Marker vs Docling: The Best Open-Source PDF-to-Markdown Tool?

Marker vs Docling: The Best Open-Source PDF-to-Markdown Tool?

When PDF quality really matters, two open-source, ML-powered converters lead the pack: Marker and IBM's Docling. Both run locally, both use models for layout and tables, and both produce excellent Markdown. So which should you use? It comes down to focus, output options, and ecosystem.

If you would rather not run models at all, file2markdown converts PDFs to clean Markdown through a browser or REST API with server-side OCR.

The Quick Answer

Use Marker when PDFs are your main input and you want fast, high-fidelity Markdown — including math and tables — with GPU acceleration.

Use Docling when you want broader document understanding (multiple formats, structured DoclingDocument/JSON output) and tight integration with RAG frameworks.

Use file2markdown when you want the quality without owning a GPU or managing models — hosted PDF and image conversion.

What Each Tool Is

Marker is an open-source pipeline specialized in converting PDFs (and some other formats) to high-quality Markdown. It uses ML models for layout, OCR, tables, and equations, and is built for throughput on a GPU. It is the go-to when PDF fidelity is the priority.

Docling (IBM Research) is a document-understanding library that parses layout with AI and exports to Markdown, JSON, or its DoclingDocument format. It covers more formats than Marker and slots neatly into LlamaIndex/LangChain loaders.

Head-to-Head Comparison

	Marker	Docling
Focus	PDF-first, high fidelity	Broad document understanding
Formats	PDF (+ some others)	PDF, DOCX, PPTX, XLSX, images
Math/equations	Strong	Good
Output formats	Markdown, JSON	Markdown, JSON, DoclingDocument
Hardware	GPU strongly recommended	GPU helps
Ecosystem	Standalone	LlamaIndex/LangChain loaders
Hosting	Local	Local
Best fit	Top PDF quality	Structured multi-format RAG

Installing and Using Each

Marker

pip install marker-pdf

from marker.converters.pdf import PdfConverter
from marker.models import create_model_dict

rendered = PdfConverter(artifact_dict=create_model_dict())("report.pdf")
print(rendered.markdown)

Docling

pip install docling

from docling.document_converter import DocumentConverter

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

Both download models on first run and reward a GPU. Marker edges ahead on pure PDF fidelity; Docling gives you more formats and structured output.

When to Reach for file2markdown Instead

Running either means managing Python, models, and ideally a GPU. If you want comparable Markdown without that overhead — or you are calling from another stack — file2markdown converts PDFs and images as a hosted service with OCR built in.

Bottom Line

Choose Marker for the best PDF-only fidelity on a GPU, Docling for broader formats and structured output in a RAG stack. For the result with zero setup, file2markdown does it in one step. See also Docling vs MarkItDown and Marker vs MarkItDown.