---
title: "Baidu's PaddleOCR-VL-1.6 Beats 235B Giants at Document Parsing With 1B Parameters"
subtitle: "PaddleOCR-VL-1.6 hits 96.33% on OmniDocBench — a 0.9B model that beats models 260x its size on document parsing"
url: "https://alphasignal.ai/news/baidu-s-paddleocr-vl-1-6-beats-235b-giants-at-document-parsing-with-1b"
author: "AlphaSignal Newsroom"
author_url: "https://alphasignal.ai/editorial-team"
company: "AlphaSignal"
company_url: "https://alphasignal.ai/company/alphasignal"
published: "2026-05-27T06:27:27.000Z"
updated: "2026-06-08T19:57:31.758Z"
type: "Model"
topics:
  - "Image"
  - "Data"
subtopics:
  - "Ocr"
  - "Datasets"
upvotes: 2853
source_url: "https://huggingface.co/PaddlePaddle/PaddleOCR-VL-1.6"
comments: 0
truncated: true
---

# Baidu's PaddleOCR-VL-1.6 Beats 235B Giants at Document Parsing With 1B Parameters

> PaddleOCR-VL-1.6 hits 96.33% on OmniDocBench — a 0.9B model that beats models 260x its size on document parsing

[PaddleOCR-VL-1.6](https://huggingface.co/PaddlePaddle/PaddleOCR-VL-1.6), Baidu's latest compact document parsing model, just set a new state-of-the-art on the authoritative OmniDocBench benchmark , and it did so with fewer than 1 billion parameters. That's not a typo. A 0.9B model is now outperforming 235B-parameter giants like Qwen3-VL and proprietary systems like Gemini 3 Pro on structured document understanding. The release is free, open-source under Apache 2.0, and available right now on Hugging Face.

## The problem it's solving

Document parsing , turning scanned PDFs, photos of forms, or image-heavy reports into structured Markdown or JSON , is one of the most critical bottlenecks in building reliable RAG (retrieval-augmented generation) pipelines. The challenge isn't just reading text; it's faithfully reconstructing tables, formulas, charts, seals, and reading order from images that may be warped, poorly lit, or photographed at an angle. Most solutions either require massive models or sacrifice accuracy on edge cases like ancient manuscripts, rare characters, or complex nested tables.

Top systems increasingly encounter difficult regions that aren't fully addressed by increasing data volume or model size alone. Long-tail document layouts, rare scripts, dense formulas, complex tables, and noisy supervision can remain underrepresented or unreliable even when the overall training corpus is large. PaddleOCR-VL-1.6's entire design philosophy is a direct answer to this problem.

## What's new in 1.6

PaddleOCR-VL-1.6 is an upgraded compact document parsing model built upon PaddleOCR-VL-1.5\. It introduces a region-aware data optimization framework that identifies weak regions from the previous model, applies targeted enhancement to those regions, and improves the reliability of supervision signals. It further adopts a progressive post-training recipe based on curated data selection and reinforcement learning, pushing model performance to a higher level through staged optimization.

The improvements are concrete and measurable. Compared to 1.5:

* **Overall score** on OmniDocBench v1.6: 94.93% → **96.33%**
* **Table TEDS** (tree edit distance similarity for tables): 91.67% → **94.76%** (+3.09%)
* **Table TEDS-Structure**: 94.37% →

_Preview only. Full text requires an AlphaSignal Pro account._

## Sources

- [PaddleOCR-VL-1.6 Model Card — Hugging Face](https://huggingface.co/PaddlePaddle/PaddleOCR-VL-1.6)
- [PaddleOCR-VL-1.6: Expanding the Frontier of Document Parsing — arXiv](https://arxiv.org/html/2606.03264v1)
- [PaddleOCR-VL-1.6 Introduction — Official Documentation](https://www.paddleocr.ai/latest/en/version3.x/algorithm/PaddleOCR-VL/PaddleOCR-VL-1.6.html)
- [PaddleOCR-VL Usage Tutorial — paddleocr.ai](https://www.paddleocr.ai/latest/en/version3.x/pipeline_usage/PaddleOCR-VL.html)
- [Baidu PaddleOCR-VL-1.6 Sets Document Parsing SOTA — AI Weekly](https://aiweekly.co/alerts/baidu-paddleocr-vl-16-sets-document-parsing-sota)