**MinerU-HTML** is an advanced HTML main content extraction tool based on Small Language Models (SLM). It can accurately identify and extract main content from complex web page HTML, automatically filtering out auxiliary elements such as navigation bars, advertisements, and metadata. **MinerU-HTML-v1.1-hunyuan0.5B-compact** is a Model Derivative of **Tencent Hunyuan 0.5B**. This model support 256k context-length, so it can support processing more complex web pages. We use a more compact output format, which reduces the inference latency and improves the speed of web page processing. Welcome to try our online document extraction tool! Supports HTML main content extraction and OCR for various document formats.

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MinerU2.5-2509-1.2B

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**MinerU2.5** is a 1.2B-parameter vision-language model for document parsing that achieves state-of-the-art accuracy with high computational efficiency. It adopts a two-stage parsing strategy: first conducting efficient global layout analysis on downsampled images, then performing fine-grained content recognition on native-resolution crops for text, formulas, and tables. Supported by a large-scale, diverse data engine for pretraining and fine-tuning, MinerU2.5 consistently outperforms both general-purpose and domain-specific models across multiple benchmarks while maintaining low computational overhead. For convenience, we provide `mineru-vl-utils`, a Python package that simplifies the process of sending requests and handling responses from MinerU2.5 Vision-Language Model. Here we give some examples to use MinerU2.5. For more information and usages, please refer to mineru-vl-utils. 📌 We strongly recommend using vllm for inference, as the `vllm-async-engine` can achieve a concurrent inference speed of **2.12 fps** on one A100.

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MinerU2.5-Pro-2604-1.2B

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**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.

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MinerU2.5-Pro-2605-1.2B

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2026.05.21 🎉🎉 We are pleased to announce the release of MinerU2.5-Pro-2605, an updated version of our model. - **Enhanced Layout Detection** To address the category misclassification issues observed in the 2604 version during layout detection, we conducted a comprehensive data cleaning process. This has substantially reduced category errors in layout detection. Notably, the missed detection rate for the `image_block` category has been significantly reduced. - **Improved Image Analysis** To overcome the limitations of the 2604 version in image analysis, we constructed a large-scale training dataset for this task. As a result, the 2605 version demonstrates markedly enhanced recognition capabilities across a wide range of charts, flowcharts, and even seals.

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