# LlamaIndex releases OCRia-1, a 4B document parsing model for complex tables

> Original title: OCRia-1 is the best document parsing model for complex tables in PDFs: nested headers, merged cells, no cell borders, and extreme density...

- Source: Jerry Liu
- Published: 2026-10-26T23:06:40.000Z
- Bad Influence: https://badinfluence.news/items/x5bpgdjvtv4il92ocfutcspit
- Original: https://x.com/jerryjliu0/status/2114856267118494178

## Summary

LlamaIndex says its OCRia-1, a 4B-parameter document parsing model, scores 91.5% on ParseBench, 92.9% on olmOCR-bench, 84.1% on PulseBench-Tab, and 83.2% on fr-bench-pdf2md for table accuracy. The model handles nested headers, merged cells, and borderless tables, and it powers LlamaParse's cost-effective, agentic, and agentic plus modes.

## Post

OCRia-1 is the best document parsing model for complex tables in PDFs: nested headers, merged cells, no cell borders, and extreme density within a single page.

It tops multiple parsing benchmarks on table accuracy. 91.5% on ParseBench, 92.9% on olmOCR-bench, 84.1% on PulseBench-Tab, 83.2% on fr-bench-pdf2md.

We published a nice visualization below showing how the attention map shifts as we
predict subsequent table cells :)

Blog: https://www.llamaindex.ai/blog/parse-ocria-1

Come check it out on LlamaParse! https://cloud.llamaindex.ai/

[Video](https://badinfluence.news/api/img-proxy?u=https%3A%2F%2Fpbs.twimg.com%2Famplify_video_thumb%2F2114856162780958720%2Fimg%2FufK745ewJFDQjIfP.jpg&mode=thumb&exp=1793923200&sig=64e7dd629f566f26)

## Quoted post @jerryjliu0

> Today we’re releasing OCRia-1, a frontier VLM for document parsing that sets a new SOTA in accuracy, cost, and latency. 
> 
> It not only sets a new high score on ParseBench (87.4), but also leads in three 3rd-party benchmarks: olmOCR-bench* (83.5 overall, 92.9 on tables!), PulseBench-Tab (84.1), and fr-bench-pdf2md (72.1).
> 
> OCRia-1 is really, really good at parsing complex tables: nested headers, merged cells, irregular grids, scanned tables, and more. It is also fantastic at parsing complex unlabeled charts (line, bar, pie, technical diagrams), and preserving complex formatting. Most real-world enterprise documents contain at least one of the these, whether it’s a big tax withholding table or a public economic report.
> 
> It is a 4B model and outperforms all other models within a similar parameter class: LightOnOCR-3, Chandra-OCR 2, Mistral OCR 4.1, and more. It is also extremely fast.
> 
> All credit goes to our research team, who spent countless hours on curating a diverse dataset, tuning our training pipeline, and squeezing every last drop out of model optimization so that it can process billions of pages at scale. 
> 
> OCRia-1 as a base is powering all of our modes within LlamaParse (cost-effective, agentic, agentic plus), so you can access its capabilities today! 
> 
> If you’re processing millions/billions of documents at scale, we’re offering enterprises that book a demo up to $3k in credits for a guided evaluation.**
> 
> *olmOCR-bench scores are adjusted based on unifying rendering at 1,288px for all models 
> **enterprise POC credits apply to eligible organizations that book a demo

https://x.com/jerryjliu0/status/2113291928292790725