MODEL PROFILE

MinerU2.5-Pro-2604-1.2B

opendatalab · MinerU2.5-Pro-2604-1.2B

**MinerU2.5-Pro** is our latest document parsing model (PDF-to-Markdown) that establishes a new industry standard. By focusing entirely on data engineering without altering the original 1.2B-parameter architecture, it delivers exceptional results across the board: **1. Defeating Leading Models on OmniDocBench v1.6** On the newly proposed, highly rigorous OmniDocBench v1.6, MinerU2.5-Pro achieves the absolute **SOTA overall score of 95.69**. It comprehensively outperforms both top-tier specialized OCR models (**GLM-OCR, PaddleOCR-VL-1.5**) and massive frontier VLMs (**Gemini 3 Pro, Qwen3-VL-235B**). **2. Massive Leap from MinerU 2.5 via Data Engineering** Compared to the previous MinerU 2.5 baseline, the overall score skyrocketed from **92.98 to 95.69**. This breakthrough was achieved not by scaling model parameters, but through meticulous data engineering—drastically expanding data scale, enriching distribution and difficulty diversity, and systematically elevating annotation quality.

Visual QAlicense:apache-2.0
Context windowNot disclosed
Input → OutputText、Image → Text
Model weightsNot verified
Profile updated2026/10/30 12:37:11
OVERVIEW

Model overview

**MinerU2.5-Pro** is our latest document parsing model (PDF-to-Markdown) that establishes a new industry standard. By focusing entirely on data engineering without altering the original 1.2B-parameter architecture, it delivers exceptional results across the board: **1. Defeating Leading Models on OmniDocBench v1.6** On the newly proposed, highly rigorous OmniDocBench v1.6, MinerU2.5-Pro achieves the absolute **SOTA overall score of 95.69**. It comprehensively outperforms both top-tier specialized OCR models (**GLM-OCR, PaddleOCR-VL-1.5**) and massive frontier VLMs (**Gemini 3 Pro, Qwen3-VL-235B**). **2. Massive Leap from MinerU 2.5 via Data Engineering** Compared to the previous MinerU 2.5 baseline, the overall score skyrocketed from **92.98 to 95.69**. This breakthrough was achieved not by scaling model parameters, but through meticulous data engineering—drastically expanding data scale, enriching distribution and difficulty diversity, and systematically elevating annotation quality.

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。