MarkPrep benchmark: honest conversion quality
Most conversion benchmarks bury the weaknesses. This one does not. Below is a qualitative parity map of how MarkPrep's browser-based conversion compares, format by format, with the markitdown.tech server-side tier and with GPU deep-learning tools like Marker and Docling. There are no invented percentages here — just a candid description of where MarkPrep is at parity, where it is equal or better, and where it loses.
MarkPrep's quality target is parity with the text-layer tier, not with GPU pipelines. On digital documents that target is met. On scanned pages that need heavy OCR, GPU tools are better, and we flag that openly.
Parity map by format
| Format | MarkPrep (local, browser) | markitdown.tech tier | GPU tools (Marker/Docling) |
|---|---|---|---|
| DOCX | Equal or better — original mammoth | Good | Good |
| XLSX | Parity | Parity | Good |
| PPTX | Parity | Good | Good |
| HTML | Parity | Good | Good |
| CSV | Parity | Parity | Good |
| JSON | Parity | Parity | Good |
| EPUB | Parity | Good | Good |
| Digital PDF | Parity — text-layer | Good | Best |
| Tables in PDF | Good, flagged in Verify | Basic | Best |
| Scanned PDF / OCR | Below GPU tools — the one gap | Limited free OCR | Best |
How to read this
Parity means MarkPrep's output matches the text-layer quality tier for that format — the same tier markitdown.tech and the open-source MarkItDown library aim at. For DOCX, MarkPrep can be equal or better because it uses the original mammoth conversion path.
Best in the GPU column reflects an honest fact: on digital PDFs with hard tables and especially on scanned pages, GPU deep-learning tools reconstruct structure more faithfully than any text-layer approach. MarkPrep flags questionable tables in its Verify tab, but for OCR-heavy scans a GPU pipeline is the right tool. For everything else — the formats most conversions actually use — MarkPrep gives you parity locally, privately, and for free.
Frequently asked questions
Is this a scored benchmark with percentages?+
No. This is a qualitative parity map, not a numeric benchmark. We do not publish fabricated accuracy percentages. The table describes, format by format, whether MarkPrep is at parity with the text-layer tier, better, or behind — so you know what to expect before you convert.
Where does MarkPrep lose?+
Scanned PDFs that require heavy OCR. GPU deep-learning tools like Marker and Docling are honestly better there, and we say so plainly. MarkPrep's local Tesseract OCR is offline and private but not a match for GPU OCR on hard scans. That is the one real gap.
Where is MarkPrep equal or better?+
On digital documents. DOCX, XLSX, PPTX, HTML, CSV, JSON, EPUB and digital text-layer PDFs land at parity with the text-layer tier, and DOCX can be equal or better thanks to the original mammoth-based path. These are the formats most everyday conversions actually use.
How do you handle PDF tables?+
MarkPrep extracts them at a good text-layer level and flags questionable tables in its Verify tab so you can review them. GPU tools reconstruct hard tables more faithfully, so for table-critical work they remain the best option. For most documents the flagged text-layer output is enough.
Why publish your weaknesses?+
Because an honest map is more useful than a marketing chart, and because this table doubles as our regression checklist. Each row is a quality target we hold ourselves to, so being candid about the OCR gap keeps us accountable and helps you pick the right tool.
See the quality for yourself
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