math benchmark
OlympiadBench is a benchmark of 8,476 bilingual math and physics problems from international and Chinese Olympiads and the Chinese college entrance exam, including text-only and multimodal problems in English and Chinese.
Updated Sep 5, 2026
Higher score ranks better on this benchmark.
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelAC | Score20.40% | Percentile100.00% | Participants1 | EvidenceC | Evaluated |
The leading models and scores on this benchmark.
The first five results on this benchmark, with official price and output speed added where the model identity can be matched.
Ranking basisThis olympiadbench AI model leaderboard uses descending score in the benchmark original unit. The leaderboard ranking keeps matched price and speed data separate from benchmark evidence.
Selection summary
QvQ-72B-Preview currently leads OlympiadBench with 20.40%. It is the top model on this specific benchmark, while the best LLM for the broader task should also be checked against other benchmarks, price, and runtime.
Use this leaderboard with the supporting benchmark results and coverage details above. A leaderboard position summarizes the selected ranking signal; it does not replace workload-specific testing.
What OlympiadBench measures and how its scores work.
OlympiadBench is a benchmark comprising 8,476 math and physics problems with expert-level annotations for step-by-step reasoning, sourced from international and Chinese Olympiads and the Chinese college entrance exam.
It measures performance using the Score metric, reported as a ratio, on text-only and multimodal problems in English and Chinese.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
LLM Stats. Benchmark scores retain their original unit. Overall score eligibility is shown separately.
Common questions about OlympiadBench.
QvQ-72B-Preview is currently ranked first with 20.40%.
The current leaders are QvQ-72B-Preview (20.40%).
No matched official input price is currently available.
No matched runtime record is currently available.
No. This benchmark measures one defined capability or task. The overall LLMBoard score uses a separate aggregation across eligible benchmark evidence.
It measures performance using the Score metric, reported as a ratio, on text-only and multimodal problems in English and Chinese.
Yes. Higher values rank better for this benchmark.
1 model results are currently shown.
No. This benchmark is shown for reference but does not contribute to the overall score.