[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"model-minimax-m2.7":3},[4,147],{"id":5,"aliases":6,"types":7,"pricing":11,"capabilities":17,"model_capabilities":23,"model_data":98,"introduction":143,"description":144,"title_focus":145,"keyword_extras":146},"minimax-m2.7",[5],[8,9,10],"openai","anthropic","openai\u002Fresponses\u002Fv1",{"flat_tokens":12},{"input_tokens":13,"output_tokens":14,"cache_read_tokens":15,"cache_write_tokens":16},"2.1","8.4","0.42","2.625",{"context_window":18,"max_output":19,"released_at":20,"modalities":21},204800,196608,"2026-03-18",[22],"text",{"groups":24,"highlights":87,"overall":91,"scenario":94,"summary":96,"weakness":97},[25,63,78],{"items":26,"name":62},[27,33,38,43,47,51,55,59],{"detail":28,"method":29,"name":30,"samples":31,"score":32},"","主\u002F客观混合","智能体协同",7,41,{"detail":28,"method":34,"name":35,"samples":36,"score":37},"客观","代码生成",10,0,{"detail":28,"method":39,"name":40,"samples":41,"score":42},"主观","代码理解与调试",12,74,{"detail":28,"method":39,"name":44,"samples":45,"score":46},"创意写作",17,70,{"detail":28,"method":29,"name":48,"samples":49,"score":50},"信息检索 \u002F RAG",13,53,{"detail":28,"method":39,"name":52,"samples":53,"score":54},"长文本处理",3,65,{"detail":28,"method":34,"name":56,"samples":57,"score":58},"数学与科学推理",14,86,{"detail":28,"method":29,"name":60,"samples":36,"score":61},"多语言能力",60,"专项进阶能力",{"items":64,"name":77},[65,69,73],{"detail":28,"method":29,"name":66,"samples":67,"score":68},"通识知识",15,57,{"detail":28,"method":39,"name":70,"samples":71,"score":72},"文本理解",5,84,{"detail":28,"method":29,"name":74,"samples":75,"score":76},"逻辑推理",20,77,"通用基础能力",{"items":79,"name":86},[80,84],{"detail":28,"method":39,"name":81,"samples":82,"score":83},"幻觉控制",9,61,{"detail":28,"method":39,"name":85,"samples":57,"score":72},"指令遵循","安全与合规能力",[88,89,90],{"name":56,"score":58},{"name":85,"score":72},{"name":70,"score":72},{"rating":92,"score":93},"良好",62,{"fit":93,"name":95},"结构化输出 \u002F 指令任务 \u002F 智能客服 \u002F 内容分析","Minimax M2.7在本轮能力评测中综合得分62，整体表现良好。其数学与科学推理（86%）和指令遵循（84%）表现突出，能够处理数学求解、科学分析和结构化推理任务，并按照明确格式和步骤稳定完成结构化任务。对企业知识库团队、AI 应用开发者和研究人员而言，它适合用于资料检索、文档问答、信息抽取和检索增强生成场景，解决从大量资料中找到相关信息，并形成可用于后续决策的回答；文本理解（84%）也为相关任务提供补充。在能力边界上，代码生成（0%）相对较弱，不适合直接承担代码编写任务；如用于研发流程，应限制在代码分析或辅助判断，并由开发者验证结果。",{"name":35,"note":28,"score":37},{"bars":99,"metrics":104,"recentAvailability":131,"summary":132},{"degraded":100,"down":101},[],[37,102,103],1,2,[105,108,111,115,119,123,127],{"label":106,"value":107},"完成率","97%",{"label":109,"value":110},"缓存命中率","1.9%",{"label":112,"unit":113,"value":114},"累计评测耗时","s","15496.1",{"label":116,"unit":117,"value":118},"Token 消耗","tokens","405,164",{"label":120,"unit":121,"value":122},"平均请求次数","次","1.0",{"label":124,"unit":125,"value":126},"TPM","tokens\u002Fmin","1,569",{"label":128,"unit":129,"value":130},"QPM","req\u002Fmin","0.6","98.66%",[133,136,139],{"label":134,"unit":113,"value":135},"延迟","44.53",{"label":137,"unit":125,"value":138},"吞吐量","1.57K",{"label":140,"unit":141,"value":142},"可用率","%","98.66","Minimax M2.7是一款侧重信息检索\u002FRAG的模型，在文本理解方面也有较好表现。它适合处理企业知识检索、资料问答与检索增强生成，可作为相关业务流程中的理解、推理或生成能力底座。","Minimax M2.7具备数学与科学推理、指令遵循、文本理解能力，可完成数学求解、科学分析和结构化推理。面向企业知识库团队、AI 应用开发者和研究人员，适合资料检索、文档问答、信息抽取和检索增强生成；可通过 SilvaMux 统一 API 一键快速接入。","指令推理模型",[56,85],{"id":5,"aliases":148,"types":150,"pricing":151,"capabilities":154,"tier":156,"priority":103,"model_capabilities":157,"model_data":198,"introduction":143,"description":144,"title_focus":145,"keyword_extras":223},[149,5],"ucloud\u002FMiniMax-M2.7",[8,9,10],{"flat_tokens":152},{"input_tokens":13,"output_tokens":14,"cache_read_tokens":153,"cache_write_tokens":15},"2.62",{"context_window":18,"max_output":19,"released_at":20,"modalities":155},[22],"tune",{"groups":158,"highlights":189,"overall":193,"scenario":195,"summary":96,"weakness":197},[159,176,183],{"items":160,"name":62},[161,163,165,167,169,171,173,174],{"detail":28,"method":29,"name":30,"samples":31,"score":162},35,{"detail":28,"method":34,"name":35,"samples":36,"score":164},80,{"detail":28,"method":39,"name":40,"samples":41,"score":166},67,{"detail":28,"method":39,"name":44,"samples":45,"score":168},66,{"detail":28,"method":29,"name":48,"samples":49,"score":170},95,{"detail":28,"method":34,"name":56,"samples":57,"score":172},79,{"detail":28,"method":39,"name":52,"samples":53,"score":68},{"detail":28,"method":29,"name":60,"samples":36,"score":175},82,{"items":177,"name":77},[178,179,181],{"detail":28,"method":29,"name":66,"samples":67,"score":42},{"detail":28,"method":39,"name":70,"samples":71,"score":180},87,{"detail":28,"method":29,"name":74,"samples":75,"score":182},50,{"items":184,"name":86},[185,187],{"detail":28,"method":39,"name":81,"samples":82,"score":186},76,{"detail":28,"method":39,"name":85,"samples":57,"score":188},81,[190,191,192],{"name":48,"score":170},{"name":70,"score":180},{"name":60,"score":175},{"rating":92,"score":194},71,{"fit":194,"name":196},"代码研发 \u002F 信息检索 \u002F 企业知识库",{"name":30,"note":28,"score":162},{"bars":199,"metrics":202,"recentAvailability":131,"summary":217},{"degraded":200,"down":201},[],[37,102,103],[203,205,207,209,211,213,215],{"label":106,"value":204},"94%",{"label":109,"value":206},"动态变化",{"label":112,"unit":113,"value":208},"20719.5",{"label":116,"unit":117,"value":210},"364,220",{"label":120,"unit":121,"value":212},"1.1",{"label":124,"unit":125,"value":214},"1,055",{"label":128,"unit":129,"value":216},"0.4",[218,220,222],{"label":134,"unit":113,"value":219},"45.26",{"label":137,"unit":125,"value":221},"1.05K",{"label":140,"unit":141,"value":142},[56,85]]