":267,"image":267,"web_search":267,"internal_reasoning":267,"input_cache_read":13,"input_cache_write":13},191,2.5e-7,{"overallScore":1042,"results":1138,"ranking":1139,"model":1140},{"cost":990,"logic":749,"speed":754,"overall":1042,"scoring":538,"toolUse":631,"hallucination":1072,"classification":162,"structuredOutput":683},{"cost":87,"logic":140,"speed":153,"overall":171,"scoring":175,"toolUse":108,"hallucination":162,"classification":140,"structuredOutput":108},{"id":1141,"documentId":1142,"active":8,"modelId":1143,"openRouterModelId":1144,"modelName":1145,"company":890,"contextLength":921,"outputCapabilities":1146,"inputCapabilities":1147,"description":1148,"maxCompletionTokens":1149,"slug":1150,"officialNodeType":13,"officialNodeParams":13,"createdAt":1151,"updatedAt":1152,"publishedAt":1153,"companyLogoMedia":1154,"pricing":1155},203,"zvjqcuo7en5tynmxsi3fdjah","mistral-medium-3.1","mistralai/mistral-medium-3.1","Mistral Medium 3.1",[694],[694,696],"Mistral Medium 3.1 is an updated version of Mistral Medium 3, which is a high-performance enterprise-grade language model designed to deliver frontier-level capabilities at significantly reduced operational cost. It balances state-of-the-art reasoning and multimodal performance with 8× lower cost compared to traditional large models, making it suitable for scalable deployments across professional and industrial use cases.\n\nThe model excels in domains such as coding, STEM reasoning, and enterprise adaptation. It supports hybrid, on-prem, and in-VPC deployments and is optimized for integration into custom workflows. Mistral Medium 3.1 offers competitive accuracy relative to larger models like Claude Sonnet 3.5/3.7, Llama 4 Maverick, and Command R+, while maintaining broad compatibility across cloud environments.","41","mistral-medium-3-1","2026-02-24T09:46:52.419Z","2026-02-24T10:18:08.310Z","2026-02-24T10:18:08.349Z",{"id":899,"documentId":900,"name":901,"alternativeText":706,"caption":706,"width":171,"height":171,"formats":13,"hash":902,"ext":334,"mime":335,"size":903,"url":904,"previewUrl":13,"provider":62,"provider_metadata":13,"createdAt":905,"updatedAt":906,"publishedAt":907,"focalPoint":13},{"id":1156,"prompt":1157,"completion":986,"request":267,"image":267,"web_search":267,"internal_reasoning":267,"input_cache_read":13,"input_cache_write":13},199,4e-7,{"overallScore":1042,"results":1159,"ranking":1160,"model":1161},{"cost":680,"logic":749,"speed":180,"overall":1042,"scoring":222,"toolUse":631,"hallucination":752,"classification":595,"structuredOutput":753},{"cost":108,"logic":140,"speed":319,"overall":171,"scoring":222,"toolUse":108,"hallucination":140,"classification":101,"structuredOutput":129},{"id":232,"documentId":1162,"active":8,"modelId":1163,"openRouterModelId":1164,"modelName":1165,"company":1166,"contextLength":1167,"outputCapabilities":1168,"inputCapabilities":1169,"description":1170,"maxCompletionTokens":1171,"slug":1172,"officialNodeType":13,"officialNodeParams":13,"createdAt":1173,"updatedAt":1174,"publishedAt":1175,"companyLogoMedia":1176,"pricing":1185},"dnsxvf7b2xxvxj8v44okeil6","deepseek-v3.2-exp","deepseek/deepseek-v3.2-exp","DeepSeek V3.2 Exp","DeepSeek","163840",[694],[694],"DeepSeek-V3.2-Exp is an experimental large language model released by DeepSeek as an intermediate step between V3.1 and future architectures. It introduces DeepSeek Sparse Attention (DSA), a fine-grained sparse attention mechanism designed to improve training and inference efficiency in long-context scenarios while maintaining output quality. Users can control the reasoning behaviour with the `reasoning` `enabled` boolean. [Learn more in our docs](https://openrouter.ai/docs/use-cases/reasoning-tokens#enable-reasoning-with-default-config)\n\nThe model was trained under conditions aligned with V3.1-Terminus to enable direct comparison. Benchmarking shows performance roughly on par with V3.1 across reasoning, coding, and agentic tool-use tasks, with minor tradeoffs and gains depending on the domain. This release focuses on validating architectural optimizations for extended context lengths rather than advancing raw task accuracy, making it primarily a research-oriented model for exploring efficient transformer designs.","79","deepseek-v3-2-exp","2026-02-23T16:11:51.198Z","2026-02-23T16:11:52.379Z","2026-02-23T16:11:52.420Z",{"id":1177,"documentId":1178,"name":1179,"alternativeText":706,"caption":706,"width":171,"height":171,"formats":13,"hash":1180,"ext":334,"mime":335,"size":1181,"url":1182,"previewUrl":13,"provider":62,"provider_metadata":13,"createdAt":1183,"updatedAt":1184,"publishedAt":1183,"focalPoint":13},2932,"hzlhbzv9zt05j40vf89ad85g","llm-deepseek.svg","llm_deepseek_428221d887",4.12,"https://n8niostorageaccount.blob.core.windows.net/n8nio-strapi-blobs-prod/assets/llm_deepseek_428221d887.svg","2026-02-12T14:35:31.196Z","2026-02-12T14:35:31.945Z",{"id":212,"prompt":1186,"completion":1187,"request":267,"image":267,"web_search":267,"internal_reasoning":267,"input_cache_read":267,"input_cache_write":267},2.1e-7,3.2e-7,{"overallScore":1042,"results":1189,"ranking":1190,"model":1191},{"cost":323,"logic":583,"speed":912,"overall":1042,"scoring":222,"toolUse":569,"hallucination":1043,"classification":595,"structuredOutput":753},{"cost":118,"logic":217,"speed":227,"overall":171,"scoring":222,"toolUse":140,"hallucination":108,"classification":101,"structuredOutput":129},{"id":1192,"documentId":1193,"active":8,"modelId":1194,"openRouterModelId":1195,"modelName":1196,"company":1197,"contextLength":727,"outputCapabilities":1198,"inputCapabilities":1199,"description":1200,"maxCompletionTokens":1201,"slug":1194,"officialNodeType":13,"officialNodeParams":13,"createdAt":1202,"updatedAt":1203,"publishedAt":1204,"companyLogoMedia":1205,"pricing":1214},196,"x4n8fwo19rnrrlh6j9md2fpl","mimo-v2-flash","xiaomi/mimo-v2-flash","MiMo-V2-Flash","Xiaomi",[694],[694],"MiMo-V2-Flash is an open-source foundation language model developed by Xiaomi. It is a Mixture-of-Experts model with 309B total parameters and 15B active parameters, adopting hybrid attention architecture. MiMo-V2-Flash supports a hybrid-thinking toggle and a 256K context window, and excels at reasoning, coding, and agent scenarios. On SWE-bench Verified and SWE-bench Multilingual, MiMo-V2-Flash ranks as the top #1 open-source model globally, delivering performance comparable to Claude Sonnet 4.5 while costing only about 3.5% as much.\n\nUsers can control the reasoning behaviour with the `reasoning` `enabled` boolean. [Learn more in our docs](https://openrouter.ai/docs/use-cases/reasoning-tokens#enable-reasoning-with-default-config).","60","2026-02-24T09:37:28.177Z","2026-02-24T10:16:29.112Z","2026-02-24T10:16:29.156Z",{"id":1206,"documentId":1207,"name":1208,"alternativeText":706,"caption":706,"width":171,"height":171,"formats":13,"hash":1209,"ext":334,"mime":335,"size":1210,"url":1211,"previewUrl":13,"provider":62,"provider_metadata":13,"createdAt":1212,"updatedAt":1213,"publishedAt":1212,"focalPoint":13},2954,"qf52bnqr4uo0jdrddvproima","llm-xiaomi.svg","llm_xiaomi_cf13f48a5a",8.27,"https://n8niostorageaccount.blob.core.windows.net/n8nio-strapi-blobs-prod/assets/llm_xiaomi_cf13f48a5a.svg","2026-02-12T14:37:09.894Z","2026-02-12T14:37:10.526Z",{"id":1215,"prompt":1216,"completion":1217,"request":267,"image":267,"web_search":267,"internal_reasoning":267,"input_cache_read":13,"input_cache_write":13},192,9e-8,2.9e-7,{"overallScore":1042,"results":1219,"ranking":1220,"model":1221},{"cost":826,"logic":752,"speed":829,"overall":1042,"scoring":199,"toolUse":569,"hallucination":826,"classification":502,"structuredOutput":754},{"cost":189,"logic":108,"speed":148,"overall":171,"scoring":232,"toolUse":140,"hallucination":153,"classification":108,"structuredOutput":118},{"id":611,"documentId":1222,"active":8,"modelId":1223,"openRouterModelId":1224,"modelName":1225,"company":782,"contextLength":783,"outputCapabilities":1226,"inputCapabilities":1227,"description":1228,"maxCompletionTokens":1229,"slug":1230,"officialNodeType":13,"officialNodeParams":13,"createdAt":1231,"updatedAt":1232,"publishedAt":1233,"companyLogoMedia":1234,"pricing":1235},"ugzfmvygv3giwt8kiu2nblv9","gpt-5.2-chat","openai/gpt-5.2-chat","GPT-5.2 Chat",[694],[786,696,694],"GPT-5.2 Chat (AKA Instant) is the fast, lightweight member of the 5.2 family, optimized for low-latency chat while retaining strong general intelligence. It uses adaptive reasoning to selectively “think” on harder queries, improving accuracy on math, coding, and multi-step tasks without slowing down typical conversations. The model is warmer and more conversational by default, with better instruction following and more stable short-form reasoning. GPT-5.2 Chat is designed for high-throughput, interactive workloads where responsiveness and consistency matter more than deep deliberation.","61","gpt-5-2-chat","2026-02-24T09:23:44.887Z","2026-02-24T09:23:59.888Z","2026-02-24T09:23:59.945Z",{"id":793,"documentId":794,"name":795,"alternativeText":706,"caption":706,"width":171,"height":171,"formats":13,"hash":796,"ext":334,"mime":335,"size":797,"url":798,"previewUrl":13,"provider":62,"provider_metadata":13,"createdAt":799,"updatedAt":800,"publishedAt":799,"focalPoint":13},{"id":595,"prompt":1236,"completion":1237,"request":267,"image":267,"web_search":715,"internal_reasoning":267,"input_cache_read":267,"input_cache_write":13},0.00000175,0.000014,{"overallScore":1239,"results":1240,"ranking":1241,"model":1242},72,{"cost":753,"logic":807,"speed":682,"overall":1239,"scoring":222,"toolUse":654,"hallucination":826,"classification":217,"structuredOutput":754},{"cost":199,"logic":129,"speed":189,"overall":175,"scoring":222,"toolUse":118,"hallucination":153,"classification":129,"structuredOutput":118},{"id":599,"documentId":1243,"active":8,"modelId":1244,"openRouterModelId":1245,"modelName":1246,"company":782,"contextLength":814,"outputCapabilities":1247,"inputCapabilities":1248,"description":1249,"maxCompletionTokens":1250,"slug":1251,"officialNodeType":13,"officialNodeParams":13,"createdAt":1252,"updatedAt":1253,"publishedAt":1254,"companyLogoMedia":1255,"pricing":1256},"fz8cr6jb0usw11i137b4qe06","gpt-5.2","openai/gpt-5.2","GPT-5.2",[694],[786,696,694],"GPT-5.2 is the latest frontier-grade model in the GPT-5 series, offering stronger agentic and long context perfomance compared to GPT-5.1. It uses adaptive reasoning to allocate computation dynamically, responding quickly to simple queries while spending more depth on complex tasks.\n\nBuilt for broad task coverage, GPT-5.2 delivers consistent gains across math, coding, sciende, and tool calling workloads, with more coherent long-form answers and improved tool-use reliability.","62","gpt-5-2","2026-02-24T09:21:40.295Z","2026-02-24T09:22:05.354Z","2026-02-24T09:22:05.400Z",{"id":793,"documentId":794,"name":795,"alternativeText":706,"caption":706,"width":171,"height":171,"formats":13,"hash":796,"ext":334,"mime":335,"size":797,"url":798,"previewUrl":13,"provider":62,"provider_metadata":13,"createdAt":799,"updatedAt":800,"publishedAt":799,"focalPoint":13},{"id":583,"prompt":1236,"completion":1237,"request":267,"image":267,"web_search":715,"internal_reasoning":267,"input_cache_read":267,"input_cache_write":13},{"overallScore":1239,"results":1258,"ranking":1259,"model":1260},{"cost":990,"logic":749,"speed":807,"overall":1239,"scoring":250,"toolUse":569,"hallucination":826,"classification":502,"structuredOutput":957},{"cost":87,"logic":140,"speed":175,"overall":175,"scoring":212,"toolUse":140,"hallucination":153,"classification":108,"structuredOutput":140},{"id":1261,"documentId":1262,"active":8,"modelId":1263,"openRouterModelId":1264,"modelName":1265,"company":890,"contextLength":727,"outputCapabilities":1266,"inputCapabilities":1267,"description":1268,"maxCompletionTokens":1269,"slug":1263,"officialNodeType":13,"officialNodeParams":13,"createdAt":1270,"updatedAt":1271,"publishedAt":1272,"companyLogoMedia":1273,"pricing":1274},201,"t8fl7mho4nfx3u9ewa0ekfuy","mistral-large-2512","mistralai/mistral-large-2512","Mistral Large 3 2512",[694],[694,696],"Mistral Large 3 2512 is Mistral’s most capable model to date, featuring a sparse mixture-of-experts architecture with 41B active parameters (675B total), and released under the Apache 2.0 license.","68","2026-02-24T09:46:01.229Z","2026-02-24T10:17:39.560Z","2026-02-24T10:17:39.621Z",{"id":899,"documentId":900,"name":901,"alternativeText":706,"caption":706,"width":171,"height":171,"formats":13,"hash":902,"ext":334,"mime":335,"size":903,"url":904,"previewUrl":13,"provider":62,"provider_metadata":13,"createdAt":905,"updatedAt":906,"publishedAt":907,"focalPoint":13},{"id":1275,"prompt":714,"completion":1008,"request":267,"image":267,"web_search":267,"internal_reasoning":267,"input_cache_read":13,"input_cache_write":13},197,{"overallScore":1239,"results":1277,"ranking":1278,"model":1279},{"cost":1043,"logic":774,"speed":749,"overall":1239,"scoring":212,"toolUse":546,"hallucination":1043,"classification":502,"structuredOutput":683},{"cost":148,"logic":87,"speed":199,"overall":175,"scoring":227,"toolUse":87,"hallucination":108,"classification":108,"structuredOutput":108},{"id":1280,"documentId":1281,"active":8,"modelId":1282,"openRouterModelId":1283,"modelName":1284,"company":1285,"contextLength":921,"outputCapabilities":1286,"inputCapabilities":1287,"description":1288,"maxCompletionTokens":1289,"slug":1282,"officialNodeType":13,"officialNodeParams":13,"createdAt":1290,"updatedAt":1291,"publishedAt":1292,"companyLogoMedia":1293,"pricing":1302},185,"wk29aje7pjcilfmg0r8k86y8","kimi-k2","moonshotai/kimi-k2","Kimi K2 0711","MoonshotAI",[694],[694],"Kimi K2 Instruct is a large-scale Mixture-of-Experts (MoE) language model developed by Moonshot AI, featuring 1 trillion total parameters with 32 billion active per forward pass. It is optimized for agentic capabilities, including advanced tool use, reasoning, and code synthesis. Kimi K2 excels across a broad range of benchmarks, particularly in coding (LiveCodeBench, SWE-bench), reasoning (ZebraLogic, GPQA), and tool-use (Tau2, AceBench) tasks. It supports long-context inference up to 128K tokens and is designed with a novel training stack that includes the MuonClip optimizer for stable large-scale MoE training.","10","2026-02-24T09:32:09.413Z","2026-02-24T10:13:10.180Z","2026-02-24T10:13:10.240Z",{"id":1294,"documentId":1295,"name":1296,"alternativeText":706,"caption":706,"width":171,"height":171,"formats":13,"hash":1297,"ext":334,"mime":335,"size":1298,"url":1299,"previewUrl":13,"provider":62,"provider_metadata":13,"createdAt":1300,"updatedAt":1301,"publishedAt":1300,"focalPoint":13},2945,"srfbtza5v4qzc7aqighhz19q","llm-moonshot.svg","llm_moonshot_5fb8e576e1",6.7,"https://n8niostorageaccount.blob.core.windows.net/n8nio-strapi-blobs-prod/assets/llm_moonshot_5fb8e576e1.svg","2026-02-12T14:36:35.097Z","2026-02-12T14:36:35.719Z",{"id":916,"prompt":714,"completion":1303,"request":267,"image":267,"web_search":267,"internal_reasoning":267,"input_cache_read":13,"input_cache_write":13},0.0000024,{"overallScore":883,"results":1305,"ranking":1307,"model":1308},{"cost":829,"logic":749,"speed":883,"overall":883,"scoring":506,"toolUse":546,"hallucination":1306,"classification":502,"structuredOutput":754},69,{"cost":153,"logic":140,"speed":254,"overall":189,"scoring":199,"toolUse":87,"hallucination":171,"classification":108,"structuredOutput":118},{"id":627,"documentId":1309,"active":8,"modelId":1310,"openRouterModelId":1311,"modelName":1312,"company":782,"contextLength":814,"outputCapabilities":1313,"inputCapabilities":1314,"description":1315,"maxCompletionTokens":1316,"slug":1310,"officialNodeType":13,"officialNodeParams":13,"createdAt":1317,"updatedAt":1318,"publishedAt":1319,"companyLogoMedia":1320,"pricing":1321},"mtykwr1iywlh9so5bc3ezts0","gpt-5-mini","openai/gpt-5-mini","GPT-5 Mini",[694],[694,696,786],"GPT-5 Mini is a compact version of GPT-5, designed to handle lighter-weight reasoning tasks. It provides the same instruction-following and safety-tuning benefits as GPT-5, but with reduced latency and cost. GPT-5 Mini is the successor to OpenAI's o4-mini model.","8","2026-02-24T09:25:49.028Z","2026-02-24T09:26:05.008Z","2026-02-24T09:26:05.052Z",{"id":793,"documentId":794,"name":795,"alternativeText":706,"caption":706,"width":171,"height":171,"formats":13,"hash":796,"ext":334,"mime":335,"size":797,"url":798,"previewUrl":13,"provider":62,"provider_metadata":13,"createdAt":799,"updatedAt":800,"publishedAt":799,"focalPoint":13},{"id":607,"prompt":1136,"completion":986,"request":267,"image":267,"web_search":715,"internal_reasoning":267,"input_cache_read":267,"input_cache_write":13},{"overallScore":806,"results":1323,"ranking":1324,"model":1325},{"cost":680,"logic":828,"speed":806,"overall":806,"scoring":222,"toolUse":599,"hallucination":681,"classification":217,"structuredOutput":683},{"cost":108,"logic":153,"speed":250,"overall":194,"scoring":222,"toolUse":129,"hallucination":129,"classification":129,"structuredOutput":108},{"id":212,"documentId":1326,"active":8,"modelId":1327,"openRouterModelId":1328,"modelName":1329,"company":1166,"contextLength":1167,"outputCapabilities":1330,"inputCapabilities":1331,"description":1332,"maxCompletionTokens":1333,"slug":1327,"officialNodeType":13,"officialNodeParams":13,"createdAt":1334,"updatedAt":1335,"publishedAt":1336,"companyLogoMedia":1337,"pricing":1338},"h69r3ilzpw0ypyl7qs8ry3ep","deepseek-chat-v3.1","deepseek/deepseek-chat-v3.1","DeepSeek V3.1",[694],[694],"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 training process, reaching up to 128K tokens, and uses FP8 microscaling for efficient inference. Users can control the reasoning behaviour with the `reasoning` `enabled` boolean. [Learn more in our docs](https://openrouter.ai/docs/use-cases/reasoning-tokens#enable-reasoning-with-default-config)\n\nThe model improves tool use, code generation, and reasoning efficiency, achieving performance comparable to DeepSeek-R1 on difficult benchmarks while responding more quickly. It supports structured tool calling, code agents, and search agents, making it suitable for research, coding, and agentic workflows. \n\nIt succeeds the [DeepSeek V3-0324](/deepseek/deepseek-chat-v3-0324) model and performs well on a variety of tasks.","7","2026-02-23T16:08:48.736Z","2026-02-23T16:09:05.084Z","2026-02-23T16:09:05.206Z",{"id":1177,"documentId":1178,"name":1179,"alternativeText":706,"caption":706,"width":171,"height":171,"formats":13,"hash":1180,"ext":334,"mime":335,"size":1181,"url":1182,"previewUrl":13,"provider":62,"provider_metadata":13,"createdAt":1183,"updatedAt":1184,"publishedAt":1183,"focalPoint":13},{"id":189,"prompt":713,"completion":1339,"request":267,"image":267,"web_search":267,"internal_reasoning":267,"input_cache_read":267,"input_cache_write":267},8e-7,{"overallScore":806,"results":1341,"ranking":1342,"model":1343},{"cost":1043,"logic":749,"speed":749,"overall":806,"scoring":522,"toolUse":546,"hallucination":561,"classification":502,"structuredOutput":754},{"cost":148,"logic":140,"speed":199,"overall":194,"scoring":189,"toolUse":87,"hallucination":212,"classification":108,"structuredOutput":118},{"id":587,"documentId":1344,"active":8,"modelId":1345,"openRouterModelId":1346,"modelName":1347,"company":782,"contextLength":814,"outputCapabilities":1348,"inputCapabilities":1349,"description":1350,"maxCompletionTokens":1351,"slug":1352,"officialNodeType":13,"officialNodeParams":13,"createdAt":1353,"updatedAt":1354,"publishedAt":1355,"companyLogoMedia":1356,"pricing":1357},"wgssbnx8sqyxr5ikgi5vzjh0","gpt-5.1-codex-mini","openai/gpt-5.1-codex-mini","GPT-5.1-Codex-Mini",[694],[696,694],"GPT-5.1-Codex-Mini is a smaller and faster version of GPT-5.1-Codex","72","gpt-5-1-codex-mini","2026-02-24T08:47:17.936Z","2026-02-24T08:47:18.992Z","2026-02-24T08:47:19.036Z",{"id":793,"documentId":794,"name":795,"alternativeText":706,"caption":706,"width":171,"height":171,"formats":13,"hash":796,"ext":334,"mime":335,"size":797,"url":798,"previewUrl":13,"provider":62,"provider_metadata":13,"createdAt":799,"updatedAt":800,"publishedAt":799,"focalPoint":13},{"id":565,"prompt":1136,"completion":986,"request":267,"image":267,"web_search":267,"internal_reasoning":267,"input_cache_read":267,"input_cache_write":267},{"overallScore":1306,"results":1359,"ranking":1360,"model":1361},{"cost":861,"logic":591,"speed":678,"overall":1306,"scoring":542,"toolUse":569,"hallucination":650,"classification":595,"structuredOutput":595},{"cost":175,"logic":212,"speed":171,"overall":199,"scoring":171,"toolUse":140,"hallucination":189,"classification":101,"structuredOutput":87},{"id":746,"documentId":1362,"active":8,"modelId":1363,"openRouterModelId":1364,"modelName":1365,"company":890,"contextLength":921,"outputCapabilities":1366,"inputCapabilities":1367,"description":1368,"maxCompletionTokens":1369,"slug":1363,"officialNodeType":13,"officialNodeParams":13,"createdAt":1370,"updatedAt":1371,"publishedAt":1372,"companyLogoMedia":1373,"pricing":1374},"ppvm6cd3krny1b9wo9l3958r","pixtral-large-2411","mistralai/pixtral-large-2411","Pixtral Large 2411",[694],[694,696],"Pixtral Large is a 124B parameter, open-weight, multimodal model built on top of [Mistral Large 2](/mistralai/mistral-large-2411). The model is able to understand documents, charts and natural images.\n\nThe model is available under the Mistral Research License (MRL) for research and educational use, and the Mistral Commercial License for experimentation, testing, and production for commercial purposes.\n\n","18","2026-02-24T09:51:09.478Z","2026-02-24T10:19:00.548Z","2026-02-24T10:19:00.589Z",{"id":899,"documentId":900,"name":901,"alternativeText":706,"caption":706,"width":171,"height":171,"formats":13,"hash":902,"ext":334,"mime":335,"size":903,"url":904,"previewUrl":13,"provider":62,"provider_metadata":13,"createdAt":905,"updatedAt":906,"publishedAt":907,"focalPoint":13},{"id":1141,"prompt":986,"completion":880,"request":267,"image":267,"web_search":267,"internal_reasoning":267,"input_cache_read":13,"input_cache_write":13},{"overallScore":1306,"results":1376,"ranking":1377,"model":1378},{"cost":323,"logic":806,"speed":829,"overall":1306,"scoring":635,"toolUse":250,"hallucination":682,"classification":217,"structuredOutput":557},{"cost":118,"logic":166,"speed":148,"overall":199,"scoring":129,"toolUse":153,"hallucination":87,"classification":129,"structuredOutput":148},{"id":1379,"documentId":1380,"active":8,"modelId":1381,"openRouterModelId":1382,"modelName":1383,"company":890,"contextLength":727,"outputCapabilities":1384,"inputCapabilities":1385,"description":1386,"maxCompletionTokens":1387,"slug":1381,"officialNodeType":13,"officialNodeParams":13,"createdAt":1388,"updatedAt":1389,"publishedAt":1390,"companyLogoMedia":1391,"pricing":1392},200,"l42aadj9itgnloq5wiju40aj","ministral-8b-2512","mistralai/ministral-8b-2512","Ministral 3 8B 2512",[694],[694,696],"A balanced model in the Ministral 3 family, Ministral 3 8B is a powerful, efficient tiny language model with vision capabilities.","66","2026-02-24T09:43:18.060Z","2026-02-24T10:17:26.766Z","2026-02-24T10:17:26.808Z",{"id":899,"documentId":900,"name":901,"alternativeText":706,"caption":706,"width":171,"height":171,"formats":13,"hash":902,"ext":334,"mime":335,"size":903,"url":904,"previewUrl":13,"provider":62,"provider_metadata":13,"createdAt":905,"updatedAt":906,"publishedAt":907,"focalPoint":13},{"id":1192,"prompt":1393,"completion":1393,"request":267,"image":267,"web_search":267,"internal_reasoning":267,"input_cache_read":13,"input_cache_write":13},1.5e-7,{"overallScore":1306,"results":1395,"ranking":1396,"model":1397},{"cost":753,"logic":749,"speed":534,"overall":1306,"scoring":514,"toolUse":631,"hallucination":751,"classification":217,"structuredOutput":754},{"cost":199,"logic":140,"speed":518,"overall":199,"scoring":194,"toolUse":108,"hallucination":118,"classification":129,"structuredOutput":118},{"id":1398,"documentId":1399,"active":8,"modelId":1400,"openRouterModelId":1401,"modelName":1402,"company":1285,"contextLength":727,"outputCapabilities":1403,"inputCapabilities":1404,"description":1405,"maxCompletionTokens":1406,"slug":1407,"officialNodeType":13,"officialNodeParams":13,"createdAt":1408,"updatedAt":1409,"publishedAt":1410,"companyLogoMedia":1411,"pricing":1412},186,"tme68ohlhkx0whljbqy57b1q","kimi-k2.5","moonshotai/kimi-k2.5","Kimi K2.5",[694],[694,696],"Kimi K2.5 is Moonshot AI's native multimodal model, delivering state-of-the-art visual coding capability and a self-directed agent swarm paradigm. Built on Kimi K2 with continued pretraining over approximately 15T mixed visual and text tokens, it delivers strong performance in general reasoning, visual coding, and agentic tool-calling.","54","kimi-k2-5","2026-02-24T09:32:43.590Z","2026-02-24T10:13:27.593Z","2026-02-24T10:13:27.650Z",{"id":1294,"documentId":1295,"name":1296,"alternativeText":706,"caption":706,"width":171,"height":171,"formats":13,"hash":1297,"ext":334,"mime":335,"size":1298,"url":1299,"previewUrl":13,"provider":62,"provider_metadata":13,"createdAt":1300,"updatedAt":1301,"publishedAt":1300,"focalPoint":13},{"id":1413,"prompt":1414,"completion":953,"request":267,"image":267,"web_search":267,"internal_reasoning":267,"input_cache_read":267,"input_cache_write":13},182,6e-7,{"overallScore":1416,"results":1417,"ranking":1418,"model":1419},68,{"cost":990,"logic":807,"speed":680,"overall":1416,"scoring":583,"toolUse":514,"hallucination":751,"classification":217,"structuredOutput":153},{"cost":87,"logic":129,"speed":118,"overall":204,"scoring":162,"toolUse":148,"hallucination":118,"classification":129,"structuredOutput":162},{"id":250,"documentId":1420,"active":8,"modelId":1421,"openRouterModelId":1422,"modelName":1423,"company":964,"contextLength":965,"outputCapabilities":1424,"inputCapabilities":1425,"description":1426,"maxCompletionTokens":1427,"slug":1421,"officialNodeType":13,"officialNodeParams":13,"createdAt":1428,"updatedAt":1429,"publishedAt":1430,"companyLogoMedia":1431,"pricing":1432},"f54k4mbya5n32p1yj8zm2qz2","gemini-2.5-flash","google/gemini-2.5-flash","Gemini 2.5 Flash",[694],[786,696,694,968,969],"Gemini 2.5 Flash is Google's state-of-the-art workhorse model, specifically designed for advanced reasoning, coding, mathematics, and scientific tasks. It includes built-in \"thinking\" capabilities, enabling it to provide responses with greater accuracy and nuanced context handling. \n\nAdditionally, Gemini 2.5 Flash is configurable through the \"max tokens for reasoning\" parameter, as described in the documentation (https://openrouter.ai/docs/use-cases/reasoning-tokens#max-tokens-for-reasoning).","14","2026-02-23T16:16:42.901Z","2026-02-23T16:16:44.279Z","2026-02-23T16:16:44.328Z",{"id":976,"documentId":977,"name":978,"alternativeText":706,"caption":706,"width":658,"height":658,"formats":13,"hash":979,"ext":334,"mime":335,"size":980,"url":981,"previewUrl":13,"provider":62,"provider_metadata":13,"createdAt":982,"updatedAt":983,"publishedAt":984,"focalPoint":13},{"id":232,"prompt":932,"completion":1433,"request":267,"image":267,"web_search":267,"internal_reasoning":267,"input_cache_read":267,"input_cache_write":267},0.0000025,{"overallScore":1416,"results":1435,"ranking":1437,"model":1438},{"cost":751,"logic":826,"speed":1436,"overall":1416,"scoring":153,"toolUse":569,"hallucination":752,"classification":217,"structuredOutput":683},81,{"cost":129,"logic":148,"speed":217,"overall":204,"scoring":254,"toolUse":140,"hallucination":140,"classification":129,"structuredOutput":108},{"id":1439,"documentId":1440,"active":8,"modelId":1441,"openRouterModelId":1442,"modelName":1443,"company":1444,"contextLength":869,"outputCapabilities":1445,"inputCapabilities":1446,"description":1447,"maxCompletionTokens":1448,"slug":1441,"officialNodeType":13,"officialNodeParams":13,"createdAt":1449,"updatedAt":1450,"publishedAt":1451,"companyLogoMedia":1452,"pricing":1461},184,"bredyezfv17zhayarb7qj4po","kat-coder-pro","kwaipilot/kat-coder-pro","KAT-Coder-Pro V1","Kwaipilot",[694],[694],"KAT-Coder-Pro V1 is KwaiKAT's most advanced agentic coding model in the KAT-Coder series. Designed specifically for agentic coding tasks, it excels in real-world software engineering scenarios, achieving 73.4% solve rate on the SWE-Bench Verified benchmark. \n\nThe model has been optimized for tool-use capability, multi-turn interaction, instruction following, generalization, and comprehensive capabilities through a multi-stage training process, including mid-training, supervised fine-tuning (SFT), reinforcement fine-tuning (RFT), and scalable agentic RL.","73","2026-02-24T09:31:45.158Z","2026-02-24T10:10:19.916Z","2026-02-24T10:10:19.962Z",{"id":1453,"documentId":1454,"name":1455,"alternativeText":706,"caption":706,"width":241,"height":241,"formats":13,"hash":1456,"ext":334,"mime":335,"size":1457,"url":1458,"previewUrl":13,"provider":62,"provider_metadata":13,"createdAt":1459,"updatedAt":1460,"publishedAt":1459,"focalPoint":13},2939,"qxihckqy1kck7bpl4rgr9i0t","llm-kwaipilot.svg","llm_kwaipilot_0ba70b4ef6",1.06,"https://n8niostorageaccount.blob.core.windows.net/n8nio-strapi-blobs-prod/assets/llm_kwaipilot_0ba70b4ef6.svg","2026-02-12T14:36:04.461Z","2026-02-12T14:36:05.794Z",{"id":1462,"prompt":1463,"completion":1464,"request":267,"image":267,"web_search":267,"internal_reasoning":267,"input_cache_read":267,"input_cache_write":13},180,2.07e-7,8.28e-7,{"overallScore":1416,"results":1466,"ranking":1467,"model":1468},{"cost":752,"logic":752,"speed":754,"overall":1416,"scoring":232,"toolUse":217,"hallucination":751,"classification":162,"structuredOutput":754},{"cost":162,"logic":108,"speed":153,"overall":204,"scoring":217,"toolUse":158,"hallucination":118,"classification":140,"structuredOutput":118},{"id":1038,"documentId":1469,"active":8,"modelId":1470,"openRouterModelId":1471,"modelName":1472,"company":726,"contextLength":783,"outputCapabilities":1473,"inputCapabilities":1474,"description":1475,"maxCompletionTokens":1476,"slug":1470,"officialNodeType":13,"officialNodeParams":13,"createdAt":1477,"updatedAt":1478,"publishedAt":1479,"companyLogoMedia":1480,"pricing":1481},"gx7rj2aasnh5h4b6c0vcj7no","qwen3-coder-plus","qwen/qwen3-coder-plus","Qwen3 Coder Plus",[694],[694],"Qwen3 Coder Plus is Alibaba's proprietary version of the Open Source Qwen3 Coder 480B A35B. It is a powerful coding agent model specializing in autonomous programming via tool calling and environment interaction, combining coding proficiency with versatile general-purpose abilities.","81","2026-02-24T09:52:00.354Z","2026-02-24T10:19:25.119Z","2026-02-24T10:19:25.165Z",{"id":736,"documentId":737,"name":738,"alternativeText":706,"caption":706,"width":171,"height":171,"formats":13,"hash":739,"ext":334,"mime":335,"size":740,"url":741,"previewUrl":13,"provider":62,"provider_metadata":13,"createdAt":742,"updatedAt":743,"publishedAt":744,"focalPoint":13},{"id":1482,"prompt":856,"completion":857,"request":267,"image":267,"web_search":267,"internal_reasoning":267,"input_cache_read":267,"input_cache_write":13},205,{"overallScore":1416,"results":1484,"ranking":1485,"model":1486},{"cost":684,"logic":635,"speed":1043,"overall":1416,"scoring":246,"toolUse":175,"hallucination":599,"classification":595,"structuredOutput":254},{"cost":101,"logic":194,"speed":87,"overall":204,"scoring":108,"toolUse":162,"hallucination":204,"classification":101,"structuredOutput":158},{"id":1487,"documentId":1488,"active":8,"modelId":1489,"openRouterModelId":1490,"modelName":1491,"company":1492,"contextLength":1493,"outputCapabilities":1494,"inputCapabilities":1495,"description":1496,"maxCompletionTokens":1497,"slug":1489,"officialNodeType":13,"officialNodeParams":13,"createdAt":1498,"updatedAt":1499,"publishedAt":1500,"companyLogoMedia":1501,"pricing":1510},194,"f1y6rqitwnkoscqjqyhf88lh","llama-4-scout","meta-llama/llama-4-scout","Llama 4 Scout","Meta","327680",[694],[694,696],"Llama 4 Scout 17B Instruct (16E) is a mixture-of-experts (MoE) language model developed by Meta, activating 17 billion parameters out of a total of 109B. It supports native multimodal input (text and image) and multilingual output (text and code) across 12 supported languages. Designed for assistant-style interaction and visual reasoning, Scout uses 16 experts per forward pass and features a context length of 10 million tokens, with a training corpus of ~40 trillion tokens.\n\nBuilt for high efficiency and local or commercial deployment, Llama 4 Scout incorporates early fusion for seamless modality integration. It is instruction-tuned for use in multilingual chat, captioning, and image understanding tasks. Released under the Llama 4 Community License, it was last trained on data up to August 2024 and launched publicly on April 5, 2025.","88","2026-02-24T09:36:27.814Z","2026-02-24T10:15:52.392Z","2026-02-24T10:15:52.428Z",{"id":1502,"documentId":1503,"name":1504,"alternativeText":706,"caption":706,"width":171,"height":171,"formats":13,"hash":1505,"ext":334,"mime":335,"size":1506,"url":1507,"previewUrl":13,"provider":62,"provider_metadata":13,"createdAt":1508,"updatedAt":1509,"publishedAt":1508,"focalPoint":13},2941,"ihdplu9orztpa6rm0qkw4wh6","llm-meta.svg","llm_meta_b970ed9df1",7.53,"https://n8niostorageaccount.blob.core.windows.net/n8nio-strapi-blobs-prod/assets/llm_meta_b970ed9df1.svg","2026-02-12T14:36:18.413Z","2026-02-12T14:36:19.833Z",{"id":1511,"prompt":1512,"completion":932,"request":267,"image":267,"web_search":267,"internal_reasoning":267,"input_cache_read":13,"input_cache_write":13},190,8e-8,{"overallScore":1416,"results":1514,"ranking":1515,"model":1516},{"cost":323,"logic":753,"speed":681,"overall":1416,"scoring":583,"toolUse":250,"hallucination":826,"classification":502,"structuredOutput":518},{"cost":118,"logic":158,"speed":158,"overall":204,"scoring":162,"toolUse":153,"hallucination":153,"classification":108,"structuredOutput":153},{"id":1517,"documentId":1518,"active":8,"modelId":1519,"openRouterModelId":1520,"modelName":1521,"company":1492,"contextLength":965,"outputCapabilities":1522,"inputCapabilities":1523,"description":1524,"maxCompletionTokens":1525,"slug":1519,"officialNodeType":13,"officialNodeParams":13,"createdAt":1526,"updatedAt":1527,"publishedAt":1528,"companyLogoMedia":1529,"pricing":1530},193,"b548ry3fu0n7b58c2bt8v6c1","llama-4-maverick","meta-llama/llama-4-maverick","Llama 4 Maverick",[694],[694,696],"Llama 4 Maverick 17B Instruct (128E) is a high-capacity multimodal language model from Meta, built on a mixture-of-experts (MoE) architecture with 128 experts and 17 billion active parameters per forward pass (400B total). It supports multilingual text and image input, and produces multilingual text and code output across 12 supported languages. Optimized for vision-language tasks, Maverick is instruction-tuned for assistant-like behavior, image reasoning, and general-purpose multimodal interaction.\n\nMaverick features early fusion for native multimodality and a 1 million token context window. It was trained on a curated mixture of public, licensed, and Meta-platform data, covering ~22 trillion tokens, with a knowledge cutoff in August 2024. Released on April 5, 2025 under the Llama 4 Community License, Maverick is suited for research and commercial applications requiring advanced multimodal understanding and high model throughput.","87","2026-02-24T09:36:03.461Z","2026-02-24T10:15:38.023Z","2026-02-24T10:15:38.084Z",{"id":1502,"documentId":1503,"name":1504,"alternativeText":706,"caption":706,"width":171,"height":171,"formats":13,"hash":1505,"ext":334,"mime":335,"size":1506,"url":1507,"previewUrl":13,"provider":62,"provider_metadata":13,"createdAt":1508,"updatedAt":1509,"publishedAt":1508,"focalPoint":13},{"id":1531,"prompt":1393,"completion":1414,"request":267,"image":267,"web_search":267,"internal_reasoning":267,"input_cache_read":13,"input_cache_write":13},189,{"overallScore":957,"results":1533,"ranking":1534,"model":1535},{"cost":751,"logic":246,"speed":752,"overall":957,"scoring":591,"toolUse":569,"hallucination":158,"classification":546,"structuredOutput":957},{"cost":129,"logic":189,"speed":166,"overall":212,"scoring":158,"toolUse":140,"hallucination":237,"classification":118,"structuredOutput":140},{"id":194,"documentId":1536,"active":8,"modelId":1537,"openRouterModelId":1538,"modelName":1539,"company":890,"contextLength":869,"outputCapabilities":1540,"inputCapabilities":1541,"description":1542,"maxCompletionTokens":1543,"slug":1537,"officialNodeType":13,"officialNodeParams":13,"createdAt":1544,"updatedAt":1545,"publishedAt":1546,"companyLogoMedia":1547,"pricing":1548},"t8hmbqr5lsqxg3652n23b5ek","codestral-2508","mistralai/codestral-2508","Codestral 2508",[694],[694],"Mistral's cutting-edge language model for coding released end of July 2025. Codestral specializes in low-latency, high-frequency tasks such as fill-in-the-middle (FIM), code correction and test generation.\n\n[Blog Post](https://mistral.ai/news/codestral-25-08)","42","2026-02-23T16:07:07.617Z","2026-02-23T16:07:53.008Z","2026-02-23T16:07:53.065Z",{"id":899,"documentId":900,"name":901,"alternativeText":706,"caption":706,"width":171,"height":171,"formats":13,"hash":902,"ext":334,"mime":335,"size":903,"url":904,"previewUrl":13,"provider":62,"provider_metadata":13,"createdAt":905,"updatedAt":906,"publishedAt":907,"focalPoint":13},{"id":166,"prompt":932,"completion":1549,"request":267,"image":267,"web_search":267,"internal_reasoning":267,"input_cache_read":267,"input_cache_write":267},9e-7,{"overallScore":1109,"results":1551,"ranking":1552,"model":1553},{"cost":1043,"logic":611,"speed":752,"overall":1109,"scoring":569,"toolUse":250,"hallucination":752,"classification":502,"structuredOutput":557},{"cost":148,"logic":199,"speed":166,"overall":217,"scoring":166,"toolUse":153,"hallucination":140,"classification":108,"structuredOutput":148},{"id":1511,"documentId":1554,"active":8,"modelId":1555,"openRouterModelId":1556,"modelName":1557,"company":1558,"contextLength":921,"outputCapabilities":1559,"inputCapabilities":1560,"description":1561,"maxCompletionTokens":1562,"slug":1563,"officialNodeType":13,"officialNodeParams":13,"createdAt":1564,"updatedAt":1565,"publishedAt":1566,"companyLogoMedia":1567,"pricing":1576},"oqjevws72eexj86bgyab5rrg","llama-3.1-nemotron-70b-instruct","nvidia/llama-3.1-nemotron-70b-instruct","Llama 3.1 Nemotron 70B Instruct","NVIDIA",[694],[694],"NVIDIA's Llama 3.1 Nemotron 70B is a language model designed for generating precise and useful responses. Leveraging [Llama 3.1 70B](/models/meta-llama/llama-3.1-70b-instruct) architecture and Reinforcement Learning from Human Feedback (RLHF), it excels in automatic alignment benchmarks. This model is tailored for applications requiring high accuracy in helpfulness and response generation, suitable for diverse user queries across multiple domains.\n\nUsage of this model is subject to [Meta's Acceptable Use Policy](https://www.llama.com/llama3/use-policy/).","31","llama-3-1-nemotron-70b-instruct","2026-02-24T09:34:40.848Z","2026-02-24T10:14:47.072Z","2026-02-24T10:14:47.115Z",{"id":1568,"documentId":1569,"name":1570,"alternativeText":706,"caption":706,"width":171,"height":171,"formats":13,"hash":1571,"ext":334,"mime":335,"size":1572,"url":1573,"previewUrl":13,"provider":62,"provider_metadata":13,"createdAt":1574,"updatedAt":1575,"publishedAt":1574,"focalPoint":13},2946,"o5yv8m5hdm1lzl24wvvbw7xq","llm-nvidia.svg","llm_nvidia_a8df17298b",1.34,"https://n8niostorageaccount.blob.core.windows.net/n8nio-strapi-blobs-prod/assets/llm_nvidia_a8df17298b.svg","2026-02-12T14:36:38.406Z","2026-02-12T14:36:39.074Z",{"id":1398,"prompt":747,"completion":747,"request":267,"image":267,"web_search":267,"internal_reasoning":267,"input_cache_read":13,"input_cache_write":13},{"overallScore":1109,"results":1578,"ranking":1579,"model":1580},{"cost":684,"logic":1416,"speed":774,"overall":1109,"scoring":627,"toolUse":250,"hallucination":1109,"classification":217,"structuredOutput":595},{"cost":101,"logic":171,"speed":212,"overall":217,"scoring":140,"toolUse":153,"hallucination":175,"classification":129,"structuredOutput":87},{"id":1482,"documentId":1581,"active":8,"modelId":1582,"openRouterModelId":1583,"modelName":1584,"company":890,"contextLength":921,"outputCapabilities":1585,"inputCapabilities":1586,"description":1587,"maxCompletionTokens":1588,"slug":1589,"officialNodeType":13,"officialNodeParams":13,"createdAt":1590,"updatedAt":1591,"publishedAt":1592,"companyLogoMedia":1593,"pricing":1594},"amnu4nhsu7zbernpuojrufxh","mistral-small-3.2-24b-instruct","mistralai/mistral-small-3.2-24b-instruct","Mistral Small 3.2 24B",[694],[696,694],"Mistral-Small-3.2-24B-Instruct-2506 is an updated 24B parameter model from Mistral optimized for instruction following, repetition reduction, and improved function calling. Compared to the 3.1 release, version 3.2 significantly improves accuracy on WildBench and Arena Hard, reduces infinite generations, and delivers gains in tool use and structured output tasks.\n\nIt supports image and text inputs with structured outputs, function/tool calling, and strong performance across coding (HumanEval+, MBPP), STEM (MMLU, MATH, GPQA), and vision benchmarks (ChartQA, DocVQA).","84","mistral-small-3-2-24b-instruct","2026-02-24T09:50:06.277Z","2026-02-24T10:18:35.081Z","2026-02-24T10:18:35.114Z",{"id":899,"documentId":900,"name":901,"alternativeText":706,"caption":706,"width":171,"height":171,"formats":13,"hash":902,"ext":334,"mime":335,"size":903,"url":904,"previewUrl":13,"provider":62,"provider_metadata":13,"createdAt":905,"updatedAt":906,"publishedAt":907,"focalPoint":13},{"id":1261,"prompt":1595,"completion":1596,"request":267,"image":267,"web_search":267,"internal_reasoning":267,"input_cache_read":13,"input_cache_write":13},6e-8,1.8e-7,{"overallScore":1109,"results":1598,"ranking":1599,"model":1600},{"cost":506,"logic":826,"speed":682,"overall":1109,"scoring":171,"toolUse":631,"hallucination":751,"classification":217,"structuredOutput":684},{"cost":227,"logic":148,"speed":189,"overall":217,"scoring":241,"toolUse":108,"hallucination":118,"classification":129,"structuredOutput":101},{"id":175,"documentId":1601,"active":8,"modelId":1602,"openRouterModelId":1603,"modelName":1604,"company":836,"contextLength":837,"outputCapabilities":1605,"inputCapabilities":1606,"description":1607,"maxCompletionTokens":1608,"slug":1609,"officialNodeType":13,"officialNodeParams":13,"createdAt":1610,"updatedAt":1611,"publishedAt":1612,"companyLogoMedia":1613,"pricing":1614},"fijma4qifpqq4r12h4e97ovz","claude-opus-4.5","anthropic/claude-opus-4.5","Claude Opus 4.5",[694],[786,696,694],"Claude Opus 4.5 is Anthropic’s frontier reasoning model optimized for complex software engineering, agentic workflows, and long-horizon computer use. It offers strong multimodal capabilities, competitive performance across real-world coding and reasoning benchmarks, and improved robustness to prompt injection. The model is designed to operate efficiently across varied effort levels, enabling developers to trade off speed, depth, and token usage depending on task requirements. It comes with a new parameter to control token efficiency, which can be accessed using the OpenRouter Verbosity parameter with low, medium, or high.\n\nOpus 4.5 supports advanced tool use, extended context management, and coordinated multi-agent setups, making it well-suited for autonomous research, debugging, multi-step planning, and spreadsheet/browser manipulation. It delivers substantial gains in structured reasoning, execution reliability, and alignment compared to prior Opus generations, while reducing token overhead and improving performance on long-running tasks.","32","claude-opus-4-5","2026-02-23T16:05:53.023Z","2026-02-23T16:05:54.235Z","2026-02-23T16:05:54.283Z",{"id":846,"documentId":847,"name":848,"alternativeText":706,"caption":706,"width":171,"height":171,"formats":13,"hash":849,"ext":334,"mime":335,"size":850,"url":851,"previewUrl":13,"provider":62,"provider_metadata":13,"createdAt":852,"updatedAt":853,"publishedAt":854,"focalPoint":13},{"id":158,"prompt":857,"completion":1615,"request":267,"image":267,"web_search":715,"internal_reasoning":267,"input_cache_read":267,"input_cache_write":267},0.000025,{"overallScore":658,"results":1617,"ranking":1618,"model":1619},{"cost":751,"logic":774,"speed":611,"overall":658,"scoring":591,"toolUse":599,"hallucination":828,"classification":502,"structuredOutput":153},{"cost":129,"logic":87,"speed":514,"overall":222,"scoring":158,"toolUse":129,"hallucination":158,"classification":108,"structuredOutput":162},{"id":1215,"documentId":1620,"active":8,"modelId":1621,"openRouterModelId":1622,"modelName":1623,"company":1558,"contextLength":921,"outputCapabilities":1624,"inputCapabilities":1625,"description":1626,"maxCompletionTokens":1627,"slug":1628,"officialNodeType":13,"officialNodeParams":13,"createdAt":1629,"updatedAt":1630,"publishedAt":1631,"companyLogoMedia":1632,"pricing":1633},"qdxggcwkcaqg90jy48ee4110","llama-3.3-nemotron-super-49b-v1.5","nvidia/llama-3.3-nemotron-super-49b-v1.5","Llama 3.3 Nemotron Super 49B V1.5",[694],[694],"Llama-3.3-Nemotron-Super-49B-v1.5 is a 49B-parameter, English-centric reasoning/chat model derived from Meta’s Llama-3.3-70B-Instruct with a 128K context. It’s post-trained for agentic workflows (RAG, tool calling) via SFT across math, code, science, and multi-turn chat, followed by multiple RL stages; Reward-aware Preference Optimization (RPO) for alignment, RL with Verifiable Rewards (RLVR) for step-wise reasoning, and iterative DPO to refine tool-use behavior. A distillation-driven Neural Architecture Search (“Puzzle”) replaces some attention blocks and varies FFN widths to shrink memory footprint and improve throughput, enabling single-GPU (H100/H200) deployment while preserving instruction following and CoT quality.\n\nIn internal evaluations (NeMo-Skills, up to 16 runs, temp = 0.6, top_p = 0.95), the model reports strong reasoning/coding results, e.g., MATH500 pass@1 = 97.4, AIME-2024 = 87.5, AIME-2025 = 82.71, GPQA = 71.97, LiveCodeBench (24.10–25.02) = 73.58, and MMLU-Pro (CoT) = 79.53. The model targets practical inference efficiency (high tokens/s, reduced VRAM) with Transformers/vLLM support and explicit “reasoning on/off” modes (chat-first defaults, greedy recommended when disabled). Suitable for building agents, assistants, and long-context retrieval systems where balanced accuracy-to-cost and reliable tool use matter.\n","44","llama-3-3-nemotron-super-49b-v1-5","2026-02-24T09:35:35.949Z","2026-02-24T10:15:21.442Z","2026-02-24T10:15:21.494Z",{"id":1568,"documentId":1569,"name":1570,"alternativeText":706,"caption":706,"width":171,"height":171,"formats":13,"hash":1571,"ext":334,"mime":335,"size":1572,"url":1573,"previewUrl":13,"provider":62,"provider_metadata":13,"createdAt":1574,"updatedAt":1575,"publishedAt":1574,"focalPoint":13},{"id":1634,"prompt":1635,"completion":1157,"request":267,"image":267,"web_search":267,"internal_reasoning":267,"input_cache_read":13,"input_cache_write":13},188,1e-7,{"overallScore":658,"results":1637,"ranking":1638,"model":1639},{"cost":611,"logic":826,"speed":635,"overall":658,"scoring":180,"toolUse":599,"hallucination":682,"classification":217,"structuredOutput":754},{"cost":217,"logic":148,"speed":502,"overall":222,"scoring":204,"toolUse":129,"hallucination":87,"classification":129,"structuredOutput":118},{"id":549,"documentId":1640,"active":8,"modelId":1641,"openRouterModelId":1642,"modelName":1643,"company":782,"contextLength":814,"outputCapabilities":1644,"inputCapabilities":1645,"description":1646,"maxCompletionTokens":1647,"slug":1641,"officialNodeType":13,"officialNodeParams":13,"createdAt":1648,"updatedAt":1649,"publishedAt":1650,"companyLogoMedia":1651,"pricing":1652},"u7ngjmufs1qgu9tww6qdy5le","gpt-5","openai/gpt-5","GPT-5",[694],[694,696,786],"GPT-5 is OpenAI’s most advanced model, offering major improvements in reasoning, code quality, and user experience. It is optimized for complex tasks that require step-by-step reasoning, instruction following, and accuracy in high-stakes use cases. It supports test-time routing features and advanced prompt understanding, including user-specified intent like \"think hard about this.\" Improvements include reductions in hallucination, sycophancy, and better performance in coding, writing, and health-related tasks.","12","2026-02-24T08:41:55.640Z","2026-02-24T08:41:56.616Z","2026-02-24T08:41:56.656Z",{"id":793,"documentId":794,"name":795,"alternativeText":706,"caption":706,"width":171,"height":171,"formats":13,"hash":796,"ext":334,"mime":335,"size":797,"url":798,"previewUrl":13,"provider":62,"provider_metadata":13,"createdAt":799,"updatedAt":800,"publishedAt":799,"focalPoint":13},{"id":534,"prompt":802,"completion":803,"request":267,"image":267,"web_search":715,"internal_reasoning":267,"input_cache_read":267,"input_cache_write":267},{"overallScore":658,"results":1654,"ranking":1655,"model":1656},{"cost":826,"logic":1416,"speed":681,"overall":658,"scoring":607,"toolUse":514,"hallucination":1306,"classification":217,"structuredOutput":595},{"cost":189,"logic":171,"speed":158,"overall":222,"scoring":148,"toolUse":148,"hallucination":171,"classification":129,"structuredOutput":87},{"id":879,"documentId":1657,"active":8,"modelId":1658,"openRouterModelId":1659,"modelName":1660,"company":890,"contextLength":1661,"outputCapabilities":1662,"inputCapabilities":1663,"description":1664,"maxCompletionTokens":1665,"slug":1658,"officialNodeType":13,"officialNodeParams":13,"createdAt":1666,"updatedAt":1667,"publishedAt":1668,"companyLogoMedia":1669,"pricing":1670},"mzfv5xco4a7t571as2u9drr5","mixtral-8x22b-instruct","mistralai/mixtral-8x22b-instruct","Mixtral 8x22B Instruct","65536",[694],[694],"Mistral's official instruct fine-tuned version of [Mixtral 8x22B](/models/mistralai/mixtral-8x22b). It uses 39B active parameters out of 141B, offering unparalleled cost efficiency for its size. Its strengths include:\n- strong math, coding, and reasoning\n- large context length (64k)\n- fluency in English, French, Italian, German, and Spanish\n\nSee benchmarks on the launch announcement [here](https://mistral.ai/news/mixtral-8x22b/).\n#moe","48","2026-02-24T09:50:31.210Z","2026-02-24T10:18:48.300Z","2026-02-24T10:18:48.368Z",{"id":899,"documentId":900,"name":901,"alternativeText":706,"caption":706,"width":171,"height":171,"formats":13,"hash":902,"ext":334,"mime":335,"size":903,"url":904,"previewUrl":13,"provider":62,"provider_metadata":13,"createdAt":905,"updatedAt":906,"publishedAt":907,"focalPoint":13},{"id":1671,"prompt":986,"completion":880,"request":267,"image":267,"web_search":267,"internal_reasoning":267,"input_cache_read":13,"input_cache_write":13},202,{"overallScore":658,"results":1673,"ranking":1674,"model":1675},{"cost":323,"logic":635,"speed":859,"overall":658,"scoring":615,"toolUse":87,"hallucination":684,"classification":162,"structuredOutput":957},{"cost":118,"logic":194,"speed":222,"overall":222,"scoring":87,"toolUse":166,"hallucination":101,"classification":140,"structuredOutput":140},{"id":1135,"documentId":1676,"active":8,"modelId":1677,"openRouterModelId":1678,"modelName":1679,"company":1492,"contextLength":921,"outputCapabilities":1680,"inputCapabilities":1681,"description":1682,"maxCompletionTokens":1683,"slug":1677,"officialNodeType":13,"officialNodeParams":13,"createdAt":1684,"updatedAt":1685,"publishedAt":1686,"companyLogoMedia":1687,"pricing":1688},"ertwky6nm3i5ju1lim3mxlwz","llama-3.3-70b-instruct","meta-llama/llama-3.3-70b-instruct","Llama 3.3 70B Instruct",[694],[694],"The Meta Llama 3.3 multilingual large language model (LLM) is a pretrained and instruction tuned generative model in 70B (text in/text out). The Llama 3.3 instruction tuned text only model is optimized for multilingual dialogue use cases and outperforms many of the available open source and closed chat models on common industry benchmarks.\n\nSupported languages: English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai.\n\n[Model Card](https://github.com/meta-llama/llama-models/blob/main/models/llama3_3/MODEL_CARD.md)","16","2026-02-24T09:35:10.799Z","2026-02-24T10:15:02.208Z","2026-02-24T10:15:02.297Z",{"id":1502,"documentId":1503,"name":1504,"alternativeText":706,"caption":706,"width":171,"height":171,"formats":13,"hash":1505,"ext":334,"mime":335,"size":1506,"url":1507,"previewUrl":13,"provider":62,"provider_metadata":13,"createdAt":1508,"updatedAt":1509,"publishedAt":1508,"focalPoint":13},{"id":1689,"prompt":1690,"completion":1691,"request":267,"image":267,"web_search":267,"internal_reasoning":267,"input_cache_read":13,"input_cache_write":13},187,1.3e-7,3.8e-7,{"overallScore":654,"results":1693,"ranking":1694,"model":1695},{"cost":267,"logic":1042,"speed":518,"overall":654,"scoring":506,"toolUse":569,"hallucination":751,"classification":595,"structuredOutput":684},{"cost":237,"logic":162,"speed":522,"overall":227,"scoring":199,"toolUse":140,"hallucination":118,"classification":101,"structuredOutput":101},{"id":1413,"documentId":1696,"active":8,"modelId":1697,"openRouterModelId":1698,"modelName":1699,"company":691,"contextLength":869,"outputCapabilities":1700,"inputCapabilities":1701,"description":1702,"maxCompletionTokens":1703,"slug":1697,"officialNodeType":13,"officialNodeParams":13,"createdAt":1704,"updatedAt":1705,"publishedAt":1706,"companyLogoMedia":1707,"pricing":1708},"rc1pejqg9qln977ngmiyoibw","grok-4","x-ai/grok-4","Grok 4",[694],[696,694],"Grok 4 is xAI's latest reasoning model with a 256k context window. It supports parallel tool calling, structured outputs, and both image and text inputs. Note that reasoning is not exposed, reasoning cannot be disabled, and the reasoning effort cannot be specified. Pricing increases once the total tokens in a given request is greater than 128k tokens. See more details on the [xAI docs](https://docs.x.ai/docs/models/grok-4-0709)","11","2026-02-24T09:28:30.632Z","2026-02-24T10:09:47.401Z","2026-02-24T10:09:47.444Z",{"id":703,"documentId":704,"name":705,"alternativeText":706,"caption":706,"width":171,"height":171,"formats":13,"hash":707,"ext":334,"mime":335,"size":708,"url":709,"previewUrl":13,"provider":62,"provider_metadata":13,"createdAt":710,"updatedAt":711,"publishedAt":710,"focalPoint":13},{"id":1709,"prompt":953,"completion":954,"request":267,"image":267,"web_search":267,"internal_reasoning":267,"input_cache_read":267,"input_cache_write":13},178,{"overallScore":650,"results":1711,"ranking":1712,"model":1713},{"cost":1043,"logic":1042,"speed":217,"overall":650,"scoring":650,"toolUse":267,"hallucination":682,"classification":502,"structuredOutput":753},{"cost":148,"logic":162,"speed":534,"overall":232,"scoring":118,"toolUse":171,"hallucination":87,"classification":108,"structuredOutput":129},{"id":1714,"documentId":1715,"active":8,"modelId":1716,"openRouterModelId":1717,"modelName":1718,"company":726,"contextLength":921,"outputCapabilities":1719,"inputCapabilities":1720,"description":1721,"maxCompletionTokens":1722,"slug":1716,"officialNodeType":13,"officialNodeParams":13,"createdAt":1723,"updatedAt":1724,"publishedAt":1725,"companyLogoMedia":1726,"pricing":1727},212,"n4lfdgvfnjkg1z1hie50zocy","qwen3-vl-8b-instruct","qwen/qwen3-vl-8b-instruct","Qwen3 VL 8B Instruct",[694],[696,694],"Qwen3-VL-8B-Instruct is a multimodal vision-language model from the Qwen3-VL series, built for high-fidelity understanding and reasoning across text, images, and video. It features improved multimodal fusion with Interleaved-MRoPE for long-horizon temporal reasoning, DeepStack for fine-grained visual-text alignment, and text-timestamp alignment for precise event localization.\n\nThe model supports a native 256K-token context window, extensible to 1M tokens, and handles both static and dynamic media inputs for tasks like document parsing, visual question answering, spatial reasoning, and GUI control. It achieves text understanding comparable to leading LLMs while expanding OCR coverage to 32 languages and enhancing robustness under varied visual conditions.","76","2026-02-24T09:53:25.451Z","2026-02-24T10:19:57.078Z","2026-02-24T10:19:57.124Z",{"id":736,"documentId":737,"name":738,"alternativeText":706,"caption":706,"width":171,"height":171,"formats":13,"hash":739,"ext":334,"mime":335,"size":740,"url":741,"previewUrl":13,"provider":62,"provider_metadata":13,"createdAt":742,"updatedAt":743,"publishedAt":744,"focalPoint":13},{"id":993,"prompt":1512,"completion":714,"request":267,"image":267,"web_search":267,"internal_reasoning":267,"input_cache_read":13,"input_cache_write":13},{"overallScore":646,"results":1729,"ranking":1730,"model":1731},{"cost":684,"logic":599,"speed":990,"overall":646,"scoring":542,"toolUse":217,"hallucination":681,"classification":217,"structuredOutput":518},{"cost":101,"logic":204,"speed":140,"overall":237,"scoring":171,"toolUse":158,"hallucination":129,"classification":129,"structuredOutput":153},{"id":1007,"documentId":1732,"active":8,"modelId":1733,"openRouterModelId":1734,"modelName":1735,"company":890,"contextLength":1736,"outputCapabilities":1737,"inputCapabilities":1738,"description":1739,"maxCompletionTokens":1740,"slug":1733,"officialNodeType":13,"officialNodeParams":13,"createdAt":1741,"updatedAt":1742,"publishedAt":1743,"companyLogoMedia":1744,"pricing":1745},"ohq1ccq3zdyjylec8pg8tqdf","mistral-small-24b-instruct-2501","mistralai/mistral-small-24b-instruct-2501","Mistral Small 3","32768",[694],[694],"Mistral Small 3 is a 24B-parameter language model optimized for low-latency performance across common AI tasks. Released under the Apache 2.0 license, it features both pre-trained and instruction-tuned versions designed for efficient local deployment.\n\nThe model achieves 81% accuracy on the MMLU benchmark and performs competitively with larger models like Llama 3.3 70B and Qwen 32B, while operating at three times the speed on equivalent hardware. [Read the blog post about the model here.](https://mistral.ai/news/mistral-small-3/)","15","2026-02-24T09:49:39.427Z","2026-02-24T10:18:22.236Z","2026-02-24T10:18:22.279Z",{"id":899,"documentId":900,"name":901,"alternativeText":706,"caption":706,"width":171,"height":171,"formats":13,"hash":902,"ext":334,"mime":335,"size":903,"url":904,"previewUrl":13,"provider":62,"provider_metadata":13,"createdAt":905,"updatedAt":906,"publishedAt":907,"focalPoint":13},{"id":1379,"prompt":909,"completion":1512,"request":267,"image":267,"web_search":267,"internal_reasoning":267,"input_cache_read":13,"input_cache_write":13},{"overallScore":646,"results":1747,"ranking":1749,"model":1750},{"cost":684,"logic":774,"speed":1748,"overall":646,"scoring":514,"toolUse":546,"hallucination":538,"classification":502,"structuredOutput":254},90,{"cost":101,"logic":87,"speed":162,"overall":237,"scoring":194,"toolUse":87,"hallucination":217,"classification":108,"structuredOutput":158},{"id":772,"documentId":1751,"active":8,"modelId":1752,"openRouterModelId":1753,"modelName":1752,"company":782,"contextLength":921,"outputCapabilities":1754,"inputCapabilities":1755,"description":1756,"maxCompletionTokens":1757,"slug":1752,"officialNodeType":13,"officialNodeParams":13,"createdAt":1758,"updatedAt":1759,"publishedAt":1760,"companyLogoMedia":1761,"pricing":1762},"p1r6n8qn0mwlz3omqpll4yvy","gpt-oss-120b","openai/gpt-oss-120b",[694],[694],"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 to run on a single H100 GPU with native MXFP4 quantization. The model supports configurable reasoning depth, full chain-of-thought access, and native tool use, including function calling, browsing, and structured output generation.","52","2026-02-24T09:26:39.068Z","2026-02-24T10:08:51.837Z","2026-02-24T10:08:51.886Z",{"id":793,"documentId":794,"name":795,"alternativeText":706,"caption":706,"width":171,"height":171,"formats":13,"hash":796,"ext":334,"mime":335,"size":797,"url":798,"previewUrl":13,"provider":62,"provider_metadata":13,"createdAt":799,"updatedAt":800,"publishedAt":799,"focalPoint":13},{"id":1763,"prompt":1764,"completion":713,"request":267,"image":267,"web_search":267,"internal_reasoning":267,"input_cache_read":13,"input_cache_write":13},175,4e-8,{"overallScore":246,"results":1766,"ranking":1767,"model":1768},{"cost":323,"logic":753,"speed":1239,"overall":246,"scoring":599,"toolUse":569,"hallucination":522,"classification":217,"structuredOutput":518},{"cost":118,"logic":158,"speed":241,"overall":241,"scoring":153,"toolUse":140,"hallucination":222,"classification":129,"structuredOutput":153},{"id":1462,"documentId":1769,"active":8,"modelId":1770,"openRouterModelId":1771,"modelName":1770,"company":782,"contextLength":921,"outputCapabilities":1772,"inputCapabilities":1773,"description":1774,"maxCompletionTokens":1775,"slug":1770,"officialNodeType":13,"officialNodeParams":13,"createdAt":1776,"updatedAt":1777,"publishedAt":1778,"companyLogoMedia":1779,"pricing":1780},"cb5sobeeqvlam4jwgnd1ic4w","gpt-oss-20b","openai/gpt-oss-20b",[694],[694],"gpt-oss-20b is an open-weight 21B parameter model released by OpenAI under the Apache 2.0 license. It uses a Mixture-of-Experts (MoE) architecture with 3.6B active parameters per forward pass, optimized for lower-latency inference and deployability on consumer or single-GPU hardware. The model is trained in OpenAI’s Harmony response format and supports reasoning level configuration, fine-tuning, and agentic capabilities including function calling, tool use, and structured outputs.","49","2026-02-24T09:27:08.221Z","2026-02-24T10:09:09.284Z","2026-02-24T10:09:09.358Z",{"id":793,"documentId":794,"name":795,"alternativeText":706,"caption":706,"width":171,"height":171,"formats":13,"hash":796,"ext":334,"mime":335,"size":797,"url":798,"previewUrl":13,"provider":62,"provider_metadata":13,"createdAt":799,"updatedAt":800,"publishedAt":799,"focalPoint":13},{"id":1781,"prompt":1782,"completion":1783,"request":267,"image":267,"web_search":267,"internal_reasoning":267,"input_cache_read":13,"input_cache_write":13},176,3e-8,1.4e-7,{"overallScore":639,"results":1785,"ranking":1786,"model":1787},{"cost":682,"logic":1042,"speed":615,"overall":639,"scoring":199,"toolUse":569,"hallucination":627,"classification":546,"structuredOutput":557},{"cost":171,"logic":162,"speed":510,"overall":254,"scoring":232,"toolUse":140,"hallucination":194,"classification":118,"structuredOutput":148},{"id":1275,"documentId":1788,"active":8,"modelId":1789,"openRouterModelId":1790,"modelName":1791,"company":1792,"contextLength":1793,"outputCapabilities":1794,"inputCapabilities":1795,"description":1796,"maxCompletionTokens":1797,"slug":1789,"officialNodeType":13,"officialNodeParams":13,"createdAt":1798,"updatedAt":1799,"publishedAt":1800,"companyLogoMedia":1801,"pricing":1810},"x8yx3fyfdjlgrdle5np91nb7","minimax-m2","minimax/minimax-m2","MiniMax M2","MiniMax","196608",[694],[694],"MiniMax-M2 is a compact, high-efficiency large language model optimized for end-to-end coding and agentic workflows. With 10 billion activated parameters (230 billion total), it delivers near-frontier intelligence across general reasoning, tool use, and multi-step task execution while maintaining low latency and deployment efficiency.\n\nThe model excels in code generation, multi-file editing, compile-run-fix loops, and test-validated repair, showing strong results on SWE-Bench Verified, Multi-SWE-Bench, and Terminal-Bench. It also performs competitively in agentic evaluations such as BrowseComp and GAIA, effectively handling long-horizon planning, retrieval, and recovery from execution errors.\n\nBenchmarked by [Artificial Analysis](https://artificialanalysis.ai/models/minimax-m2), MiniMax-M2 ranks among the top open-source models for composite intelligence, spanning mathematics, science, and instruction-following. Its small activation footprint enables fast inference, high concurrency, and improved unit economics, making it well-suited for large-scale agents, developer assistants, and reasoning-driven applications that require responsiveness and cost efficiency.\n\nTo avoid degrading this model's performance, MiniMax highly recommends preserving reasoning between turns. Learn more about using reasoning_details to pass back reasoning in our [docs](https://openrouter.ai/docs/use-cases/reasoning-tokens#preserving-reasoning-blocks).","75","2026-02-24T09:41:55.562Z","2026-02-24T10:16:45.256Z","2026-02-24T10:16:45.313Z",{"id":1802,"documentId":1803,"name":1804,"alternativeText":706,"caption":706,"width":171,"height":171,"formats":13,"hash":1805,"ext":334,"mime":335,"size":1806,"url":1807,"previewUrl":13,"provider":62,"provider_metadata":13,"createdAt":1808,"updatedAt":1809,"publishedAt":1808,"focalPoint":13},2943,"gqprjjw8gcb3qcodleiw6q1a","llm-minimax.svg","llm_minimax_a801917273",4.87,"https://n8niostorageaccount.blob.core.windows.net/n8nio-strapi-blobs-prod/assets/llm_minimax_a801917273.svg","2026-02-12T14:36:26.358Z","2026-02-12T14:36:27.367Z",{"id":1517,"prompt":713,"completion":856,"request":267,"image":267,"web_search":267,"internal_reasoning":267,"input_cache_read":267,"input_cache_write":13},{"overallScore":639,"results":1812,"ranking":1813,"model":1814},{"cost":990,"logic":828,"speed":754,"overall":639,"scoring":153,"toolUse":546,"hallucination":1306,"classification":502,"structuredOutput":254},{"cost":87,"logic":153,"speed":153,"overall":254,"scoring":254,"toolUse":87,"hallucination":171,"classification":108,"structuredOutput":158},{"id":1671,"documentId":1815,"active":8,"modelId":1816,"openRouterModelId":1817,"modelName":1818,"company":890,"contextLength":921,"outputCapabilities":1819,"inputCapabilities":1820,"description":1821,"maxCompletionTokens":1822,"slug":1816,"officialNodeType":13,"officialNodeParams":13,"createdAt":1823,"updatedAt":1824,"publishedAt":1825,"companyLogoMedia":1826,"pricing":1827},"tyo5a90km7i5kvtr7th0k28e","mistral-medium-3","mistralai/mistral-medium-3","Mistral Medium 3",[694],[694,696],"Mistral Medium 3 is a high-performance enterprise-grade language model designed to deliver frontier-level capabilities at significantly reduced operational cost. It balances state-of-the-art reasoning and multimodal performance with 8× lower cost compared to traditional large models, making it suitable for scalable deployments across professional and industrial use cases.\n\nThe model excels in domains such as coding, STEM reasoning, and enterprise adaptation. It supports hybrid, on-prem, and in-VPC deployments and is optimized for integration into custom workflows. Mistral Medium 3 offers competitive accuracy relative to larger models like Claude Sonnet 3.5/3.7, Llama 4 Maverick, and Command R+, while maintaining broad compatibility across cloud environments.","39","2026-02-24T09:46:28.871Z","2026-02-24T10:17:53.542Z","2026-02-24T10:17:53.579Z",{"id":899,"documentId":900,"name":901,"alternativeText":706,"caption":706,"width":171,"height":171,"formats":13,"hash":902,"ext":334,"mime":335,"size":903,"url":904,"previewUrl":13,"provider":62,"provider_metadata":13,"createdAt":905,"updatedAt":906,"publishedAt":907,"focalPoint":13},{"id":1828,"prompt":1157,"completion":986,"request":267,"image":267,"web_search":267,"internal_reasoning":267,"input_cache_read":13,"input_cache_write":13},198,[1830,1849,1868,1885,1912,1930,1948,1965,1982,2000],{"overallScore":639,"results":1831,"ranking":1832,"model":1833},{"cost":680,"logic":826,"speed":631,"overall":639,"scoring":222,"toolUse":217,"hallucination":751,"classification":502,"structuredOutput":518},{"cost":108,"logic":148,"speed":506,"overall":254,"scoring":222,"toolUse":158,"hallucination":118,"classification":108,"structuredOutput":153},{"id":222,"documentId":1834,"active":8,"modelId":1835,"openRouterModelId":1836,"modelName":1837,"company":1166,"contextLength":1167,"outputCapabilities":1838,"inputCapabilities":1839,"description":1840,"maxCompletionTokens":1841,"slug":1842,"officialNodeType":13,"officialNodeParams":13,"createdAt":1843,"updatedAt":1844,"publishedAt":1845,"companyLogoMedia":1846,"pricing":1847},"l82abfeknxnws4mke34hwiry","deepseek-v3.2","deepseek/deepseek-v3.2","DeepSeek V3.2",[694],[694],"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 that reduces training and inference cost while preserving quality in long-context scenarios. A scalable reinforcement learning post-training framework further improves reasoning, with reported performance in the GPT-5 class, and the model has demonstrated gold-medal results on the 2025 IMO and IOI. V3.2 also uses a large-scale agentic task synthesis pipeline to better integrate reasoning into tool-use settings, boosting compliance and generalization in interactive environments.\n\nUsers can control the reasoning behaviour with the `reasoning` `enabled` boolean. [Learn more in our docs](https://openrouter.ai/docs/use-cases/reasoning-tokens#enable-reasoning-with-default-config)","51","deepseek-v3-2","2026-02-23T16:11:00.177Z","2026-02-23T16:11:01.316Z","2026-02-23T16:11:01.365Z",{"id":1177,"documentId":1178,"name":1179,"alternativeText":706,"caption":706,"width":171,"height":171,"formats":13,"hash":1180,"ext":334,"mime":335,"size":1181,"url":1182,"previewUrl":13,"provider":62,"provider_metadata":13,"createdAt":1183,"updatedAt":1184,"publishedAt":1183,"focalPoint":13},{"id":199,"prompt":1848,"completion":1157,"request":267,"image":267,"web_search":267,"internal_reasoning":267,"input_cache_read":267,"input_cache_write":267},2.8e-7,{"overallScore":631,"results":1850,"ranking":1851,"model":1852},{"cost":753,"logic":148,"speed":682,"overall":631,"scoring":658,"toolUse":217,"hallucination":826,"classification":546,"structuredOutput":153},{"cost":199,"logic":232,"speed":189,"overall":250,"scoring":101,"toolUse":158,"hallucination":153,"classification":118,"structuredOutput":162},{"id":1689,"documentId":1853,"active":8,"modelId":1854,"openRouterModelId":1855,"modelName":1856,"company":1492,"contextLength":1857,"outputCapabilities":1858,"inputCapabilities":1859,"description":1860,"maxCompletionTokens":1861,"slug":1854,"officialNodeType":13,"officialNodeParams":13,"createdAt":1862,"updatedAt":1863,"publishedAt":1864,"companyLogoMedia":1865,"pricing":1866},"y192swyu75tjlk8fukm8t453","llama-3.1-405b-instruct","meta-llama/llama-3.1-405b-instruct","Llama 3.1 405B Instruct","130815",[694],[694],"The highly anticipated 400B class of Llama3 is here! Clocking in at 128k context with impressive eval scores, the Meta AI team continues to push the frontier of open-source LLMs.\n\nMeta's latest class of model (Llama 3.1) launched with a variety of sizes & flavors. This 405B instruct-tuned version is optimized for high quality dialogue usecases.\n\nIt has demonstrated strong performance compared to leading closed-source models including GPT-4o and Claude 3.5 Sonnet in evaluations.\n\nTo read more about the model release, [click here](https://ai.meta.com/blog/meta-llama-3-1/). Usage of this model is subject to [Meta's Acceptable Use Policy](https://llama.meta.com/llama3/use-policy/).","17","2026-02-24T09:33:12.859Z","2026-02-24T10:13:48.569Z","2026-02-24T10:13:48.610Z",{"id":1502,"documentId":1503,"name":1504,"alternativeText":706,"caption":706,"width":171,"height":171,"formats":13,"hash":1505,"ext":334,"mime":335,"size":1506,"url":1507,"previewUrl":13,"provider":62,"provider_metadata":13,"createdAt":1508,"updatedAt":1509,"publishedAt":1508,"focalPoint":13},{"id":757,"prompt":1867,"completion":1867,"request":267,"image":267,"web_search":267,"internal_reasoning":267,"input_cache_read":13,"input_cache_write":13},0.0000035,{"overallScore":627,"results":1869,"ranking":1870,"model":1871},{"cost":553,"logic":774,"speed":1072,"overall":627,"scoring":180,"toolUse":569,"hallucination":681,"classification":217,"structuredOutput":254},{"cost":222,"logic":87,"speed":237,"overall":495,"scoring":204,"toolUse":140,"hallucination":129,"classification":129,"structuredOutput":158},{"id":506,"documentId":1872,"active":8,"modelId":1873,"openRouterModelId":1874,"modelName":1875,"company":964,"contextLength":965,"outputCapabilities":1876,"inputCapabilities":1877,"description":1878,"maxCompletionTokens":1879,"slug":1873,"officialNodeType":13,"officialNodeParams":13,"createdAt":1880,"updatedAt":1881,"publishedAt":1882,"companyLogoMedia":1883,"pricing":1884},"nhsc2w29fw5c1ldgz170tlvf","gemini-2.5-pro","google/gemini-2.5-pro","Gemini 2.5 Pro",[694],[694,696,786,968,969],"Gemini 2.5 Pro is Google’s state-of-the-art AI model designed for advanced reasoning, coding, mathematics, and scientific tasks. It employs “thinking” capabilities, enabling it to reason through responses with enhanced accuracy and nuanced context handling. Gemini 2.5 Pro achieves top-tier performance on multiple benchmarks, including first-place positioning on the LMArena leaderboard, reflecting superior human-preference alignment and complex problem-solving abilities.","13","2026-02-24T08:33:44.539Z","2026-02-24T08:34:04.452Z","2026-02-24T08:34:04.499Z",{"id":976,"documentId":977,"name":978,"alternativeText":706,"caption":706,"width":658,"height":658,"formats":13,"hash":979,"ext":334,"mime":335,"size":980,"url":981,"previewUrl":13,"provider":62,"provider_metadata":13,"createdAt":982,"updatedAt":983,"publishedAt":984,"focalPoint":13},{"id":250,"prompt":802,"completion":803,"request":267,"image":715,"web_search":267,"internal_reasoning":267,"input_cache_read":267,"input_cache_write":267},{"overallScore":623,"results":1886,"ranking":1887,"model":1888},{"cost":680,"logic":826,"speed":646,"overall":623,"scoring":199,"toolUse":87,"hallucination":1072,"classification":162,"structuredOutput":753},{"cost":108,"logic":148,"speed":180,"overall":180,"scoring":232,"toolUse":166,"hallucination":162,"classification":140,"structuredOutput":129},{"id":1889,"documentId":1890,"active":8,"modelId":1891,"openRouterModelId":1892,"modelName":1893,"company":1894,"contextLength":921,"outputCapabilities":1895,"inputCapabilities":1896,"description":1897,"maxCompletionTokens":1898,"slug":1891,"officialNodeType":13,"officialNodeParams":13,"createdAt":1899,"updatedAt":1900,"publishedAt":1901,"companyLogoMedia":1902,"pricing":1910},214,"th413iqhwv7vvvxuequ6clk2","trinity-mini","arcee-ai/trinity-mini","Trinity Mini","Arcee AI",[694],[694],"Trinity Mini is a 26B-parameter (3B active) sparse mixture-of-experts language model featuring 128 experts with 8 active per token. Engineered for efficient reasoning over long contexts (131k) with robust function calling and multi-step agent workflows.","69","2026-02-24T09:54:14.623Z","2026-02-24T10:20:34.514Z","2026-02-24T10:20:34.552Z",{"id":1903,"documentId":1904,"name":1905,"alternativeText":706,"caption":706,"width":171,"height":171,"formats":13,"hash":1906,"ext":334,"mime":335,"size":708,"url":1907,"previewUrl":13,"provider":62,"provider_metadata":13,"createdAt":1908,"updatedAt":1909,"publishedAt":1908,"focalPoint":13},2929,"q279es7rp2gqvpdjtpwwqcdc","llm-arcee-ai.svg","llm_arcee_ai_dcab734eeb","https://n8niostorageaccount.blob.core.windows.net/n8nio-strapi-blobs-prod/assets/llm_arcee_ai_dcab734eeb.svg","2026-02-12T14:35:20.179Z","2026-02-12T14:35:20.993Z",{"id":864,"prompt":1911,"completion":1393,"request":267,"image":267,"web_search":267,"internal_reasoning":267,"input_cache_read":13,"input_cache_write":13},4.5e-8,{"overallScore":623,"results":1913,"ranking":1914,"model":1915},{"cost":680,"logic":774,"speed":752,"overall":623,"scoring":522,"toolUse":217,"hallucination":522,"classification":217,"structuredOutput":254},{"cost":108,"logic":87,"speed":166,"overall":180,"scoring":189,"toolUse":158,"hallucination":222,"classification":129,"structuredOutput":158},{"id":180,"documentId":1916,"active":8,"modelId":1917,"openRouterModelId":1918,"modelName":1919,"company":964,"contextLength":965,"outputCapabilities":1920,"inputCapabilities":1921,"description":1922,"maxCompletionTokens":1923,"slug":1924,"officialNodeType":13,"officialNodeParams":13,"createdAt":1925,"updatedAt":1926,"publishedAt":1927,"companyLogoMedia":1928,"pricing":1929},"djev6if5xnvpgk57qz54yfoh","gemini-2.5-flash-lite","google/gemini-2.5-flash-lite","Gemini 2.5 Flash Lite",[694],[694,696,786,968,969],"Gemini 2.5 Flash-Lite is a lightweight reasoning model in the Gemini 2.5 family, optimized for ultra-low latency and cost efficiency. It offers improved throughput, faster token generation, and better performance across common benchmarks compared to earlier Flash models. By default, \"thinking\" (i.e. multi-pass reasoning) is disabled to prioritize speed, but developers can enable it via the [Reasoning API parameter](https://openrouter.ai/docs/use-cases/reasoning-tokens) to selectively trade off cost for intelligence. ","35","gemini-2-5-flash-lite","2026-02-24T08:31:38.587Z","2026-02-24T08:31:39.567Z","2026-02-24T08:31:39.604Z",{"id":976,"documentId":977,"name":978,"alternativeText":706,"caption":706,"width":658,"height":658,"formats":13,"hash":979,"ext":334,"mime":335,"size":980,"url":981,"previewUrl":13,"provider":62,"provider_metadata":13,"createdAt":982,"updatedAt":983,"publishedAt":984,"focalPoint":13},{"id":241,"prompt":1635,"completion":1157,"request":267,"image":267,"web_search":267,"internal_reasoning":267,"input_cache_read":267,"input_cache_write":267},{"overallScore":611,"results":1931,"ranking":1932,"model":1933},{"cost":859,"logic":826,"speed":267,"overall":611,"scoring":212,"toolUse":546,"hallucination":684,"classification":162,"structuredOutput":753},{"cost":194,"logic":148,"speed":538,"overall":502,"scoring":227,"toolUse":87,"hallucination":101,"classification":140,"structuredOutput":129},{"id":542,"documentId":1934,"active":8,"modelId":1935,"openRouterModelId":1936,"modelName":1937,"company":1050,"contextLength":1051,"outputCapabilities":1938,"inputCapabilities":1939,"description":1940,"maxCompletionTokens":1941,"slug":1942,"officialNodeType":13,"officialNodeParams":13,"createdAt":1943,"updatedAt":1944,"publishedAt":1945,"companyLogoMedia":1946,"pricing":1947},"k2kx1gel3cjd9zst9vqiy2zv","glm-4.7","z-ai/glm-4.7","GLM 4.7",[694],[694],"GLM-4.7 is Z.AI’s latest flagship model, featuring upgrades in two key areas: enhanced programming capabilities and more stable multi-step reasoning/execution. It demonstrates significant improvements in executing complex agent tasks while delivering more natural conversational experiences and superior front-end aesthetics.","58","glm-4-7","2026-02-24T08:40:34.909Z","2026-02-24T08:40:41.915Z","2026-02-24T08:40:41.975Z",{"id":1061,"documentId":1062,"name":1063,"alternativeText":706,"caption":706,"width":171,"height":171,"formats":13,"hash":1064,"ext":334,"mime":335,"size":1065,"url":1066,"previewUrl":13,"provider":62,"provider_metadata":13,"createdAt":1067,"updatedAt":1068,"publishedAt":1067,"focalPoint":13},{"id":522,"prompt":1157,"completion":1008,"request":267,"image":267,"web_search":267,"internal_reasoning":267,"input_cache_read":267,"input_cache_write":267},{"overallScore":611,"results":1949,"ranking":1950,"model":1951},{"cost":323,"logic":1042,"speed":678,"overall":611,"scoring":250,"toolUse":217,"hallucination":650,"classification":267,"structuredOutput":557},{"cost":118,"logic":162,"speed":171,"overall":502,"scoring":212,"toolUse":158,"hallucination":189,"classification":87,"structuredOutput":148},{"id":1828,"documentId":1952,"active":8,"modelId":1953,"openRouterModelId":1954,"modelName":1955,"company":890,"contextLength":727,"outputCapabilities":1956,"inputCapabilities":1957,"description":1958,"maxCompletionTokens":1959,"slug":1953,"officialNodeType":13,"officialNodeParams":13,"createdAt":1960,"updatedAt":1961,"publishedAt":1962,"companyLogoMedia":1963,"pricing":1964},"gyii20hm4hh8at7s6jgvimwg","ministral-14b-2512","mistralai/ministral-14b-2512","Ministral 3 14B 2512",[694],[694,696],"The largest model in the Ministral 3 family, Ministral 3 14B offers frontier capabilities and performance comparable to its larger Mistral Small 3.2 24B counterpart. A powerful and efficient language model with vision capabilities.","65","2026-02-24T09:42:25.695Z","2026-02-24T10:16:59.528Z","2026-02-24T10:16:59.575Z",{"id":899,"documentId":900,"name":901,"alternativeText":706,"caption":706,"width":171,"height":171,"formats":13,"hash":902,"ext":334,"mime":335,"size":903,"url":904,"previewUrl":13,"provider":62,"provider_metadata":13,"createdAt":905,"updatedAt":906,"publishedAt":907,"focalPoint":13},{"id":1487,"prompt":713,"completion":713,"request":267,"image":267,"web_search":267,"internal_reasoning":267,"input_cache_read":13,"input_cache_write":13},{"overallScore":607,"results":1966,"ranking":1967,"model":1968},{"cost":323,"logic":569,"speed":752,"overall":607,"scoring":514,"toolUse":514,"hallucination":510,"classification":502,"structuredOutput":153},{"cost":118,"logic":222,"speed":166,"overall":506,"scoring":194,"toolUse":148,"hallucination":227,"classification":108,"structuredOutput":162},{"id":1156,"documentId":1969,"active":8,"modelId":1970,"openRouterModelId":1971,"modelName":1972,"company":890,"contextLength":921,"outputCapabilities":1973,"inputCapabilities":1974,"description":1975,"maxCompletionTokens":1976,"slug":1970,"officialNodeType":13,"officialNodeParams":13,"createdAt":1977,"updatedAt":1978,"publishedAt":1979,"companyLogoMedia":1980,"pricing":1981},"if3klj7g0dvys0biy6ixh9nq","ministral-3b-2512","mistralai/ministral-3b-2512","Ministral 3 3B 2512",[694],[694,696],"The smallest model in the Ministral 3 family, Ministral 3 3B is a powerful, efficient tiny language model with vision capabilities.","67","2026-02-24T09:42:54.474Z","2026-02-24T10:17:11.404Z","2026-02-24T10:17:11.439Z",{"id":899,"documentId":900,"name":901,"alternativeText":706,"caption":706,"width":171,"height":171,"formats":13,"hash":902,"ext":334,"mime":335,"size":903,"url":904,"previewUrl":13,"provider":62,"provider_metadata":13,"createdAt":905,"updatedAt":906,"publishedAt":907,"focalPoint":13},{"id":1112,"prompt":1635,"completion":1635,"request":267,"image":267,"web_search":267,"internal_reasoning":267,"input_cache_read":13,"input_cache_write":13},{"overallScore":607,"results":1983,"ranking":1984,"model":1985},{"cost":680,"logic":506,"speed":681,"overall":607,"scoring":627,"toolUse":250,"hallucination":267,"classification":217,"structuredOutput":557},{"cost":108,"logic":227,"speed":158,"overall":506,"scoring":140,"toolUse":153,"hallucination":241,"classification":129,"structuredOutput":148},{"id":1634,"documentId":1986,"active":8,"modelId":1987,"openRouterModelId":1988,"modelName":1989,"company":1492,"contextLength":921,"outputCapabilities":1990,"inputCapabilities":1991,"description":1992,"maxCompletionTokens":1993,"slug":1994,"officialNodeType":13,"officialNodeParams":13,"createdAt":1995,"updatedAt":1996,"publishedAt":1997,"companyLogoMedia":1998,"pricing":1999},"jd4rx3ccjhiqxy6nmjhb51v6","llama-3.1-70b-instruct","meta-llama/llama-3.1-70b-instruct","Llama 3.1 70B Instruct",[694],[694],"Meta's latest class of model (Llama 3.1) launched with a variety of sizes & flavors. This 70B instruct-tuned version is optimized for high quality dialogue usecases.\n\nIt has demonstrated strong performance compared to leading closed-source models in human evaluations.\n\nTo read more about the model release, [click here](https://ai.meta.com/blog/meta-llama-3-1/). Usage of this model is subject to [Meta's Acceptable Use Policy](https://llama.meta.com/llama3/use-policy/).","46","llama-3-1-70b-instruct","2026-02-24T09:33:37.772Z","2026-02-24T10:14:20.999Z","2026-02-24T10:14:21.049Z",{"id":1502,"documentId":1503,"name":1504,"alternativeText":706,"caption":706,"width":171,"height":171,"formats":13,"hash":1505,"ext":334,"mime":335,"size":1506,"url":1507,"previewUrl":13,"provider":62,"provider_metadata":13,"createdAt":1508,"updatedAt":1509,"publishedAt":1508,"focalPoint":13},{"id":1439,"prompt":1157,"completion":1157,"request":267,"image":267,"web_search":267,"internal_reasoning":267,"input_cache_read":13,"input_cache_write":13},{"overallScore":546,"results":2001,"ranking":2002,"model":2003},{"cost":684,"logic":267,"speed":611,"overall":546,"scoring":267,"toolUse":514,"hallucination":538,"classification":546,"structuredOutput":267},{"cost":101,"logic":237,"speed":514,"overall":510,"scoring":250,"toolUse":148,"hallucination":217,"classification":118,"structuredOutput":166},{"id":1531,"documentId":2004,"active":8,"modelId":2005,"openRouterModelId":2006,"modelName":2007,"company":1492,"contextLength":921,"outputCapabilities":2008,"inputCapabilities":2009,"description":2010,"maxCompletionTokens":2011,"slug":2012,"officialNodeType":13,"officialNodeParams":13,"createdAt":2013,"updatedAt":2014,"publishedAt":2015,"companyLogoMedia":2016,"pricing":2017},"i27utpeslkmztkt2xaf9p4nm","llama-3.1-8b-instruct","meta-llama/llama-3.1-8b-instruct","Llama 3.1 8B Instruct",[694],[694],"Meta's latest class of model (Llama 3.1) launched with a variety of sizes & flavors. This 8B instruct-tuned version is fast and efficient.\n\nIt has demonstrated strong performance compared to leading closed-source models in human evaluations.\n\nTo read more about the model release, [click here](https://ai.meta.com/blog/meta-llama-3-1/). Usage of this model is subject to [Meta's Acceptable Use Policy](https://llama.meta.com/llama3/use-policy/).","30","llama-3-1-8b-instruct","2026-02-24T09:34:03.855Z","2026-02-24T10:14:33.105Z","2026-02-24T10:14:33.151Z",{"id":1502,"documentId":1503,"name":1504,"alternativeText":706,"caption":706,"width":171,"height":171,"formats":13,"hash":1505,"ext":334,"mime":335,"size":1506,"url":1507,"previewUrl":13,"provider":62,"provider_metadata":13,"createdAt":1508,"updatedAt":1509,"publishedAt":1508,"focalPoint":13},{"id":1280,"prompt":2018,"completion":1782,"request":267,"image":267,"web_search":267,"internal_reasoning":267,"input_cache_read":13,"input_cache_write":13},2e-8,{"left":267,"top":267,"width":282,"height":282,"rotate":267,"vFlip":7,"hFlip":7,"body":2020},"\u003Cpath fill=\"currentColor\" d=\"m229.66 218.34l-50.07-50.06a88.11 88.11 0 1 0-11.31 11.31l50.06 50.07a8 8 0 0 0 11.32-11.32M40 112a72 72 0 1 1 72 72a72.08 72.08 0 0 1-72-72\"/>",{"left":267,"top":267,"width":282,"height":282,"rotate":267,"vFlip":7,"hFlip":7,"body":2022},"\u003Cpath fill=\"currentColor\" d=\"M222 128a6 6 0 0 1-6 6H54.49l61.75 61.76a6 6 0 1 1-8.48 8.48l-72-72a6 6 0 0 1 0-8.48l72-72a6 6 0 0 1 8.48 8.48L54.49 122H216a6 6 0 0 1 6 6\"/>",{"left":267,"top":267,"width":282,"height":282,"rotate":267,"vFlip":7,"hFlip":7,"body":2024},"\u003Cpath fill=\"currentColor\" d=\"m220.24 132.24l-72 72a6 6 0 0 1-8.48-8.48L201.51 134H40a6 6 0 0 1 0-12h161.51l-61.75-61.76a6 6 0 0 1 8.48-8.48l72 72a6 6 0 0 1 0 8.48\"/>",{"left":267,"top":267,"width":282,"height":282,"rotate":267,"vFlip":7,"hFlip":7,"body":2026},"\u003Cpath fill=\"currentColor\" d=\"M197.58 129.06L146 110l-19-51.62a15.92 15.92 0 0 0-29.88 0L78 110l-51.62 19a15.92 15.92 0 0 0 0 29.88L78 178l19 51.62a15.92 15.92 0 0 0 29.88 0L146 178l51.62-19a15.92 15.92 0 0 0 0-29.88ZM137 164.22a8 8 0 0 0-4.74 4.74L112 223.85L91.78 169a8 8 0 0 0-4.78-4.78L32.15 144L87 123.78a8 8 0 0 0 4.78-4.78L112 64.15L132.22 119a8 8 0 0 0 4.74 4.74L191.85 144ZM144 40a8 8 0 0 1 8-8h16V16a8 8 0 0 1 16 0v16h16a8 8 0 0 1 0 16h-16v16a8 8 0 0 1-16 0V48h-16a8 8 0 0 1-8-8m104 48a8 8 0 0 1-8 8h-8v8a8 8 0 0 1-16 0v-8h-8a8 8 0 0 1 0-16h8v-8a8 8 0 0 1 16 0v8h8a8 8 0 0 1 8 8\"/>",1775243297116]