We introduce PaddleOCR-VL-1.6, an upgraded compact document parsing model built upon PaddleOCR-VL-1.5. PaddleOCR-VL-1.6 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. PaddleOCR-VL-1.6 achieves a new state-of-the-art score of 96.33% on OmniDocBench v1.6, sets new records on OmniDocBench v1.5 and Real5-OmniDocBench as well, and demonstrates strong competitiveness against top-tier VLMs. The model architecture is fully compatible with PaddleOCR-VL-1.5, enabling zero-cost plug-and-play migration.
🚀 New SOTA Accuracy: OmniDocBench v1.6 achieves 96.33%, setting new state-of-the-art records on OmniDocBench v1.5 and Real5-OmniDocBench as well. It delivers comprehensive leading performance across text, formula, and table recognition, surpassing both open-source and closed-source solutions.
⚡ Fully Upgraded Capabilities: Significant improvements in table, Chinese ancient document, and Chinese rare character recognition, along with notable enhancements in seal/stamp recognition, text spotting, chart recognition, and more diverse scenarios.
🔄 Seamless Migration: The model architecture is fully compatible with PaddleOCR-VL-1.5 — zero adaptation cost, plug-and-play replacement.
2026.05.28 🚀 We release PaddleOCR-VL-1.6. PaddleOCR-VL-1.6 achieves a new state-of-the-art score of 96.33% on OmniDocBench v1.6, sets new records on OmniDocBench v1.5 and Real5-OmniDocBench as well, and demonstrates strong competitiveness against top-tier VLMs. The model architecture is fully compatible with PaddleOCR-VL-1.5, enabling zero-cost plug-and-play migration.
# The following command installs the PaddlePaddle version for CUDA 12.6. For other CUDA versions and the CPU version, please refer to https://www.paddlepaddle.org.cn/en/install/quick?docurl=/documentation/docs/en/develop/install/pip/linux-pip_en.html
python -m pip install paddlepaddle-gpu==3.2.1 -i https://www.paddlepaddle.org.cn/packages/stable/cu126/
python -m pip install -U "paddleocr[doc-parser]>=3.6.0"
Please ensure that you install PaddlePaddle framework version 3.2.1 or above, along with the special version of safetensors. For macOS users, please use Docker to set up the environment.
from paddleocr import PaddleOCRVL
pipeline = PaddleOCRVL(pipeline_version="v1.6")
output = pipeline.predict("https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/paddleocr_vl_demo.png")
for res in output:
res.print()
res.save_to_json(save_path="output")
res.save_to_markdown(save_path="output")
Accelerate VLM Inference via Optimized Inference Servers#
Start the VLM inference server: You can start the vLLM inference service using one of two methods:
Currently, the PaddleOCR-VL-1.6-0.9B model facilitates seamless inference via the transformers library, supporting comprehensive text spotting and the recognition of complex elements including formulas, tables, charts, and seals. Below is a simple script we provide to support inference using the PaddleOCR-VL-1.5-0.9B model with transformers.
Note. Note: We currently recommend using the official method for inference, as it is faster and supports page-level document parsing. The example code below only supports element-level recognition and text spotting.
# ensure the transformers v5 is installed
python -m pip install "transformers>=5.0.0"
Performance metrics are cited from the OmniDocBench official leaderboard, except for Gemini-3 Pro, Qwen3-VL-235B-A22B-Instruct and our model, which were evaluated independently.
Across all five diverse and challenging scenarios—scanning, warping, screen-photography, illumination, and skew—PaddleOCR-VL-1.6 consistently sets new SOTA records#
Real5-OmniDocBench is a brand-new benchmark oriented toward real-world scenarios, which we constructed based on the OmniDocBench v1.5 dataset. The dataset comprises five distinct scenarios: Scanning, Warping, Screen-photography, Illumination, and Skew. For further details, please refer to Real5-OmniDocBench.
We would like to thank PaddleFormers, Keye, MinerU, OmniDocBench for providing valuable code, model weights and benchmarks. We also appreciate everyone's contribution to this open-source project!
If you find PaddleOCR-VL-1.6 helpful, feel free to give us a star and citation.
@misc{zhang2026paddleocrvl16expandingfrontierdocument,
title={PaddleOCR-VL-1.6: Expanding the Frontier of Document Parsing with Under-Optimized Region Refinement and Progressive Post-Training},
author={Zelun Zhang and Hongen Liu and Suyin Liang and Yubo Zhang and Yiqing Xiang and Jiaxuan Liu and Ting Sun and Manhui Lin and Yue Zhang and Changda Zhou and Tingquan Gao and Cheng Cui and Yi Liu and Dianhai Yu and Yanjun Ma},
year={2026},
eprint={2606.03264},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2606.03264},
}
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