reasoning benchmark
HellaSwag
A challenging commonsense natural language inference dataset that uses Adversarial Filtering to create questions trivial for humans (>95% accuracy) but difficult for state-of-the-art models, requiring completion of sentence endings based on physical situations and everyday activities
Updated Aug 11, 2026
Models27
Model coverage27
MetricScore
EvidenceB
HellaSwag Ranking
Higher score ranks better on this benchmark.
HellaSwag Score Distribution
A closer view of the leading scores on this benchmark.
HellaSwag Highlights
The leading models and scores on this benchmark.
What is HellaSwag?
What HellaSwag measures and how its scores work.
A challenging commonsense natural language inference dataset that uses Adversarial Filtering to create questions trivial for humans (>95% accuracy) but difficult for state-of-the-art models, requiring completion of sentence endings based on physical situations and everyday activities
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
- Family
- HellaSwag
- Modality
- text
- Primary category
- reasoning
- Score direction
- higher
- LLMBoard eligible
- Yes
- Evaluation key
- hellaswag|llm-stats-current
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
FAQ
Common questions about HellaSwag.
Which model scores highest on HellaSwag?
Claude 3 Opus is currently ranked first with 95.4%.
What does HellaSwag measure?
A challenging commonsense natural language inference dataset that uses Adversarial Filtering to create questions trivial for humans (>95% accuracy) but difficult for state-of-the-art models, requiring completion of sentence endings based on physical situations and everyday activities
Is a higher score better?
Yes. Higher values rank better for this benchmark.
How many models are compared?
27 model results are currently shown.
Does this benchmark affect the overall score?
Yes. This benchmark can contribute to the current LLMBoard capability score.