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

Leaderboard

Answer is final-answer accuracy (%); Process measures step-level derivation quality. Subject columns report answer accuracy (%): Mech. = Mechanics, E&M = Electromagnetism, Mod. = Modern Physics, Therm. = Thermodynamics, and Opt. = Optics. Bold values mark the best model score in each metric.

Rank	Model	Answer ↑	Process ↑	Mech.	E&M	Mod.	Therm.	Opt.
—	Human baseline	48.5	65.2	47.6	42.3	54.2	51.7	57.3
🥇 1	Grok-4.2 †	33.7	47.6	34.0	26.6	33.0	35.4	25.4
🥈 2	Claude-Opus-4.6	28.1	49.6	27.2	20.9	24.7	33.1	21.3
🥉 3	o3-mini †⋆	26.2	43.9	25.3	20.4	26.8	28.4	19.6
4	GPT-5.2 †	24.0	42.6	22.1	18.6	23.9	28.4	17.4
5	Gemini-3-Pro †	21.8	41.4	21.9	15.5	20.0	23.7	16.1
6	Kimi-K2-Thinking †	20.0	32.7	19.3	13.2	15.6	22.8	9.6
6	Qwen3-VL-235B-A22B	20.0	36.8	19.3	15.8	19.2	22.0	14.4
8	Claude-Sonnet-4.5	18.5	33.6	17.5	14.7	18.5	23.7	12.6
9	Gemini-2.5 †	17.9	36.8	16.9	14.2	20.7	20.9	13.5
10	DeepSeek-V3 ⋆	17.0	34.6	18.7	10.3	14.5	19.3	10.9
11	Qwen3-VL-32B	15.9	31.5	15.2	10.1	13.8	17.8	12.2
12	Qwen-VL-Max	14.3	30.7	14.6	8.5	12.0	16.5	9.6
13	Qwen3-VL-8B	11.6	23.2	12.2	5.4	10.9	12.6	6.1
14	GPT-4o	10.4	25.0	8.8	6.5	10.2	13.8	10.0
15	Qwen2.5-VL-72B	9.8	21.5	9.1	6.2	9.1	11.1	6.1
16	Dolphin-Mistral-24B	8.6	20.6	6.1	7.5	10.1	9.9	7.0
17	LLaMA-3.1-70B ⋆	7.5	20.2	4.9	6.0	10.5	7.9	6.6
18	Qwen2.5-VL-7B	1.7	3.7	2.4	0.5	0.7	2.0	0.5

† Reasoning model evaluated with extended thinking. ⋆ Text-only model evaluated without diagram input.

Accessing the dataset

The benchmark data is hosted on Hugging Face. Its dataset viewer shows the available fields and lets you inspect examples before downloading. You can load it with the Hugging Face datasets library:

from datasets import load_dataset

physelite = load_dataset("physelite/PhysElite")
print(physelite)

Citation

If you use PhysElite in your research, please cite:

@inproceedings{
anonymous2026physelite,
title={PhysElite:  How Far Are {LLM}s from Solving Olympiad-Level Physics Problems?},
author={Anonymous},
booktitle={The Fortieth Annual Conference on Neural Information Processing Systems Evaluations and Datasets Track},
year={2026},
url={https://openreview.net/forum?id=f3ceI8ijyu}
}