instruction following benchmark
Multi-IF
Multi-IF benchmarks LLMs on multi-turn and multilingual instruction following. It expands upon IFEval by incorporating multi-turn sequences and translating English prompts into 7 other languages, resulting in 4,501 multilingual conversations with three turns each. The benchmark reveals that current leading LLMs struggle with maintaining accuracy in multi-turn instructions and shows higher error rates for non-Latin script languages.
Updated Aug 11, 2026
Models20
Model coverage20
MetricScore
EvidenceB
Multi-IF Ranking
Higher score ranks better on this benchmark.
Multi-IF Score Distribution
A closer view of the leading scores on this benchmark.
Multi-IF Highlights
The leading models and scores on this benchmark.
What is Multi-IF?
What Multi-IF measures and how its scores work.
Multi-IF benchmarks LLMs on multi-turn and multilingual instruction following. It expands upon IFEval by incorporating multi-turn sequences and translating English prompts into 7 other languages, resulting in 4,501 multilingual conversations with three turns each. The benchmark reveals that current leading LLMs struggle with maintaining accuracy in multi-turn instructions and shows higher error rates for non-Latin script languages.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
- Family
- Multi-IF
- Modality
- text
- Primary category
- instruction following
- Score direction
- higher
- LLMBoard eligible
- Yes
- Evaluation key
- multi-if|llm-stats-current
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
FAQ
Common questions about Multi-IF.
Which model scores highest on Multi-IF?
Qwen3-235B-A22B-Thinking-2507 is currently ranked first with 80.6%.
What does Multi-IF measure?
Multi-IF benchmarks LLMs on multi-turn and multilingual instruction following. It expands upon IFEval by incorporating multi-turn sequences and translating English prompts into 7 other languages, resulting in 4,501 multilingual conversations with three turns each. The benchmark reveals that current leading LLMs struggle with maintaining accuracy in multi-turn instructions and shows higher error rates for non-Latin script languages.
Is a higher score better?
Yes. Higher values rank better for this benchmark.
How many models are compared?
20 model results are currently shown.
Does this benchmark affect the overall score?
Yes. This benchmark can contribute to the current LLMBoard capability score.