TheoremQA
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.
Qwen2 72B Instruct from Alibaba Cloud / Qwen Team currently leads the TheoremQA leaderboard with a score of 0.444 across 6 evaluated AI models.
Qwen2 72B Instruct leads with 44.4%, followed by Qwen2.5 32B Instruct at 44.1% and Qwen2.5-Coder 32B Instruct at 43.1%.
Progress Over Time
Interactive timeline showing model performance evolution on TheoremQA
TheoremQA Leaderboard
| Context | Cost | License | ||||
|---|---|---|---|---|---|---|
| 1 |  Alibaba Cloud / Qwen Team | 72B | — | — | ||
| 2 | Alibaba Cloud / Qwen Team | 33B | — | — | ||
| 3 | Alibaba Cloud / Qwen Team | 32B | 128K | $0.09 / $0.09 | ||
| 4 | Alibaba Cloud / Qwen Team | 15B | — | — | ||
| 5 | Alibaba Cloud / Qwen Team | 7B | — | — | ||
| 6 | Alibaba Cloud / Qwen Team | 8B | — | — |
FAQ
Common questions about TheoremQA.
More evaluations to explore
Related benchmarks in the same category
A challenging dataset of 448 multiple-choice questions written by domain experts in biology, physics, and chemistry. Questions are Google-proof and extremely difficult, with PhD experts reaching 65% accuracy.
A more robust and challenging multi-task language understanding benchmark that extends MMLU by expanding multiple-choice options from 4 to 10, eliminating trivial questions, and focusing on reasoning-intensive tasks. Features over 12,000 curated questions across 14 domains and causes a 16-33% accuracy drop compared to original MMLU.
All 30 problems from the 2025 American Invitational Mathematics Examination (AIME I and AIME II), testing olympiad-level mathematical reasoning with integer answers from 000-999. Used as an AI benchmark to evaluate large language models' ability to solve complex mathematical problems requiring multi-step logical deductions and structured symbolic reasoning.
Massive Multitask Language Understanding benchmark testing knowledge across 57 diverse subjects including STEM, humanities, social sciences, and professional domains
A verified subset of 500 software engineering problems from real GitHub issues, validated by human annotators for evaluating language models' ability to resolve real-world coding issues by generating patches for Python codebases.
Humanity's Last Exam (HLE) is a multi-modal academic benchmark with 2,500 questions across mathematics, humanities, and natural sciences, designed to test LLM capabilities at the frontier of human knowledge with unambiguous, verifiable solutions