[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-d4c2c83a-f1ea-4a1e-848e-5f1169e9d42b":3},{"id":4,"title":5,"summary":6,"original_url":7,"source_id":8,"tags":9,"published_at":23,"created_at":24,"modified_at":25,"is_published":26,"publish_type":27,"image_url":13,"view_count":28},"d4c2c83a-f1ea-4a1e-848e-5f1169e9d42b","微软MAI-Thinking-1：清洁训练的35B MoE推理模型，对位Claude Opus 4.6与Sonnet 4.6","2026年6月2日Build大会，Microsoft AI发布首个自研推理模型MAI-Thinking-1：35B激活参数、约1T总参数的稀疏MoE，256K上下文。未对第三方模型蒸馏，数据为可追溯商业授权语料；「Hill-Climbing Machine」是迭代闭环的一部分，安全性与能力奖励在同一RL回路统一训练。AIME 2025达97.0%、AIME 2026达94.5%，SWE-Bench Pro与Claude Opus 4.6基本持平；Anthropic合作的1276项Surge盲测中，用户偏好度超过Claude Sonnet 4.6。模型与微软自研加速器和内部RL框架共设计，支持Chat Completions API与函数调用，瞄准企业级Agent编码。Mustafa Suleyman将其定位为迈向「Humanist Superintelligence」的一步，强调模型应保持辅助性、拒绝以安全为名拒绝合法请求。这与OpenAI、Anthropic纯能力竞赛形成对照，数据可解释性、低推理成本与可控对齐正成为新一轮推理模型的差异化战场。","https:\u002F\u002Fmicrosoft.ai\u002Fnews\u002Fintroducing-mai-thinking-1","8922c55c-aa1b-4abb-8812-8e59cea78b3d",[10,14,17,20],{"id":11,"name":12,"slug":12,"description":13,"color":13},"120fa59a-ff6f-4537-9bf5-f818df636a0e","benchmark",null,{"id":15,"name":16,"slug":16,"description":13,"color":13},"dca4d0ab-7994-43a7-839e-7756fc77344a","claude",{"id":18,"name":19,"slug":19,"description":13,"color":13},"e82b2d09-81b2-43d1-977e-e018443b3c14","coding-agent",{"id":21,"name":22,"slug":22,"description":13,"color":13},"7e89b5cc-57db-4f37-bc6d-28919a73931c","model-release","2026-06-02T06:14:00Z","2026-06-19T14:16:36.632605Z","2026-06-19T14:16:36.632616Z",true,"agent",3]