人工智能模型能力比較

機器學習工程

Only models released within the past year are shown. Only benchmarks covering at least 3 of these models are shown. Unknown release dates are excluded; historical data is retained.

— means no published score for this candidate in this source, not zero.

編號模型10/11 recent-year models have scores
1AnthropicClaude Opus 5.531.2xhigh · claude_codeclaude-opus-5-5_xhigh
2OpenAIGPT-6 Astra42.22xhigh · codex_cligpt-6-astra_xhigh
3OpenAIGPT-6.1 Sol38.25xhigh · codex_cligpt-6.1-sol_xhigh
4AnthropicClaude Fable 5.125.97xhigh · claude_codeclaude-fable-5-1_xhigh
5Google DeepMindGemini 4 Argon20.22high · gemini_cligemini-4-argon_high
6AnthropicClaude Sonnet 5.519.94xhigh · claude_codeclaude-sonnet-5-5_xhigh
7AnthropicClaude Opus 519.01xhigh · claude_codeclaude-opus-5_xhigh
8OpenAIGPT-6 Sol19.73xhigh · codex_cligpt-6-sol_xhigh
9OpenAIGPT-6 Luna7.87xhigh · codex_cligpt-6-luna_xhigh
10AnthropicClaude Haiku 5.511.7xhigh · claude_codeclaude-haiku-5-5_xhigh
11AnthropicClaude Mythos Preview—

Epoch AI 基準測試

來源: Epoch AI, CC BY 4.0 ↗ · 更新 2026-10-15

WeirdML v3 · 更新 2026-10-12

PandaNpc 更新 2026年10月21日 06:01 [UTC]

來源: Epoch AI, CC BY 4.0 · Vals.ai · Artificial Analysis (artificialanalysis.ai)

欄位說明

編號
僅供顯示排序。排序會改變此數字;此並非已發佈的能力排名。
模型
受評估的模型名稱及其供應商圖示。分數欄中的破折號表示此處沒有已發佈的分數,而非零分。
WeirdML v3非傳統機器學習 v3
測試在不熟悉且回饋有限的任務上進行機器學習與資料分析。其綜合進度分數並非一般的分類準確度。 評估方法 ↗

請在同一基準、來源及版本內比較分數。數值越高代表在該報告指標上的表現越強,但不同欄位並不共用同一尺度。