Llamaindex出的ExtractBench基准测文档提取。商业VLM长文档会截断，LlamaExtract Agentic Plus更准还更便宜。
ExtractBench是一个面向模式引导文档提取的新基准，评估集涵盖370份企业文档、4869页、8个业务领域和67种文档类型。该基准同时评测值准确性、记录完整性、来源可追溯性和成本，其中值F1对顺序不敏感。测试发现，商业VLM在短文档上表现良好，但在长文档上常截断记录列表；编码智能体准确率更高但成本明显更高。LlamaExtract Agentic Plus在三个指标上均排第一，成本仅为编码智能体的很小一部分。
ExtractBench: A Benchmark for Schema-Guided Enterprise Document Extraction
Enterprise workflows increasingly rely on agents for \emph{schema-guided extraction}: given a document and a user-defined schema, the agent faithfully follows the schema to produce the correct output with source evidence as grounding metadata. We present ExtractBench, a benchmark for schema-guided extraction and, to our knowledge, the first to score value accuracy, record completeness at scale, grounding, and measured cost together. The evaluation system contains 4,869 pages across 370 enterprise documents, 8 business domains, and 67 document types, with clear tags differentiating their challenge scenarios. The scalable schema and ground-truth curation pipeline combines independent-system agreement for real documents, known values for synthetic lists, and human verification for forms. We report order-insensitive value F1 for value accuracy, plus two grounding metrics for source traceability: word- and page-level F1. Commercial VLMs perform well on short documents but often truncate record lists on long ones, while coding agents retain higher accuracy at much higher cost. LlamaExtract Agentic Plus ranks first on all three metrics, with accuracy comparable to coding agents at a fraction of the cost. Dataset and evaluation code are available on \href{https://huggingface.co/datasets/llamaindex/ExtractBench}{HuggingFace} and \href{https://github.com/run-llama/ExtractBench}{GitHub}.