multimodal benchmark
MME-RealWorld Leaderboard
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 17, 2026
Models1
Model coverage1
MetricScore
EvidenceB
MME-RealWorld Ranking
Higher score ranks better on this benchmark.
Sort by
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelAC | Score61.6% | Percentile100.0% | Participants1 | EvidenceC | Evaluated |
MME-RealWorld Highlights
The leading models and scores on this benchmark.
The Top AI Models for MME-RealWorld
The first five results on this benchmark, with official price and output speed added where the model identity can be matched.
Ranking basisThis mme-realworld AI model leaderboard uses descending score in the benchmark's original unit. The leaderboard ranking keeps matched price and speed data separate from benchmark evidence.
- 01ACQwen2.5-Omni-7BAlibaba Cloud / Qwen Team
- Score
- 61.6%
- Price
- $0.10 input / $0.40 output per 1M tokens
Strengths
- Ranks #1 of 1 compared models
- 100th percentile on this benchmark
- C evidence result
Considerations
- This result measures MME-RealWorld, not total model capability
Selection summary
Best AI Models for MME-RealWorld
Qwen2.5-Omni-7B currently leads MME-RealWorld with 61.6%. It is the top model on this specific benchmark, while the best LLM for the broader task should also be checked against other benchmarks, price and runtime.
Use this leaderboard with the supporting benchmark results and coverage details above. A leaderboard position summarizes the selected ranking signal; it does not replace workload-specific testing.
What is MME-RealWorld?
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.
- Family
- MME-RealWorld
- Modality
- multimodal
- Primary category
- multimodal
- Score direction
- higher
- LLMBoard eligible
- No
- Evaluation key
- mme-realworld|llm-stats-current
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
FAQ
Common questions about MME-RealWorld.
Which model scores highest on MME-RealWorld?
Qwen2.5-Omni-7B is currently ranked first with 61.6%.
What does MME-RealWorld measure?
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.
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.