math benchmark
ScienceQA is a multimodal science question answering benchmark with 21,208 multiple-choice questions across natural science, language science, and social science.
Updated Sep 5, 2026
Higher score ranks better on this benchmark.
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelMI | Score91.30% | 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 scienceqa 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
Phi-3.5-vision-instruct currently leads ScienceQA with 91.30%. 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 ScienceQA measures and how its scores work.
ScienceQA is a benchmark covering 3 subjects, 26 topics, 127 categories, and 379 skills, with text and image modalities, detailed explanations, and Chain-of-Thought reasoning.
It measures Score on multiple-choice science questions and is used to diagnose multi-hop reasoning ability.
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 ScienceQA.
Phi-3.5-vision-instruct is currently ranked first with 91.30%.
The current leaders are Phi-3.5-vision-instruct (91.30%).
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 Score on multiple-choice science questions and is used to diagnose multi-hop reasoning ability.
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.