Qianfan-OCR: A Unified End-to-End Model for Document Intelligence
We present Qianfan-OCR, a 4B-parameter end-to-end vision-language model that unifies document parsing, layout analysis, and document understanding within a single architecture. It performs direct image-to-Markdown conversion and supports diverse prompt-driven tasks including table extraction, chart understanding, document QA, and key information extraction. To address the loss of explicit layout analysis in end-to-end OCR, we propose Layout-as-Thought, an optional thinking phase triggered by special think tokens that generates structured layout representations -- bounding boxes, element types, and reading order -- before producing final outputs, recovering layout grounding capabilities while improving accuracy on complex layouts. Qianfan-OCR ranks first among end-to-end models on OmniDocBench v1.5 (93.12) and OlmOCR Bench (79.8), achieves competitive results on OCRBench, CCOCR, DocVQA, and ChartQA against general VLMs of comparable scale, and attains the highest average score on public key information extraction benchmarks, surpassing Gemini-3.1-Pro, Seed-2.0, and Qwen3-VL-235B. The model is publicly accessible via the Baidu AI Cloud Qianfan platform.
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Daxiang Dong
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2026-03-18T09:49:22.485Z
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daxiang
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Mingming Zheng
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2026-03-18T09:49:30.335Z
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Mingming Zheng
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Dong Xu
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Chunhua Luo
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2026-03-18T09:49:42.265Z
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chunhua luo
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Bairong Zhuang
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Yuxuan Li
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Ruoyun He
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Haoran Wang
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Wenyu Zhang
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Wenbo Wang
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Yicheng Wang
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2026-03-19T13:01:50.998Z
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yicheng
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Xue Xiong
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Ayong Zheng
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2026-03-18T09:50:29.836Z
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Zheng
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Xiaoying Zuo
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Shawing Zuo
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Ziwei Ou
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Jingnan Gu
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Quanhao Guo
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Jianmin Wu
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Dawei Yin
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Dou Shen
Qianfan-OCR: A Unified End-to-End Model for Document Intelligence
We present Qianfan-OCR, a 4B-parameter end-to-end vision-language model that unifies document parsing, layout analysis, and document understanding within a single architecture. It performs direct image-to-Markdown conversion and supports diverse prompt-driven tasks including table extraction, chart understanding, document QA, and key information extraction. To address the loss of explicit layout analysis in end-to-end OCR, we propose Layout-as-Thought, an optional thinking phase triggered by special think tokens that generates structured layout representations -- bounding boxes, element types, and reading order -- before producing final outputs, recovering layout grounding capabilities while improving accuracy on complex layouts. Qianfan-OCR ranks first among end-to-end models on OmniDocBench v1.5 (93.12) and OlmOCR Bench (79.8), achieves competitive results on OCRBench, CCOCR, DocVQA, and ChartQA against general VLMs of comparable scale, and attains the highest average score on public key information extraction benchmarks, surpassing Gemini-3.1-Pro, Seed-2.0, and Qwen3-VL-235B. The model is publicly accessible via the Baidu AI Cloud Qianfan platform.
Qianfan-OCR is a 4B-parameter vision-language model that unifies document parsing, layout analysis, and understanding while maintaining strong performance across multiple OCR benchmarks through its Layout-as-Thought mechanism.
vision-language model end-to-end model Markdown conversion layout analysis document understanding think tokens structured layout representations bounding boxes element types reading order layout grounding OCR benchmarks VLMs key information extraction