language benchmark
Winogrande
WinoGrande: An Adversarial Winograd Schema Challenge at Scale. A large-scale dataset of 44,000 pronoun resolution problems designed to test machine commonsense reasoning. Uses adversarial filtering to reduce spurious biases and provides a more robust evaluation of whether AI systems truly understand commonsense or exploit statistical shortcuts. Current best AI methods achieve 59.4-79.1% accuracy, significantly below human performance of 94.0%.
Updated Aug 11, 2026
Models22
Model coverage22
MetricScore
EvidenceB
Winogrande Ranking
Higher score ranks better on this benchmark.
Winogrande Score Distribution
A closer view of the leading scores on this benchmark.
Winogrande Highlights
The leading models and scores on this benchmark.
What is Winogrande?
What Winogrande measures and how its scores work.
WinoGrande: An Adversarial Winograd Schema Challenge at Scale. A large-scale dataset of 44,000 pronoun resolution problems designed to test machine commonsense reasoning. Uses adversarial filtering to reduce spurious biases and provides a more robust evaluation of whether AI systems truly understand commonsense or exploit statistical shortcuts. Current best AI methods achieve 59.4-79.1% accuracy, significantly below human performance of 94.0%.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
- Family
- Winogrande
- Modality
- text
- Primary category
- language
- Score direction
- higher
- LLMBoard eligible
- Yes
- Evaluation key
- winogrande|llm-stats-current
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
FAQ
Common questions about Winogrande.
Which model scores highest on Winogrande?
GPT-4 is currently ranked first with 87.5%.
What does Winogrande measure?
WinoGrande: An Adversarial Winograd Schema Challenge at Scale. A large-scale dataset of 44,000 pronoun resolution problems designed to test machine commonsense reasoning. Uses adversarial filtering to reduce spurious biases and provides a more robust evaluation of whether AI systems truly understand commonsense or exploit statistical shortcuts. Current best AI methods achieve 59.4-79.1% accuracy, significantly below human performance of 94.0%.
Is a higher score better?
Yes. Higher values rank better for this benchmark.
How many models are compared?
22 model results are currently shown.
Does this benchmark affect the overall score?
Yes. This benchmark can contribute to the current LLMBoard capability score.