# MMBench

- Category: Detection & evals
- Official site: https://mmbench.opencompass.org.cn/
- HTML: https://www.88z.ai/en/tools/mmbench
- Markdown: https://www.88z.ai/en/tools/mmbench.md

A comprehensive multimodal benchmark for large models powered by OpenCompass

## What is MMBench?

MMBench is a comprehensive evaluation benchmark dedicated to assessing the capabilities of Multimodal Large Language Models (MLLMs).As a key component of the OpenCompass open-source evaluation framework, it aims to objectively evaluate model performance on tasks combining vision and language through standardized testing procedures.

The benchmark covers multiple dimensions ranging from basic perception to complex reasoning, helping developers and researchers fully understand the true capability boundaries of models when processing mixed image-text inputs.Through MMBench, users can obtain detailed data on model robustness, accuracy, and generalization abilities.

For specific evaluation metrics, dataset composition, and the latest leaderboard information, please refer to the official MMBench website.

## Key features

- **Multi-dimensiona**：Covers multimodal task testing across perception, reasoning, logic, and other dimensions.
- **Standardized Ben**：Provides unified evaluation standards and processes to ensure comparability of results across different models.
- **OpenCompass Inte**：Deeply integrated into the OpenCompass evaluation framework, supporting convenient automated evaluation.
- **Comprehensive Da**：Includes diverse image-text paired data to simulate real-world application scenarios.
- **Open and Transpa**：Evaluation methods and results are public, promoting community understanding of multimodal model capabilities.

## How to use it

1. Visit the official MMBench website to learn about the benchmark details.
2. Consult the official documentation for supported datasets and evaluation metric descriptions.
3. Configure the OpenCompass environment or related dependencies according to the guide.
4. Select the target multimodal model and run the evaluation script.
5. Analyze the generated evaluation report to compare model scores across different dimensions.

## Where it fits

- **Model Selection**：Helps enterprises choose the most suitable multimodal model for business needs based on objective data before deployment.
- **Academic Researc**：Provides researchers with standardized baselines for validating new multimodal algorithms or architectures.
- **Capability Diagn**：Identifies weaknesses in specific visual question answering or reasoning tasks to guide subsequent optimization.

## Pricing and free quota

Pricing and any free quota follow the official site.

## Who it is for

- AI Researchers
- Multimodal Model Developers
- Technical Evaluation Experts
- Open Source Community Contributors

## FAQ

### What types of models does MMBench primarily evaluate?

It primarily evaluates Multimodal Large Language Models (MLLMs), which are models capable of processing both image and text inputs.

### How can I participate in MMBench evaluation?

Typically, you need to submit your model via the OpenCompass framework or run local evaluation scripts following the official guidelines.Please check the official website documentation for details.

### How is MMBench different from other benchmarks?

MMBench emphasizes multi-dimensional, fine-grained capability decomposition and provides a standardized evaluation experience through OpenCompass.

## Tags

Multimodal Evaluation · OpenCompass

Features, pricing and availability follow the official site. Except for 88z.ai’s own API pages, this is a third-party product; 88z.ai only provides a directory introduction.