# DeepSeek-OCR 2 vs Nanonets-OCR2-3B (slim)

> DeepSeek-OCR 2 scores 49.2 (D) to Nanonets-OCR2-3B's 48.1 (D) for ocr. Prices, MCP, x402, uptime and agent notes side by side.

- Full: https://www.anchorterminal.com/compare/deepseek-ocr-2-vs-nanonets-ocr2-3b.md (~3,050 tokens) · this version ~680 tokens · JSON https://www.anchorterminal.com/compare/deepseek-ocr-2-vs-nanonets-ocr2-3b.json · canonical https://www.anchorterminal.com/compare/deepseek-ocr-2-vs-nanonets-ocr2-3b
- Index: https://www.anchorterminal.com/llms.txt · API: https://www.anchorterminal.com/api/v1/index.json · MCP: https://www.anchorterminal.com/mcp · Updated: 2026-11-10
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)

DeepSeek-OCR 2 scores 49.2 (D) on agent readiness against Nanonets-OCR2-3B's 48.1 (D), and leads in 2 of 4 scored categories. Nanonets-OCR2-3B leads on agent interface. Both do ocr.

- DeepSeek-OCR 2: D 49.2, rank #147 of 160 open-weight models · https://www.anchorterminal.com/tools/deepseek-ocr-2.min.md
- Nanonets-OCR2-3B: D 48.1, rank #153 of 160 open-weight models · https://www.anchorterminal.com/tools/nanonets-ocr2-3b.min.md
- DeepSeek-OCR 2, good for Teams that want an Apache 2.0 OCR model on a small GPU and already run vLLM Ahead on Reach, 65 against 55; Risk, 50 against 40.
- Nanonets-OCR2-3B, good for Researchers and teams evaluating page-to-Markdown OCR who can wait for a licence before shipping Ahead on Agent interface, 70 against 35.

| Category | DeepSeek-OCR 2 | Nanonets-OCR2-3B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 65 | 55 |
| Risk (20%) | 50 | 40 |
| Agent interface (10%) | 35 | 70 |
| Documentation and upkeep (10%) | 22 | 25 |

| Fact (where they differ) | DeepSeek-OCR 2 | Nanonets-OCR2-3B |
| --- | --- | --- |
| Vendor | DeepSeek | Nanonets |
| Licence | Apache-2.0 | None stated (the base model, Qwen2.5-VL-3B-Instruct, is under the Qwen Research licence) |
| Last release | 2026-02-03 | 2025-10-16 |

Why the edge:
- Agent interface, Nanonets-OCR2-3B +35: DeepSeek-OCR 2 35 (Chat template 10, no Jinja template in the repository and none in tokenizer_config.json.); Nanonets-OCR2-3B 70 (Chat template 20, chat_template.jinja in the repository (1,017 characters), Qwen's ChatML format with image placeholders.)
- Reach, DeepSeek-OCR 2 +10: DeepSeek-OCR 2 65 (Memory 30, the only build from DeepSeek is the BF16 safetensors file, 6.78 GB, and 6.78 GB × 1.2 = 8.1 GB, within 16 GB.); Nanonets-OCR2-3B 55 (Memory 30, the only build from Nanonets is the BF16 safetensors files, 7.51 GB, and 7.51 GB × 1.2 = 9.0 GB, within 16 GB.)
- Risk, DeepSeek-OCR 2 +10: DeepSeek-OCR 2 50 (File safety 15, safetensors only, no .bin, .pt or .pth files (25), less 10 because the card's documented way to load it is trust_remote_code, which runs the rep…); Nanonets-OCR2-3B 40 (File safety 25, safetensors only, no .bin, .pt or .pth files and no Python in the repository.)