PDF to Markdown

PDF to Markdown is a web-based document conversion tool that uses an in-house AI model to turn PDF files into structured Markdown. Rather than dumping raw text, it attempts to reconstruct the document's original layout — headings, lists, tables, equations, and reading order — so the output is actually usable in downstream workflows.

Who it's for

The tool is aimed at researchers, developers, technical writers, and knowledge managers who regularly work with PDFs and need the content in a format they can edit, version, or feed into other systems. If you're building a RAG pipeline, maintaining a documentation site, or importing papers into Obsidian or Notion, the structured Markdown output is a more practical starting point than plain text extraction.

How it works

Conversions run through three modes you choose based on document type and how much fidelity you need.

Fast is the lightest option, suited for simple text-based PDFs where layout complexity is low. Balanced is the recommended default for most documents, handling mixed layouts at the same credit cost. Precision applies heavier processing for documents with tables, equations, or tricky column structures — useful when the default output misses structure that matters.

All three modes combine OCR, layout analysis, and structure recovery. Scanned PDFs are supported when the source scan quality is high enough for OCR to work reliably, though results on low-quality scans are not guaranteed.

What the output looks like

The Markdown output uses semantic headings, ordered and unordered lists, and recovered table structure where the model can infer it. Images are referenced, equations are preserved inline where possible, and links are extracted. The result is copyable and downloadable directly from the interface.

Before uploading anything, you can test output quality using built-in sample previews — a research paper, invoice, contract, and mixed-layout document — without spending any credits. That's a practical way to calibrate expectations for your specific document type before committing.

API and batch access

For teams or developers integrating PDF conversion into a pipeline, the tool exposes a REST API that accepts either a multipart file upload or a remote URL via a JSON fileUrl parameter. Authentication uses an API key generated from your account settings. The API playground on the homepage lets you test requests and inspect responses directly in the browser.

Batch uploads are supported for signed-in users on the homepage, making it straightforward to queue multiple files without scripting each one individually.

Pricing model

The service runs on a credit system. Fast and Balanced modes cost 1 credit per page. Precision costs 2 credits per page. There is a minimum charge of 3 credits per conversion regardless of how short the document is — so single-page files are not cheaper than three-page ones.

That minimum is worth factoring in if your workflow involves many short documents. For longer technical documents where structure recovery matters, the per-page cost is more predictable and the Precision mode's higher rate is easier to justify.

Practical fit

The tool sits in a space between basic PDF text extractors and full document intelligence platforms. It does not claim perfect output on every file — table and equation recovery is described as best-effort, and complex layouts can still produce imperfect results. What it offers is a cleaner starting point than raw extraction, with enough structure preserved to reduce manual cleanup for most standard document types.

For teams preparing content for LLM ingestion, the structured output reduces the noise that comes from unformatted text dumps, which tends to matter for chunking quality and retrieval accuracy in RAG setups.