# Mme Realworld Mmbench Eval

> Evaluates multimodal large language models on real-world visual perception and reasoning tasks, as well as fine-grained visual-language understanding across multiple dimensions. Use when the user wants to benchmark on MME-Realworld, MMBench, or asks about evaluating this task. Reports Overall score.

- Skill: `qhjqhj00/mme-realworld-mmbench-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/mme-realworld-mmbench-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/mme-realworld-mmbench-eval/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Productivity
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/mme-realworld-mmbench-eval

---


# mme-realworld-mmbench-eval

> Awaker2.5-VL: Stably Scaling MLLMs with Parameter-Efficient Mixture of Experts — Long et al. (2024) (arXiv:2411.10669, 2024)

## What this evaluates

Evaluates multimodal large language models on real-world visual perception and reasoning tasks, as well as fine-grained visual-language understanding across multiple dimensions.

## Datasets

- **MME-Realworld** — total 29429; splits: test (-1); repo https://mme-realworld.github.io/home_page.html
- **MMBench** — total ?; splits: test (-1); repo https://mmbench.opencompass.org.cn/leaderboard

## Metrics

- `Overall score` **(primary)** — range: percent
  - Percentage of correctly answered questions averaged across all sub-tasks or evaluation dimensions.
- `Perception score` — range: percent
  - Accuracy on tasks requiring visual recognition and detail extraction.
- `Reasoning score` — range: percent
  - Accuracy on tasks requiring logical deduction and multi-step visual reasoning.

## Input / output format

**Input**: Image(s) paired with text prompts or questions.

**Output**: Text responses generated by the model.

## Scoring recipe

```python
def compute_metric(predictions, gold_labels):
    correct = sum(1 for pred, gold in zip(predictions, gold_labels) if pred == gold)
    return (correct / len(gold_labels)) * 100
```

## Common pitfalls

- Competitor results are directly cited from external leaderboards rather than re-run under identical conditions.
- Scores are aggregated averages across diverse sub-tasks, which may mask performance variations on specific capabilities like OCR or spatial reasoning.

## Evidence (verbatim from paper)

> We conduct evaluation on the latest two multimodal large model benchmarks: (1) MME-RealWorld *[[15]]*: this benchmark considers images from domains such as autonomous driving, remote sensing, video surveillance, newspapers, street views, and financial charts. It contains 29,429 annotations, covering 43 sub-tasks, with each task having at least 100 questions. (2) MMBench *[[12]]*: this benchmark is a visual-language model evaluation benchmark developed by the OpenCompass research team. It enables a granular assessment of capabilities ranging from perception to cognition, covering 20 fine-grained evaluation dimensions including object detection, text recognition, action recognition, image understanding, and relational reasoning. All compared models are ranked by the average/overall scores.

## Citation

```bibtex
@misc{long2024awaker25vl,
  title={Awaker2.5-VL: Stably Scaling MLLMs with Parameter-Efficient Mixture of Experts},
  author={Long et al. (2024)},
  year={2024},
  note={arXiv:2411.10669}
}
```

- arXiv: 2411.10669