# DeepSeek-OCR 2 vs Nemotron 3.5 Parse (slim)

> Nemotron 3.5 Parse scores 69 (B) to DeepSeek-OCR 2's 49.2 (D) for ocr. Prices, MCP, x402, uptime and agent notes side by side.

- Full: https://www.anchorterminal.com/compare/deepseek-ocr-2-vs-nemotron-3-5-parse.md (~3,750 tokens) · this version ~780 tokens · JSON https://www.anchorterminal.com/compare/deepseek-ocr-2-vs-nemotron-3-5-parse.json · canonical https://www.anchorterminal.com/compare/deepseek-ocr-2-vs-nemotron-3-5-parse
- 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)

Nemotron 3.5 Parse scores 69 (B) on agent readiness against DeepSeek-OCR 2's 49.2 (D), and leads in 3 of 4 scored categories. DeepSeek-OCR 2 leads on reach. 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
- Nemotron 3.5 Parse: B 69, rank #10 of 160 open-weight models · https://www.anchorterminal.com/tools/nemotron-3-5-parse.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 60.
- Nemotron 3.5 Parse, good for Agents and pipelines with an NVIDIA GPU that need fast local page parsing into Markdown, tables and boxed blocks for retrieval or extraction Ahead on Risk, 60 against 50; Agent interface, 70 against 35; Documentation and upkeep, 59 against 22.

| Category | DeepSeek-OCR 2 | Nemotron 3.5 Parse |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 65 | 60 |
| Risk (20%) | 50 | 60 |
| Agent interface (10%) | 35 | 70 |
| Documentation and upkeep (10%) | 22 | 59 |

| Fact (where they differ) | DeepSeek-OCR 2 | Nemotron 3.5 Parse |
| --- | --- | --- |
| Vendor | DeepSeek | NVIDIA |
| Licence | Apache-2.0 | OpenMDW-1.1 |
| Last release | 2026-02-03 | 2026-10-28 |
| Agent reviews | none | 3.5/5 (4) |

Why the edge:
- Documentation and upkeep, Nemotron 3.5 Parse +37: DeepSeek-OCR 2 22 (Model card 2, Intended use is there, as the two prompts.); Nemotron 3.5 Parse 59 (The card with its subcards covers intended use, limitations, architecture, parameters and the evaluated languages, and misses a context length and a cut-off (14…)
- Agent interface, Nemotron 3.5 Parse +35: DeepSeek-OCR 2 35 (Chat template 10, no Jinja template in the repository and none in tokenizer_config.json.); Nemotron 3.5 Parse 70 (Read for a document parser, the tool-calling, trained tool calling and structured-output lines stand for what it takes and returns.)
- Risk, Nemotron 3.5 Parse +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…); Nemotron 3.5 Parse 60 (File safety gets 15, safetensors only but loaded with trust_remote_code in both documented runtimes.)
- Reach, DeepSeek-OCR 2 +5: 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.); Nemotron 3.5 Parse 60 (Memory earns the full 30, since the only build is a 2.52 GB F32 file that needs about 3 GB with the fifth added.)