MODEL PROFILE

Qianfan-OCR

baidu · Qianfan-OCR

**Qianfan-OCR** is a **4B-parameter end-to-end document intelligence model** developed by the Baidu Qianfan Team. It unifies document parsing, layout analysis, and document understanding within a single vision-language architecture. Unlike traditional multi-stage OCR pipelines that chain separate layout detection, text recognition, and language comprehension modules, Qianfan-OCR performs **direct image-to-Markdown conversion** and supports a broad range of prompt-driven tasks — from structured document parsing and table extraction to chart understanding, document question answering, and key information extraction — all within one model. - 🏆 **#1 End-to-End Model on OmniDocBench v1.5** — Achieves **93.12** overall score, surpassing DeepSeek-OCR-v2 (91.09), Gemini-3 Pro (90.33), and all other end-to-end models - 🏆 **#1 End-to-End Model on OlmOCR Bench** — Scores **79.8** - 🏆 **#1 on Key Information Extraction** — Overall mean score of **87.9** across five public KIE benchmarks, surpassing Gemini-3.1-Pro, Gemini-3-Pro, Seed-2.0, and Qwen3-VL-235B-A22B - 🧠 **Layout-as-Thought** — An innovative optional thinking phase that recovers explicit layout analysis within the end-to-end paradigm via `⟨think⟩` tokens - 🌍 **192 Languages** — Multilingual OCR support across diverse scripts - ⚡ **Efficient Deployment** — Achieves **1.024 PPS** (pages per second) with W8A8 quantization on a single A100 GPU

Visual QAlicense:apache-2.0
Context windowNot disclosed
Input → OutputText、Image → Text
Model weightsNot verified
Profile updated2026/10/31 02:09:00
OVERVIEW

Model overview

**Qianfan-OCR** is a **4B-parameter end-to-end document intelligence model** developed by the Baidu Qianfan Team. It unifies document parsing, layout analysis, and document understanding within a single vision-language architecture. Unlike traditional multi-stage OCR pipelines that chain separate layout detection, text recognition, and language comprehension modules, Qianfan-OCR performs **direct image-to-Markdown conversion** and supports a broad range of prompt-driven tasks — from structured document parsing and table extraction to chart understanding, document question answering, and key information extraction — all within one model. - 🏆 **#1 End-to-End Model on OmniDocBench v1.5** — Achieves **93.12** overall score, surpassing DeepSeek-OCR-v2 (91.09), Gemini-3 Pro (90.33), and all other end-to-end models - 🏆 **#1 End-to-End Model on OlmOCR Bench** — Scores **79.8** - 🏆 **#1 on Key Information Extraction** — Overall mean score of **87.9** across five public KIE benchmarks, surpassing Gemini-3.1-Pro, Gemini-3-Pro, Seed-2.0, and Qwen3-VL-235B-A22B - 🧠 **Layout-as-Thought** — An innovative optional thinking phase that recovers explicit layout analysis within the end-to-end paradigm via `⟨think⟩` tokens - 🌍 **192 Languages** — Multilingual OCR support across diverse scripts - ⚡ **Efficient Deployment** — Achieves **1.024 PPS** (pages per second) with W8A8 quantization on a single A100 GPU

Key features

特点摘要待依据来源补齐,不凭模型名称推断能力。

Limitations & considerations

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PRICING & COST

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INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

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Frequently asked questions

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Sources & traceability

许可证:apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。