MDisBetter vs Docling — AI PDF Parsing Compared
Docling is IBM Research's document conversion library, published 2024 and built on a stack of layout-recognition and vision-language models. It's genuinely impressive on complex layouts. MDisBetter is a hosted service with similar goals. Both produce Markdown; the difference is in setup, ops, and edge-case handling.
| Feature | MDisBetter | Docling |
|---|---|---|
| PDF to Markdown | ✓ | ✓ |
| Layout-aware models | ✓ | ✓ |
| Tables from PDF | ✓ | ✓ |
| Equations as LaTeX | ✓ | ✓ |
| OCR for scanned PDFs | ✓ | ✓ |
| Setup | None — call API | Python + GPU + model downloads (~5GB) |
| Inference cost | ~$0.001/page | GPU time + ops |
| Air-gapped use | Enterprise tier | ✓ |
Frequently asked questions
Is Docling more accurate than MDisBetter?
On most documents, output quality is comparable. Docling has a slight edge on figure/diagram regions where its vision-language model adds context. MDisBetter has an edge on operational simplicity and continuous improvement (new model versions deploy automatically; with Docling you upgrade and re-test).
How hard is Docling to set up?
Several hours for first-time users: Python environment, dependencies, GPU drivers (or accept slow CPU inference), download ~5GB of model weights. Subsequent runs are fast. Compare to <code>npm install</code> + API key for MDisBetter.
Can Docling handle the same languages as MDisBetter?
Both cover the major Western and East Asian languages. For OCR specifically, both use modern engines with broad language support. For very low-resource languages, neither is reliable enough for production without human review.
Cost at scale: which is cheaper?
Below ~50k pages/month: MDisBetter's paid tiers are cheaper than the GPU + ops cost of running Docling. Above ~500k pages/month: Docling on a dedicated GPU pool wins on raw compute. The crossover depends on your engineering hourly rate and tolerance for ops work.
Can I evaluate both on my own data?
Strongly recommended. Pick 20 representative PDFs from your domain, run both, compare the output. Quality varies more by document type than by tool, so domain-specific evaluation beats generic benchmarks.