IFEval: Instruction-Following Evaluation
IFEval measures LLM ability to follow verifiable formatting instructions (e.g., output length, keyword inclusion, capitalization) with exact-match checking. High benchmark score alone doesn't make a model the right pick — weigh it against pricing, API availability, and release date.
Models ranked
12
tracked on this benchmark
Score band
94.8 – 38.5
best → lowest tracked
Snapshot trend
+3.87
Apr 29 → Jun 26 · 1 models
Leaderboard
Tracked models ranked by Instruction Following Score (higher is better).
Notes: Official Intern Science model card reports Agents-A1 at 94.82 on IFEval. Evaluator/source: Intern Science self-reported model-card/project benchmark table. Public harness details are not fully reproducible from the seed row. Confidence: medium for reported value, low-medium for reproducibility. Recommended seed value: 94.82.
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 IFEval benchmark measure?
IFEval measures LLM ability to follow verifiable formatting instructions (e.g., output length, keyword inclusion, capitalization) with exact-match checking. On this page it lists 12 tracked model variants where higher is better.
Is a higher Instruction-Following Evaluation 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 Instruction-Following Evaluation data?
This benchmark was last reviewed on Apr 15, 2026. The tracked score average moved +3.87 points across the last 3 snapshots.
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Last reviewed: Apr 15, 2026