Code-Vision: Evaluating Multimodal LLMs Logic Understanding and Code Generation Capabilities

Hanbin Wang; Xiaoxuan Zhou; Zhipeng Xu; Keyuan Cheng; Yuxin Zuo; Kai Tian; Jingwei Song; Junting Lu; Wenhui Hu; Xueyang Liu

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arXiv · 2502.11829

Code-Vision: Evaluating Multimodal LLMs Logic Understanding and Code Generation Capabilities

Hanbin Wang·Xiaoxuan Zhou·Zhipeng Xu·Keyuan Cheng·Yuxin Zuo·Kai Tian·Jingwei Song·Junting Lu·Wenhui Hu·Xueyang Liu

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Abstract

This paper introduces Code-Vision, a benchmark designed to evaluate the logical understanding and code generation capabilities of Multimodal Large Language Models (MLLMs). It challenges MLLMs to generate a correct program that fulfills specific functionality requirements based on a given flowchart, which visually represents the desired algorithm or process. Code-Vision comprises three subsets: HumanEval-V, Algorithm, and MATH, which evaluate MLLMs' coding abilities across basic programming, algorithmic, and mathematical problem-solving domains. Our experiments evaluate 12 MLLMs on Code-Vision. Experimental results demonstrate that there is a large performance difference between proprietary and open-source models. On Hard problems, GPT-4o can achieve 79.3% pass@1, but the best open-source model only achieves 15%. Further experiments reveal that Code-Vision can pose unique challenges compared to other multimodal reasoning benchmarks MMCode and MathVista. We also explore the reason for the poor performance of the open-source models. All data and codes are available at https://github.com/wanghanbinpanda/CodeVision.

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Hanbin Wang, Xiaoxuan Zhou, Zhipeng Xu, Keyuan Cheng, Yuxin Zuo, Kai Tian, Jingwei Song, Junting Lu, Wenhui Hu, Xueyang Liu. 2025-02-17. Code-Vision: Evaluating Multimodal LLMs Logic Understanding and Code Generation Capabilities. https://arxiv.org/abs/2502.11829

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