math benchmark
A theorem-driven question answering dataset containing 800 high-quality questions covering 350+ theorems from Math, Physics, EE&CS, and Finance. Designed to evaluate AI models' capabilities to apply theorems to solve challenging university-level science problems.
Updated Aug 11, 2026
Higher score ranks better on this benchmark.
| 01 | AC | 44.4% | 100.0% | 6 | C | |
| 02 | AC | 44.1% | 80.0% | 6 | C | |
| 03 | AC | 43.1% | 60.0% | 6 | C | |
| 04 | AC | 43.0% | 40.0% | 6 | C | |
| 05 | AC | 34.0% | 20.0% | 6 | C | |
| 06 | AC | 25.3% | 0.0% | 6 | C |
A closer view of the leading scores on this benchmark.
The leading models and scores on this benchmark.
What TheoremQA measures and how its scores work.
A theorem-driven question answering dataset containing 800 high-quality questions covering 350+ theorems from Math, Physics, EE&CS, and Finance. Designed to evaluate AI models' capabilities to apply theorems to solve challenging university-level science problems.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
Common questions about TheoremQA.
Qwen2 72B Instruct is currently ranked first with 44.4%.
A theorem-driven question answering dataset containing 800 high-quality questions covering 350+ theorems from Math, Physics, EE&CS, and Finance. Designed to evaluate AI models' capabilities to apply theorems to solve challenging university-level science problems.
Yes. Higher values rank better for this benchmark.
6 model results are currently shown.
Yes. This benchmark can contribute to the current LLMBoard capability score.