multimodal benchmark
A comprehensive evaluation benchmark for Multimodal Large Language Models featuring over 13,366 high-resolution images and 29,429 question-answer pairs across 43 subtasks and 5 real-world scenarios. The largest manually annotated multimodal benchmark to date, designed to test MLLMs on challenging high-resolution real-world scenarios.
Updated Aug 11, 2026
Higher score ranks better on this benchmark.
| 01 | AC | 61.6% | 100.0% | 1 | C |
The leading models and scores on this benchmark.
What MME-RealWorld measures and how its scores work.
A comprehensive evaluation benchmark for Multimodal Large Language Models featuring over 13,366 high-resolution images and 29,429 question-answer pairs across 43 subtasks and 5 real-world scenarios. The largest manually annotated multimodal benchmark to date, designed to test MLLMs on challenging high-resolution real-world scenarios.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
Common questions about MME-RealWorld.
Qwen2.5-Omni-7B is currently ranked first with 61.6%.
A comprehensive evaluation benchmark for Multimodal Large Language Models featuring over 13,366 high-resolution images and 29,429 question-answer pairs across 43 subtasks and 5 real-world scenarios. The largest manually annotated multimodal benchmark to date, designed to test MLLMs on challenging high-resolution real-world scenarios.
Yes. Higher values rank better for this benchmark.
1 model results are currently shown.
No. This benchmark is shown for reference but does not contribute to the overall score.