markitdown vs Docling vs Marker: Which Document Converter Is Right for You?

Document-to-Markdown conversion has become a foundational preprocessing step for AI applications. Getting content from PDFs, Word files, and web pages into clean structured text is essential for RAG pipelines, LLM prompts, knowledge bases, and documentation workflows. Three open-source libraries have emerged as the most capable choices: markitdown (Microsoft), Docling (IBM), and Marker (VikParuchuri). All three are MIT-licensed. All three convert documents to Markdown. But their architectures, format support, and ideal use cases differ substantially.

The choice between them depends primarily on what you are converting, how complex the source documents are, and whether you need a single library for many file types or maximum accuracy for a specific format.

markitdown is a Python library built by Microsoft Research, originally developed as part of the AutoGen multi-agent research framework. It converts a broad range of formats — PDF, DOCX, XLSX, PPTX, HTML, CSV, TXT, and images with optional OCR — through a unified API.

The architecture uses format-specific parsers for each file type: pdfminer for PDFs, python-docx for Word, openpyxl for Excel, python-pptx for PowerPoint, and markdownify for HTML. This breadth is markitdown's core advantage. One library, one API, all major office and web formats handled. The optional markitdown[all] package installs all dependencies for full format coverage including image OCR via Azure Document Intelligence.

markitdown is best suited for general-purpose document processing pipelines where you need one tool to handle many different file types. The API is minimal — instantiate MarkItDown, call convert() with a file path — and the library is straightforward to integrate. It reached over 100,000 GitHub stars within its first two years, largely because it solved the multi-format conversion problem without requiring multiple specialized tools.

The trade-off is that markitdown prioritizes breadth and simplicity over peak extraction fidelity for any single format. For PDFs with complex multi-column academic layouts or dense table structures, accuracy can be lower than specialized tools built specifically for PDF understanding.

Docling is an open-source document processing library from IBM Research, designed from the ground up for high-fidelity document understanding. Its core differentiator is a trained layout analysis model that processes documents before extracting text — rather than extracting text first and reconstructing structure from it.

The layout model identifies page regions — headings, body paragraphs, tables, figures, lists, column boundaries — and processes each region type with appropriate extraction logic before reassembling into structured Markdown. This pipeline adds computational overhead compared to markitdown, but it substantially improves output quality for visually complex documents, particularly multi-column PDFs, financial reports with embedded tables, and regulatory documents with rich formatting.

Docling's PDF handling is its strongest capability. For research papers with two-column layouts, financial tables with spanning headers, and dense regulatory documents, Docling typically produces more accurate structural output than markitdown or Marker for native PDFs.

The trade-offs are computational weight — Docling's layout model inference adds latency and may require GPU resources for throughput at scale — and a narrower format focus. Docling's coverage of XLSX, PPTX, and HTML is more limited than markitdown, making it less suitable for mixed-format pipelines.

Marker is a PDF-to-Markdown tool built by Vik Paruchuri, creator of the Surya OCR library. Unlike markitdown and Docling, Marker is focused exclusively on PDFs — it does not handle Word, Excel, PowerPoint, or HTML. Within that focused scope, it is optimized for accuracy on scanned and visually complex PDFs.

Marker is built on Surya, a multilingual OCR engine trained specifically for document understanding rather than general image text recognition. Surya applies layout detection to identify columns, tables, and text regions before running OCR, which produces more accurate character recognition and better reading-order reconstruction than general-purpose OCR tools.

For scanned PDFs, academic papers, legal documents, and historical archives where the content exists only as scanned images, Marker with Surya OCR produces excellent results. The accuracy on difficult documents — low-contrast scans, mixed languages, handwriting adjacent to print — is a genuine differentiator.

The trade-off is scope. Marker processes only PDFs. For any other format, you need a separate tool. Surya's model inference also requires meaningful compute resources — GPU acceleration is recommended for throughput. For simple native PDFs without complex layouts, markitdown's pdfminer extraction is faster and produces comparable results.

Use markitdown when you need one library for many file types, want a simple API and fast local setup, or are building a general-purpose document processing pipeline. It is the most versatile option and the most straightforward to deploy. The markitdown[all] package handles every format the library supports with a single pip install.

Use Docling when your primary input is native PDFs with complex multi-column layouts, nested tables, or rich visual structure, and when accuracy is more important than processing speed. IBM's layout model is among the strongest available for high-fidelity PDF understanding on documents that were programmatically generated.

Use Marker when you are processing scanned PDFs at scale and need the highest possible OCR accuracy, or when working primarily with academic papers, legal documents, or historical archives that exist only as image-based PDFs. Surya's OCR accuracy on difficult scans is Marker's strongest differentiator.