WinoGrande
44,000 adversarially filtered Winograd schema problems for commonsense reasoning, created at scale to reduce dataset biases.
Models ranked
4
tracked on this benchmark
Score band
97.5 – 76.1
best → lowest tracked
Snapshot trend
+19.81
Jun 25 → Oct 15 · 3 models
Leaderboard
Tracked models ranked by Accuracy (higher is better).
Notes: Cloudflare first-party HF card, Results > Decision Index table ('WinoGrande (accuracy)'). Posed as a decision over the benchmark's options in Cloudflare's internal run of the Decision Index 0.2.1 suite, not standard generative eval; not comparable to LLM rows without the variant.
Notes: Cloudflare first-party HF card, Results > Decision Index table ('WinoGrande (accuracy)'). Posed as a decision over the benchmark's options in Cloudflare's internal run of the Decision Index 0.2.1 suite, not standard generative eval; not comparable to LLM rows without the variant.
Notes: Cloudflare first-party HF card, Results > Decision Index table ('WinoGrande (accuracy)'). Posed as a decision over the benchmark's options in Cloudflare's internal run of the Decision Index 0.2.1 suite, not standard generative eval; not comparable to LLM rows without the variant.
How to read this benchmark
This benchmark scores models where higher is better. Use scores for directional filtering and shortlisting, not universal quality ranking; then validate pricing, context window, provider availability, and fit for your workload.
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.
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Last reviewed: Apr 15, 2026