MarkPrep vs Docling
Docling is IBM's open-source document toolkit, and its table structure recognition is best-in-class. On complex, nested, or borderless tables, Docling reconstructs the underlying grid more accurately than a text-layer tool can. If your work hinges on getting tables exactly right, Docling is honestly the stronger parser.
MarkPrep is not trying to win that fight. It targets the text-layer quality tier while running entirely in your browser — no Python, no GPU, no upload — and it adds the AI-ready tooling that a parsing library leaves out. The trade is table precision for zero-install convenience, privacy, and LLM-ready output.
Side by side
| MarkPrep | Docling | |
|---|---|---|
| Where it runs | Browser (WebAssembly), local | Python, GPU-friendly |
| Install required | None — open a web page | Python + dependencies |
| Table structure recognition | Good, flagged in Verify | Best-in-class |
| Files leave your device | Never | Never (you run it locally) |
| Token counting | Live, per-model | None |
| Chunking for RAG | Heading-aware preview + JSONL | None |
| OCR | Local Tesseract, offline | Available in pipeline |
| License / price | Free unlimited; Pro $4/mo | MIT, free |
When to use which
Use Docling when table structure is the whole game — financial statements, scientific tables, dense reports — and you have a Python environment, ideally with a GPU. Its recognition models are genuinely ahead of MarkPrep on those inputs, and the MIT license makes it easy to embed.
Use MarkPrep when you want conversion without installing anything, when the document must stay on your device, or when the goal is to feed an LLM. MarkPrep flags questionable tables in its Verify tab so you can catch them, and its live token count, context-window verdict, heading-aware chunk preview, and JSONL export with heading-path and page metadata make the output retrieval-ready in seconds.
Frequently asked questions
Which is better at PDF tables?+
Docling is. IBM's toolkit has best-in-class table structure recognition, and on dense or nested tables it will reconstruct rows and columns more faithfully than MarkPrep's text-layer approach. If your documents live and die by table fidelity, Docling is the stronger engine.
Why choose MarkPrep then?+
Because Docling is a Python toolkit that is heavier to run and benefits from a GPU, while MarkPrep is a web page. There is nothing to install, nothing leaves your device, and it is free for unlimited use. MarkPrep also adds token counting, a context-window verdict, and heading-aware chunking that Docling does not include.
Does Docling count tokens or chunk for RAG?+
Docling focuses on high-quality document parsing and structured output. It does not provide a live per-model token count, a context-window verdict, or MarkPrep's heading-aware chunk preview with JSONL export. MarkPrep is built specifically to shape output for retrieval and prompting.
Is Docling open source?+
Yes. Docling is MIT-licensed and maintained under the docling-project organization, which makes it a dependable library to embed in a Python pipeline. If you are comfortable running Python and want maximum table accuracy, it is an excellent choice.
Can I use both together?+
Yes. Use Docling when table structure is critical and you have a Python environment, and use MarkPrep for zero-install, private, LLM-ready conversion of everyday documents. They complement each other well.
Try it in your browser
No install, no Python, no upload. Convert a file and see the token count in seconds.