reasoning benchmark
SimpleQA
SimpleQA is a factuality benchmark developed by OpenAI that measures the short-form factual accuracy of large language models. The benchmark contains 4,326 short, fact-seeking questions that are adversarially collected and designed to have single, indisputable answers. Questions cover diverse topics from science and technology to entertainment, and the benchmark also measures model calibration by evaluating whether models know what they know.
Updated Aug 11, 2026
Models46
Model coverage46
MetricScore
EvidenceB
SimpleQA Ranking
Higher score ranks better on this benchmark.
SimpleQA Score Distribution
A closer view of the leading scores on this benchmark.
SimpleQA Highlights
The leading models and scores on this benchmark.
What is SimpleQA?
What SimpleQA measures and how its scores work.
SimpleQA is a factuality benchmark developed by OpenAI that measures the short-form factual accuracy of large language models. The benchmark contains 4,326 short, fact-seeking questions that are adversarially collected and designed to have single, indisputable answers. Questions cover diverse topics from science and technology to entertainment, and the benchmark also measures model calibration by evaluating whether models know what they know.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
- Family
- SimpleQA
- Modality
- text
- Primary category
- reasoning
- Score direction
- higher
- LLMBoard eligible
- Yes
- Evaluation key
- simpleqa|llm-stats-current
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
FAQ
Common questions about SimpleQA.
Which model scores highest on SimpleQA?
DeepSeek-V3.2-Exp is currently ranked first with 97.1%.
What does SimpleQA measure?
SimpleQA is a factuality benchmark developed by OpenAI that measures the short-form factual accuracy of large language models. The benchmark contains 4,326 short, fact-seeking questions that are adversarially collected and designed to have single, indisputable answers. Questions cover diverse topics from science and technology to entertainment, and the benchmark also measures model calibration by evaluating whether models know what they know.
Is a higher score better?
Yes. Higher values rank better for this benchmark.
How many models are compared?
46 model results are currently shown.
Does this benchmark affect the overall score?
Yes. This benchmark can contribute to the current LLMBoard capability score.