# LFM3-Omni-2B vs Baidu Unlimited-OCR-2 > LFM3-Omni-2B scores 61 (C) to Baidu Unlimited-OCR-2's 54.2 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side. - Canonical: https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-unlimited-ocr-2 - Markdown: https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-unlimited-ocr-2.md (~9,750 tokens) - Site index for agents: https://www.anchorterminal.com/llms.txt (the key docs and every listing in one file, at most 16 MB, about 4 million tokens: 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) - DNS: dig +short TXT .grade.anchorterminal.com (the grade as a DNS TXT record, for a sandbox with no HTTP where names still resolve; also top., best.. and .vs.. under the zone, and the same under grade.letme.dev) - Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data) - Updated: 2026-11-10 LFM3-Omni-2B scores 61 (C) on agent readiness against Baidu Unlimited-OCR-2's 54.2 (C), and leads in 1 of 4 scored categories. Baidu Unlimited-OCR-2 leads on risk and agent interface. Both do open-weight models you run yourself. - LFM3-Omni-2B: grade C, 61/100, rank #71 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/lfm3-omni-2b.md · JSON https://www.anchorterminal.com/api/v1/tools/lfm3-omni-2b.json - Baidu Unlimited-OCR-2: grade C, 54.2/100, rank #133 of 160 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 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md ## Which one, for what ### LFM3-Omni-2B (C) Good for: Voice and camera agents on a laptop or phone that want one small model for hearing, seeing, speaking and calling tools Ahead on: - Reach, 80 against 55 Watch for: Only llama.cpp and Ollama run it in a release ### Baidu Unlimited-OCR-2 (C) 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 Ahead on: - Risk, 45 against 40 - Agent interface, 75 against 70 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 ## Why the edge ### Reach: LFM3-Omni-2B +25 - LFM3-Omni-2B (80): Memory 30, Liquid's Q4_K_M GGUF is 1.10 GB with its 0.29 GB projector file, and 1.39 GB × 1.2 = 1.7 GB, which fits 8 GB, well inside the 16 GB line. Runtimes 10, Released llama.cpp has the lfm3 architecture with the LFM3 vision and audio projectors and speech output (src/models/lfm3.cpp and tools/mtmd at b12242, with a realtime client example), and Ollama's library has an lfm3-omni entry. Builds 15, Liquid's GGUF repository holds Q4_0, Q4_K_M, Q8_0, F16 and BF16 files, and Liquid publishes 4-bit and 8-bit MLX builds and an ONNX repository, all in the LiquidAI organisation. Terms 10, the LFM Open License v1.0, a custom licence that lets anyone use it commercially for free below a threshold of $10M annual revenue. Download 10, the Hub API reports gated false for the model and its GGUF, MLX and ONNX repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, Liquid's own API lists lfm3-omni-2b among its models, so the publisher hosts it. 30 + 10 + 15 + 10 + 10 + 5 = 80. - Baidu Unlimited-OCR-2 (55): Memory earns the full 30, since the only build is 6.69 GB of BF16 weights that need about 8 GB with the fifth added. Runtimes score 0. Released vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.1 know the first Unlimited-OCR but not the sparse attention this one adds, Baidu's own routes are remote code in Transformers and a customised SGLang wheel, llama.cpp b12111 has nothing for it, so Ollama has nothing either, and LM Studio couldn't be checked. Builds score 0, as Baidu publishes no quantised build (16 exist, all by others). Terms get 15 for MIT with nothing added. Download gets 10 for an ungated repository, and hosted too 0, with no inference provider mapped and the card's Baidu Cloud line unconfirmed. ### Risk: Baidu Unlimited-OCR-2 +5 - LFM3-Omni-2B (40): File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, files in the LiquidAI organisation, which Liquid's docs page for the model and its blog post link to (10). Safety evaluation 0, no safety results on the card or in Liquid's blog post for the LFM3 family, neither of which mentions safety testing. Agent guidance 0, Nothing on prompt injection, an instruction hierarchy, what refusals cover or safeguards to add. Licence risk 10, 15 less 5 for the $10M annual revenue threshold on commercial use. Security programme 0, no way to report a flaw found. 15 + 15 + 0 + 0 + 10 + 0 = 40. - Baidu Unlimited-OCR-2 (45): File safety gets 15, safetensors only but loaded with trust_remote_code or through a prebuilt SGLang wheel from the repository. Provenance gets 15 for files in Baidu's verified organisation, linked from its GitHub organisation, with hashes for every file. Licence risk keeps 15, since MIT has none of the deductions. Safety evaluation, agent guidance and the security programme score 0, with no safety results, no cautions about use and no security contact found. ### Agent interface: Baidu Unlimited-OCR-2 +5 - LFM3-Omni-2B (70): Chat template 20, chat_template.jinja in the repository (13,513 characters), with roles, images, video and audio, tools, deferred tools and tool results. Tool-calling format 25, the card and Liquid's tool-use page document tools passed through `tools=` in apply_chat_template, calls returned as XML and results sent back as tool messages. Trained tool calling 20, the template loops over every entry in an assistant turn's tool_calls, so several calls fit in one turn, and the card documents tool calling from spoken or written requests. Structured output 0, the card doesn't mention JSON or structured output for this model. Reasoning control 0, the card says this is a non-reasoning model and that `enable_thinking=True` is not supported, so there is no thinking to turn off or budget. Context 5, 32K tokens of input and output per the card. 20 + 25 + 20 + 0 + 0 + 5 = 70. - Baidu Unlimited-OCR-2 (75): Read for a document parser, the tool-calling, trained tool calling and structured-output lines stand for what it takes and returns. Baidu documents the page images, the prompt, the request settings and the block output, and ships a converter for the server's output (25). It was trained to read many pages in one request (20). The block format with classes, boxes and HTML tables, and its conversion to Markdown, are documented with how to get them (15). The prompt is built in code, with no Jinja template in the repository (10). Reasoning control scores 0, and the context 5, trained at up to 64K tokens. ## Score by category | Category | Weight | LFM3-Omni-2B | Baidu Unlimited-OCR-2 | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 80 | 55 | LFM3-Omni-2B +25 | | Risk | 20% (20 this run) | 40 | 45 | Baidu Unlimited-OCR-2 +5 | | Agent interface | 10% (10 this run) | 70 | 75 | Baidu Unlimited-OCR-2 +5 | | Documentation and upkeep | 10% (10 this run) | 49 | 50 | Baidu Unlimited-OCR-2 +1 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **61 · C** | **54.2 · C** | | ## Facts side by side | Fact | LFM3-Omni-2B | Baidu Unlimited-OCR-2 | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | Liquid AI | Baidu | | Hosted endpoint | no (local only) | no (local only) | | Transports | | | | Auth | None | None | | Pricing | Free | Free | | x402 | no | no | | Licence | LFM Open License v1.0 (free commercial use below $10M annual revenue) | 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 | yes | no | | Last release | 2026-11-03 | 2026-10-26 | | Terms last updated | no document linked | no document linked | | Privacy policy last updated | no document linked | no document linked | ## How the scores have moved ### LFM3-Omni-2B No score change since it was listed. ### Baidu Unlimited-OCR-2 - +0.7 on 4 November 2026 (D to C): company ## Verdicts **LFM3-Omni-2B.** One 2.17B model that sees, listens and talks on an 8 GB machine and turns spoken requests into tool calls. Only llama.cpp and Ollama run it in a release. The context is 32K, it doesn't reason, Liquid hasn't answered a question on the repository, and there are no safety results or data sources. **Baidu Unlimited-OCR-2.** An MIT document parser of 3.3B parameters that reads up to hundreds of pages in one request and returns ordered layout blocks with boxes and HTML tables. ParseBench's independent run puts content faithfulness at 89.16 and charts at 1.06. It needs an NVIDIA GPU with about 12 GB and runs only through Baidu's remote code or its own SGLang build. ## Before you call either ### LFM3-Omni-2B 1. Start it with `llama-server -hf LiquidAI/LFM3-Omni-2B-GGUF:Q4_K_M --jinja` or `ollama run lfm3-omni` 2. Greedy decoding for text. For speech use audio_temperature 0.4 and audio_repetition_penalty 1.2, per the card 3. The context is 32K, and llama.cpp samples video at 2 frames a second up to 384 frames 4. Parse tool calls yourself from the XML it returns 5. It doesn't reason, so don't pass enable_thinking 6. Validate JSON output yourself ### 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.dev13+g75b9b06b02-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, LFM3-Omni-2B or Baidu Unlimited-OCR-2? LFM3-Omni-2B scores 61 (C) on agent readiness against Baidu Unlimited-OCR-2's 54.2 (C), and leads in 1 of 4 scored categories. Baidu Unlimited-OCR-2 leads on risk and agent interface. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-unlimited-ocr-2.json, and with the fewest tokens: https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-unlimited-ocr-2.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "lfm3-omni-2b", "b": "unlimited-ocr-2"}`. 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