Datalab 发布 OmniExtractBench 基准测试，其 Agentic Plus 模型在测试中取得 93.94% 成绩

他们使用 Agentic Plus 模式测试了该模型，通过修复基准测试中移除空值的兼容性问题后，在测试中取得了 93.94% 的成绩，与当前最高分持平或略高。

Recently, Datalab released OmniExtractBench, a benchmark that combines several benchmarks (our own ExtractBench, LongExtractBench, other vendors) to measure structured document quality. The benchmark measured our "Agentic Plus" extraction model, our most powerful mode for complex document extraction. We found a compatibility workaround in the benchmark’s integration that stripped null from fields that allowed it - removing a valid way to represent missing information. We restored that option while preserving the schema’s structure and required fields. With the same scorer, we were able to get to 93.94% on the benchmark. This puts it slightly higher (or at least equal) to the highest published scores. There's a lot of vendor benchmarks, but ultimately it's also important to evaluate over your own data. We'd be happy to help you get that set up. PR: github.com/datalab-to/omn… Our Agentic Plus mode for LlamaExtract is legitimately quite good, would love to you to try it out and let us know your thoughts! cloud.llamaindex.ai📊 6 ⚡

本条情报归属 AITOP「模型」核心栏目，重点聚焦北美前沿实验室与硅谷科技巨头。在跨区域产业观察坐标中，该动态直接关联「中美大模型技术代差与商业化对照」「AI 开发者工具链、Coding Agent 与工程化实践」研究主线，反映出当前技术演进与商业落地的最新风向。涉及核心实体与技术议题包括：OmniExtractBench、Agentic Plus、LlamaExtract、Datalab，读者可通过下方标签进入对应专题页查阅历史脉络与关联对比。

信源核验说明：本文原始线索采编自「Jerry Liu」（@jerryjliu0），经 AITOP 多维评分管线结构化提炼。