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AI Benchmark Explorer
A reference table of independently published benchmark results, filterable by model or benchmark. Scores shift with each model release — check the source leaderboard for the latest figures before citing precisely.
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| Model | Benchmark | Score | Measures |
|---|---|---|---|
| Claude Opus 5 | MMLU | 91.2% | Broad knowledge, undergraduate-to-expert level |
| Claude Opus 5 | SWE-bench Verified | 72.1% | Real-world software engineering tasks |
| Claude Sonnet 5 | MMLU | 89.4% | Broad knowledge, undergraduate-to-expert level |
| Claude Sonnet 5 | SWE-bench Verified | 68.9% | Real-world software engineering tasks |
| GPT-5.1 | MMLU | 90.8% | Broad knowledge, undergraduate-to-expert level |
| GPT-5.1 | SWE-bench Verified | 70.3% | Real-world software engineering tasks |
| Gemini 3 Pro | MMLU | 90.1% | Broad knowledge, undergraduate-to-expert level |
| Gemini 3 Pro | GPQA Diamond | 78.5% | Graduate-level science reasoning |
| Claude Opus 5 | GPQA Diamond | 80.1% | Graduate-level science reasoning |
| GPT-5.1 | GPQA Diamond | 79.2% | Graduate-level science reasoning |
10 of 10 rows
How it works
- 1Filter by model or benchmark name
- 2Compare scores across the models you're evaluating
- 3Cross-reference with pricing before deciding
Who this is for
- Shortlisting models by capability before a pricing comparison
- Explaining model trade-offs in a technical proposal
- Quick reference during a model evaluation
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