[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"article-ai-models-2026-which-one-to-use-zh":3,"tags-ai-models-2026-which-one-to-use-zh":36,"related-lang-ai-models-2026-which-one-to-use-zh":41,"related-posts-ai-models-2026-which-one-to-use-zh":45,"series-model-release-9416ba34-e6b5-4ff0-9eeb-ea16f70e769b":82},{"id":4,"title":5,"content":6,"summary":7,"source":8,"source_url":9,"author":10,"image_url":11,"keywords":12,"language":24,"translated_content":10,"views":25,"is_premium":26,"created_at":27,"updated_at":27,"cover_image":11,"published_at":28,"rewrite_status":29,"rewrite_error":10,"rewritten_from_id":30,"slug":31,"category":32,"related_article_id":33,"status":34,"google_indexed_at":35,"x_posted_at":10,"tweet_text":10,"title_rewritten_at":10,"title_original":10,"key_takeaways":10,"topic_cluster_id":10,"embedding":10,"is_canonical_seed":26},"9416ba34-e6b5-4ff0-9eeb-ea16f70e769b","2026 AI 模型怎麼選","\u003Cp data-speakable=\"summary\">2026 年選 AI \u003Ca href=\"\u002Fnews\u002Fhycop-modular-interpretable-pde-surrogates-zh\">模型\u003C\u002Fa>要看任務。Gemini 3.1 Pro 偏推理，\u003Ca href=\"\u002Ftag\u002Fclaude\">Claude\u003C\u002Fa> 寫作最穩，Grok 在部分 coding 測試領先。\u003C\u002Fp>\u003Cp>說真的，2026 的 AI 模型選擇很像選工具。沒有一個模型包辦全部。\u003Ca href=\"https:\u002F\u002Fopenai.com\u002F\" target=\"_blank\" rel=\"noopener\">OpenAI\u003C\u002Fa>、\u003Ca href=\"https:\u002F\u002Fwww.anthropic.com\u002F\" target=\"_blank\" rel=\"noopener\">Anthropic\u003C\u002Fa>、\u003Ca href=\"https:\u002F\u002Fdeepmind.google\u002F\" target=\"_blank\" rel=\"noopener\">Google DeepMind\u003C\u002Fa>、\u003Ca href=\"https:\u002F\u002Fx.ai\u002F\" target=\"_blank\" rel=\"noopener\">xAI\u003C\u002Fa> 都有各自拿手的地方。\u003C\u002Fp>\u003Cp>這篇要講的很直接。你如果在挑 \u003Ca href=\"https:\u002F\u002Fplatform.openai.com\u002Fdocs\" target=\"_blank\" rel=\"noopener\">GPT\u003C\u002Fa>、\u003Ca href=\"https:\u002F\u002Fdocs.anthropic.com\u002F\" target=\"_blank\" rel=\"noopener\">Claude\u003C\u002Fa>、\u003Ca href=\"https:\u002F\u002Fai.google.dev\u002F\" target=\"_blank\" rel=\"noopener\">Gemini\u003C\u002Fa>、Grok，重點不是誰最強。重點是誰最適合你的工作流。下面這些數字，差距其實蠻明顯。\u003C\u002Fp>\u003Ctable>\u003Cthead>\u003Ctr>\u003Cth>模型\u003C\u002Fth>\u003Cth>Coding\u003C\u002Fth>\u003Cth>Reasoning\u003C\u002Fth>\u003Cth>Writing\u003C\u002Fth>\u003Cth>每 1M tokens API 價格\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Ctd>GPT-5.4\u003C\u002Ftd>\u003Ctd>74.9% SWE-bench\u003C\u002Ftd>\u003Ctd>92.8% GPQA\u003C\u002Ftd>\u003Ctd>Canvas 編輯\u003C\u002Ftd>\u003Ctd>$2.50 \u002F $15\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Claude Opus 4.6\u003C\u002Ftd>\u003Ctd>74%+ SWE-bench\u003C\u002Ftd>\u003Ctd>91.3% GPQA\u003C\u002Ftd>\u003Ctd>128K 輸出，文筆自然\u003C\u002Ftd>\u003Ctd>$15 \u002F $75\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Gemini 3.1 Pro\u003C\u002Ftd>\u003Ctd>63.8% SWE-bench\u003C\u002Ftd>\u003Ctd>94.3% GPQA\u003C\u002Ftd>\u003Ctd>Docs 整合\u003C\u002Ftd>\u003Ctd>$2 \u002F $12\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Grok 4\u003C\u002Ftd>\u003Ctd>75% SWE-bench\u003C\u002Ftd>\u003Ctd>表現有競爭力\u003C\u002Ftd>\u003Ctd>風格較不受限\u003C\u002Ftd>\u003Ctd>$2 \u002F $15\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003Ch2>2026 的重點是分工，不是通吃\u003C\u002Fh2>\u003Cp>以前很多人買模型，想找一個萬用解。現在這套思路開始失靈。模型能力拉開後，最佳選擇會跟任務綁死。你寫程式、做研究、寫文件、跑客服，答案都可能不同。\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1777878654011-1rbu.png\" alt=\"2026 AI 模型怎麼選\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>這件事對開發者很重要。因為你不是只在選一個聊天機器人。你是在選 API、延遲、價格、上下文長度，還有整個產品體驗。模型分工越細，團隊越不能只看排行榜第一名。\u003C\u002Fp>\u003Cp>從資料看，coding、reasoning、寫作這三條線已經分開了。Grok 4 在 \u003Ca href=\"\u002Ftag\u002Fswe-bench\">SWE-bench\u003C\u002Fa> 看到 75%。GPT-5.4 是 74.9%。Claude Opus 4.6 也有 74%+。這三個數字很接近，代表實作細節會放大差異。\u003C\u002Fp>\u003Cul>\u003Cli>Grok 4：SWE-bench 75%\u003C\u002Fli>\u003Cli>GPT-5.4：SWE-bench 74.9%\u003C\u002Fli>\u003Cli>Claude Opus 4.6：SWE-bench 74%+\u003C\u002Fli>\u003Cli>Gemini 3.1 Pro：GPQA 94.3%\u003C\u002Fli>\u003C\u002Ful>\u003Ch2>Claude 為什麼常被拿來寫文件\u003C\u002Fh2>\u003Cp>如果你的工作是長文件、提案、報告、產品規格，Claude 很常是第一個值得試的模型。原因很簡單。它的文字比較順，段落結構也比較穩。講白了，就是比較像人寫的，不容易一段一段散掉。\u003C\u002Fp>\u003Cp>Claude Opus 4.6 的 128K 輸出也很實用。這代表它能一次處理更長的內容。對團隊來說，這種能力會直接影響編輯成本。少一次重寫，就少一次人力浪費。\u003C\u002Fp>\u003Cp>\u003Ca href=\"\u002Ftag\u002Fanthropic\">Anthropic\u003C\u002Fa> 也不是只靠模型分數吃飯。它已經深度進入開發者工具圈。像 \u003Ca href=\"https:\u002F\u002Fwww.cursor.com\u002F\" target=\"_blank\" rel=\"noopener\">Cursor\u003C\u002Fa>、\u003Ca href=\"https:\u002F\u002Fwindsurf.com\u002F\" target=\"_blank\" rel=\"noopener\">Windsurf\u003C\u002Fa> 都跟 Claude 的使用情境很貼近。模型好不好是一回事，工具順不順又是另一回事。\u003C\u002Fp>\u003Cblockquote>“Claude is the best model for writing and coding assistants.” — Andrew Ng\u003C\u002Fblockquote>\u003Ch2>推理能力這條線，Gemini 很強\u003C\u002Fh2>\u003Cp>如果你在做數學、研究、分析、資料整理，Gemini 3.1 Pro 很值得看。它在 GPQA 拿到 94.3%。這個數字比 GPT-5.4 的 92.8% 還高，也比 Claude Opus 4.6 的 91.3% 高一截。\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1777878651962-joe5.png\" alt=\"2026 AI 模型怎麼選\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>這種差距在日常聊天\u003Ca href=\"\u002Fnews\u002Fllms-procedural-execution-diagnostic-study-zh\">不一定\u003C\u002Fa>看得出來。可是在需要多步推理的場景，差 1% 到 3% 就可能影響答案品質。尤其是你把模型接進內部知識庫、研究助理、文件摘要流程時，穩定性比嘴快重要。\u003C\u002Fp>\u003Cp>Gemini 3.1 Pro 的另一個優勢是價格。表格裡它是最便宜的那個，輸入 $2、輸出 $12，都是每 1M tokens。對要大量跑資料的團隊來說，這種差異會直接反映在帳單上。\u003C\u002Fp>\u003Cul>\u003Cli>Gemini 3.1 Pro：GPQA 94.3%\u003C\u002Fli>\u003Cli>GPT-5.4：GPQA 92.8%\u003C\u002Fli>\u003Cli>Claude Opus 4.6：GPQA 91.3%\u003C\u002Fli>\u003Cli>Gemini 3.1 Pro：$2 \u002F $12\u003C\u002Fli>\u003C\u002Ful>\u003Ch2>寫作、coding、推理，三者差很多\u003C\u002Fh2>\u003Cp>很多人會把模型當成同一種東西。其實不是。寫作看的是語氣、結構、長文一致性。coding 看的是修 bug、理解 repo、補測試。推理看的是多步思考和錯誤控制。這三件事根本不是同一個考題。\u003C\u002Fp>\u003Cp>所以你看 benchmark 時，不能只盯一個分數。Grok 4 在 SWE-bench 領先，GPT-5.4 在推理和整體平衡上很強，Claude 則在長文和自然語氣上更穩。每個模型都像有自己的主場。\u003C\u002Fp>\u003Cp>如果你是產品經理或技術主管，最好先問自己三件事。你的任務是產文、寫 code，還是做分析。你的資料是不是很長。你的成本能不能撐住高用量。這三題比「哪個模型最強」更有用。\u003C\u002Fp>\u003Cul>\u003Cli>寫作：Claude 通常最穩\u003C\u002Fli>\u003Cli>推理：Gemini 3.1 Pro 很突出\u003C\u002Fli>\u003Cli>coding：Grok 4 和 GPT-5.4 很接近\u003C\u002Fli>\u003Cli>成本：Gemini 3.1 Pro 最便宜\u003C\u002Fli>\u003C\u002Ful>\u003Ch2>企業買單時，別只看聊天畫面\u003C\u002Fh2>\u003Cp>企業場景最常踩雷的地方，是把模型和系統混為一談。客服機器人、內部知識助理、銷售輔助工具，真正決定效果的，常常不是模型本體，而是檢索、路由、權限和人工接手。\u003C\u002Fp>\u003Cp>講白了，模型只是大腦的一部分。你還要有資料來源、上下文管理、錯誤回復機制。沒有這些，換再強的 L\u003Ca href=\"\u002Fnews\u002Fpersistent-visual-memory-lvml-visual-drift-zh\">LM\u003C\u002Fa> 也只是換一個比較會講話的前端。\u003C\u002Fp>\u003Cp>這也是為什麼很多 SaaS 公司在做 AI 功能時，會把重點放在工作流。模型負責生成，系統負責控管。這種架構才有機會把 AI 真正接進日常營運。\u003C\u002Fp>\u003Cul>\u003Cli>檢索比單純聊天更重要\u003C\u002Fli>\u003Cli>路由決定答案是否對題\u003C\u002Fli>\u003Cli>人工接手仍然必要\u003C\u002Fli>\u003Cli>成本要看整體流程，不只看 token 單價\u003C\u002Fli>\u003C\u002Ful>\u003Ch2>如果是我，我會這樣選\u003C\u002Fh2>\u003Cp>如果只想先挑一個通用模型，我會先看 GPT-5.4。理由很現實。它的生態系最大，文件多，工具多，整合也最方便。對多數團隊來說，這種省事很值錢。\u003C\u002Fp>\u003Cp>如果是寫作導向，我會先試 Claude。你要寫長文、提案、產品說明、內部文件，它通常比較不會讓你改到懷疑人生。如果是研究、分析、數學題，我會先試 Gemini 3.1 Pro。\u003C\u002Fp>\u003Cp>如果是 coding，我會看你用哪個編輯器。因為 \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fanthropics\u002Fclaude-code\" target=\"_blank\" rel=\"noopener\">Claude Code\u003C\u002Fa>、\u003Ca href=\"\u002Ftag\u002Fcursor\">Cursor\u003C\u002Fa>、\u003Ca href=\"\u002Ftag\u002Fwindsurf\">Windsurf\u003C\u002Fa> 這些工具，會直接影響你實際感受到的速度。模型分數很重要，但工作流更重要。真的。\u003C\u002Fp>\u003Ch2>這個市場接下來會怎麼走\u003C\u002Fh2>\u003Cp>我覺得 2026 之後，模型市場會更像資料庫或雲端服務。大家不會只問誰最大。大家會問，誰最適合這個工作。這種分化會讓產品設計更細，也會讓採購更務實。\u003C\u002Fp>\u003Cp>對台灣開發者來說，最實際的做法不是追每一次發表會。是把你的任務拆開。先看寫作、推理、coding、客服四種情境，再各自做測試。你會很快發現，最貴的不一定最好，最強的也不一定最省事。\u003C\u002Fp>\u003Cp>如果你現在就在選模型，我的建議很簡單。先用一週做 A\u002FB 測試。再看準確率、人工修改時間、每月 token 成本。最後才決定要不要換。別被排行榜帶著走，因為你的產品不是排行榜。\u003C\u002Fp>","2026 年選 AI 模型要看任務。Gemini 3.1 Pro 偏推理，Claude 寫作最穩，Grok 在部分 coding 測試領先。","gurusup.com","https:\u002F\u002Fgurusup.com\u002Fblog\u002Fai-comparisons",null,"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1777878654011-1rbu.png",[13,14,15,16,17,18,19,20,21,22,23],"AI 模型","GPT-5.4","Claude Opus 4.6","Gemini 3.1 Pro","Grok 4","LLM 選擇","API 成本","SWE-bench","GPQA","AI 寫作","AI coding","zh",2,false,"2026-05-04T07:10:32.636088+00:00","2026-05-04T07:10:32.402+00:00","done","b339b110-086c-4311-b230-794c2ee1ac75","ai-models-2026-which-one-to-use-zh","model-release","a0374fea-8855-45af-a854-5c3449ab50e6","published","2026-05-04T09:00:13.41+00:00",[37,39],{"name":17,"slug":38},"grok-4",{"name":13,"slug":40},"ai-模型",{"id":33,"slug":42,"title":43,"language":44},"ai-models-2026-which-one-to-use-en","AI Models in 2026: Which One to Use","en",[46,52,58,64,70,76],{"id":47,"slug":48,"title":49,"cover_image":50,"image_url":50,"created_at":51,"category":32},"5b5fa24f-5259-4e9e-8270-b08b6805f281","minimax-m1-open-hybrid-attention-reasoning-model-zh","MiniMax-M1：開源 1M Token 推理模型","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1778797859209-ea1g.png","2026-05-14T22:30:38.636592+00:00",{"id":53,"slug":54,"title":55,"cover_image":56,"image_url":56,"created_at":57,"category":32},"b1da56ac-8019-4c6b-a8dc-22e6e22b1cb5","gemini-omni-video-review-text-rendering-zh","Gemini Omni 影片模型怎麼了","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1778779280109-lrrk.png","2026-05-14T17:20:42.608312+00:00",{"id":59,"slug":60,"title":61,"cover_image":62,"image_url":62,"created_at":63,"category":32},"d63e9d93-e613-4bbf-8135-9599fde11d08","why-xiaomi-mimo-v25-pro-changes-coding-agents-zh","為什麼 Xiaomi 的 MiMo-V2.5-Pro 改變的是 Coding …","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1778689858139-v38e.png","2026-05-13T16:30:27.893951+00:00",{"id":65,"slug":66,"title":67,"cover_image":68,"image_url":68,"created_at":69,"category":32},"8f0c9185-52f9-46f2-82c6-5baec126ba2e","openai-realtime-audio-models-live-voice-zh","OpenAI 即時音訊模型瞄準語音互動","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1778451657895-2iu7.png","2026-05-10T22:20:32.443798+00:00",{"id":71,"slug":72,"title":73,"cover_image":74,"image_url":74,"created_at":75,"category":32},"52106dc2-4eba-4ca0-8318-fa646064de97","anthropic-10-finance-ai-agents-zh","Anthropic推10款金融AI 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