math benchmark
A Universal Olympiad Level Mathematic Benchmark for Large Language Models containing 4,428 competition-level problems with rigorous human annotation, categorized into over 33 sub-domains and spanning more than 10 distinct difficulty levels
Updated Aug 17, 2026
Higher score ranks better on this benchmark.
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelMI | Score81.9% | Percentile100.0% | Participants2 | EvidenceC | Evaluated |
| Rank02 | ModelMI | Score76.6% | Percentile0.0% | Participants2 | EvidenceC | Evaluated |
The leading models and scores on this benchmark.
A closer view of the leading 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 omnimath AI model leaderboard uses descending score in the benchmark's original unit. The leaderboard ranking keeps matched price and speed data separate from benchmark evidence.
Selection summary
Phi 4 Reasoning Plus currently leads OmniMath with 81.9%. 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 OmniMath measures and how its scores work.
A Universal Olympiad Level Mathematic Benchmark for Large Language Models containing 4,428 competition-level problems with rigorous human annotation, categorized into over 33 sub-domains and spanning more than 10 distinct difficulty levels
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
Common questions about OmniMath.
Phi 4 Reasoning Plus is currently ranked first with 81.9%.
A Universal Olympiad Level Mathematic Benchmark for Large Language Models containing 4,428 competition-level problems with rigorous human annotation, categorized into over 33 sub-domains and spanning more than 10 distinct difficulty levels
Yes. Higher values rank better for this benchmark.
2 model results are currently shown.
No. This benchmark is shown for reference but does not contribute to the overall score.