AI agents are changing the requirements for document parsing. What matters is semantic correctness: parsed output must preserve the structure and meaning needed for autonomous decisions, including correct table structure, precise chart data, semantically meaningful formatting, and visual grounding. Existing benchmarks do not fully capture this setting for enterprise automation, relying on narrow document distributions and text-similarity metrics that miss agent-critical failures. We introduce ParseBench, a benchmark of ${\sim}2{,}000$ human-verified pages from enterprise documents spanning insurance, finance, and government, organized around five capability dimensions: tables, charts, content faithfulness, semantic formatting, and visual grounding. Across 14 methods spanning vision-language models, specialized document parsers, and LlamaParse, the benchmark reveals a fragmented capability landscape: no method is consistently strong across all five dimensions. LlamaParse Agentic achieves the highest overall score at 84.9%, and the benchmark highlights the remaining capability gaps across current systems. Dataset and evaluation code are available on https://huggingface.co/datasets/llamaindex/ParseBench and https://github.com/run-llama/ParseBench.

翻译：AI智能体正在改变文档解析的需求。语义正确性至关重要：解析输出必须保留自主决策所需的结构和含义，包括正确的表格结构、精确的图表数据、具有语义意义的格式以及视觉定位。现有基准测试未能完全捕捉企业自动化场景中的这一需求，它们依赖于窄范围的文档分布和文本相似性指标，而这些指标会遗漏对智能体至关重要的错误。我们提出ParseBench基准测试，包含来自保险、金融和政府领域的企业文档中约2000个经过人工验证的页面，围绕五个能力维度组织：表格、图表、内容忠实度、语义格式和视觉定位。在涵盖视觉语言模型、专用文档解析器和LlamaParse的14种方法中，该基准测试揭示了碎片化的能力格局：没有一种方法在所有五个维度上表现持续强劲。LlamaParse Agentic以84.9%的最高总分领先，而该基准测试突显了当前系统间仍存在的能力差距。数据集和评估代码可在https://huggingface.co/datasets/llamaindex/ParseBench 和https://github.com/run-llama/ParseBench 获取。