ScienceQA Visual
ScienceQA Visual is a multimodal science question answering benchmark consisting of 21,208 multiple-choice questions from elementary and high school science curricula. The dataset covers 3 subjects (natural science, language science, social science), 26 topics, 127 categories, and 379 skills. 48.7% of questions include image context requiring multimodal reasoning. Questions are annotated with lectures (83.9%) and explanations (90.5%) to support chain-of-thought reasoning for science question answering.
Phi-4-multimodal-instruct from Microsoft currently leads the ScienceQA Visual leaderboard with a score of 0.975 across 1 evaluated AI models.
Phi-4-multimodal-instruct leads with 97.5%.
Progress Over Time
Interactive timeline showing model performance evolution on ScienceQA Visual
ScienceQA Visual Leaderboard
| Context | Cost | License | ||||
|---|---|---|---|---|---|---|
| 1 | Microsoft | 6B | 128K | $0.05 / $0.10 |
FAQ
Common questions about ScienceQA Visual.
More evaluations to explore
Related benchmarks in the same category
A challenging dataset of 448 multiple-choice questions written by domain experts in biology, physics, and chemistry. Questions are Google-proof and extremely difficult, with PhD experts reaching 65% accuracy.
A more robust and challenging multi-task language understanding benchmark that extends MMLU by expanding multiple-choice options from 4 to 10, eliminating trivial questions, and focusing on reasoning-intensive tasks. Features over 12,000 curated questions across 14 domains and causes a 16-33% accuracy drop compared to original MMLU.
All 30 problems from the 2025 American Invitational Mathematics Examination (AIME I and AIME II), testing olympiad-level mathematical reasoning with integer answers from 000-999. Used as an AI benchmark to evaluate large language models' ability to solve complex mathematical problems requiring multi-step logical deductions and structured symbolic reasoning.
Massive Multitask Language Understanding benchmark testing knowledge across 57 diverse subjects including STEM, humanities, social sciences, and professional domains
A verified subset of 500 software engineering problems from real GitHub issues, validated by human annotators for evaluating language models' ability to resolve real-world coding issues by generating patches for Python codebases.
Humanity's Last Exam (HLE) is a multi-modal academic benchmark with 2,500 questions across mathematics, humanities, and natural sciences, designed to test LLM capabilities at the frontier of human knowledge with unambiguous, verifiable solutions