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language benchmark

CommonSenseQA

CommonSenseQA is a multiple-choice question answering dataset that requires different types of commonsense knowledge to predict correct answers. It contains 12,102 questions with one correct answer and four distractors, designed to test semantic reasoning and conceptual relationships. Questions are created based on ConceptNet concepts and require prior world knowledge for accurate reasoning.

Updated Aug 11, 2026

Models1
Model coverage1
MetricScore
EvidenceB

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  • FAQ

CommonSenseQA Ranking

Higher score ranks better on this benchmark.

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01MAMistral NeMo InstructMistral AI70.4%100.0%1CAug 11, 2026

CommonSenseQA Highlights

The leading models and scores on this benchmark.

Rank #1Mistral NeMo Instruct70.4%

What is CommonSenseQA?

What CommonSenseQA measures and how its scores work.

CommonSenseQA is a multiple-choice question answering dataset that requires different types of commonsense knowledge to predict correct answers. It contains 12,102 questions with one correct answer and four distractors, designed to test semantic reasoning and conceptual relationships. Questions are created based on ConceptNet concepts and require prior world knowledge for accurate reasoning.

Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.

Family
CommonSenseQA
Modality
text
Primary category
language
Score direction
higher
LLMBoard eligible
No
Evaluation key
commonsenseqa|llm-stats-current

Benchmark scores retain their original unit. Overall score eligibility is shown separately.

FAQ

Common questions about CommonSenseQA.

Which model scores highest on CommonSenseQA?

Mistral NeMo Instruct is currently ranked first with 70.4%.

What does CommonSenseQA measure?

CommonSenseQA is a multiple-choice question answering dataset that requires different types of commonsense knowledge to predict correct answers. It contains 12,102 questions with one correct answer and four distractors, designed to test semantic reasoning and conceptual relationships. Questions are created based on ConceptNet concepts and require prior world knowledge for accurate reasoning.

Is a higher score better?

Yes. Higher values rank better for this benchmark.

How many models are compared?

1 model results are currently shown.

Does this benchmark affect the overall score?

No. This benchmark is shown for reference but does not contribute to the overall score.