Docling vs MarkItDown: Which Is Better for Document-to-Markdown Conversion?

Docling vs MarkItDown: Which Is Better for Document-to-Markdown Conversion?

You are building a RAG pipeline or feeding documents into an LLM. You need clean Markdown output from PDFs, DOCX files, or spreadsheets. Two Python libraries keep coming up: IBM's Docling and Microsoft's MarkItDown. This post walks through the real differences so you can pick the right one — or know when to skip both.

If you do not want to run Python at all, file2markdown handles the same conversions through a browser or REST API with no setup required.

The Quick Answer

Use Docling when document structure matters: dense tables, multi-column layouts, academic PDFs, or any document where losing formatting would degrade retrieval quality.

Use MarkItDown when you need fast, lightweight text extraction across many file types and the documents are straightforward.

Use file2markdown when you want a hosted API or a no-code web interface that handles PDF, DOCX, XLSX, and more without managing Python dependencies.

What Each Tool Is

Docling (by IBM Research) is an open-source document understanding library. It uses AI-based layout detection to parse complex document structure — tables, figures, reading order, multi-column text — and exports to Markdown, JSON, or its own DoclingDocument format. It is heavier, slower, and more accurate.

MarkItDown (by Microsoft) is a lightweight converter designed specifically to produce LLM-friendly Markdown. It wraps existing libraries (pdfminer, python-docx, openpyxl, etc.) with a consistent interface. It is faster, easier to install, and supports a broader range of file types including images, audio, and EPUB.

Head-to-Head Comparison

	Docling	MarkItDown
PDF table extraction	Excellent (AI layout detection)	Basic (text-based)
Multi-column PDFs	Handles correctly	Often merges columns
Format support	PDF, DOCX, PPTX, XLSX, images	PDF, DOCX, PPTX, XLSX, CSV, images, audio, EPUB, HTML
Speed	Slow (GPU helps)	Fast
Install size	Large (PyTorch dependency)	Small
OCR support	Yes (built-in)	Limited
Output formats	Markdown, JSON, DoclingDocument	Markdown only
Maintenance	IBM Research	Microsoft

Installing and Using Each

Docling

pip install docling

from docling.document_converter import DocumentConverter

converter = DocumentConverter()
result = converter.convert("report.pdf")
print(result.document.export_to_markdown())

The first run downloads model weights, so expect a delay. Subsequent runs are faster. For large document sets in a RAG pipeline, Docling's structured output integrates well with LlamaIndex and LangChain document loaders.

MarkItDown

pip install markitdown

from markitdown import MarkItDown

md = MarkItDown()
result = md.convert("report.pdf")
print(result.text_content)

MarkItDown is quick to run and produces readable Markdown immediately. The simpler API makes it easy to drop into an existing pipeline. For a deeper look at what MarkItDown does and where it falls short, see what MarkItDown is.

Where Each Shines

Docling wins on complex PDFs

If your documents include research papers, financial reports, or government PDFs with intricate table layouts, Docling's AI-based approach produces significantly cleaner output. It correctly identifies headers, caption text, and table boundaries instead of dumping raw character streams. This matters for RAG — a garbled table sent to an embedding model produces poor retrieval. See the full pipeline in RAG document prep: PDF to Markdown to chunks.

MarkItDown wins on breadth and speed

MarkItDown handles more than 15 file formats out of the box and converts quickly enough for real-time use cases. If you are processing thousands of simple DOCX or HTML files, MarkItDown is the faster path. It also handles formats Docling does not — audio transcription, EPUB, and email files — making it more useful for mixed-format pipelines.

Neither wins on ease of use

Both tools require Python, dependency management, and code. If your team does not want to maintain a local conversion environment — or you need a conversion API without infrastructure overhead — file2markdown provides the same output through a REST API or web UI. See how to automate PDF to Markdown with Python using the file2markdown API instead.

Practical Guidance by Use Case

RAG pipeline with complex PDFs: Docling. Better structure means better chunk boundaries and fewer hallucinations from poorly formatted context. Once chunked, see chunking Markdown for vector databases.

Mixed file types (DOCX, XLSX, images): MarkItDown. Broader format support with less setup. For a head-to-head comparison between MarkItDown and file2markdown specifically, see file2markdown vs MarkItDown.

No Python environment / hosted API: file2markdown. Converts PDFs, spreadsheets, presentations, and images via a drag-and-drop interface or curl call.

Table-heavy documents: Docling for local processing; file2markdown for hosted extraction. See the guide on extracting tables from PDFs to Markdown.

Frequently Asked Questions

Is Docling better than MarkItDown for RAG?

For most RAG use cases involving complex PDFs, yes. Docling's AI layout detection preserves table structure and reading order more accurately than MarkItDown's text-layer extraction. The trade-off is speed and a heavier installation with PyTorch as a dependency.

Can I use Docling and MarkItDown together?

Yes. A common pattern is to try MarkItDown first for speed, then fall back to Docling for documents where the output quality is insufficient. You can route based on file type or a simple quality heuristic on the output.

Does MarkItDown support OCR for scanned PDFs?

MarkItDown has limited OCR support. For scanned documents, Docling performs better out of the box. For a hosted OCR-to-Markdown solution, file2markdown handles scanned PDFs without any local setup.

Which tool has better long-term support?

Both are actively maintained: Docling by IBM Research (open-source) and MarkItDown by Microsoft (open-source). Both have growing communities and frequent releases. Neither is at risk of abandonment in the near term.