HellaSwag
Commonsense sentence-completion benchmark using adversarially filtered wrong answers. Top LLMs now exceed 95% accuracy. High benchmark score alone doesn't make a model the right pick — weigh it against pricing, API availability, and release date.
Models ranked
48
tracked on this benchmark
Score band
96.4 – 78.9
best → lowest tracked
Snapshot trend
+2.06
Sep 1 → Mar 6 · 47 models
Leaderboard
Tracked models ranked by Accuracy (higher is better).
How to read this benchmark
This benchmark scores models where higher is better. Scores are useful for directional filtering and shortlisting — not for universal quality ranking. Prefer benchmarks closest to your workload, then validate the linked model pages for pricing, context window, and provider availability.
Trust this score when
- There is a fresh timestamped snapshot (or multiple snapshots) for this benchmark.
- The model list covers the same version family you can actually deploy today.
- Top candidates overlap with your required routing and feature requirements.
Be cautious when
- There is only one benchmark snapshot or the dataset appears stale.
- The benchmark metric direction is opposite of your decision objective.
- The score difference between options is narrow and likely within implementation variance.
FAQ
What does the HellaSwag benchmark measure?
Commonsense sentence-completion benchmark using adversarially filtered wrong answers. Top LLMs now exceed 95% accuracy. On this page it lists 48 tracked model variants where higher is better.
Is a higher HellaSwag score always better?
For this benchmark, higher is better. A high score helps you shortlist, but confirm pricing, context window, and provider availability on each model page before committing — the top scorer is not always the right pick for your workload or budget.
How current is this HellaSwag data?
This benchmark was last reviewed on Apr 15, 2026. The tracked score average moved +2.06 points across the last 2 snapshots.
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Last reviewed: Apr 15, 2026