mineru vs markitdown
Two of the top projects in Data and scraping for AI, side by side: who leads on each score part and who each one suits, then setup, license, activity and what each review found.
mineru
Document parser that converts PDFs, images and Office files to Markdown and JSON
markitdown
Python tool that converts PDF, Office, HTML and audio files to Markdown for LLMs
Verdict
mineru ranks higher, 78 to 71. Of the 5 score parts we measure for both, mineru leads on 3, markitdown on 1 and 1 is even.
- Adoptionmarkitdown leads by 18 points, 95 to 77: a wide gap.
- FreshnessEven at 100.
- Maintenancemineru leads by 9 points, 99 to 90.
- Easy to runmineru leads by 17 points, 50 to 33: a wide gap.
- Agent-readymineru leads by 70 points, 70 to 0: a wide gap.
Pick mineru if you are
Engineers feeding PDFs and Office documents to LLM pipelines or agents
- Handles scanned PDFs, tables and formulas, with four quality tiers from fast preview to advanced
- Stable doc/tier/page/block locators allow paged reading and citation of long documents
Pick markitdown if you are
Developers feeding documents into LLM and RAG pipelines
- Covers many formats through one CLI and Python API (convert, convert_local, convert_stream)
- Built-in converters run offline; install only needed formats via extras like [pdf,docx,pptx]
| What we compare | mineru | markitdown |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 77, popular | 95, widely used |
| Freshness | 100, active | 100, active |
| Maintenance | 99, healthy | 90, healthy |
| Easy to run | 50, easy | 33, some setup |
| Agent-ready | 70, partly | 0, none |
| Facts from GitHub and the README | ||
| Stars | 81.8k | 190.3k |
| License | custom license (read the license) | MIT (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Oct 2026 | Sep 2026 |
| Language | Python | Python |
| Docker | Yes | Yes |
| GPU | Optional | Not needed |
| arm64 or Apple Silicon | Mentioned | Not stated |
mineru
MinerU parses PDFs, scanned images, Office, OpenDocument, EPUB, OFD, HTML and CSV files into Markdown, HTML, LaTeX, DOCX and structured content lists. It offers four parsing tiers (flash, basic, standard, advanced), a local document library with search, and page/block locators for citations. It ships a CLI, Python SDK, API, router and Gradio WebUI, plus an agent skill.
Who it is for: Engineers feeding PDFs and Office documents to LLM pipelines or agents
Strengths
- Handles scanned PDFs, tables and formulas, with four quality tiers from fast preview to advanced
- Stable doc/tier/page/block locators allow paged reading and citation of long documents
- Small models run on ONNX CPU; the VLM can run on llama.cpp, vLLM or LMDeploy
- Documents are not uploaded to the official service unless remote parsing is configured
Weaknesses
- Office, HTML, EPUB, OFD and CSV files only get flash-tier parsing; higher tiers are PDF and image only
- Docker deployment for non-NVIDIA devices is pending an update
- Highest throughput needs an NVIDIA GPU and mineru[full]; Windows needs torch installed separately
- License is reported as NOASSERTION, so terms need checking before use
- GPU optional
- Docker + Compose
- Needs Python >=3.10,<3.15, llama.cpp, vLLM or LMDeploy (VLM runtime), ONNX or Torch
- Models: MinerU VLM, ONNX small models, Torch small models, llama.cpp, vLLM
markitdown
MarkItDown is a Python library and CLI that converts PDF, Word, PowerPoint, Excel, images, audio, HTML, CSV/JSON/XML, ZIP, EPUB and YouTube URLs into Markdown, keeping headings, lists, tables and links. Optional backends add LLM image descriptions, Azure Document Intelligence, Azure Content Understanding, and OCR via the markitdown-ocr plugin. Output targets LLM pipelines rather than high-fidelity human-facing documents.
Who it is for: Developers feeding documents into LLM and RAG pipelines
Strengths
- Covers many formats through one CLI and Python API (convert, convert_local, convert_stream)
- Built-in converters run offline; install only needed formats via extras like [pdf,docx,pptx]
- Third-party plugin system, plus an LLM-vision OCR plugin using any OpenAI-compatible client
- MIT license, active releases, Python 3.10 through 3.14
Weaknesses
- Not meant for high-fidelity conversion; output is tuned for text analysis tools
- Video and structured field extraction need billable Azure Content Understanding
- convert() accepts URIs and files with process privileges; untrusted input must be sanitized
- LLM image descriptions currently limited to pptx and image files; OCR needs a plugin
- no GPU
- Docker
- Needs Python 3.10-3.14, Azure Document Intelligence (optional), Azure Content Understanding (optional), OpenAI-compatible client (optional)
- Models: OpenAI-compatible vision models (e.g. gpt-4o)
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