CPI-Bench: A Comprehensive,Practical and Intelligent Benchmark for Real-World Image Editing
2026-08-14 • Computer Vision and Pattern Recognition
Computer Vision and Pattern Recognition
AI summaryⓘ
The authors created CPI-Bench, a new benchmark to better test image editing models in realistic and complex situations. Existing tests only handle simple tasks and can't properly compare different models or reflect real-world use. CPI-Bench includes three parts: general editing tasks (including multi-image editing), common user scenarios, and tasks requiring advanced reasoning. Their tests show CPI-Bench can clearly highlight differences between models and aligns well with human judgments, making it useful for improving image editing systems.
image editing modelsbenchmarkmulti-image editingreasoning tasksmodel evaluationreal-world deploymentperformance differentiationuser experience alignment
Authors
Qinye Zhou, Jun Zheng, Yongchao Du, Yuan Wang, Zhengrui Chen, Zuan Gao, Taihang Hu, Chao Lin, Yefeng Shen, Xingjian Wang, Zhao Wang, Zhengtao Wu, Xiaoli Xu, Zhengze Xu, Hao Yan, Denghui Yang, Yuhang Yu, Huayu Zhang, Mingzhou Zhang, Mengting Chen
Abstract
With the rapid advancement of image editing models and their widespread application across various domains, there is an increasingly urgent need to deploy these model capabilities directly into real-world scenarios. However, existing benchmarks remain confined to simple single-image tasks, suffering from limited coverage dimensions and an inability to effectively differentiate performance among diverse models. Consequently, they fail to reliably evaluate model performance in complex multi-image editing, highly demanding reasoning instructions, and practical deployment settings. To address these limitations, we propose CPI-Bench, a Comprehensive, Practical andIntelligent benchmark for real-world image editing. CPI-Bench comprises three core subsets: CPI-General-Bench, which comprehensively covers diverse editing tasks and pioneers the inclusion of multi-image editing evaluation; CPI-Practical-Bench, which focuses on high-frequency real-user application scenarios; and CPI-Intelligent-Bench, which is dedicated to evaluating capabilities in highly demanding reasoning-based editing. Evaluation results of mainstream image editing models based on CPI-Bench demonstrate that CPI-Bench enhances performance differentiation among models. It provides a comprehensive and reliable quantification of gaps in general editing capabilities, practical deployment efficacy, and advanced reasoning-based editing, offering invaluable guidance for the future optimization of image editing models. Crucially, our ranking analysis reveals that CPI-Bench achieves the highest alignment with the Arena Image Edit Leaderboard, indicating it faithfully captures the preferences and perceptual judgments of human evaluators, serving as a robust proxy for real-world user experience.