剖析大模型在真实文档场景下的失败原因，揭示性能与实际部署间的差距。

Recent Large Multimodal Models (LMMs) have achieved remarkable progress on OCR-centric document understanding and processing tasks. Existing benchmarks primarily evaluate LMMs across diverse tasks to reflect practical document-processing workflows or analyze how document characteristics influence model performance. However, they provide limited insight into the reliability of LMMs under real-world document acquisition conditions, where factors such as lighting, screen displays, imaging quality, and capture methods can substantially affect performance. To bridge this gap, we present CC-OCR v2, a comprehensive benchmark for attributing LMM failures in real-world document processing. CC-OCR v2 provides a unified evaluation framework covering five core document-processing capabilities through 16 subtasks, 74 application scenarios, and 7,093 samples. The benchmark spans five evaluation tracks, ten document categories, and 32 languages in the recognition suite. Beyond task-level evaluation, each sample is annotated with ten fine-grained document factors, enabling systematic attribution of model failures across document types and acquisition conditions. Extensive experiments on 17 representative LMMs reveal substantial performance variation across tasks, document categories, and real-world conditions. Moreover, models with comparable overall accuracy often exhibit fundamentally different failure patterns under specific document factors, highlighting a significant gap between benchmark-level performance and reliable deployment in practical applications. The dataset and evaluation toolkit are publicly available at https://github.com/eioss/CC-OCR-V2.