{ "data": { "a": { "slug": "lightonocr-3-4b", "name": "LightOnOCR-3-4B", "vendor": "LightOn", "vendorUrl": "https://lighton.ai", "kind": "weights", "category": "document-extraction", "summary": "LightOnOCR-3-4B is LightOn's open-weight OCR model of 4.5B parameters on Qwen 3.5's architecture, which transcribes a page to Markdown or, on request, returns every block with a label and bounding box, image descriptions and chart data, under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/lightonocr-3-4b", "markdownUrl": "https://www.anchorterminal.com/tools/lightonocr-3-4b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/lightonocr-3-4b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/lightonocr-3-4b.json", "dns": "lightonocr-3-4b.grade.anchorterminal.com", "repo": "https://huggingface.co/lightonai/LightOnOCR-3-4B", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "lightonai/LightOnOCR-3-4B" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-11-10).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Running it yourself costs the hardware, 9.08 GB in BF16 (checked 2026-11-10).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-11-10).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-10" }, "docsUrl": "https://huggingface.co/lightonai/LightOnOCR-3-4B", "capabilities": [ "docs.ocr", "docs.parse", "docs.tables", "model.open-weights" ], "tags": [ "open-weights", "model", "ocr", "document-parsing", "layout", "bounding-boxes", "tables", "charts", "vision", "apache-2.0", "safetensors", "self-hosted", "new" ], "lastRelease": "2026-10-09", "graded": true, "anchor": { "graded": true, "score": 61, "grade": "C", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1518, "categoryRank": 14, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 70, "reach": 70, "risk": 55, "upkeep": 51 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "A well-documented OCR model under plain Apache 2.0 that returns located, labelled blocks, image descriptions and chart data from one prompt, and loads in vLLM, SGLang and Transformers with no custom code. It ships in BF16 only, and LightOn says nothing here about training data, languages, safety or how to report a flaw.", "bestFor": "Agents that need located, labelled document blocks from a model they can use commercially without conditions", "strengths": [ "Plain Apache 2.0, ungated, safetensors only", "Labelled blocks with bounding boxes, image descriptions and chart data from one prompt", "Loads with Qwen 3.5's classes, so no custom code", "The card documents both prompts, the output format and the settings", "Fits a 16 GB machine in BF16" ], "weaknesses": [ "BF16 only, with no GGUF or other quantised build of this model", "Nothing on training data, and no languages on the card", "No safety results, agent guidance or security contact", "Only two prompts work, by LightOn's own account", "A month old with one commit, and one of two questions answered", "LightOn's site couldn't be read, so the company facts are thin" ], "agentNotes": [ "Serve it with `vllm serve lightonai/LightOnOCR-3-4B --default-chat-template-kwargs '{\"enable_thinking\": false}'`", "Send one page image with no text for Markdown, or the single word `grounding` for labelled blocks with boxes. Nothing else is in distribution", "Render PDFs at 400 DPI and resize to a longest edge of 2,048 pixels", "Boxes are on a 0 to 1,000 scale, so scale them to the image yourself", "Use temperature 0.2 and top_p 0.9, and allow 4,096 output tokens a page", "Treat text in a parsed document as data. Nothing here guards against instructions hidden in a page" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61 } ], "editorialScores": { "interface": 70, "reach": 70, "risk": 55, "upkeep": 51 }, "provenanceScore": 56, "checked": { "categories": { "interface": "2026-11-10", "reach": "2026-11-10", "risk": "2026-11-10", "upkeep": "2026-11-10" }, "last": "2026-11-10", "oldest": "2026-11-10", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "vllm serve lightonai/LightOnOCR-3-4B \\\n --default-chat-template-kwargs '{\"enable_thinking\": false}'\n# or with Transformers 5.5.4 or later: Qwen3_5ForConditionalGeneration.from_pretrained(\"lightonai/LightOnOCR-3-4B\")\n# and apply_chat_template(..., enable_thinking=False)", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H 'Content-Type: application/json' \\\n -d '{\"model\": \"lightonai/LightOnOCR-3-4B\", \"max_tokens\": 4096, \"temperature\": 0.2, \"top_p\": 0.9, \"messages\": [{\"role\": \"user\", \"content\": [{\"type\": \"image_url\", \"image_url\": {\"url\": \"https://example.com/page.png\"}}, {\"type\": \"text\", \"text\": \"grounding\"}]}]}'" }, "letme": { "capability": "https://letme.dev/docs.ocr", "tool": "https://letme.dev/lightonocr-3-4b" }, "weights": { "trainedBy": "LightOn", "builtOn": "Qwen 3.5's vision-language architecture (per the card; the metadata names no base model)", "params": "4.5B", "goodFor": "Turning page images into Markdown with labelled, located blocks, described images and chart data, on a single GPU under a plain Apache 2.0 licence.", "runtimes": [ "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "LightOnOCR-3-4B (BF16 safetensors)", "url": "https://huggingface.co/lightonai/LightOnOCR-3-4B/blob/main/model.safetensors", "who": "publisher", "precision": "bf16", "sizeGB": 9.08, "memoryGB": 12, "format": "safetensors", "runsOn": [ "vLLM", "SGLang", "Transformers" ], "note": "The only build from LightOn" } ], "run": "vllm serve lightonai/LightOnOCR-3-4B --default-chat-template-kwargs '{\"enable_thinking\": false}'", "runBuild": "LightOnOCR-3-4B (BF16 safetensors)", "checked": "2026-11-10", "minMemoryGB": 12 }, "family": { "lab": "LightOn", "family": "LightOnOCR", "url": "https://www.anchorterminal.com/open-weights/#lighton", "others": [ { "name": "LightOnOCR-3-1B", "url": "https://huggingface.co/lightonai/LightOnOCR-3-1B", "params": "1.0B (1,005,647,872 by the Hub's safetensors count)" }, { "name": "LightOnOCR-3-0.8B", "url": "https://huggingface.co/lightonai/LightOnOCR-3-0.8B", "params": "0.85B (852,985,920 by the Hub's safetensors count)" }, { "name": "LightOnOCR-2-1B", "url": "https://huggingface.co/lightonai/LightOnOCR-2-1B", "params": "1.0B (1,005,647,872 by the Hub's safetensors count)" } ] }, "area": "web-data", "provenance": { "legalEntity": "LightOn (named on the card as the publisher; its site couldn't be read to confirm the legal entity)", "domain": "lighton.ai", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/lightonai/LightOnOCR-3-4B/commits/main", "securityTxt": "unknown", "checked": "2026-11-10", "notes": [ "lighton.ai and www.lighton.ai answered 500 for robots.txt on 10 November 2026, so nothing on the site was read.", "The Hub marks the lightonai organisation as verified, and LightOn's GitHub repository for the models links to it.", "Nothing is hosted under this listing, so Apache 2.0 stands in for terms of service and no privacy policy is scored.", "The changelog is the commit history, one commit on 9 October 2026." ], "score": 56 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/lightonocr-3-4b.json" }, "answer": "Nemotron 3.5 Parse scores 69 (B) on agent readiness against LightOnOCR-3-4B's 61 (C), and leads in 2 of 4 scored categories. LightOnOCR-3-4B leads on reach.", "b": { "slug": "nemotron-3-5-parse", "name": "Nemotron 3.5 Parse", "vendor": "NVIDIA", "vendorUrl": "https://developer.nvidia.com/nemotron", "kind": "weights", "category": "document-extraction", "summary": "Nemotron 3.5 Parse is NVIDIA's open-weight document parser, 0.63B parameters, that turns a page image into text with layout classes, bounding boxes and reading order, and renders Markdown, HTML tables and JSON, under OpenMDW-1.1.", "url": "https://www.anchorterminal.com/tools/nemotron-3-5-parse", "markdownUrl": "https://www.anchorterminal.com/tools/nemotron-3-5-parse.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/nemotron-3-5-parse.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/nemotron-3-5-parse.json", "dns": "nemotron-3-5-parse.grade.anchorterminal.com", "repo": "https://huggingface.co/nvidia/Nemotron-3.5-Parse", "license": "OpenMDW-1.1", "transports": [], "packages": [ { "registry": "huggingface", "name": "nvidia/Nemotron-3.5-Parse" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository isn't gated (checked 2026-10-22). The card's line about running hf auth login 'if repository access requires authentication' doesn't apply today.", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under OpenMDW-1.1 (the Linux Foundation's permissive model licence), with the tokenizer under MIT. NVIDIA sells no per-call access to these weights in this listing. Running them costs the hardware, a 2.52 GB F32 safetensors file loaded in BF16 on one NVIDIA GPU; NVIDIA's reference hardware is one H100. No inference provider serves it on Hugging Face (checked 2026-10-22).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-22).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-01" }, "docsUrl": "https://huggingface.co/nvidia/Nemotron-3.5-Parse/blob/main/docs/serving.md", "capabilities": [ "docs.parse", "docs.ocr", "docs.tables", "model.open-weights" ], "tags": [ "open-weights", "model", "document-parsing", "ocr", "layout", "tables", "charts", "bounding-boxes", "multilingual", "vision", "safetensors", "self-hosted", "new" ], "lastRelease": "2026-10-28", "graded": true, "anchor": { "graded": true, "score": 69, "grade": "B", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1518, "categoryRank": 4, "methodology": "0.7", "run": "2026-10-01", "scores": { "capability": 83, "interface": 70, "reach": 60, "risk": 60, "upkeep": 59 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-01" }, "negative": 0, "verdict": "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.", "bestFor": "Agents and pipelines with an NVIDIA GPU that need fast local page parsing into Markdown, tables and boxed blocks for retrieval or extraction", "strengths": [ "Tiny, 0.63B parameters in one 2.52 GB safetensors file, about 3 GB in memory", "Layout classes, bounding boxes and reading order on every block, with helpers for Markdown, HTML tables, JSON and CSV", "OpenMDW-1.1 with nothing added, ungated, files hashed in NVIDIA's own organisation", "An OpenAI-compatible server through vLLM, a Dockerfile, a CLI with single, double and triple pass, and a runnable ParseBench reproduction bundle", "Earlier Parse releases kept in their own repositories, with a dated pointer to this one", "NVIDIA's API catalogue serves it as a free endpoint, and vLLM v0.33.0 registers its architecture natively" ], "weaknesses": [ "Loading runs the publisher's Python with trust_remote_code, in Transformers and in vLLM through NVIDIA's plugin", "Only vLLM, from v0.33.0, runs it from its own code, the card still pins an earlier vLLM, and NVIDIA publishes no quantised build", "One page per request with a 16,384-token context, so documents are parsed page by page", "Its ParseBench result is NVIDIA's own run; LlamaIndex's leaderboard row copies the card's numbers", "No safety results, training data without named datasets or a cut-off, and nobody has asked anything in its repository yet" ], "agentNotes": [ "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", "To serve it, `hf download nvidia/Nemotron-3.5-Parse --local-dir nemotron-parse`, `pip install \".[vllm,client]\" \"vllm\u003e=0.25.0,\u003c0.33\"` 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`", "Send one RGB page per request with the prompt `\u003c/s\u003e\u003cs\u003e\u003cpredict_bbox\u003e\u003cpredict_classes\u003e\u003coutput_markdown\u003e\u003cpredict_text_in_pic\u003e`, 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", "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", "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", "Boxes are normalised to the page content; scale x by the image width and y by its height", "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" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3.5, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "B", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 69 } ], "editorialScores": { "capability": 83, "interface": 70, "reach": 60, "risk": 60, "upkeep": 59 }, "provenanceScore": 78, "checked": { "categories": { "capability": "2026-11-01", "interface": "2026-11-01", "reach": "2026-11-01", "risk": "2026-11-01", "upkeep": "2026-11-01" }, "last": "2026-11-01", "lastMoved": "2026-11-01", "oldest": "2026-11-01", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "python3.12 -m venv .venv \u0026\u0026 . .venv/bin/activate\npip install \"transformers==5.14.1\" \"torchvision==0.26.0\" \"timm==1.0.22\"\n# then AutoModel.from_pretrained(\"nvidia/Nemotron-3.5-Parse\", trust_remote_code=True, dtype=torch.bfloat16)\n# prompt: \u003c/s\u003e\u003cs\u003e\u003cpredict_bbox\u003e\u003cpredict_classes\u003e\u003coutput_markdown\u003e\u003cpredict_text_in_pic\u003e", "http": "# after: hf download nvidia/Nemotron-3.5-Parse --local-dir nemotron-parse \u0026\u0026 cd nemotron-parse \u0026\u0026 pip install \".[vllm,client]\" \"vllm\u003e=0.25.0,\u003c0.33\"\n# vllm serve . --served-model-name nemotron-parse --dtype bfloat16 --trust-remote-code --max-model-len 16384 --limit-mm-per-prompt '{\"image\":1}' --chat-template chat_template.jinja\ncurl http://localhost:8000/v1/chat/completions -H 'Content-Type: application/json' -d '{\"model\":\"nemotron-parse\",\"temperature\":0,\"max_tokens\":8744,\"top_k\":1,\"repetition_penalty\":1.1,\"skip_special_tokens\":false,\"messages\":[{\"role\":\"user\",\"content\":[{\"type\":\"text\",\"text\":\"\u003c/s\u003e\u003cs\u003e\u003cpredict_bbox\u003e\u003cpredict_classes\u003e\u003coutput_markdown\u003e\u003cpredict_text_in_pic\u003e\"},{\"type\":\"image_url\",\"image_url\":{\"url\":\"data:image/png;base64,...\"}}]}]}'" }, "letme": { "capability": "https://letme.dev/docs.parse", "tool": "https://letme.dev/nemotron-3-5-parse" }, "sameCompany": [ "nemotron-3-5-super-vl", "nvidia-nemo-retriever", "nemo-guardrails", "nemotron-3-5-asr-streaming-0-6b", "nemotron-3-5-transcribe-30b-a4b", "parakeet-tdt-0-6b-v3", "parakeet-unified-en-0-6b", "cosmos-reason2-2b", "nemotron-3-5-bolt-e4b-4gb", "nemotron-3-nano-30b-a3b", "nemotron-3-nano-4b", "nemotron-3-nano-omni-30b-a3b-reasoning", "nemotron-3-super-120b-a12b", "nemotron-3-ultra-550b-a55b", "nemotron-3-5-lightning-30b-a3b" ], "weights": { "trainedBy": "NVIDIA", "builtOn": "nvidia/C-RADIOv4-SO400M (the vision encoder the card names; NVIDIA's own, under the NVIDIA Open Model License)", "params": "0.63B", "context": 16384, "goodFor": "Turning scanned or rendered document pages into Markdown, HTML tables and JSON blocks with layout classes, boxes and reading order, fast on one GPU, for retrieval, extraction and data-curation pipelines.", "runtimes": [ "vLLM" ], "builds": [ { "name": "Nemotron-3.5-Parse (safetensors)", "url": "https://huggingface.co/nvidia/Nemotron-3.5-Parse/blob/main/model.safetensors", "who": "publisher", "precision": "fp32", "sizeGB": 2.52, "memoryGB": 8, "format": "safetensors", "runsOn": [ "Transformers (remote code)", "vLLM (NVIDIA's plugin)" ], "note": "Stored in F32 and loaded in BF16 per the card. The only build from NVIDIA; 2,523,266,640 bytes, SHA-256 81dd7609c589f369..." } ], "run": "hf download nvidia/Nemotron-3.5-Parse --local-dir nemotron-parse \u0026\u0026 cd nemotron-parse \u0026\u0026 pip install \".[vllm,client]\" \"vllm\u003e=0.25.0,\u003c0.33\" \u0026\u0026 vllm serve . --served-model-name nemotron-parse --dtype bfloat16 --trust-remote-code --max-model-len 16384 --no-enable-prefix-caching --limit-mm-per-prompt '{\"image\":1}' --chat-template chat_template.jinja", "runBuild": "Nemotron-3.5-Parse (safetensors)", "checked": "2026-11-01", "minMemoryGB": 8 }, "family": { "lab": "NVIDIA (Nemotron)", "family": "Nemotron 3", "url": "https://www.anchorterminal.com/open-weights/#nvidia", "others": [ { "name": "Nemotron 3 Nano 30B-A3B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16", "params": "30B total, 3B active (31.6B per the safetensors index)", "listing": "https://www.anchorterminal.com/tools/nemotron-3-nano-30b-a3b" }, { "name": "Nemotron 3 Nano 30B-A3B Base", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-Base-BF16", "params": "30B total, 3B active" }, { "name": "Nemotron 3 Super 120B-A12B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16", "params": "120B total, 12B active", "listing": "https://www.anchorterminal.com/tools/nemotron-3-super-120b-a12b" }, { "name": "Nemotron 3 Super 120B-A12B Base", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-Base-BF16", "params": "120B total, 12B active" }, { "name": "Nemotron 3 Nano 4B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16", "params": "4B", "listing": "https://www.anchorterminal.com/tools/nemotron-3-nano-4b" }, { "name": "Nemotron 3 Nano Omni 30B-A3B Reasoning", "url": "https://huggingface.co/nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16", "params": "30B total, 3B active", "listing": "https://www.anchorterminal.com/tools/nemotron-3-nano-omni-30b-a3b-reasoning" }, { "name": "Nemotron 3 Ultra 550B-A55B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16", "params": "550B total, 55B active", "listing": "https://www.anchorterminal.com/tools/nemotron-3-ultra-550b-a55b" }, { "name": "Nemotron 3 Ultra 550B-A55B Base", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-Base-BF16", "params": "550B total, 55B active" }, { "name": "Nemotron 3 Ultra 550B-A55B GenRM", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-GenRM", "params": "550B total, 55B active" }, { "name": "Nemotron 3.5 Lightning 30B-A3B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16", "params": "30B total, 3B active", "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-lightning-30b-a3b" }, { "name": "Nemotron 3.5 Lightning 30B-A3B Base", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-Base-BF16", "params": "30B total, 3B active" }, { "name": "Nemotron 3.5 Super VL 120B-A12B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16", "params": "120B total, 12B active", "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-super-vl" }, { "name": "Nemotron 3.5 Super VL 120B-A12B Base", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-Base-BF16", "params": "120B total, 12B active" }, { "name": "Nemotron 3.5 Bolt 74B-A9B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Bolt-74B-A9B-BF16", "params": "74B total, 9B active" }, { "name": "Nemotron 3.5 Bolt E4B 4GB", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-4GB", "params": "8B total, 4B effective", "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-bolt-e4b-4gb" }, { "name": "Nemotron Labs 3 Elastic 30B-A3B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-Labs-3-Elastic-30B-A3B-BF16", "params": "30B total, 3B active" }, { "name": "Nemotron Labs 3 Puzzle 75B-A9B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-Labs-3-Puzzle-75B-A9B-BF16", "params": "75B total, 9B active" }, { "name": "Nemotron 3 Labs Ultra Math SFT", "url": "https://huggingface.co/nvidia/Nemotron-3-Labs-Ultra-Math-SFT", "params": "550B total, 55B active" }, { "name": "Nemotron 3 Labs Ultra Math RL", "url": "https://huggingface.co/nvidia/Nemotron-3-Labs-Ultra-Math-RL", "params": "550B total, 55B active" }, { "name": "Nemotron Labs 3 Competitive Coding 550B-A55B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-Labs-3-Competitive-Coding-550B-A55B-NVFP4", "params": "550B total, 55B active" }, { "name": "nemotron-3.5-asr-streaming-0.6b", "url": "https://huggingface.co/nvidia/nemotron-3.5-asr-streaming-0.6b", "params": "0.6B (638M per the safetensors count)", "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-asr-streaming-0-6b" }, { "name": "Nemotron 3.5 Transcribe 30B-A4B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Transcribe-30B-A4B", "params": "30B total, about 4.3B active (33,697,781,029 by the Hub's safetensors count)", "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-transcribe-30b-a4b" } ], "olderCount": 47 }, "area": "web-data", "provenance": { "legalEntity": "NVIDIA Corporation", "domain": "nvidia.com", "domainRegistered": "1993-04-20", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/nvidia/Nemotron-3.5-Parse/commits/main", "securityTxt": "unknown", "checked": "2026-10-22", "notes": [ "The card says the model was developed by NVIDIA as part of Nemotron, and the files sit in NVIDIA's verified nvidia organisation on Hugging Face, which developer.nvidia.com links to. The domain registration date is carried from the RDAP check made for nemotron-3-super-120b-a12b on 21 October 2026.", "Nothing is hosted in this listing, so the OpenMDW-1.1 licence stands in for terms of service and no privacy policy is scored. The privacy subcard links NVIDIA's privacy policy.", "The changelog is the repository's commit history, four commits from 18 to 21 October 2026 (the release, refreshed regression references, a fix to malformed block parsing in the reference pipelines, and a merged pull request widening the vLLM range), all by NVIDIA organisation members. The repository was created on 3 September 2026 and its earlier history isn't public.", "www.nvidia.com/.well-known/security.txt was not requested; earlier NVIDIA listings record a 403. The card links NVIDIA's vulnerability submission page." ], "score": 78 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/nemotron-3-5-parse.json", "live": { "slug": "nemotron-3-5-parse", "securityTxt": { "url": "https://nvidia.com/.well-known/security.txt", "state": "unknown", "checkedAt": "2026-11-10T16:08:00.271598775Z" }, "domain": { "domain": "nvidia.com", "registered": "1993-04-20", "source": "https://rdap.verisign.com/com/v1/domain/nvidia.com", "checkedAt": "2026-11-08T15:03:11.895084282Z" }, "pages": [ { "url": "https://huggingface.co/nvidia/Nemotron-3.5-Parse/commits/main", "kind": "changelog", "status": 200, "checkedAt": "2026-11-10T16:23:30.724078931Z", "changedAt": "2026-11-10T16:23:30.724078931Z", "fingerprint": "655ab448397f" } ], "updatedAt": "2026-11-10T16:23:30.724078931Z" } }, "edges": [ { "by": 10, "category": "reach", "leads": "lightonocr-3-4b", "name": "Reach", "reasons": { "a": "Memory 30, the only build from LightOn is the BF16 safetensors file, 9.08 GB, and 9.08 GB × 1.2 = 10.9 GB, within 16 GB. No runtime library has a build of this model. Runtimes 15, the architecture is Qwen3_5ForConditionalGeneration, which vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 run from the safetensors file, three at 5 each. Builds 0, LightOn publishes the BF16 weights only. Terms 15, Apache 2.0, OSI-approved, with nothing added, in the card's metadata and licence section. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it, and the card names no hosted API for it. 30 + 15 + 0 + 15 + 10 + 0 = 70.", "b": "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. Runtimes score 5. vLLM v0.33.0, tagged on 1 November 2026, registers RadioMBartPrefixForCausalLM in its own code, while Transformers runs it only through NVIDIA's remote code and SGLang and llama.cpp have nothing for it; Ollama and LM Studio couldn't be checked. Builds score 0, as NVIDIA publishes no quantised build. Terms get 10 because OpenMDW-1.1 is permissive with nothing added but isn't OSI-approved. Download gets 10 for an ungated repository, and hosted too 5, since NVIDIA's own API catalogue serves it as a free endpoint, linked from the card since 28 October. build.nvidia.com's robots.txt carries Content-Signal ai-input=no, recorded as a fact about the site. 30+5+0+10+10+5 = 60." }, "scores": { "a": 70, "b": 60 } }, { "by": 8, "category": "upkeep", "leads": "nemotron-3-5-parse", "name": "Documentation and upkeep", "reasons": { "a": "Model card 11, Intended use, a limitation (instructions other than its two prompts are out of distribution), the architecture (Qwen 3.5's) and the size (4B) are on the card. Training data 0, Nothing on the card about the training data. Last change 15, one commit, the release on 9 October 2026, 32 days before this check and within 90. Responsiveness 10, Of the 2 discussions that aren't pull requests (both opened on 9 October 2026), 1 has a reply from a lightonai member, which is some. Version policy 5, LightOnOCR-2-1B stays in its own repository, and a commit to its card on 8 October 2026 named this model as its new version. File health 10, Config, template and card agree on the Qwen 3.5 classes and on thinking off by default. 11 + 0 + 15 + 10 + 5 + 10 = 51.", "b": "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)." }, "scores": { "a": 51, "b": 59 } }, { "by": 5, "category": "risk", "leads": "nemotron-3-5-parse", "name": "Risk", "reasons": { "a": "File safety 25, one safetensors file, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, the files sit in the lightonai organisation, which the Hub marks as verified and LightOn's GitHub repository for the models links to (10), and the Hub gives a SHA-256 for the large files (5). Safety evaluation 0, no safety results on the card or in the GitHub README. Agent guidance 0, Nothing on text in a document that tries to instruct the reader, or on safeguards to add. Licence risk 15, Apache 2.0 has no use policy, field-of-use limit, threshold or change clause, and nothing is added on the card. Security programme 0, no way to report a flaw found on the card or in the GitHub README. lighton.ai couldn't be read for a security.txt. 25 + 15 + 0 + 0 + 15 + 0 = 55.", "b": "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." }, "scores": { "a": 55, "b": 60 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "LightOn", "b": "NVIDIA", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "", "b": "", "name": "Transports" }, { "a": "None", "b": "None", "name": "Auth" }, { "a": "Free", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Apache-2.0", "b": "OpenMDW-1.1", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-10-09", "b": "2026-10-28", "name": "Last release" }, { "a": "no document linked", "b": "no document linked", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "none", "b": "3.5/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Nemotron 3.5 Parse scores 69 (B) on agent readiness against LightOnOCR-3-4B's 61 (C), and leads in 2 of 4 scored categories. LightOnOCR-3-4B leads on reach.", "question": "Which is better for AI agents, LightOnOCR-3-4B or Nemotron 3.5 Parse?" } ], "goodFor": [ { "aheadOn": [ "Reach, 70 against 60" ], "also": null, "goodFor": "Agents that need located, labelled document blocks from a model they can use commercially without conditions", "slug": "lightonocr-3-4b", "watchFor": "BF16 only, with no GGUF or other quantised build of this model" }, { "aheadOn": [ "Risk, 60 against 55", "Documentation and upkeep, 59 against 51" ], "also": null, "goodFor": "Agents and pipelines with an NVIDIA GPU that need fast local page parsing into Markdown, tables and boxed blocks for retrieval or extraction", "slug": "nemotron-3-5-parse", "watchFor": "Loading runs the publisher's Python with trust_remote_code, in Transformers and in vLLM through NVIDIA's plugin" } ], "history": { "lightonocr-3-4b": null, "nemotron-3-5-parse": [ { "causes": [ "product" ], "date": "2026-11-01", "delta": 2.5, "from": 66.5, "fromGrade": "B", "to": 69, "toGrade": "B" } ] }, "job": { "capability": "docs.ocr", "name": "OCR" }, "others": [ { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-parse-vs-surya-ocr-2-5.json", "title": "Nemotron 3.5 Parse vs Surya OCR 2.5", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-parse-vs-surya-ocr-2-5" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-parse-vs-xiaomi-ocr-0.json", "title": "Nemotron 3.5 Parse vs Xiaomi-OCR-0", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-parse-vs-xiaomi-ocr-0" }, { "json": "https://www.anchorterminal.com/compare/chandra-ocr-2-vs-lightonocr-3-4b.json", "title": "Chandra OCR 2 vs LightOnOCR-3-4B", "url": "https://www.anchorterminal.com/compare/chandra-ocr-2-vs-lightonocr-3-4b" }, { "json": "https://www.anchorterminal.com/compare/chandra-ocr-2-vs-nemotron-3-5-parse.json", "title": "Chandra OCR 2 vs Nemotron 3.5 Parse", "url": "https://www.anchorterminal.com/compare/chandra-ocr-2-vs-nemotron-3-5-parse" }, { "json": "https://www.anchorterminal.com/compare/deepseek-ocr-2-vs-lightonocr-3-4b.json", "title": "DeepSeek-OCR 2 vs LightOnOCR-3-4B", "url": "https://www.anchorterminal.com/compare/deepseek-ocr-2-vs-lightonocr-3-4b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-ocr-2-vs-nemotron-3-5-parse.json", "title": "DeepSeek-OCR 2 vs Nemotron 3.5 Parse", "url": "https://www.anchorterminal.com/compare/deepseek-ocr-2-vs-nemotron-3-5-parse" }, { "json": "https://www.anchorterminal.com/compare/hunyuanocr-vs-lightonocr-3-4b.json", "title": "HunyuanOCR 1.5 vs LightOnOCR-3-4B", "url": "https://www.anchorterminal.com/compare/hunyuanocr-vs-lightonocr-3-4b" }, { "json": "https://www.anchorterminal.com/compare/hunyuanocr-vs-nemotron-3-5-parse.json", "title": "HunyuanOCR 1.5 vs Nemotron 3.5 Parse", "url": "https://www.anchorterminal.com/compare/hunyuanocr-vs-nemotron-3-5-parse" }, { "json": "https://www.anchorterminal.com/compare/lightonocr-3-4b-vs-nanonets-ocr2-3b.json", "title": "LightOnOCR-3-4B vs Nanonets-OCR2-3B", "url": "https://www.anchorterminal.com/compare/lightonocr-3-4b-vs-nanonets-ocr2-3b" }, { "json": "https://www.anchorterminal.com/compare/lightonocr-3-4b-vs-surya-ocr-2-5.json", "title": "LightOnOCR-3-4B vs Surya OCR 2.5", "url": "https://www.anchorterminal.com/compare/lightonocr-3-4b-vs-surya-ocr-2-5" }, { "json": "https://www.anchorterminal.com/compare/lightonocr-3-4b-vs-unlimited-ocr-2.json", "title": "LightOnOCR-3-4B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/lightonocr-3-4b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/lightonocr-3-4b-vs-xiaomi-ocr-0.json", "title": "LightOnOCR-3-4B vs Xiaomi-OCR-0", "url": "https://www.anchorterminal.com/compare/lightonocr-3-4b-vs-xiaomi-ocr-0" }, { "json": "https://www.anchorterminal.com/compare/nanonets-ocr2-3b-vs-nemotron-3-5-parse.json", "title": "Nanonets-OCR2-3B vs Nemotron 3.5 Parse", "url": "https://www.anchorterminal.com/compare/nanonets-ocr2-3b-vs-nemotron-3-5-parse" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 10, "edge": "lightonocr-3-4b", "key": "reach", "lightonocr-3-4b": 70, "name": "Reach", "nemotron-3-5-parse": 60, "weight": 25 }, { "by": 5, "edge": "nemotron-3-5-parse", "key": "risk", "lightonocr-3-4b": 55, "name": "Risk", "nemotron-3-5-parse": 60, "weight": 20 }, { "by": 0, "edge": "", "key": "interface", "lightonocr-3-4b": 70, "name": "Agent interface", "nemotron-3-5-parse": 70, "weight": 10 }, { "by": 8, "edge": "nemotron-3-5-parse", "key": "upkeep", "lightonocr-3-4b": 51, "name": "Documentation and upkeep", "nemotron-3-5-parse": 59, "weight": 10 } ], "summary": "Nemotron 3.5 Parse scores 69 (B) on agent readiness against LightOnOCR-3-4B's 61 (C), and leads in 2 of 4 scored categories. LightOnOCR-3-4B leads on reach. Both do ocr.", "tasks": null, "tutorials": [], "verdicts": { "lightonocr-3-4b": "A well-documented OCR model under plain Apache 2.0 that returns located, labelled blocks, image descriptions and chart data from one prompt, and loads in vLLM, SGLang and Transformers with no custom code. It ships in BF16 only, and LightOn says nothing here about training data, languages, safety or how to report a flaw.", "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." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/lightonocr-3-4b-vs-nemotron-3-5-parse", "json": "https://www.anchorterminal.com/compare/lightonocr-3-4b-vs-nemotron-3-5-parse.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/lightonocr-3-4b-vs-nemotron-3-5-parse.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/lightonocr-3-4b-vs-nemotron-3-5-parse.min.md" }, "markdown": "Nemotron 3.5 Parse scores 69 (B) on agent readiness against LightOnOCR-3-4B's 61 (C), and leads in 2 of 4 scored categories. LightOnOCR-3-4B leads on reach. Both do ocr.\n\n- LightOnOCR-3-4B: grade C, 61/100, rank #73 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/lightonocr-3-4b.md · JSON https://www.anchorterminal.com/api/v1/tools/lightonocr-3-4b.json\n- 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\n- Best document parsing, OCR and extraction APIs for AI agents: https://www.anchorterminal.com/best/document-extraction/index.md\n- All 215 documents comparisons: https://www.anchorterminal.com/compare/document-extraction/index.md\n\n## Which one, for what\n\n### LightOnOCR-3-4B (C)\n\nGood for: Agents that need located, labelled document blocks from a model they can use commercially without conditions\n\nAhead on:\n- Reach, 70 against 60\n\nWatch for: BF16 only, with no GGUF or other quantised build of this model\n\n### Nemotron 3.5 Parse (B)\n\nGood for: Agents and pipelines with an NVIDIA GPU that need fast local page parsing into Markdown, tables and boxed blocks for retrieval or extraction\n\nAhead on:\n- Risk, 60 against 55\n- Documentation and upkeep, 59 against 51\n\nWatch for: Loading runs the publisher's Python with trust_remote_code, in Transformers and in vLLM through NVIDIA's plugin\n\n\n## Why the edge\n\n### Reach: LightOnOCR-3-4B +10\n\n- LightOnOCR-3-4B (70): Memory 30, the only build from LightOn is the BF16 safetensors file, 9.08 GB, and 9.08 GB × 1.2 = 10.9 GB, within 16 GB. No runtime library has a build of this model. Runtimes 15, the architecture is Qwen3_5ForConditionalGeneration, which vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 run from the safetensors file, three at 5 each. Builds 0, LightOn publishes the BF16 weights only. Terms 15, Apache 2.0, OSI-approved, with nothing added, in the card's metadata and licence section. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it, and the card names no hosted API for it. 30 + 15 + 0 + 15 + 10 + 0 = 70.\n- 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. Runtimes score 5. vLLM v0.33.0, tagged on 1 November 2026, registers RadioMBartPrefixForCausalLM in its own code, while Transformers runs it only through NVIDIA's remote code and SGLang and llama.cpp have nothing for it; Ollama and LM Studio couldn't be checked. Builds score 0, as NVIDIA publishes no quantised build. Terms get 10 because OpenMDW-1.1 is permissive with nothing added but isn't OSI-approved. Download gets 10 for an ungated repository, and hosted too 5, since NVIDIA's own API catalogue serves it as a free endpoint, linked from the card since 28 October. build.nvidia.com's robots.txt carries Content-Signal ai-input=no, recorded as a fact about the site. 30+5+0+10+10+5 = 60.\n\n### Documentation and upkeep: Nemotron 3.5 Parse +8\n\n- LightOnOCR-3-4B (51): Model card 11, Intended use, a limitation (instructions other than its two prompts are out of distribution), the architecture (Qwen 3.5's) and the size (4B) are on the card. Training data 0, Nothing on the card about the training data. Last change 15, one commit, the release on 9 October 2026, 32 days before this check and within 90. Responsiveness 10, Of the 2 discussions that aren't pull requests (both opened on 9 October 2026), 1 has a reply from a lightonai member, which is some. Version policy 5, LightOnOCR-2-1B stays in its own repository, and a commit to its card on 8 October 2026 named this model as its new version. File health 10, Config, template and card agree on the Qwen 3.5 classes and on thinking off by default. 11 + 0 + 15 + 10 + 5 + 10 = 51.\n- 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).\n\n### Risk: Nemotron 3.5 Parse +5\n\n- LightOnOCR-3-4B (55): File safety 25, one safetensors file, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, the files sit in the lightonai organisation, which the Hub marks as verified and LightOn's GitHub repository for the models links to (10), and the Hub gives a SHA-256 for the large files (5). Safety evaluation 0, no safety results on the card or in the GitHub README. Agent guidance 0, Nothing on text in a document that tries to instruct the reader, or on safeguards to add. Licence risk 15, Apache 2.0 has no use policy, field-of-use limit, threshold or change clause, and nothing is added on the card. Security programme 0, no way to report a flaw found on the card or in the GitHub README. lighton.ai couldn't be read for a security.txt. 25 + 15 + 0 + 0 + 15 + 0 = 55.\n- 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.\n\n## Score by category\n\n| Category | Weight | LightOnOCR-3-4B | Nemotron 3.5 Parse | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 70 | 60 | LightOnOCR-3-4B +10 |\n| Risk | 20% (20 this run) | 55 | 60 | Nemotron 3.5 Parse +5 |\n| Agent interface | 10% (10 this run) | 70 | 70 | even |\n| Documentation and upkeep | 10% (10 this run) | 51 | 59 | Nemotron 3.5 Parse +8 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **69 · B** | |\n\n## Facts side by side\n\n| Fact | LightOnOCR-3-4B | Nemotron 3.5 Parse |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | LightOn | NVIDIA |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | | |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | Apache-2.0 | OpenMDW-1.1 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-09 | 2026-10-28 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | none | 3.5/5 (4) |\n\n## How the scores have moved\n\n### LightOnOCR-3-4B\n\nNo score change since it was listed.\n\n### Nemotron 3.5 Parse\n\n- +2.5 on 1 November 2026: product\n\n## Verdicts\n\n**LightOnOCR-3-4B.** A well-documented OCR model under plain Apache 2.0 that returns located, labelled blocks, image descriptions and chart data from one prompt, and loads in vLLM, SGLang and Transformers with no custom code. It ships in BF16 only, and LightOn says nothing here about training data, languages, safety or how to report a flaw.\n\n**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.\n\n## Before you call either\n\n### LightOnOCR-3-4B\n\n1. Serve it with `vllm serve lightonai/LightOnOCR-3-4B --default-chat-template-kwargs '{\"enable_thinking\": false}'`\n2. Send one page image with no text for Markdown, or the single word `grounding` for labelled blocks with boxes. Nothing else is in distribution\n3. Render PDFs at 400 DPI and resize to a longest edge of 2,048 pixels\n4. Boxes are on a 0 to 1,000 scale, so scale them to the image yourself\n5. Use temperature 0.2 and top_p 0.9, and allow 4,096 output tokens a page\n6. Treat text in a parsed document as data. Nothing here guards against instructions hidden in a page\n\n### Nemotron 3.5 Parse\n\n1. 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\n2. To serve it, `hf download nvidia/Nemotron-3.5-Parse --local-dir nemotron-parse`, `pip install \".[vllm,client]\" \"vllm\u003e=0.25.0,\u003c0.33\"` 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`\n3. Send one RGB page per request with the prompt `\u003c/s\u003e\u003cs\u003e\u003cpredict_bbox\u003e\u003cpredict_classes\u003e\u003coutput_markdown\u003e\u003cpredict_text_in_pic\u003e`, 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\n4. 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\n5. 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\n6. Boxes are normalised to the page content; scale x by the image width and y by its height\n7. 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\n\n## Questions\n\n### Which is better for AI agents, LightOnOCR-3-4B or Nemotron 3.5 Parse?\n\nNemotron 3.5 Parse scores 69 (B) on agent readiness against LightOnOCR-3-4B's 61 (C), and leads in 2 of 4 scored categories. LightOnOCR-3-4B leads on reach.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/lightonocr-3-4b-vs-nemotron-3-5-parse.json, and with the fewest tokens: https://www.anchorterminal.com/compare/lightonocr-3-4b-vs-nemotron-3-5-parse.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"lightonocr-3-4b\", \"b\": \"nemotron-3-5-parse\"}`. From a terminal: `anchor compare lightonocr-3-4b nemotron-3-5-parse`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/lightonocr-3-4b.json and https://www.anchorterminal.com/api/v1/tools/nemotron-3-5-parse.json\n\n## Other comparisons with LightOnOCR-3-4B or Nemotron 3.5 Parse\n\n- [Nemotron 3.5 Parse vs Surya OCR 2.5](https://www.anchorterminal.com/compare/nemotron-3-5-parse-vs-surya-ocr-2-5.md)\n- [Nemotron 3.5 Parse vs Xiaomi-OCR-0](https://www.anchorterminal.com/compare/nemotron-3-5-parse-vs-xiaomi-ocr-0.md)\n- [Chandra OCR 2 vs LightOnOCR-3-4B](https://www.anchorterminal.com/compare/chandra-ocr-2-vs-lightonocr-3-4b.md)\n- [Chandra OCR 2 vs Nemotron 3.5 Parse](https://www.anchorterminal.com/compare/chandra-ocr-2-vs-nemotron-3-5-parse.md)\n- [DeepSeek-OCR 2 vs LightOnOCR-3-4B](https://www.anchorterminal.com/compare/deepseek-ocr-2-vs-lightonocr-3-4b.md)\n- [DeepSeek-OCR 2 vs Nemotron 3.5 Parse](https://www.anchorterminal.com/compare/deepseek-ocr-2-vs-nemotron-3-5-parse.md)\n- [HunyuanOCR 1.5 vs LightOnOCR-3-4B](https://www.anchorterminal.com/compare/hunyuanocr-vs-lightonocr-3-4b.md)\n- [HunyuanOCR 1.5 vs Nemotron 3.5 Parse](https://www.anchorterminal.com/compare/hunyuanocr-vs-nemotron-3-5-parse.md)\n- [LightOnOCR-3-4B vs Nanonets-OCR2-3B](https://www.anchorterminal.com/compare/lightonocr-3-4b-vs-nanonets-ocr2-3b.md)\n- [LightOnOCR-3-4B vs Surya OCR 2.5](https://www.anchorterminal.com/compare/lightonocr-3-4b-vs-surya-ocr-2-5.md)\n- [LightOnOCR-3-4B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/lightonocr-3-4b-vs-unlimited-ocr-2.md)\n- [LightOnOCR-3-4B vs Xiaomi-OCR-0](https://www.anchorterminal.com/compare/lightonocr-3-4b-vs-xiaomi-ocr-0.md)\n- [Nanonets-OCR2-3B vs Nemotron 3.5 Parse](https://www.anchorterminal.com/compare/nanonets-ocr2-3b-vs-nemotron-3-5-parse.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from (https://www.anchorterminal.com/terms/#data)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-11-11", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.7", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run", "terms": "https://www.anchorterminal.com/terms/#data" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "LightOnOCR-3-4B vs Nemotron 3.5 Parse", "url": "" } ], "description": "Nemotron 3.5 Parse scores 69 (B) to LightOnOCR-3-4B's 61 (C) for ocr. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "LightOnOCR-3-4B C 61", "Nemotron 3.5 Parse B 69", "scores" ], "h1": "LightOnOCR-3-4B vs Nemotron 3.5 Parse", "image": "https://www.anchorterminal.com/assets/og/default.png", "path": "/compare/lightonocr-3-4b-vs-nemotron-3-5-parse", "published": "2026-10-01", "section": "tools", "title": "LightOnOCR-3-4B vs Nemotron 3.5 Parse for AI agents (2026)", "toc": null, "updated": "2026-11-10", "url": "https://www.anchorterminal.com/compare/lightonocr-3-4b-vs-nemotron-3-5-parse" }, "tokens": { "markdown": 3350, "slim": 680 }, "version": 1 }