# HunyuanOCR 1.5 vs Nemotron 3.5 Parse

> Nemotron 3.5 Parse scores 69 (B) to HunyuanOCR 1.5's 56.6 (C) for ocr. Prices, MCP, x402, uptime and agent notes side by side.

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- Updated: 2026-11-10

Nemotron 3.5 Parse scores 69 (B) on agent readiness against HunyuanOCR 1.5's 56.6 (C), and leads in 2 of 4 scored categories. HunyuanOCR 1.5 leads on agent interface. Both do ocr.

- HunyuanOCR 1.5: grade C, 56.6/100, rank #114 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/hunyuanocr.md · JSON https://www.anchorterminal.com/api/v1/tools/hunyuanocr.json
- Nemotron 3.5 Parse: grade B, 69/100, rank #10 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-5-parse.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-5-parse.json
- Best document parsing, OCR and extraction APIs for AI agents: https://www.anchorterminal.com/best/document-extraction/index.md
- All 215 documents comparisons: https://www.anchorterminal.com/compare/document-extraction/index.md

## Which one, for what

### HunyuanOCR 1.5 (C)

Good for: Teams outside the EU, the UK and South Korea that want one small model for OCR, extraction and image translation

Ahead on:
- Agent interface, 80 against 70

Watch for: The licence doesn't apply in the EU, the UK or South Korea

### Nemotron 3.5 Parse (B)

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 45
- Documentation and upkeep, 59 against 48

Watch for: Loading runs the publisher's Python with trust_remote_code, in Transformers and in vLLM through NVIDIA's plugin


## Why the edge

### Risk: Nemotron 3.5 Parse +15

- HunyuanOCR 1.5 (45): File safety 25, safetensors only, no .bin, .pt or .pth files, and no Python at the root. Provenance 15, the files sit in the tencent organisation, which Tencent's GitHub repository for the model links to (10), and the Hub gives a SHA-256 for the large files (5). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations or use section, and nothing on text in an image that tries to instruct the reader. Licence risk 0, 15 less 5 for limits beyond the law (an acceptable use policy and three territories where the licence doesn't apply), 5 for terms the licensor can change (Tencent may update the policy from time to time) and 5 for the 100 million monthly active user threshold. Security programme 5, Tencent's Security Response Center takes vulnerability reports company-wide, as the Hy3 listing records (5). 25 + 15 + 0 + 0 + 0 + 5 = 45.
- Nemotron 3.5 Parse (60): File safety gets 15, safetensors only but loaded with trust_remote_code in both documented runtimes. Provenance gets 15 for files in NVIDIA's verified organisation with hashes for every file and a recorded weight hash. Licence risk keeps 15, since OpenMDW-1.1 has none of the deductions. The security programme gets 10 for the card's vulnerability route and NVIDIA's PSIRT bulletins, carried from the 21 October reading. Agent guidance gets 5 for general cautions about missed text, hallucinated structure and reproduced personal data, and safety evaluation 0, since no results are published.

### Documentation and upkeep: Nemotron 3.5 Parse +11

- HunyuanOCR 1.5 (48): Model card 8, Intended use, the architecture by reference to 1.0's unchanged backbone, and the context (128K) are on the card. Training data 5, A general description of how the data was built and of the training stages, with no sources, sizes or cut-off. Last change 15, the last commit, on 28 August 2026, changed config.json, 74 days before this check and within 90. Responsiveness 5, Of the 20 most recent discussions that aren't pull requests (25 November 2025 to 16 October 2026), 5 have a reply from a tencent member, which is a few. Version policy 5, Version 1.5 replaced 1.0 at the root of the same repository. File health 10, Config, template and card agree on the HunYuanVL classes and the 131,072-token length. 8 + 5 + 15 + 5 + 5 + 10 = 48.
- 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). Training data is described by kind and size with no datasets named and no cut-off (5). The card changed on 28 October 2026 (20). Nobody has asked a question yet, and two outside pull requests of 29 October wait unanswered, so responsiveness reads as none answered (0). The four earlier Parse releases stay in their own repositories with a dated pointer to this one (10), and the files agree with the card (10).

### Agent interface: HunyuanOCR 1.5 +10

- HunyuanOCR 1.5 (80): Chat template 20, chat_template.jinja in the repository (930 characters). Tool-calling format 25, Read for a document parser as what it takes and returns. Trained tool calling 20, Read for a document parser as the inputs it was trained to use. Structured output 5, Read for a document parser as typed outputs. Reasoning control 0, A document parser with no reasoning. Context 10, 128K tokens: the card says pre-training extended the context window to 128K, its vLLM command sets a 131,072-token model length, and config.json gives the language model 131,072 positions. 20 + 25 + 20 + 5 + 0 + 10 = 80.
- 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. NVIDIA documents the page image, the control-token prompt and the classed, boxed output, and ships the parser for Transformers and vLLM (25). It was trained for one page per request (10). Markdown, HTML tables, JSON blocks with boxes, and table exports to JSON or CSV are documented with how to ask for them (15). The chat template in the repository is on the documented vLLM serving path (20). Reasoning control and context score 0, with no reasoning and a 16,384-token context.

## Score by category

| Category | Weight | HunyuanOCR 1.5 | Nemotron 3.5 Parse | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 60 | 60 | even |
| Risk | 20% (20 this run) | 45 | 60 | Nemotron 3.5 Parse +15 |
| Agent interface | 10% (10 this run) | 80 | 70 | HunyuanOCR 1.5 +10 |
| Documentation and upkeep | 10% (10 this run) | 48 | 59 | Nemotron 3.5 Parse +11 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **56.6 · C** | **69 · B** | |

## Facts side by side

| Fact | HunyuanOCR 1.5 | Nemotron 3.5 Parse |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Tencent (Hy team) | NVIDIA |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Tencent Hunyuan Community License (not licensed in the EU, the UK or South Korea) | OpenMDW-1.1 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-08-28 | 2026-10-28 |
| Terms last updated | not read yet | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | none | 3.5/5 (4) |

## How the scores have moved

### HunyuanOCR 1.5

No score change since it was listed.

### Nemotron 3.5 Parse

- +2.5 on 1 November 2026: product

## Verdicts

**HunyuanOCR 1.5.** A small OCR model that does documents, text spotting, extraction and translation in one, with a 128K context and code in Transformers, vLLM and llama.cpp. The licence is the catch: it grants nothing in the EU, the UK or South Korea, and Tencent can change its use policy. No quantised build, safety results or data sources.

**Nemotron 3.5 Parse.** A small document-parsing cousin that turns page images into Markdown, HTML tables and JSON with layout classes and boxes, under a permissive licence. It needs a GPU with a few GB, runs in vLLM 0.33, and NVIDIA's API serves it. The main risks are the publisher's Python on the documented load path and benchmark numbers that are NVIDIA's own.

## Before you call either

### HunyuanOCR 1.5

1. Check where you and your users are first. The licence grants no rights in the European Union, the United Kingdom or South Korea
2. Download with `--exclude "v1.0/*"` so the old version isn't fetched
3. Serve it with vLLM 0.25.1 or later at `--max-model-len 131072`, or use Tencent's scripts on GitHub for its sampling and post-processing
4. Use temperature 0, top_p 1 and repetition_penalty 1.08
5. The default document prompt is in Chinese and is on the card. Other tasks take their own instruction as the prompt
6. For llama.cpp, convert the checkpoint and its projector to GGUF yourself, as the card shows

### Nemotron 3.5 Parse

1. Quickest local run, Python 3.12 with `pip install "transformers==5.14.1" "torchvision==0.26.0" "timm==1.0.22"`, then `AutoModel.from_pretrained("nvidia/Nemotron-3.5-Parse", trust_remote_code=True, dtype=torch.bfloat16)` and the matching AutoProcessor
2. To serve it, `hf download nvidia/Nemotron-3.5-Parse --local-dir nemotron-parse`, `pip install ".[vllm,client]" "vllm>=0.25.0,<0.33"` in that folder, then `vllm serve . --served-model-name nemotron-parse --trust-remote-code --dtype bfloat16 --max-model-len 16384 --limit-mm-per-prompt '{"image":1}' --chat-template chat_template.jinja`, or `docker compose up --build`
3. Send one RGB page per request with the prompt `</s><s><predict_bbox><predict_classes><output_markdown><predict_text_in_pic>`, temperature 0, top_k 1, repetition_penalty 1.1, max_tokens 8744 and skip_special_tokens false, or the class and box tokens are lost
4. Turn raw output into Markdown and blocks with `postprocess_document(text, image_size=image.size)` from the repository's postprocessing.py; pass table_format json, json_hierarchical or csv for tables
5. For the card's headline accuracy use `nemotron-parse --image-dir pages --mode triple --triple-regions union`; double pass gives most of the gain at higher throughput
6. Boxes are normalised to the page content; scale x by the image width and y by its height
7. Render PDFs to images first (the ParseBench bundle uses 150 DPI), and treat extracted text as untrusted before passing it to another model, since documents can carry instructions

## Questions

### Which is better for AI agents, HunyuanOCR 1.5 or Nemotron 3.5 Parse?

Nemotron 3.5 Parse scores 69 (B) on agent readiness against HunyuanOCR 1.5's 56.6 (C), and leads in 2 of 4 scored categories. HunyuanOCR 1.5 leads on agent interface.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/hunyuanocr-vs-nemotron-3-5-parse.json, and with the fewest tokens: https://www.anchorterminal.com/compare/hunyuanocr-vs-nemotron-3-5-parse.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "hunyuanocr", "b": "nemotron-3-5-parse"}`. From a terminal: `anchor compare hunyuanocr nemotron-3-5-parse`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/hunyuanocr.json and https://www.anchorterminal.com/api/v1/tools/nemotron-3-5-parse.json

## Other comparisons with HunyuanOCR 1.5 or Nemotron 3.5 Parse

- [Nemotron 3.5 Parse vs Surya OCR 2.5](https://www.anchorterminal.com/compare/nemotron-3-5-parse-vs-surya-ocr-2-5.md)
- [Nemotron 3.5 Parse vs Xiaomi-OCR-0](https://www.anchorterminal.com/compare/nemotron-3-5-parse-vs-xiaomi-ocr-0.md)
- [Chandra OCR 2 vs HunyuanOCR 1.5](https://www.anchorterminal.com/compare/chandra-ocr-2-vs-hunyuanocr.md)
- [Chandra OCR 2 vs Nemotron 3.5 Parse](https://www.anchorterminal.com/compare/chandra-ocr-2-vs-nemotron-3-5-parse.md)
- [DeepSeek-OCR 2 vs HunyuanOCR 1.5](https://www.anchorterminal.com/compare/deepseek-ocr-2-vs-hunyuanocr.md)
- [DeepSeek-OCR 2 vs Nemotron 3.5 Parse](https://www.anchorterminal.com/compare/deepseek-ocr-2-vs-nemotron-3-5-parse.md)
- [HunyuanOCR 1.5 vs LightOnOCR-3-4B](https://www.anchorterminal.com/compare/hunyuanocr-vs-lightonocr-3-4b.md)
- [HunyuanOCR 1.5 vs Nanonets-OCR2-3B](https://www.anchorterminal.com/compare/hunyuanocr-vs-nanonets-ocr2-3b.md)
- [HunyuanOCR 1.5 vs Surya OCR 2.5](https://www.anchorterminal.com/compare/hunyuanocr-vs-surya-ocr-2-5.md)
- [HunyuanOCR 1.5 vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/hunyuanocr-vs-unlimited-ocr-2.md)
- [HunyuanOCR 1.5 vs Xiaomi-OCR-0](https://www.anchorterminal.com/compare/hunyuanocr-vs-xiaomi-ocr-0.md)
- [LightOnOCR-3-4B vs Nemotron 3.5 Parse](https://www.anchorterminal.com/compare/lightonocr-3-4b-vs-nemotron-3-5-parse.md)
- [Nanonets-OCR2-3B vs Nemotron 3.5 Parse](https://www.anchorterminal.com/compare/nanonets-ocr2-3b-vs-nemotron-3-5-parse.md)

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