reasoning benchmark
Frontier Science is a benchmark of exceptionally challenging scientific reasoning problems spanning advanced natural-science domains, designed to test expert-level scientific understanding and multi-step reasoning.
Updated Aug 17, 2026
Higher score ranks better on this benchmark.
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelMI | Score58.2% | Percentile100.0% | 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 frontier science 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
MAI-Code-1-Flash currently leads Frontier Science with 58.2%. 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 Frontier Science measures and how its scores work.
Frontier Science is a benchmark of exceptionally challenging scientific reasoning problems spanning advanced natural-science domains, designed to test expert-level scientific understanding and multi-step reasoning.
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 Frontier Science.
MAI-Code-1-Flash is currently ranked first with 58.2%.
Frontier Science is a benchmark of exceptionally challenging scientific reasoning problems spanning advanced natural-science domains, designed to test expert-level scientific understanding and multi-step reasoning.
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.