---
dataset_info:
  features:
  - name: id
    dtype: string
  - name: turncase
    dtype: string
  - name: prompt_category
    dtype: string
  - name: eval_focus
    dtype: string
  - name: prompt
    dtype: string
  - name: images_by_turn
    sequence:
      sequence:
        dtype: image
  - name: rubrics
    sequence: string
  splits:
  - name: train
    num_bytes: 3506927656
    num_examples: 1191
  download_size: 3506927656
  dataset_size: 3506927656
  configs:
  - config_name: default
    data_files:
    - split: train
      path: data/*.parquet
---

VisuAlToolBench is a challenging benchmark to assess tool-enabled visual perception, transformation, and reasoning in multimodal LLMs. It evaluates whether models can not only think about images but also think with images by actively manipulating visuals (e.g., crop, edit, enhance) and integrating general-purpose tools to solve complex tasks. The dataset contains single-turn and multi-turn tasks across diverse domains, each accompanied by detailed rubrics for systematic evaluation. Parquet files under `data/` are auto-indexed by the Hub and power the Dataset Viewer.

Paper: [BEYOND SEEING: Evaluating Multimodal LLMs on Tool-enabled Image Perception, Transformation, and Reasoning](https://static.scale.com/uploads/654197dc94d34f66c0f5184e/vtb_paper.pdf)