[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"article-opcode-supports-deepseek-glm-qwen-gpt-models-zh":3,"article-related-opcode-supports-deepseek-glm-qwen-gpt-models-zh":32,"series-tools-4a2a7043-32f7-4520-9f3c-820d19f00b74":83},{"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":25,"views":29,"created_at":30,"published_at":31,"topic_cluster_id":11},"4a2a7043-32f7-4520-9f3c-820d19f00b74","opcode-supports-deepseek-glm-qwen-gpt-models-zh","OpenCode 支援多家模型，切換更省事","\u003Cp data-speakable=\"summary\">OpenCode 讓開發者能在 DeepSeek、GLM、\u003Ca href=\"\u002Ftag\u002Fqwen\">Qwen\u003C\u002Fa> 和 GPT 之間快速切換，少改程式，多做比較。\u003C\u002Fp>\u003Cp>這次更新的重點很直白。\u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fopencode-ai\u002Fopencode\" target=\"_blank\" rel=\"noopener\">OpenCode\u003C\u002Fa> 把多家模型放進同一個工作流裡，開發者不用每換一家就重寫 client。對常測模型的人來說，這比多一個花俏功能實用太多。\u003C\u002Fp>\u003Cp>它支援 \u003Ca href=\"https:\u002F\u002Fwww.deepseek.com\u002F\" target=\"_blank\" rel=\"noopener\">DeepSeek\u003C\u002Fa> V4 Flash、\u003Ca href=\"https:\u002F\u002Fwww.zhipuai.cn\u002F\" target=\"_blank\" rel=\"noopener\">GLM\u003C\u002Fa>-5.2、\u003Ca href=\"https:\u002F\u002Fwww.qwenlm.ai\u002F\" target=\"_blank\" rel=\"noopener\">Qwen\u003C\u002Fa>3.8 Max，還有 \u003Ca href=\"https:\u002F\u002Fopenai.com\u002F\" target=\"_blank\" rel=\"noopener\">GPT\u003C\u002Fa> 5.6 Luna。這種支援清單看起來像小事，實際上會直接影響團隊願不願意試新模型。\u003C\u002Fp>\u003Cp>因為現在大家比的不只模型分數。API 格式、工具鏈相容性、切換\u003Ca href=\"\u002Fnews\u002Fkimi-k3-gpu-api-cost-comparison-zh\">成本\u003C\u002Fa>，才是日常開發最常撞到的牆。\u003C\u002Fp>\u003Ctable>\u003Cthead>\u003Ctr>\u003Cth>模型\u003C\u002Fth>\u003Cth>API 介面\u003C\u002Fth>\u003Cth>對開發者的意義\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Ctd>DeepSeek V4 Flash\u003C\u002Ftd>\u003Ctd>OpenAI-compatible API\u003C\u002Ftd>\u003Ctd>容易接進既有 OpenAI 工具\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Qwen3.8 Max\u003C\u002Ftd>\u003Ctd>Anthropic Messages\u003C\u002Ftd>\u003Ctd>可搭配支援訊息格式的工具\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>GLM-5.2\u003C\u002Ftd>\u003Ctd>來源未明確說明\u003C\u002Ftd>\u003Ctd>仍可在 OpenCode 內統一切換\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>GPT 5.6 Luna\u003C\u002Ftd>\u003Ctd>來源未明確說明\u003C\u002Ftd>\u003Ctd>同一介面下可直接選用\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003Ch2>API 相容性決定模型能不能上線\u003C\u002Fh2>\u003Cp>模型好不好，常常不是第一關。第一關是能不能接進現有系統。你如果已經有一套 \u003Ca href=\"\u002Ftag\u002Fopenai\">OpenAI\u003C\u002Fa>-style client，再來一個要改 header、改 payload、改 streaming 邏輯的 API，心情通常不會太好。\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1786363377470-lo30.png\" alt=\"OpenCode 支援多家模型，切換更省事\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>OpenCode 的價值就在這裡。它把模型選擇變成產品層的切換，而不是工程層的重做。這對個人開發者很有感，對團隊更有感，因為團隊最怕的就是每次測新模型都要開一輪整合會議。\u003C\u002Fp>\u003Cp>這也解釋了為什麼 \u003Ca href=\"https:\u002F\u002Fdocs.anthropic.com\u002Fen\u002Fapi\u002Fmessages\" target=\"_blank\" rel=\"noopener\">Anthropic Messages\u003C\u002Fa>、OpenAI-compatible API 這些字眼會這麼重要。它們聽起來像規格細節，實際上是採用門檻。門檻低，模型才有機會進到真實專案。\u003C\u002Fp>\u003Cul>\u003Cli>OpenAI-compatible API 可直接接既有工具。\u003C\u002Fli>\u003Cli>Anthropic Messages 格式讓另一批工具更好整合。\u003C\u002Fli>\u003Cli>OpenCode 把模型切換成本壓低。\u003C\u002Fli>\u003Cli>開發者能更快比較輸出品質與速度。\u003C\u002Fli>\u003C\u002Ful>\u003Cblockquote>“The API is the product.” — \u003Ca href=\"https:\u002F\u002Ftwitter.com\u002Fandrewchen\" target=\"_blank\" rel=\"noopener\">Andrew Chen\u003C\u002Fa>\u003C\u002Fblockquote>\u003Cp>這句話很老，但還是準。很多模型看起來很強，實際上卡在接法太麻煩，最後只停在 Demo 階段。\u003C\u002Fp>\u003Cp>OpenCode 這次做的事，就是把 Demo 和日常使用之間那道牆削薄一點。這種工具不會上新聞頭條，卻很容易進\u003Ca href=\"\u002Ftag\u002F開發者工具\">開發者工具\u003C\u002Fa>箱。\u003C\u002Fp>\u003Ch2>四個模型放一起，比單看分數更有意思\u003C\u002Fh2>\u003Cp>這份支援清單的訊號很清楚。\u003Ca href=\"https:\u002F\u002Fwww.deepseek.com\u002F\" target=\"_blank\" rel=\"noopener\">DeepSeek\u003C\u002Fa>、\u003Ca href=\"https:\u002F\u002Fwww.zhipuai.cn\u002F\" target=\"_blank\" rel=\"noopener\">GLM\u003C\u002Fa>、\u003Ca href=\"https:\u002F\u002Fwww.qwenlm.ai\u002F\" target=\"_blank\" rel=\"noopener\">Qwen\u003C\u002Fa>、\u003Ca href=\"https:\u002F\u002Fopenai.com\u002F\" target=\"_blank\" rel=\"noopener\">OpenAI\u003C\u002Fa> 都能在同一個入口被比較。這代表市場競爭不再只看模型本體，還看誰的接入體驗比較順。\u003C\u002Fp>\u003Cp>對產品團隊來說，這會改變選型方式。以前可能先問哪個模型分數高，現在會先問哪個模型最容易接、成本多少、延遲能不能接受。這三件事常常比 \u003Ca href=\"\u002Ftag\u002Fbenchmark\">benchmark\u003C\u002Fa> 更早決定成敗。\u003C\u002Fp>\u003Cp>也因為這樣，工具層的角色變重了。當 \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fopencode-ai\u002Fopencode\" target=\"_blank\" rel=\"noopener\">OpenCode\u003C\u002Fa> 幫你把切換流程整理好，團隊就能把時間花在 prompt、資料、測試集，而不是一直修 SDK。\u003C\u002Fp>\u003Cul>\u003Cli>DeepSeek 適合想用 OpenAI-compatible 路線的人。\u003C\u002Fli>\u003Cli>Qwen 對偏好 Anthropic Messages 的工具更友善。\u003C\u002Fli>\u003Cli>GLM 與 GPT 也能放進同一個比較框架。\u003C\u002Fli>\u003Cli>多模型並行測試會比單模型押注更常見。\u003C\u002Fli>\u003C\u002Ful>\u003Cp>我覺得這才是重點。模型供應商一直推新版本，但真正留下來的，往往是讓你少踩坑的那個 client。\u003C\u002Fp>\u003Cp>對\u003Ca href=\"\u002Ftag\u002F台灣開發者\">台灣開發者\u003C\u002Fa>來說，這種工具特別實際。你不用先賭哪家模型會贏，只要先把切換成本壓低，就能慢慢挑。\u003C\u002Fp>\u003Ch2>為什麼這種工具會越來越重要\u003C\u002Fh2>\u003Cp>LLM 市場現在很像一個超大工具箱。每家都說自己快、便宜、準，但真正進到專案，大家還是會先看能不能接到現有流程。這也是為什麼 API 相容性會比宣傳文案更有份量。\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1786363376005-ivei.png\" alt=\"OpenCode 支援多家模型，切換更省事\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>過去團隊可能只養一條模型路線，現在更常見的是多模型並用。\u003Ca href=\"\u002Fnews\u002Fdeepseek-codex-ai-coding-costs-reset-zh\">Code\u003C\u002Fa> review 用一個，摘要用一個，客服草稿再用一個。這種拆分方式會讓工具層的價值更明顯。\u003C\u002Fp>\u003Cp>OpenCode 沒有試圖包辦一切。它做的是把選擇變簡單。這種簡單很務實，也很少被吹成神話，但開發現場就是吃這套。\u003C\u002Fp>\u003Cul>\u003Cli>模型切換越便宜，試錯就越快。\u003C\u002Fli>\u003Cli>工具鏈相容性比單次表現更重要。\u003C\u002Fli>\u003Cli>多模型工作流會變成常態。\u003C\u002Fli>\u003C\u002Ful>\u003Cp>從產業角度看，這也會壓縮供應商的護城河。當用戶能更快換家，供應商就得在品質、價格、延遲三條線一起拼。\u003C\u002Fp>\u003Cp>這對開發者是好事。你不用被單一平台綁住，也不用為了換模型就重寫一堆整合碼。\u003C\u002Fp>\u003Ch2>接下來該盯什麼\u003C\u002Fh2>\u003Cp>如果你平常就會測不同 LLM，這次更新值得直接試。先把 OpenCode 接到你現有的工作流，再比較 DeepSeek、GLM、Qwen 和 GPT 的輸出差異，會比只看宣傳頁更準。\u003C\u002Fp>\u003Cp>我會建議先看三件事：回應速度、工具相容性、以及你團隊最常用的 API 格式。只要這三項過關，新模型才有機會真的進專案。\u003C\u002Fp>\u003Cp>接下來最值得觀察的，是更多工具會不會跟進支援多家模型的統一介面。若這條路繼續走下去，開發者挑模型的方式會更像挑套件，而不是重新選平台。\u003C\u002Fp>\u003Cp>我的判斷很直接：\u003Ca href=\"\u002Fnews\u002Fai-weekly-2026-w33-zh\">2026\u003C\u002Fa> 年能留住開發者的，不是誰喊得最大聲，而是誰讓切換最省事。\u003C\u002Fp>","OpenCode 新增 DeepSeek V4 Flash、GLM-5.2、Qwen3.8 Max、GPT 5.6 Luna 支援，讓開發者更容易在不同 API 之間切換。","zhuanlan.zhihu.com","https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F2068789834517328427",null,"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1786363377470-lo30.png","tools","zh","d5325d38-a48e-43b7-b373-2ecefff808cd",[17,18,19,20,21,22,23,24],"OpenCode","DeepSeek","GLM","Qwen","GPT","LLM","API 相容性","模型切換",[26,27,28],"OpenCode 把 DeepSeek、GLM、Qwen、GPT 放進同一個切換流程。","API 相容性和工具鏈支援，已經和模型品質一樣重要。","多模型並用會更常見，開發者會更在意切換成本。",0,"2026-08-10T12:02:30.043727+00:00","2026-08-10T12:02:30.021+00:00",{"tags":33,"relatedLang":42,"relatedPosts":46},[34,36,38,40],{"name":21,"slug":35},"gpt",{"name":20,"slug":37},"qwen",{"name":18,"slug":39},"deepseek",{"name":17,"slug":41},"opencode",{"id":15,"slug":43,"title":44,"language":45},"opcode-supports-deepseek-glm-qwen-gpt-models-en","OpenCode now supports DeepSeek, GLM, Qwen, GPT","en",[47,53,59,65,71,77],{"id":48,"slug":49,"title":50,"cover_image":51,"image_url":51,"created_at":52,"category":13},"7c5f393f-e45a-45d0-98fe-28abc173ba11","baidu-wenxin-search-to-agent-template-zh","百度文心把搜索底子变成Agent能力","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1786365239713-gwqo.png","2026-08-10T12:33:19.194341+00:00",{"id":54,"slug":55,"title":56,"cover_image":57,"image_url":57,"created_at":58,"category":13},"fc9bd7b8-542f-4c91-9965-60ecb3ae417e","deepseek-codex-ai-coding-costs-reset-zh","DeepSeek 接入 Codex 後，AI 編程成本必須重算","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1786321973140-5qz2.png","2026-08-10T00:32:32.710718+00:00",{"id":60,"slug":61,"title":62,"cover_image":63,"image_url":63,"created_at":64,"category":13},"1b9f3648-fe4a-445d-bdc0-fda420e42c6b","token-ciyuan-zhongwen-fenciqi-shice-zh","用詞元實測中文分詞器","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1786320173760-2fu6.png","2026-08-10T00:02:31.714597+00:00",{"id":66,"slug":67,"title":68,"cover_image":69,"image_url":69,"created_at":70,"category":13},"0afe05ab-6675-498f-b179-531a8460290b","openai-api-pricing-august-2026-token-costs-zh","OpenAI API 單價落差到 180 美元","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1786300368036-5pzh.png","2026-08-09T18:32:26.525684+00:00",{"id":72,"slug":73,"title":74,"cover_image":75,"image_url":75,"created_at":76,"category":13},"e9622f39-b7db-4d48-bea9-84bc671402cc","usage-limits-chatgpt-enterprise-edu-controls-zh","ChatGPT Enterprise 與 Edu 的用量上限，必須是核心管理控制","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1786298570504-ubv3.png","2026-08-09T18:02:22.954575+00:00",{"id":78,"slug":79,"title":80,"cover_image":81,"image_url":81,"created_at":82,"category":13},"b13d8b40-53c1-4249-a342-b0f40834ba06","prepare-for-gemini-3-5-pro-on-launch-day-zh","Gemini 3.5 Pro 上線日準備清單","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1786276970495-cnpl.png","2026-08-09T12:02:25.680556+00:00",[84,89,94,99,104,109,114,119,124,129],{"id":85,"slug":86,"title":87,"created_at":88},"855cd52f-6fab-46cc-a7c1-42195e8a0de4","surepath-real-time-mcp-policy-controls-zh","SurePath 推出即時 MCP 政策控管","2026-03-26T07:57:40.77233+00:00",{"id":90,"slug":91,"title":92,"created_at":93},"9b19ab54-edef-4dbd-9ce4-a51e4bae4ebb","mcp-in-2026-the-ai-tool-layer-teams-use-zh","2026 年 MCP：團隊真的在用的 AI 工具層","2026-03-26T08:01:46.589694+00:00",{"id":95,"slug":96,"title":97,"created_at":98},"af9c46c3-7a28-410b-9f04-32b3de30a68c","prompting-in-2026-what-actually-works-zh","2026 提示工程，真正有用的是什麼","2026-03-26T08:08:12.453028+00:00",{"id":100,"slug":101,"title":102,"created_at":103},"05553086-6ed0-4758-81fd-6cab24b575e0","garry-tan-open-sources-claude-code-toolkit-zh","Garry Tan 開源 Claude Code 工具包","2026-03-26T08:26:20.068737+00:00",{"id":105,"slug":106,"title":107,"created_at":108},"042a73a2-18a2-433d-9e8f-9802b9559aac","github-ai-projects-to-watch-in-2026-zh","2026 必看 20 個 GitHub AI 專案","2026-03-26T08:28:09.619964+00:00",{"id":110,"slug":111,"title":112,"created_at":113},"a5f94120-ac0d-4483-9a8b-63590071ac6a","claude-code-vs-cursor-2026-zh","Claude Code 與 Cursor 深度對比：202…","2026-03-26T13:27:14.279193+00:00",{"id":115,"slug":116,"title":117,"created_at":118},"0975afa1-e0c7-4130-a20d-d890eaed995e","practical-github-guide-learning-ml-2026-zh","2026 機器學習入門 GitHub 實用指南","2026-03-27T01:16:49.712576+00:00",{"id":120,"slug":121,"title":122,"created_at":123},"bfdb467a-290f-4a80-b3a9-6f081afb6dff","aiml-2026-student-ai-ml-lab-repo-review-zh","AIML-2026：像課綱的學生實驗 Repo","2026-03-27T01:21:51.467798+00:00",{"id":125,"slug":126,"title":127,"created_at":128},"80cabc3e-09fc-4ff5-8f07-b8d68f5ae545","ai-trending-github-repos-and-research-feeds-zh","AI Trending：把 AI 資源收成一張表","2026-03-27T01:31:35.262183+00:00",{"id":130,"slug":131,"title":132,"created_at":133},"3ce6e6e2-bac5-463e-9f8d-45caabcc61f7","awesome-ai-for-science-research-tools-map-zh","AI 科研工具清單，開始像地圖了","2026-03-27T01:46:50.521945+00:00"]