Docling vs Nougat
Which should you use? A side-by-side look at what each one does, who it suits, what it costs, how hard it is to set up, and where each falls short.
Open source · MIT
Python library that converts PDFs and office files into structured text for AI use.
Open source · MIT
Converts academic PDFs into markup text, preserving LaTeX math and tables.
The short answer
Choose Docling if you need…
- Python developers building retrieval or RAG pipelines over PDFs and office files
- Teams that need tables and reading order preserved when extracting document content
- Users who need local processing for sensitive or air-gapped documents
- People who want a command-line tool to convert a document to Markdown
Choose Nougat if you need…
- Researchers who want editable text from arXiv-style papers with equations and tables
- People building datasets of scientific documents in markup form
- Developers who want a local PDF-to-markup conversion step in a pipeline
- Users processing mostly English, Latin-alphabet academic papers
Side-by-side
| Docling | Nougat | |
|---|---|---|
| Type | Library | AI model / agent |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 70k | 10.1k |
| Setup difficulty | Beginner | Intermediate |
| How you get it | Install with a package manager | Install with a package manager |
| Main language | Python | Python |
| Last code update | 5 days ago | 21 months ago |
What each one is
Docling
Docling parses many document formats, including PDF, Word, PowerPoint, Excel, HTML and email, and turns them into a structured representation. It exports that content to formats such as Markdown, HTML and JSON. People use it to prepare documents for generative AI and retrieval pipelines, and it can run locally for sensitive data.
Nougat
Nougat is a neural model from Meta's research team that reads scientific PDF papers and converts them into a lightweight markup format (.mmd) that is mostly compatible with Mathpix Markdown. It is built to handle LaTeX math and tables, which ordinary PDF text extraction often mangles. You run it from the command line or start a local API to process documents.
Best for
Docling
- Python developers building retrieval or RAG pipelines over PDFs and office files
- Teams that need tables and reading order preserved when extracting document content
- Users who need local processing for sensitive or air-gapped documents
- People who want a command-line tool to convert a document to Markdown
Nougat
- Researchers who want editable text from arXiv-style papers with equations and tables
- People building datasets of scientific documents in markup form
- Developers who want a local PDF-to-markup conversion step in a pipeline
- Users processing mostly English, Latin-alphabet academic papers
Strengths
Docling
- Supports a wide range of input formats in one library, per the README feature list
- Can run locally, which the README highlights for sensitive data and air-gapped setups
- Includes a CLI, a Python API, an MCP server and an API server option
Nougat
- Designed specifically for LaTeX math and tables in academic PDFs
- Offers a command-line tool, a local API, and pip installation
- Code is MIT licensed and the project is backed by a published arXiv paper
Not ideal for
Docling
- Users who need a no-code desktop app, since the README centers on pip install and CLI or Python use
- Projects that need metadata extraction such as title and authors, which the README lists as coming soon
- Chemistry-heavy work, since molecular structure understanding is also listed as coming soon
Nougat
- Documents in Chinese, Russian, Japanese and similar languages, which the README says will not work
- Non-academic PDFs like invoices or forms, since the model was trained on arXiv and PMC papers
- Users without a GPU who need fast throughput, since CPU use may be slow and can trigger false failure detections
- Commercial projects needing unrestricted model use, because the model weights are licensed CC-BY-NC
Trade-offs
Docling
- Advanced features such as VLM pipelines and audio or video parsing may need extra setup or models; the README does not detail requirements for each
- Some features are newer, as shown in the What's new section, so behavior may change between releases
Nougat
- Model weights are CC-BY-NC, so they cannot be used commercially despite the MIT-licensed code
- Works poorly outside English-style scientific documents and can return [MISSING_PAGE] responses
- Setup on Windows with GPU requires installing a matching PyTorch version first
Getting started
Nougat
Install from PyPI
pip install nougat-ocrInstall from the repository
pip install git+https://github.com/facebookresearch/nougatFrequently asked questions
What is the difference between Docling and Nougat?
Docling: Python library that converts PDFs and office files into structured text for AI use. Nougat: Converts academic PDFs into markup text, preserving LaTeX math and tables. Docling suits python developers building retrieval or rag pipelines over pdfs and office files, while Nougat suits researchers who want editable text from arxiv-style papers with equations and tables.
Are Docling and Nougat free?
Docling: Free and open source. Nougat: Free and open source.
Which is easier to set up, Docling or Nougat?
Docling is easier to get started with (rated beginner); Nougat is rated intermediate and usually needs more setup.
Is Docling or Nougat more popular?
Docling has more GitHub stars (70k) than Nougat (10.1k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to Docling and Nougat?
Other options people compare them with include MarkItDown, pdf-inspector, Rich, Showdown, remark.
Keep comparing
Built from our catalogue entries for both products: open-source details come from each project's README (AI-assisted, refreshed weekly) and GitHub data; AI-tool details are editorial. Spotted something wrong? Let us know from either product's page.