Chandra vs dots.ocr
Dots.ocr is faster; only Chandra supports handwriting, forms.
Chandradatalab/chandra | dots.ocrrednote/dots-ocr | |
|---|---|---|
| Type | Open source | Open source |
| Price | 2 credits / page | 2 credits / page |
| Per 1,000 pages | $2.00 | $2.00 |
| Typical speed | 2.0 s / page | 1.5 s / page |
| Best for | Complex layouts and handwriting | Multilingual document parsing |
| Inputs | PDF, images | PDF, images |
| Languages | 40+ languages | 100+ languages |
| Max pages per request | 500 | 500 |
| Tables | ✓ Yes | ✓ Yes |
| Handwriting | ✓ Yes | No |
| Math | ✓ Yes | ✓ Yes |
| Bounding boxes | ✓ Yes | ✓ Yes |
| Confidence | No | No |
| Forms | ✓ Yes | No |
| Multilingual | ✓ Yes | ✓ Yes |
| On OCRRouter | Coming soon | Coming soon |
Prices are what you pay on OCRRouter. Speed is the provider's typical figure, not a benchmark we ran.
What each one is
Chandra
Datalab's OCR model that keeps layout and handles forms, tables and handwriting.
Chandra detailsdots.ocr
Single vision model from rednote that does layout detection and content recognition together.
dots.ocr detailsUse one, fall back to the other
Request body
{
"model": "datalab/chandra",
"fallbacks": ["rednote/dots-ocr"],
"file_url": "https://example.com/document.pdf"
}