IFEval: Instruction-Following Evaluation
About
IFEval measures LLM ability to follow verifiable formatting instructions (e.g., output length, keyword inclusion, capitalization) with exact-match checking.Compare model scores alongside pricing, API availability, and release date — high benchmark score alone doesn't make a model the right pick.
How to read this benchmark
This benchmark uses a scoring system where higher is better. Scores are useful for directional filtering and model shortlisting, not for universal quality ranking.
Interpretation checklist: prefer benchmarks that are closest to your workload style, then validate against the linked model pages for pricing, context window, and provider availability.
Trust when:
- There is a fresh timestamped snapshot (or multiple snapshots) for this benchmark.
- Model list covers the same version family you can actually deploy today.
- Top candidates overlap with your required routing and feature requirements.
Don't trust when:
- There is only one benchmark snapshot or the dataset appears stale.
- Benchmark metric direction is opposite of your decision objective.
- The score difference between options is narrow and likely within implementation variance.
Current modeled score band for tracked entries is roughly 92.6 – 77.8.
Window
Apr 19 to May 28
last 3 snapshots
Mean delta
-0.76
score points
Coverage
1
models in latest snapshot
Leaderboard preview (top 5)
- 1. Qwen3.5-397B-A17B92.6
- 2. GPT-5.592.1
- 3. LFM2.5 8B A1B91.8
- 4. Kimi K2.689.8
- 5. Llama 3 70B Instruct77.8