KinghtH/VL-RouterBench

VL-RouterBench: A Benchmark for Vision–Language Model Routing

Overview

We provides a clean, reproducible implementation of VL-RouterBench, a benchmark and toolkit for routing across a pool of Vision–Language Models (VLMs) under both performance and performance–cost objectives.

📦 Data Preparation

VL-RouterBench converts VLMEvalKit outputs into a unified routing benchmark.

To make data setup easier, we provide a pre-packaged archive vlm_router_data.tar.gz that contains everything needed to run the pipeline. You can download it from any of the following channels and extract it under the repo root:

• Google Drive: vlm_router_data.tar.gz
• Baidu Netdisk: vlm_router_data.tar.gz (code: xb1s)
• Hugging Face: vlm_router_data.tar.gz

After downloading, extract it as:

tar -xzf vlm_router_data.tar.gz

By default, the pipeline expects the following directories (relative to repo root):

vlm_router_data/
  VLMEvalKit_evaluation/   # required (for is_correct / evaluation)
  VLMEvalKit_inference/    # required for accurate output-token counting (Step 2)
  TSV_images/              # optional (for TSV-packed image datasets)

Notes:

• VLMEvalKit_evaluation/ is used by Step 1 & 4 (contains correctness signals).
• VLMEvalKit_inference/ is used by Step 2 (extract real model outputs to count output tokens).
• TSV_images/ is used by routers for training and inference to make routing decisions.

📝 Citation

If you find this benchmark useful, please cite:

@misc{huang2025vlrouterbenchbenchmarkvisionlanguagemodel,
      title={VL-RouterBench: A Benchmark for Vision-Language Model Routing}, 
      author={Zhehao Huang and Baijiong Lin and Jingyuan Zhang and Jingying Wang and Yuhang Liu and Ning Lu and Tao Li and Xiaolin Huang},
      year={2025},
      eprint={2512.23562},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2512.23562}, 
}

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