multimodal benchmark
MM IF-Eval
A challenging multimodal instruction-following benchmark that includes both compose-level constraints for output responses and perception-level constraints tied to input images, with comprehensive evaluation pipeline.
Updated Aug 11, 2026
Models1
Model coverage1
MetricScore
EvidenceB
MM IF-Eval Ranking
Higher score ranks better on this benchmark.
| 01 | MA | 52.7% | 100.0% | 1 | C |
MM IF-Eval Highlights
The leading models and scores on this benchmark.
What is MM IF-Eval?
What MM IF-Eval measures and how its scores work.
A challenging multimodal instruction-following benchmark that includes both compose-level constraints for output responses and perception-level constraints tied to input images, with comprehensive evaluation pipeline.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
- Family
- MM IF-Eval
- Modality
- multimodal
- Primary category
- multimodal
- Score direction
- higher
- LLMBoard eligible
- No
- Evaluation key
- mm-if-eval|llm-stats-current
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
FAQ
Common questions about MM IF-Eval.
Which model scores highest on MM IF-Eval?
Pixtral-12B is currently ranked first with 52.7%.
What does MM IF-Eval measure?
A challenging multimodal instruction-following benchmark that includes both compose-level constraints for output responses and perception-level constraints tied to input images, with comprehensive evaluation pipeline.
Is a higher score better?
Yes. Higher values rank better for this benchmark.
How many models are compared?
1 model results are currently shown.
Does this benchmark affect the overall score?
No. This benchmark is shown for reference but does not contribute to the overall score.