Both services can supply Markdown to a custom pipeline. Choose by output and operational requirements; LlamaParse is not limited to a LlamaIndex Python object.

Current interface facts

LlamaParse’s official documentation describes parsing PDF pages, scans, tables and charts into Markdown, text or JSON. It provides API-based access, so a framework dependency is an integration choice rather than an inherent restriction. LlamaParse documentation, checked 10 September 2026.

pdfToMarkdown accepts URL, Base64 or raw PDF input at one synchronous conversion endpoint. Its canonical success has complete: true, markdown, pages and request_id. Keep the request identity for account recovery.

Compare on a fixed corpus

Test the same documents and selected pages. Record lost cells, reading-order errors, output format, total elapsed time and actual cost. A feature list does not establish better extraction quality. No current LlamaParse head-to-head benchmark is published here.

Keep integration choices separate

Your parser need not choose your chunker, embedding model or vector store. Carry source and section metadata into whichever downstream stack you use. For retrieval-specific evaluation, continue to the RAG comparison.

Consult each provider’s current pricing before budgeting.

Convert a document

Use the tested Python, JavaScript or curl workflow. Choose one input: a public PDF URL, Base64 bytes, or a raw PDF upload. Raw uploads avoid Base64 expansion; all three send the document to an external processor.

The public demo converts page 1 with a watermark, at 3 requests per minute per IP. New accounts receive 20 trial pages once. Further account conversions use paid credits: monthly pages expire, top-ups do not. The API reference owns authentication, limits, replay and retention.