PhysElite: How Far Are LLMs from Solving Olympiad-Level Physics Problems?

Understanding how (multimodal) large language models perform on physics problems requires benchmarks that reflect the difficulty and breadth of expert-level physical reasoning. Existing physics benchmarks remain limited in the following two important ways: (1) short of high-difficulty datasets, and (2) lack of comprehensive coverage of visual forms, knowledge points, and step-by-step solution processes. As a result, model performance on current datasets may not be fully representative of their ability to solve complex physics problems. To address these issues, we present \textsc{PhysElite}, a large-scale bilingual multimodal benchmark for Olympiad-level physics reasoning. \textsc{PhysElite} contains 11,586 Olympiad-tier problems. For each problem, we provide corresponding visual diagrams, step-by-step bilingual Chinese--English solution derivations, and the final answer. We benchmark 18 open-source and closed-source MLLMs, and find that even the strongest model reaches only 33.7\% answer accuracy. We additionally conduct step-level process evaluation to diagnose where models fail in the reasoning chain. The benchmark results and dataset will be publicly available.