LlamaIndex 发布 ParseBench-2：6 维度评测 40 款 OCR 工具
做 RAG 或文档处理的都该看看：LlamaIndex 把 40 款 OCR 工具按 6 个维度拆开评测，Opus 5.5 表格强图表弱，Extend 和 Datalab 各有强项，选型时能直接对着自己场景挑。数据集也开源了。
Jerry Liu 团队发布文档解析基准 ParseBench-2，首日覆盖 40 款工具，包括专用 OCR 服务、前沿模型和开源方案，并按表格、图表、表单等 6 个维度分别打分。基准包含 4300+ 文档页（较 ParseBench-1 翻倍），为 330+ 种错误类型逐一标注严重度，让指标分数与人工判断对齐，区别于 OmniDocBench 的确定性指标和 ParseArena 的 LLM-as-a-judge 方法。
分维度看结论更细：Opus 5.5 擅长表格但图表较弱；专用服务商各有侧重，Extend 在表单上表现好，Datalab 在表格上占优。总体分只是复合平均，团队建议按自己业务关心的错误类型自定义严重度权重。数据集已在 Hugging Face 开放，排行榜托管在 parsebench.ai。
同时团队发布 OCRia-1.5 文档解析模型家族，为 LlamaParse 各模式提供支撑，在复杂表格、表单和图表解析上较上一代有明显提升。ParseBench-1 已有 5 万+ 月下载量，被 NVIDIA、Cohere、Perplexity 等公司采用。
There are thousands of OCR tools out there, and ParseBench-2 represents our attempt to not only build a standardized benchmark, but a framework for understanding which tools do better on which dimensions. On day 1, we’ve benchmarked 40 different tools across 6 different dimensions: * specialized OCR tools * frontier models * open-weight solutions We’ve aggressively tried to improve the metrics from ParseBench-1 to reflect real penalties while remaining quantitative. *** For what it’s worth, I would take the overall score with a grain of salt, since it’s a simple composite average. A lot of the insights/nuances in the benchmark come from analyzing each individual dimension and understanding the tagging behind each failure case. It’s fascinating to see where models and other vendors do well - we don’t top the charts in every dimension relative to price point! ✅ frontier models are hillclimbing certain dimensions while ignoring others. e.g. opus 5.5 is quite good at tables, while bad at charts ✅ specialized OCR providers do well on various dimensions, e.g. Extend on forms, Datalab on tables, (we also fixed reducto r-1 chart evals) There has been some feedback on annotations and metrics that we’re continuing to take into account, and will create new versions soon. Also reminder that if you care about certain errors more than others, you're welcome to customize the severities for the use cases you care about. We’re going to be adding a ton more models to the leaderboard, come check it out: parsebench.ai Hugging Face: huggingface.co/datasets/llama…
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「AI 开发者工具链、Coding Agent 与工程化实践」「中美大模型技术代差与商业化对照」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：ParseBench、LlamaParse、OCR、LlamaIndex，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。截至目前，全网已有 11 家独立信源对该事件进行交叉印证与跟进报道（含 TestingCatalog、techcrunch、Thomas Wolf 等），属于近期高关注度行业信号。
