[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-slug-github-copilot-kimi-k2-7":3,"news-related-fa8a0c53-dd89-4870-8e6e-978ce038a919":31},{"id":4,"title":5,"summary":6,"content":6,"original_url":7,"source_id":8,"tags":9,"translations":23,"news_slug":24,"published_at":25,"created_at":26,"modified_at":27,"is_published":28,"publish_type":29,"image_url":13,"view_count":30},"fa8a0c53-dd89-4870-8e6e-978ce038a919","GitHub Copilot 上线 Kimi K2.7：开源权重模型首次进入默认模型选择器","7 月 1 日，GitHub 把 Moonshot AI 的 Kimi K2.7 Code 推上 Copilot 模型选择器的 GA 通道。这是 Copilot 史上第一次出现 open-weight 选项，长期由 OpenAI、Anthropic、Google、Microsoft 自家模型垄断的格局被打破。\n\n更值得关注的是部署方式：模型托管在 Microsoft Azure 上，由 GitHub 计费并对接企业合规。这意味着 Microsoft 不仅愿意把中国厂商的模型跑在自家云上，还要替它接 enterprise SLA 和数据治理——典型的「基础设施中立、模型层开放」打法。\n\nK2.7 Code 的产品定位很清晰：以「低成本选项」切入，优先对 Pro\u002FPro+\u002FMax 个人档开放，Business\u002FEnterprise 默认关闭，需管理员在策略里手动打开。这与 GitHub 早期对 Llama 类模型的 BYOM（Bring Your Own Model）路线不同——这次是 GitHub 自己提供一键可用的体验，开源权重正式进入 IDE 的「零摩擦」阶段。\n\n模型能力侧，K2.7 Code 是 K2 系列的代码分支：K2.6 四月底拿下 AI 编程挑战赛冠军，K2.7 又把「过度思考」砍掉三成，长程任务更经济。这种「跑分+成本」双指标，已经让开源权重模型具备进入头部 IDE 的入门票。\n\n开源权重模型首次出现在 GitHub 旗舰产品的默认列表里，意味着头部 IDE 的模型层从「封闭俱乐部」走向「分层服务」：底层由闭源旗舰占领高价值推理任务，开源权重承担成本敏感和合规优先的场景。下一步值得观察的是，Microsoft 是否会把同类部署开放给 GLM、DeepSeek 等其他中国头部模型，以及开源权重 IDE 模型是否会反推 Anthropic\u002FOpenAI 的价格曲线。","https:\u002F\u002Fgithub.blog\u002Fchangelog\u002F2026-07-01-kimi-k2-7-is-now-available-in-github-copilot\u002F","998df6db-96e6-4b8e-8be1-cfa00a6cd177",[10,14,17,20],{"id":11,"name":12,"slug":12,"description":13,"color":13},"a8002d98-9df1-4ab9-94d4-a7625af634c4","china-ai",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},"01598627-1ea6-4b27-a5d8-874971571a71","llm",{"id":21,"name":22,"slug":22,"description":13,"color":13},"b9bd9039-fcdb-41a8-b85b-fc1587def2b9","open-source",[],"github-copilot-kimi-k2-7","2026-07-03T06:00:00Z","2026-07-02T22:06:53.149774Z","2026-08-19T02:08:40.142862Z",true,"agent",95,{"items":32},[33,38,43,48,53,58],{"id":34,"title":35,"news_slug":36,"published_at":37},"49d0aaf8-6fcf-4bf9-83fb-19a016ae2784","CompactionRL:把上下文压缩塞进 RL 循环,GLM-5.2 训练管线吃下 5–7pp 编码代理增益","compactionrl-glm-5-2","2026-07-10T12:08:00+00:00",{"id":39,"title":40,"news_slug":41,"published_at":42},"61222f5d-b7e6-4200-8517-3b1972040d24","小米开源 MiMo Code：把 Coding Agent 拆成「计算-记忆-演化」三段式，长程编程首次跑通","xiaomi-mimo-code-compute-memory-evolution","2026-06-11T04:00:00+00:00",{"id":44,"title":45,"news_slug":46,"published_at":47},"fc0cd2d4-cc5f-4f97-b91f-08719e41e8ec","Qwen3.6-27B：27B密集模型超越397B MoE，单卡部署的编程新选择","qwen-3-6-27b-dense-beats-397b-moe-coding-77pct","2026-04-24T03:30:00+00:00",{"id":49,"title":50,"news_slug":51,"published_at":52},"d4d40e4b-04e9-45cf-a04c-792aca45b152","Kimi K2.6开源发布：万亿参数MoE模型的长时编程与Agent Swarm突破","kimi-k2-6-trillion-moe-1t-32b-active-256k","2026-04-24T03:00:00+00:00",{"id":54,"title":55,"news_slug":56,"published_at":57},"f6e4aab0-7693-4c2c-bb66-c1641fc2cc3e","Ox Alpha 谜底揭晓:智谱 GLM-5.3-Flash,MIT 开源 320B MoE","ox-alpha-glm-5-3-flash-reveal","2026-08-27T13:30:00+00:00",{"id":59,"title":60,"news_slug":61,"published_at":62},"804ab59a-a8d6-4b61-bf74-8f6f2bdae83c","智谱把 Flash 做成一件正经事:一次说清 GLM-5.3-Flash 的架构和 benchmark 真相","glm-5-3-flash-hybrid-attention-architecture","2026-08-27T08:00:00+00:00"]