Solving expert-level multimodal tasks is a key milestone towards general intelligence. As the capabilities of multimodal large language models (MLLMs) continue to improve, evaluation of such advanced multimodal intelligence becomes necessary yet challenging. In this work, we introduce ProBench, a benchmark of open-ended user queries that require professional expertise and advanced reasoning. ProBench consists of 4,000 high-quality samples independently submitted by professionals based on their daily productivity demands. It spans across 10 fields and 56 sub-fields, including science, arts, humanities, coding, mathematics, and creative writing. Experimentally, we evaluate and compare 24 latest models using MLLM-as-a-Judge. Our results reveal that although the best open-source models rival the proprietary ones, ProBench presents significant challenges in visual perception, textual understanding, domain knowledge and advanced reasoning, thus providing valuable directions for future multimodal AI research efforts.
@article{arxiv.2503.06885,
title = {ProBench: Judging Multimodal Foundation Models on Open-ended Multi-domain Expert Tasks},
author = {Yan Yang and Dongxu Li and Haoning Wu and Bei Chen and Liu Liu and Liyuan Pan and Junnan Li},
journal= {arXiv preprint arXiv:2503.06885},
year = {2025}
}