[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"tag-ai-benchmarks":3},{"tag":4,"articles":11},{"id":5,"name":6,"slug":7,"article_count":8,"description_zh":9,"description_en":10},"bc621db1-f621-43ab-9821-f832ef6ceff5","AI benchmarks","ai-benchmarks",3,"AI 基準測試用來比較模型在推理、知識問答、程式能力與長上下文等面向的表現，像 ARC-AGI-2、GPQA、MMLU 這類分數常被拿來判斷新模型是否真的進步，也能看出各家在成本與能力之間的取捨。","AI benchmarks measure how models perform on reasoning, knowledge QA, coding, and long-context tasks. Scores from tests like ARC-AGI-2, GPQA, and MMLU help compare new releases, track real progress, and expose trade-offs between capability, cost, and reliability.",[12],{"id":13,"slug":14,"title":15,"summary":16,"category":17,"image_url":18,"cover_image":18,"language":19,"created_at":20},"7c188c00-8556-4f77-8a36-ac458322ad19","llm-stats-ai-benchmarks-compare-zh","5 個最值得先看的 AI 基準","300+ 個 AI 基準集中比較，先看 5 項就能判斷模型在推理、寫碼、視覺與工具呼叫上的實力。","industry","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780973269412-nyhe.png","zh","2026-06-09T02:47:22.6013+00:00"]