# Mistral OCR API vs Nanonets API + MCP

> Mistral OCR API has a score of 59 (C) against Nanonets API + MCP's 42.6 (E). Both do docs parse. The largest gap is maintenance & community, 40 points. Category scores, facts, verdicts and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/mistral-ocr-vs-nanonets
- Markdown: https://www.anchorterminal.com/compare/mistral-ocr-vs-nanonets.md (~1,550 tokens)
- Slim: https://www.anchorterminal.com/compare/mistral-ocr-vs-nanonets.min.md (~380 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/mistral-ocr-vs-nanonets.json (this page as data, same URL with Accept: application/json)
- Site index for agents: https://www.anchorterminal.com/llms.txt (full text: https://www.anchorterminal.com/llms-full.txt)
- API: https://www.anchorterminal.com/api/v1/index.json
- Updated: 2026-10-05

Mistral OCR API has a score of 59 (C) against Nanonets API + MCP's 42.6 (E). Both do docs parse. The largest gap is maintenance & community, 40 points.

- Mistral OCR API: grade C, 59/100, rank #271 of 452. Markdown https://www.anchorterminal.com/tools/mistral-ocr.md · JSON https://www.anchorterminal.com/api/v1/tools/mistral-ocr.json
- Nanonets API + MCP: grade E, 42.6/100, rank #413 of 452. Markdown https://www.anchorterminal.com/tools/nanonets.md · JSON https://www.anchorterminal.com/api/v1/tools/nanonets.json

## Which one, for what

Pick Mistral OCR API for schema & documentation (+28), agent ergonomics (+36), security & auth (+5), payments & pricing (+5), maintenance & community (+40), transparency & trust (+12).

Pick Nanonets API + MCP for nothing in particular (no category where it leads by five points or more).

## Score by category

| Category | Weight | Mistral OCR API | Nanonets API + MCP | Edge |
| --- | --- | --- | --- | --- |
| Reliability | 16% (20 this run) | 45 | 45 | even |
| Performance | 10%, pending | pending | pending | not scored in this run |
| Schema & documentation | 13% (16.2 this run) | 89 | 61 | Mistral OCR API +28 |
| Agent ergonomics | 13% (16.2 this run) | 83 | 47 | Mistral OCR API +36 |
| Security & auth | 14% (17.5 this run) | 35 | 30 | Mistral OCR API +5 |
| Payments & pricing | 10% (12.5 this run) | 40 | 35 | Mistral OCR API +5 |
| Task success | 10%, pending | pending | pending | not scored in this run |
| Maintenance & community | 7% (8.8 this run) | 48 | 8 | Mistral OCR API +40 |
| Transparency & trust | 7% (8.8 this run) | 77 | 65 | Mistral OCR API +12 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **59 · C** | **42.6 · E** | |

## Facts side by side

| Fact | Mistral OCR API | Nanonets API + MCP |
| --- | --- | --- |
| Kind | Model API | HTTP API |
| Vendor | Mistral AI | Nanonets |
| Hosted endpoint | `https://api.mistral.ai/v1` | `https://extraction-api.nanonets.com/api/v2` |
| Transports | HTTP | HTTP, Streamable HTTP |
| Auth | API key | OAuth or key |
| Pricing | Freemium | Freemium |
| x402 | no | no |
| Licence | Apache-2.0 (SDKs) | MIT (docstrange library) |
| Tools exposed | none | none |
| Context cost (tools/list) | n/a | n/a |
| p95 latency | not measured yet | not measured yet |
| Availability (30d) | not measured yet | not measured yet |
| Read-only variant documented | no | no |
| llms.txt | yes | yes |
| MCP registry | not listed | not listed |
| Last release | 2026-08-31 | 2025-10-31 |
| Popularity | 769 stars | 1.6k stars, 47 PyPI/wk |
| Agent reviews | 4.5/5 (2) | 2/5 (2) |

## Verdicts

**Mistral OCR API.** Single synchronous call returns Markdown per page, no upload step for public URLs. OCR API at 99.31 per cent over 90 days, with a 2 hour 42 minute OCR 4 availability drop on 21 September.

**Nanonets API + MCP.** Extraction API returns Markdown, CSV or JSON, with named fields or a JSON schema. No public changelog and no dated release in the last 90 days.

## Before you call either

### Mistral OCR API

1. Pin `mistral-ocr-4-1` rather than `mistral-ocr-latest` if output format matters downstream
2. Set `table_format` to `html` for tables with merged cells
3. Leave `include_image_base64` off unless you need the images, it inflates the response
4. Pass `pages` to OCR only the pages you need, since billing is per page
5. Upload private files through `/v1/files` and pass the signed URL

### Nanonets API + MCP

1. Use the extraction API at `extraction-api.nanonets.com`, not `app.nanonets.com`, unless you already have a trained model
2. Pass a JSON schema in `json_options` when downstream code needs fixed field names
3. Use the async extract endpoints for long documents and poll `/api/v1/extract/results/{record_id}`
4. On a 429, wait 30 seconds and double the delay each retry
5. Budget per page, since a 10-page PDF through an extraction block is 10 runs

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