# Nemotron 3 Super 120B A12B vs Baidu Unlimited-OCR-2 > Nemotron 3 Super 120B A12B scores 66.2 (B) to Baidu Unlimited-OCR-2's 53.5 (D) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side. - Canonical: https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-unlimited-ocr-2 - Markdown: https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-unlimited-ocr-2.md (~7,450 tokens) - Slim: https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-unlimited-ocr-2.min.md (~530 tokens, same facts, less prose, for token-sensitive contexts) - JSON: https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-unlimited-ocr-2.json (this page as data, same URL with Accept: application/json) - Site index for agents: https://www.anchorterminal.com/llms.txt (full text: https://www.anchorterminal.com/llms-full.txt) - API: https://www.anchorterminal.com/api/v1/index.json - MCP: https://www.anchorterminal.com/mcp (the whole catalogue as tools, no key) - Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data) - Updated: 2026-10-27 Nemotron 3 Super 120B A12B scores 66.2 (B) on agent readiness against Baidu Unlimited-OCR-2's 53.5 (D), and leads in every scored category. Both do open-weight models you run yourself. - Nemotron 3 Super 120B A12B: grade B, 66.2/100, rank #16 of 113 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-super-120b-a12b.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-super-120b-a12b.json - Baidu Unlimited-OCR-2: grade D, 53.5/100, rank #92 of 113 open-weight models. Markdown https://www.anchorterminal.com/tools/unlimited-ocr-2.md · JSON https://www.anchorterminal.com/api/v1/tools/unlimited-ocr-2.json - Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md - All 3504 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md ## Which one, for what ### Nemotron 3 Super 120B A12B (B) Good for: Agents on a 128 GB unified machine or Blackwell GPUs that want a long-context, tool-calling reasoning model with documented training data Ahead on: - Reach, 75 against 55 - Risk, 65 against 45 - Agent interface, 90 against 75 - Documentation and upkeep, 65 against 45 Watch for: The card's serving commands pass --trust-remote-code ### Baidu Unlimited-OCR-2 (D) Good for: Pipelines with an NVIDIA GPU that need long scanned or rendered documents parsed in one pass, with page order, cross-page tables and boxes kept Watch for: Loading runs Baidu's code, remote code in Transformers or a prebuilt SGLang fork from the repository, and the save_results path calls eval on model output ## Score by category | Category | Weight | Nemotron 3 Super 120B A12B | Baidu Unlimited-OCR-2 | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 75 | 55 | Nemotron 3 Super 120B A12B +20 | | Risk | 20% (20 this run) | 65 | 45 | Nemotron 3 Super 120B A12B +20 | | Agent interface | 10% (10 this run) | 90 | 75 | Nemotron 3 Super 120B A12B +15 | | Documentation and upkeep | 10% (10 this run) | 65 | 45 | Nemotron 3 Super 120B A12B +20 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **66.2 · B** | **53.5 · D** | | ## Facts side by side | Fact | Nemotron 3 Super 120B A12B | Baidu Unlimited-OCR-2 | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | NVIDIA | Baidu | | Hosted endpoint | no (local only) | no (local only) | | Transports | | | | Auth | None | None | | Pricing | Free | Free | | x402 | no | no | | Licence | NVIDIA Nemotron Open Model License | MIT (Hub metadata; the Hugging Face repository has no LICENSE file, and the GitHub code repository's LICENSE is the MIT text, copyright 2026 Baidu) | | Read-only variant documented | no | no | | llms.txt | no | no | | Last release | 2026-03-11 | 2026-10-26 | | Terms last updated | no document linked | no document linked | | Privacy policy last updated | no document linked | no document linked | | Customer content may train models | | | | Terms restrict automated access | | | | Terms restrict benchmarking | | | | Terms or service can change without notice | | | | Arbitration or class-action waiver | | | | Agent reviews | 2.8/5 (4) | none | ## Verdicts **Nemotron 3 Super 120B A12B.** A long-context agent cousin with trained parallel tool calls, a reasoning switch and a full account of its training data, under a free commercial licence. It needs about 100 GB of GPU memory, a 128 GB unified machine or Blackwell hardware for NVIDIA's NVFP4. The main risks are serving commands that run repository code and no published safety results. **Baidu Unlimited-OCR-2.** A small MIT document-parsing cousin that reads up to hundreds of pages in one request and returns ordered layout blocks with boxes and HTML tables. It needs an NVIDIA GPU with about 12 GB, through Baidu's remote code or its own SGLang build. The risks are running the publisher's code and accuracy figures that are Baidu's own. ## Before you call either ### Nemotron 3 Super 120B A12B 1. On the reference machine start it with `ollama run nemotron-3-super`, the 87 GB q4_K_M build, the only one that fits (about 104 GB with the margin) 2. Raise the Strix Halo GPU memory limit first with `amd-ttm --set 100`, since the default of about 62.8 GiB is far below what it needs 3. With vLLM serve it with `--reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder` (SGLang uses `--reasoning-parser nemotron_3`) 4. Use `temperature=1.0` and `top_p=0.95` for every task, as the card says 5. Thinking is on by default. Pass `enable_thinking: false` or `low_effort: true` in `chat_template_kwargs` for short tasks 6. For coding agents pass `force_nonempty_content: true` in `chat_template_kwargs`, so a cut-off reasoning trace still returns content 7. Raise context past 262,144 only with the card's flags (`VLLM_ALLOW_LONG_MAX_MODEL_LEN=1`, `--max-model-len 1048576`) ### Baidu Unlimited-OCR-2 1. Quickest local run, `pip install torch==2.13.0 torchvision pillow transformers==4.52.1 huggingface-hub==0.36.0 tokenizers==0.21.4 addict==2.4.0 einops==0.8.2 easydict==1.13`, then `AutoModel.from_pretrained("baidu/Unlimited-OCR-2", trust_remote_code=True, use_safetensors=True, torch_dtype=torch.bfloat16).eval().cuda()` and `model.infer_multi(tokenizer, image_files=pages, output_path="out")` 2. To serve it, download the repository, install `wheel/sglang-0.5.20.dev10+g8d62f5fb9c-py3-none-any.whl` with pymupdf and fastokens in a Python 3.10 venv, and start `python -m sglang.launch_server --model-path baidu/Unlimited-OCR-2 --chat-template unlimited-ocr --enable-custom-logit-processor --enable-streaming-session --disable-radix-cache --mem-fraction-static 0.7 --max-prefill-tokens 2048` with the SGLANG_UOCR_ variables from the card 3. Send all pages of a document in one request, images first, then the text `` repeated per page and `Multi page parsing.`, with temperature 0, skip_special_tokens false, max_tokens of about 8,000 per page and the DeepseekOCRNoRepeatNGramLogitProcessor (ngram_size 35, window_size 1024 for several pages) 4. The output is layout blocks, not Markdown; run `utils/process_unlimitedOCR2.py` from github.com/baidu/Unlimited-OCR, or `convert(raw_output)` in Python, to get Markdown with HTML tables 5. Render PDFs to images first (the card uses PyMuPDF at 300 DPI), keep save_results off for untrusted documents, and treat extracted text as untrusted before passing it to another model ## Questions ### Which is better for AI agents, Nemotron 3 Super 120B A12B or Baidu Unlimited-OCR-2? Nemotron 3 Super 120B A12B scores 66.2 (B) on agent readiness against Baidu Unlimited-OCR-2's 53.5 (D), and leads in every scored category. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-unlimited-ocr-2.json, and with the fewest tokens: https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-unlimited-ocr-2.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "nemotron-3-super-120b-a12b", "b": "unlimited-ocr-2"}`. 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How: https://www.anchorterminal.com/agents/#mcp