PhysicsFinals
Progress Over Time
Interactive timeline showing model performance evolution on PhysicsFinals
PhysicsFinals Leaderboard
| Context | Cost | License | ||||
|---|---|---|---|---|---|---|
| 1 |  Google | — | — | — | ||
| 2 | Google | — | — | — |
What is PhysicsFinals?
PHYSICS is a comprehensive benchmark for university-level physics problem solving, containing 1,297 expert-annotated problems covering six core areas: classical mechanics, quantum mechanics, thermodynamics and statistical mechanics, electromagnetism, atomic physics, and optics. Each problem requires advanced physics knowledge and mathematical reasoning. Even advanced models like o3-mini achieve only 59.9% accuracy.
PhysicsFinals is a text benchmark evaluating models on math, physics, and reasoning tasks. LLM Stats tracks 2 models on this benchmark, scored on a 0–1 scale. The current average is 0.6, with the leader at 0.6.
Compare leaders on the best AI for math, best AI for physics and best AI for reasoning leaderboards.
Current leaders
Gemini 1.5 Pro from Google currently leads the PhysicsFinals leaderboard with a score of 0.639 across 2 evaluated AI models.
Source paper
- Title
- Physics: Benchmarking Foundation Models on University-Level Physics Problem Solving
- Authors
- Kaiyue Feng, Yilun Zhao, Yixin Liu, Tianyu Yang, and 3 others
- Published
- arXiv
- 2503.21821
Abstract
We introduce Physics, a benchmark for evaluating foundation models on advanced physics problem solving. It contains 1034 annotated, open-ended problems from PhD-qualifying exams across six core physics domains. Solving these problems requires applying domain-specific knowledge to physical setups and carrying out multi-step mathematical reasoning. Each problem includes a worked solution and independently checked reference answers. An LLM-based grading protocol evaluates individual answers and complete multi-part problems, supporting numeric, symbolic, and text responses. We evaluate 53 proprietary and open-weight models. On test problems without figures, high accuracy is concentrated in a small group of thinking models, with the strongest open-weight models approaching proprietary performance. Most open-weight models still solve fewer than half of these problems. Our analysis of Qwen3.5-9B reveals failures in applying physics knowledge and interpreting figures, with misread figures affecting several subquestions.
FAQ
Common questions about the PhysicsFinals benchmark and leaderboard.