HunyuanOCR 1.5 vs Nemotron 3.5 Parse

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.

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).

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.