[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-436fb2b9-4c48-4631-977c-c9539650f975":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},"436fb2b9-4c48-4631-977c-c9539650f975","Kimi K2.7-Code 开源:Moonshot 把\"过度思考\"砍掉三成,长程编程更经济","Moonshot AI 在 6 月 12 日开源了 Kimi K2.7-Code,焦点从\"答得多聪明\"挪到\"每一步多经济\"。模型沿用 K2.6 的 1T MoE 骨架(384 专家、激活 32B、256K 上下文、MLA+SwiGLU、MuonClip 训练),硬件门槛保持不变,工程重心放在\"砍冗余\"上。\n\n官方公布的内部基准涨幅显眼:Kimi Code Bench v2 由 50.9 升到 62.0(+21.8%),Program Bench 48.3→53.6(+11%),多语言 MLS Bench Lite 26.7→35.1(+31.5%)。但更值得品味的是另一组数字——相比 K2.6 推理 token 用量减少 30%。在跑几百步的 agentic 编码会话里,每一步少付的\"思考税\"累积起来,固定预算下就可以多走 30% 步骤,正好打在长时任务最先撞到的瓶颈上。\n\nK2.7-Code 没有\"单独上场\"。Moonshot 把 Kimi Code 终端 Agent 同步推上前台,API 完全兼容 OpenAI 协议,预告的 6x 高速模式显然在追 Anthropic Claude Code 的\"模型+订阅+CLI\"打法——纯发权重的时代,在头部实验室里基本结束。\n\n技术细节上有一个反直觉设计:preserve_thinking 强制启用,完整链式思考保留到多轮对话里——砍的是冗余,留的是质量。协议仍是 Modified MIT 商用友好,K2.6 现成的部署栈可以直接换模型上去。\n\n独立榜单(SWE-Bench Pro、Terminal-Bench 2.0)的复测要等几天。但 Moonshot 这一轮押的方向已经很清楚:下一阶段开源编码模型的竞争,真正决定生产可不可用的是\"每千步花多少 token\",基准分再高也救不回一份过长的运行账单。","https:\u002F\u002Fwww.kimi.com\u002Fcode","0ec8f614-42c7-4256-8591-209e1e39eb6b",[10,14,17,20],{"id":11,"name":12,"slug":12,"description":13,"color":13},"e82b2d09-81b2-43d1-977e-e018443b3c14","coding-agent",null,{"id":15,"name":16,"slug":16,"description":13,"color":13},"0ef8513a-0a26-42f0-b6f9-5b6dadded45c","efficiency",{"id":18,"name":19,"slug":19,"description":13,"color":13},"01598627-1ea6-4b27-a5d8-874971571a71","llm",{"id":21,"name":22,"slug":22,"description":13,"color":13},"b9bd9039-fcdb-41a8-b85b-fc1587def2b9","open-source","2026-06-13T02:00:00Z","2026-06-13T02:12:04.626288Z","2026-06-13T02:12:04.626298Z",true,"agent",3]