[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-4d436945-18e9-4d69-a4c8-c1e3e975ab33":3},{"id":4,"title":5,"summary":6,"original_url":7,"source_id":8,"tags":9,"published_at":32,"created_at":33,"modified_at":34,"is_published":35,"publish_type":36,"image_url":13,"view_count":37},"4d436945-18e9-4d69-a4c8-c1e3e975ab33","MiniMax M3发布：稀疏注意力打通百万token上下文，开源模型编程能力逼近闭源前沿","6月1日，MiniMax发布M3大模型，首个将顶级编程能力、百万token上下文窗口与原生多模态三者合一的开源模型。核心突破在于自主研发的MSA（MiniMax Sparse Attention）稀疏注意力机制：传统Transformer注意力为二次复杂度——token翻倍计算量约增四倍，MSA采用KV块选择机制，只对最相关的键值缓存块进行计算，在百万token级别将每token计算量降至原来的1\u002F10，预填充速度提升约9倍，解码速度提升约15倍。\n\n在官方基准测试中，M3在SWE-Bench Pro上得分59%，超越GPT-5.5和Gemini 3.1 Pro，逼近Claude Opus 4.7；在BrowseComp自主浏览任务上以83.5分超越Opus 4.7（79.3）。模型支持文本、图像、视频输入，开放权重计划于发布后10天内释出。\n\n观点：长上下文和高计算成本长期制约开源模型在真实场景中的表现，MiniMax M3通过稀疏注意力架构为这一痛点提供了新的解决路径。基准数据来自厂商自测，开源权重释放后社区复现将给出更客观的答案。","https:\u002F\u002Fwww.minimax.io\u002Fblog\u002Fminimax-m3","70524a06-fc44-487c-ac6b-4a0186f66a45",[10,14,17,20,23,26,29],{"id":11,"name":12,"slug":12,"description":13,"color":13},"7ac06d8e-b074-4147-abfc-ffaa4c6b8744","ai-efficiency",null,{"id":15,"name":16,"slug":16,"description":13,"color":13},"e82b2d09-81b2-43d1-977e-e018443b3c14","coding-agent",{"id":18,"name":19,"slug":19,"description":13,"color":13},"0a93ec8e-ea39-4693-81de-563ca8c173f7","inference",{"id":21,"name":22,"slug":22,"description":13,"color":13},"01598627-1ea6-4b27-a5d8-874971571a71","llm",{"id":24,"name":25,"slug":25,"description":13,"color":13},"7e89b5cc-57db-4f37-bc6d-28919a73931c","model-release",{"id":27,"name":28,"slug":28,"description":13,"color":13},"499f4b56-819d-49a3-9609-33e775143b86","multimodal",{"id":30,"name":31,"slug":31,"description":13,"color":13},"b9bd9039-fcdb-41a8-b85b-fc1587def2b9","open-source","2026-06-04T01:00:00Z","2026-06-04T01:08:43.891152Z","2026-06-04T01:08:43.891164Z",true,"agent",3]