[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"article-glm-5-2-open-source-1m-context-long-tasks-zh":3,"article-related-glm-5-2-open-source-1m-context-long-tasks-zh":31,"series-model-release-8d0595e5-788b-417c-a309-15d00e4558b8":75},{"id":4,"slug":5,"title":6,"content":7,"summary":8,"source":9,"source_url":10,"author":11,"image_url":12,"cover_image":12,"category":13,"language":14,"translated_content":11,"related_article_id":15,"keywords":16,"key_takeaways":23,"views":27,"created_at":28,"published_at":29,"topic_cluster_id":30},"8d0595e5-788b-417c-a309-15d00e4558b8","glm-5-2-open-source-1m-context-long-tasks-zh","GLM-5.2 開源：1M 上下文上線","\u003Cp data-speakable=\"summary\">智譜開源 GLM-5.2，主打 1M 上下文、Coding 與長程任務。\u003C\u002Fp>\u003Cp>智譜今天正式推出並開源 \u003Ca href=\"https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F2050513163658044507\" target=\"_blank\" rel=\"noopener\">GLM-5.2\u003C\u002Fa>，把重點放在 1M 上下文、\u003Ca href=\"\u002Fnews\u002Fai-code-review-rollout-with-human-oversight-zh\">程式\u003C\u002Fa>開發與長程任務。官方同時宣布，模型已開放 API、\u003Ca href=\"https:\u002F\u002Fhuggingface.co\u002F\" target=\"_blank\" rel=\"noopener\">Hugging Face\u003C\u002Fa>、\u003Ca href=\"https:\u002F\u002Fmodelscope.cn\u002F\" target=\"_blank\" rel=\"noopener\">ModelScope\u003C\u002Fa> 與開發工具接入。\u003C\u002Fp>\u003Cp>這次更新不只是把上下文拉長，還把訓練、推理與部署一起往工程可用性靠攏。智譜稱，GLM-5.2 在全球百萬用戶參與的 Code Arena 前端開發評估中拿到「全球可用模型第一」。\u003C\u002Fp>\u003Ctable>\u003Cthead>\u003Ctr>\u003Cth>項目\u003C\u002Fth>\u003Cth>數值\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Ctd>上下文窗口\u003C\u002Ftd>\u003Ctd>1M\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Code Arena 排名\u003C\u002Ftd>\u003Ctd>全球可用模型第一\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>FrontierSWE\u003C\u002Ftd>\u003Ctd>比 Opus 4.8 低 1%\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>SWE-Marathon\u003C\u002Ftd>\u003Ctd>比 Opus 4.8 低 13%\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Terminal-Bench 2.1\u003C\u002Ftd>\u003Ctd>比 Opus 4.8 低 4%\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>MCP-Atlas\u003C\u002Ftd>\u003Ctd>比 Opus 4.8 低 0.8%\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>一次長程交付處理量\u003C\u002Ftd>\u003Ctd>88萬 tokens\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>FLOPs 優化\u003C\u002Ftd>\u003Ctd>單位 token 降至 2.9 倍\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003Ch2>發生了什麼\u003C\u002Fh2>\u003Cp>GLM-5.2 的核心變化，是把 1M 上下文做成可用能力，而不是只停在規格數字。智譜表示，它先擴展了 1M Coding \u003Ca href=\"\u002Ftag\u002Fagent\">Agent\u003C\u002Fa> 訓練環境，再把自動化研究、效能優化等任務納入訓練，目標是減少\u003Ca href=\"\u002Ftag\u002F長上下文\">長上下文\u003C\u002Fa>在數十萬 \u003Ca href=\"\u002Ftag\u002Ftoken\">token\u003C\u002Fa> 後常見的退化。\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1782065872389-e3e7.png\" alt=\"GLM-5.2 開源：1M 上下文上線\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>在能力面，這版模型主打三個方向：更穩的 Coding 體感、更長的任務續航，以及更容易落地的工程整合。官方給出的結果顯示，它在前端、後端與長程任務上的成功率都較 GLM-5.1 提升，並在多個主流編程基準上維持\u003Ca href=\"\u002Ftag\u002F開源模型\">開源模型\u003C\u002Fa>中的前列位置。\u003C\u002Fp>\u003Cp>技術棧上，GLM-5.2 也補齊了部署端。它已適配 \u003Ca href=\"https:\u002F\u002Fdocs.vllm.ai\u002F\" target=\"_blank\" rel=\"noopener\">vLLM\u003C\u002Fa>、\u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fsgl-project\u002Fsglang\" target=\"_blank\" rel=\"noopener\">SGLang\u003C\u002Fa> 與 transformers，並提供 MIT License，允許下載、私有部署與商用。對企業來說，這代表從拿到模型到接進現有推理服務，路徑更短。\u003C\u002Fp>\u003Cul>\u003Cli>1M 無損上下文，面向長鏈路任務\u003C\u002Fli>\u003Cli>支援 effort level，平衡能力、速度和成本\u003C\u002Fli>\u003Cli>已適配 vLLM、SGLang、transformers\u003C\u002Fli>\u003Cli>MIT License，允許下載、部署和商用\u003C\u002Fli>\u003C\u002Ful>\u003Cp>另外，智譜還引入 IndexShare、改進 MTP 投機解碼，並用自研 Slime 框架支撐 Agentic RL 與 OPD 訓練。官方說法是，這些改動一起壓低長上下文推理成本，同時提高接受長度與訓練效率。\u003C\u002Fp>\u003Ch2>為什麼重要\u003C\u002Fh2>\u003Cp>對開發者來說，GLM-5.2 的重點不是單純「更會寫程式」，而是能把一次任務拉長到完整工程週期。官方舉例稱，它曾在一次長程任務中處理 88 萬 tokens，完成開發、聯調、測試到上線，涵蓋 Web、移動端與小程式，這類工作過去通常要多人協作數週。\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1782065868349-rpg9.png\" alt=\"GLM-5.2 開源：1M 上下文上線\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>這會\u003Ca href=\"\u002Fnews\u002Fcudf-turns-pandas-code-into-gpu-runs-zh\">直接\u003C\u002Fa>影響團隊怎麼切任務。當模型能吃下更長的程式庫、更多歷史紀錄與更多測試輸出，開發者就能少做上下文裁切，少來回餵資料，也更容易把重構、除錯、文件更新放進同一條工作流。\u003C\u002Fp>\u003Cp>對產業面來看，GLM-5.2 釋放的是開源模型、國產算力與 API 商用同步推進的\u003Ca href=\"\u002Fnews\u002Fricoh-weaviate-ai-ready-enterprise-data-zh\">訊號\u003C\u002Fa>。它已在華為昇騰、平頭哥、摩爾線程、寒武紀、昆侖芯、沐曦、海光、壁仞等平台完成推理適配，意味著企業可以更直接把模型放進國產晶片集群與既有推理棧。\u003C\u002Fp>\u003Cp>智譜也把這版模型接入 GLM Coding Plan、AutoClaw 與 ZCode，指向的已不是單一編碼助手，而是覆蓋設計、法務、重構與辦公流程的長程智能體工具鏈。問題不再只是模型會不會寫，而是它能不能穩定接手一段跨天、跨週的工作流。\u003C\u002Fp>\u003Cp>如果 1M 上下文真的能在真實工程裡穩定運作，下一個被改寫的可能不是 \u003Ca href=\"\u002Ftag\u002Fbenchmark\">benchmark\u003C\u002Fa>，而是團隊分工本身。\u003C\u002Fp>","智譜開源 GLM-5.2，主打 1M 上下文、Coding 與長程任務，並同步開放 API、Hugging Face、ModelScope 與多種推理框架接入。","zhuanlan.zhihu.com","https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F2050513163658044507",null,"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1782065872389-e3e7.png","model-release","zh","54271426-cb3c-4580-b96d-04a260cae6a0",[17,18,19,20,21,22],"GLM-5.2","智譜","1M上下文","開源模型","長程任務","Coding",[24,25,26],"GLM-5.2 以 1M 上下文和 Coding\u002F長程任務為主軸，並同步開放多個部署與分發管道。","官方把重點放在工程可用性：長上下文退化、推理成本與部署適配都被一起處理。","對開發團隊來說，這類模型的價值在於接管更長工作流，而不是只生成單段程式碼。",1,"2026-06-21T18:17:26.052006+00:00","2026-06-21T18:17:26.045+00:00","0ccb5d2e-69f1-4354-a3e0-cb370221cd95",{"tags":32,"relatedLang":34,"relatedPosts":38},[33],{"name":20,"slug":20},{"id":15,"slug":35,"title":36,"language":37},"glm-5-2-open-source-1m-context-long-tasks-en","GLM-5.2开源：1M上下文冲刺长程任务","en",[39,45,51,57,63,69],{"id":40,"slug":41,"title":42,"cover_image":43,"image_url":43,"created_at":44,"category":13},"e64f73a5-2832-464c-a70b-13de61872630","apple-intelligence-ai-everyday-experiences-zh","Apple 把 AI 直接塞進日常 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