Head to head · Work · updated Oct 27, 2026
Docling vs LlamaParse
Docling scores 64 to LlamaParse's 18 on public evidence for extract data from documents, and they rank #2 and #4 there. Both are free to start. Numbers below are the same public signals the ranking uses, recomputed nightly — nothing here is sponsored.
Docling
Turns PDFs, Office files and scans into structured markdown or JSON, layout and tables intact.
+ Reads a table as a table, and does it on your own machine with nothing sent anywhere.
− Heavy scanned documents want a GPU; CPU-only runs are slow on long PDFs.
LlamaParse
Parsing service aimed at the documents that break parsers: nested tables, forms, figures.
+ Holds up on financial statements and forms where plain text extraction turns to soup.
− Per-page pricing and a cloud round trip; large archives add up fast.
Side by side
public evidence| Docling | LlamaParse | |
|---|---|---|
| Evidence score / 100 | 64 | 18 |
| GitHub stars | 69,684 | closed source |
| Hacker News stories, 30d | 1 | 1 |
| Release-feed mentions, 30d | 0 | 0 |
| Last activity | 0 days ago | – |
| Pricing | Open source · MIT; runs locally, no API key | Free tier · Free pages each day, then per-page credits |
| Platforms | cli, api, linux, mac, windows | api, web |
| Maker | docling-project (IBM Research) | LlamaIndex |
Which one?
Pick Docling if
- you want the stronger public evidence (64 vs 18)
- you want open source — self-host it, read it, fork it
- you need it on cli, linux, mac
- a bigger GitHub community matters (69,684 vs 0 stars)
Best for feeding your own documents to a model without uploading them.
Pick LlamaParse if
- you need it on web
Best for a few thousand hard PDFs rather than a million easy ones.