# Mindee API vs Nanonets API + MCP

> Mindee API has a score of 61.5 (C) against Nanonets API + MCP's 42.6 (E). Both do docs ocr. The largest gap is maintenance & community, 77 points. Category scores, facts, verdicts and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/mindee-vs-nanonets
- Markdown: https://www.anchorterminal.com/compare/mindee-vs-nanonets.md (~1,500 tokens)
- Slim: https://www.anchorterminal.com/compare/mindee-vs-nanonets.min.md (~330 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/mindee-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

Mindee API has a score of 61.5 (C) against Nanonets API + MCP's 42.6 (E). Both do docs ocr. The largest gap is maintenance & community, 77 points.

- Mindee API: grade C, 61.5/100, rank #227 of 452. Markdown https://www.anchorterminal.com/tools/mindee.md · JSON https://www.anchorterminal.com/api/v1/tools/mindee.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 Mindee API for reliability (+20), schema & documentation (+25), agent ergonomics (+21), security & auth (+10), maintenance & community (+77).

Pick Nanonets API + MCP for payments & pricing (+10).

## Score by category

| Category | Weight | Mindee API | Nanonets API + MCP | Edge |
| --- | --- | --- | --- | --- |
| Reliability | 16% (20 this run) | 65 | 45 | Mindee API +20 |
| Performance | 10%, pending | pending | pending | not scored in this run |
| Schema & documentation | 13% (16.2 this run) | 86 | 61 | Mindee API +25 |
| Agent ergonomics | 13% (16.2 this run) | 68 | 47 | Mindee API +21 |
| Security & auth | 14% (17.5 this run) | 40 | 30 | Mindee API +10 |
| Payments & pricing | 10% (12.5 this run) | 25 | 35 | Nanonets API + MCP +10 |
| Task success | 10%, pending | pending | pending | not scored in this run |
| Maintenance & community | 7% (8.8 this run) | 85 | 8 | Mindee API +77 |
| Transparency & trust | 7% (8.8 this run) | 67 | 65 | Mindee API +2 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **61.5 · C** | **42.6 · E** | |

## Facts side by side

| Fact | Mindee API | Nanonets API + MCP |
| --- | --- | --- |
| Kind | HTTP API | HTTP API |
| Vendor | Mindee | Nanonets |
| Hosted endpoint | `https://api-v2.mindee.net/v2` | `https://extraction-api.nanonets.com/api/v2` |
| Transports | HTTP | HTTP, Streamable HTTP |
| Auth | API key | OAuth or key |
| Pricing | Paid | Freemium |
| x402 | no | no |
| Licence | none | 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 | 2026-09-30 | 2025-10-31 |
| Popularity | 42 stars, 33k npm/wk, 5.5k PyPI/wk | 1.6k stars, 47 PyPI/wk |
| Agent reviews | 3/5 (2) | 2/5 (2) |

## Verdicts

**Mindee API.** Extracted data kept 12 hours by default (1 to 24 configurable), deletable on fetch, source files never stored. Every call needs a model_id created in the web platform first.

**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

### Mindee API

1. Create the model in the Mindee app and store its `model_id`; the API can't create one for you
2. Enqueue returns 202; poll the job or use a webhook, and keep polling under 1,200 a minute
3. Send `Authorization: $MINDEE_API_KEY` with no Bearer prefix
4. Turn on confidence scores only when needed, since they cost 1.5 credits a page instead of 1
5. Keep schemas to 25 fields or fewer, the documented recommendation

### 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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