### [Open VLM Leaderboard](https://free.ilovefree.com/en)

**Published:** 2025-07-15T13:29:52

**Author:** ilovefree

**Excerpt:** The Open VLM Leaderboard provides a centralized, free p…

The Open VLM Leaderboard provides a centralized, free platform for researchers and developers to evaluate and compare the performance of Vision-Language Models (VLMs) and Large Multi-modality Models (LMMs) across multiple benchmarks.

## Benchmarking Multimodal Models on Visual Understanding Tasks

Comparing and selecting the right VLM for a specific application requires reliable performance data. The Open VLM Leaderboard serves as a critical resource for:

-   **Model Selection**: Users can filter models by evaluation dimensions, size, and type to identify top performers for their needs.
-   **Developer Evaluation**: VLM developers can utilize VLMEvalKit to assess their own models and contribute them to the leaderboard.
-   **Reproducibility**: The open-source nature of VLMEvalKit promotes reproducible evaluation results.
-   **Performance Tracking**: The leaderboard updates regularly, keeping users informed about the latest VLM advancements.

### Automated Evaluation on DocVQA, MMMU, and AI2D

The Open VLM Leaderboard presents performance metrics such as "Avg Score" (normalized 0-100, where higher is better) and "Avg Rank" (where lower is better). These metrics are derived from evaluations across a wide array of image and video understanding benchmarks. Users can also download detailed evaluation results for deeper analysis.

## HuggingFace Integration with Reproducible Evaluation Pipeline

VLMEvalKit, the framework powering the leaderboard, simplifies the evaluation process. It handles data downloading, preprocessing, prediction inference, and metric calculation. Developers only need to implement a `generate_inner()` function for new models. The platform is built with Gradio and integrated into Hugging Face Spaces.

### 50+ Vision-Language Models Ranked

The leaderboard covers a substantial number of models and benchmarks:

-   **Supported Models**: Over 200 LMMs and 285 VLMs, including commercial APIs like GPT-4o and Gemini-1.5, and open-source options such as LLaVA and QwenVLPlus.
-   **Supported Benchmarks**: More than 70 image and video benchmarks, alongside 31 multi-modal benchmarks. Examples include MMBench, MMStar, MMMU\_VAL, MathVista, OCRBench, AI2D, HallusionBench, and MMVet.

VLMEvalKit employs generation-based evaluation for all VLMs, with optional LLM-based answer extraction. It aims for consistent prompt templates across models on a given benchmark, though custom templates are possible.

### Free on HuggingFace Spaces

The Open VLM Leaderboard is a free resource. Its description explicitly states "Free AI Vision Model Rankings," and the underlying VLMEvalKit is also open-source, ensuring no subscription tiers or hidden costs.

## Focused on Academic Benchmarks, May Not Reflect Production Use

While the Open VLM Leaderboard offers significant value, users should be aware of certain limitations:

-   **Accuracy Reproducibility**: VLMEvalKit may not always reproduce the exact accuracy numbers reported in original research papers for all third-party benchmarks. This can be due to differences in evaluation approaches, such as using generation-based evaluation versus PPL-based evaluation.
-   **Prompt Template Customization**: Although the framework aims for consistent prompt templates, some VLMs might require specific templates not yet covered, necessitating developer implementation.
-   Data Contamination Risk: A general concern with any AI leaderboard is the potential for data contamination. Models might be over-optimized for specific benchmarks, leading to inflated scores that don’t fully reflect real-world performance. This can complicate the assessment of open-source models.
-   Version Dependencies: Evaluating certain VLMs may require specific `transformers` and `torchvision` versions, which can introduce technical constraints during the evaluation process.



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