Best PDF to Markdown Tools Comparison (2026)

pdfs2markdown.com Best for privacy

Our own converter runs entirely in your browser using PDF.js and a custom heuristic engine. Your documents never leave your device — there's no server to receive them. It handles headings (inferred from font size), bold/italic (from font metadata), tables (grid alignment detection), and multi-column layouts.

What it does well: Privacy, speed for small/medium documents, zero setup. Where it falls short: Complex multi-column academic papers with dense tables can produce imperfect output. Image-only/scanned PDFs return no text (OCR is not included). To understand exactly how the conversion engine works, see our technical deep-dive.

Verdict: Best free browser option. Ideal for anyone who doesn't want their documents uploaded to a third-party server.

marker Best open-source quality

marker is a Python library that uses a local ML model (surya) to produce high-fidelity Markdown from PDFs. Unlike heuristic-only converters, it understands document layout at a semantic level — correctly handling mixed column widths, spanning tables, footnotes, and even basic equations.

What it does well: Output quality on complex documents is the best available in open-source. Runs locally (privacy-preserving). Handles multi-column layouts that break simpler tools. Where it falls short: Requires Python and a reasonably modern GPU for practical speed; CPU-only is slow for large files. Not suitable for quick one-off conversions.

Verdict: Go-to choice for automated pipelines processing complex documents. If you're building a RAG system that ingests PDFs, start here. See also: using marker output in LangChain pipelines.

Pandoc Good for simple documents

Pandoc is the universal document converter, but its PDF→Markdown path is weak. It works by extracting text from the PDF and converting it, which means it loses most structural information. Headings rarely survive, tables are mangled, and multi-column layouts produce garbled output.

When to use it: Pandoc is excellent for Markdown→PDF (the reverse direction). For PDF→Markdown on simple, single-column, text-heavy documents, it produces acceptable plain text. For anything with structure, use marker instead.

Verdict: Fine for scripting simple conversions; don't use it on complex PDFs.

LlamaParse Best for RAG pipelines

LlamaParse is a document parsing API built specifically for RAG use cases. It returns Markdown that is optimized for chunking and embedding — with clean section boundaries, consistent heading levels, and table formatting that survives the chunking process.

The free tier allows 1,000 pages/day, which covers most developer experimentation. Paid tiers are priced per page.

What it does well: Output tuned for LLM consumption. Integrates directly with LlamaIndex (and LangChain via adapters). Handles scanned PDFs via built-in OCR. Where it falls short: Documents are processed on LlamaIndex's servers — privacy-sensitive content requires trust in their data handling. See our LangChain/LlamaIndex integration guide for setup details.

Verdict: Top pick if you're building a RAG pipeline and want a managed solution with no infrastructure to maintain.

Mathpix Best for scientific papers

Mathpix is purpose-built for scientific documents. Its killer feature is LaTeX extraction: it correctly identifies mathematical notation and converts it to LaTeX syntax within Markdown. For research papers from journals like arXiv, it produces output that other tools completely fail at.

Verdict: If you're processing scientific literature with equations, Mathpix is in a category of its own. For general business documents, overkill.

Claude API (Anthropic) Most flexible

Claude 3 and newer models accept PDF files as input and produce structured Markdown output. The advantage over dedicated tools: Claude understands semantic context. It knows that a number in a specific position is a section number, not page content. It handles unusual document structures gracefully.

What it does well: Variety of document types, edge cases, context-aware extraction. Where it falls short: Cost scales with document length. A 100-page PDF can cost $0.50–2.00 to process depending on model and content density. Not suitable for high-volume pipelines without cost controls. See our API comparison guide for code examples.

Verdict: Best for occasional, high-value conversions where output quality justifies per-page cost.