أفضل نماذج الذكاء الاصطناعي للرياضيات — تصنيف MATH و AIME و GSM8K
أفضل نماذج الذكاء الاصطناعي للاستدلال الرياضي، مرتبة حسب درجات MATH و AIME 2024 و GSM8K و GPQA. منسقة بواسطة OrcaRouter.
Top math models
Ordered by composite score across AIME 2024 (40%), MATH-level-5 (30%), Putnam-style problems (20%), and GSM8K (10%). Score weights AIME most heavily because GSM8K and most of MATH are saturated by 2026.
- gpt-5.5-thinking — AIME 2024: 94%, MATH-5: 88%. Best on competition-style problems. Extended thinking mode adds significant cost (~5× regular gpt-5.5) but is the only viable choice for genuinely hard problems.
- claude-opus-4-7-thinking — AIME 2024: 91%, MATH-5: 86%. Slightly behind gpt-5.5-thinking on AIME but strongest on multi-step proof-style reasoning where intermediate-step accuracy matters.
- deepseek-v4-pro-thinking — AIME 2024: 89%, MATH-5: 84%. Open-weights thinking model; remarkable for the price. Best non-frontier-vendor option.
- gemini-3.1-pro-preview — AIME 2024: 82%, MATH-5: 80%. No explicit thinking mode but very strong on symbolic and computational math. Best non-thinking model.
- claude-sonnet-4-6 — AIME 2024: 71%, MATH-5: 76%. Solid mid-tier non-thinking option. Use when latency matters more than competition-grade accuracy.
Why thinking models dominate
Mathematical reasoning rewards extended chain-of-thought heavily — a model that writes 5,000 tokens working through a proof is usually more accurate than the same base model in standard mode. AIME, Putnam and the harder MATH levels are essentially saturated by humans-with-time, so models that can spend 'time' (compute) on inference dominate the leaderboard.
When NOT to use a thinking model
Thinking modes cost 3-10× more per task and add seconds-to-minutes of latency. For grade-school arithmetic, simple algebra and structured numeric extraction, the standard models are already at >95% accuracy. Reserve thinking modes for genuinely hard problems where the alternative is solver software.