{
  "data": {
    "a": {
      "slug": "smollm3-3b",
      "name": "SmolLM3-3B",
      "vendor": "Hugging Face",
      "vendorUrl": "https://huggingface.co",
      "kind": "weights",
      "category": "open-weights",
      "summary": "SmolLM3-3B is Hugging Face's open-weight dense 3B instruct model with switchable reasoning, tool calling and a 65,536-token native context, published in BF16 safetensors under Apache 2.0 with its training data and recipes.",
      "url": "https://www.anchorterminal.com/tools/smollm3-3b",
      "markdownUrl": "https://www.anchorterminal.com/tools/smollm3-3b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/smollm3-3b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/smollm3-3b.json",
      "repo": "https://huggingface.co/HuggingFaceTB/SmolLM3-3B",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "HuggingFaceTB/SmolLM3-3B"
        },
        {
          "registry": "huggingface",
          "name": "HuggingFaceTB/SmolLM3-3B-ONNX"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Running it yourself costs the hardware, 6.15 GB for the BF16 files (checked 2026-10-21).",
      "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 2026-10-21).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-10-21"
      },
      "docsUrl": "https://huggingface.co/HuggingFaceTB/SmolLM3-3B",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "dense",
        "small",
        "reasoning",
        "tool-calling",
        "multilingual",
        "apache-2.0",
        "safetensors",
        "onnx",
        "self-hosted"
      ],
      "lastRelease": "2025-09-10",
      "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": 1217,
        "categoryRank": 43,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "interface": 80,
          "reach": 85,
          "risk": 65,
          "upkeep": 54
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "A tiny, fully open cousin with documented tool calling and switchable thinking, for local subtasks and routing on laptops and CPUs. It needs about 8 GB in BF16, or under 3 GB in Hugging Face's 4-bit ONNX build for ONNX Runtime. The main risks are a 64K native context, no safety results and no repository changes since September 2025.",
        "bestFor": "Agents that want a small, fully transparent tool-calling cousin on a laptop, CPU or edge device",
        "strengths": [
          "Training data, mixture weights, configs and intermediate checkpoints all published",
          "Plain Apache 2.0, no gate, no account",
          "About 7.4 GB in BF16, with Hugging Face's own ONNX builds down to 4-bit for ONNX Runtime",
          "Tool-call format and vLLM parser documented on the card",
          "Thinking turns off per request",
          "Safetensors only, with a SHA-256 for each shard and a Jinja template"
        ],
        "weaknesses": [
          "65,536 tokens trained; 131,072 needs a YaRN edit to config.json",
          "No change to the files since 10 September 2025 and no publisher reply in discussions since November 2025",
          "No safety evaluation or agent-use guidance",
          "No structured-output documentation",
          "No Ollama or LM Studio catalogue entry and no hosted provider",
          "The chat template renders tool results as user turns and drops tool_calls from earlier assistant messages"
        ],
        "agentNotes": [
          "Start it with `vllm serve HuggingFaceTB/SmolLM3-3B --enable-auto-tool-choice --tool-call-parser=hermes`, or tool calls come back as text",
          "Pass `chat_template_kwargs` with `enable_thinking` false for short tasks, since thinking is on by default; a `/think` or `/no_think` flag in the system prompt overrides the kwarg",
          "Put earlier tool calls in the assistant message content, since the template ignores a `tool_calls` field when it renders history",
          "Add the YaRN `rope_scaling` block (factor 2.0, original 65536) to config.json before sending more than 65,536 tokens",
          "Use temperature 0.6 and top_p 0.95, the card's recommended sampling",
          "Needs Transformers v4.53.0 or later; for llama.cpp use a GGUF from ggml-org or another converter, since Hugging Face publishes none",
          "Validate JSON output yourself or use the runtime's guided decoding"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 3.3,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "C",
            "methodology": "0.6",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 61
          }
        ],
        "editorialScores": {
          "interface": 80,
          "reach": 85,
          "risk": 65,
          "upkeep": 54
        },
        "provenanceScore": 67,
        "checked": {
          "categories": {
            "interface": "2026-10-21",
            "reach": "2026-10-21",
            "risk": "2026-10-21",
            "upkeep": "2026-10-21"
          },
          "last": "2026-10-21",
          "oldest": "2026-10-21",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "pip install -U transformers vllm\nvllm serve HuggingFaceTB/SmolLM3-3B --enable-auto-tool-choice --tool-call-parser=hermes",
        "http": "curl http://localhost:8000/v1/chat/completions -H \"Content-Type: application/json\" -d '{\n  \"model\": \"HuggingFaceTB/SmolLM3-3B\",\n  \"messages\": [{\"role\": \"user\", \"content\": \"Give me a brief explanation of gravity in simple terms.\"}],\n  \"temperature\": 0.6,\n  \"top_p\": 0.95,\n  \"max_tokens\": 16384,\n  \"chat_template_kwargs\": {\"enable_thinking\": false}\n}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/smollm3-3b"
      },
      "sameCompany": [
        "hugging-face-inference-endpoints",
        "carbon-x"
      ],
      "weights": {
        "trainedBy": "Hugging Face",
        "builtOn": "HuggingFaceTB/SmolLM3-3B-Base",
        "params": "3.08B",
        "context": 65536,
        "goodFor": "Small fully open instruct model with switchable reasoning and tool calling, text only, multilingual.",
        "runtimes": [
          "llama.cpp",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "SmolLM3-3B",
            "url": "https://huggingface.co/HuggingFaceTB/SmolLM3-3B",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 6.2,
            "memoryGB": 8,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)"
            ]
          },
          {
            "name": "SmolLM3-3B-ONNX fp16",
            "url": "https://huggingface.co/HuggingFaceTB/SmolLM3-3B-ONNX/blob/main/onnx/model_fp16.onnx_data",
            "who": "publisher",
            "precision": "fp16",
            "sizeGB": 6.2,
            "memoryGB": 8,
            "format": "onnx",
            "note": "ONNX for Transformers.js and ONNX Runtime, none of the runtimes listed here"
          },
          {
            "name": "SmolLM3-3B-ONNX int8",
            "url": "https://huggingface.co/HuggingFaceTB/SmolLM3-3B-ONNX/blob/main/onnx/model_int8.onnx_data",
            "who": "publisher",
            "precision": "int8",
            "sizeGB": 3.1,
            "memoryGB": 8,
            "format": "onnx",
            "note": "ONNX for Transformers.js and ONNX Runtime, none of the runtimes listed here"
          },
          {
            "name": "SmolLM3-3B-ONNX uint8",
            "url": "https://huggingface.co/HuggingFaceTB/SmolLM3-3B-ONNX/blob/main/onnx/model_uint8.onnx_data",
            "who": "publisher",
            "precision": "uint8",
            "sizeGB": 3.1,
            "memoryGB": 8,
            "format": "onnx",
            "note": "ONNX for Transformers.js and ONNX Runtime, none of the runtimes listed here"
          },
          {
            "name": "SmolLM3-3B-ONNX q4",
            "url": "https://huggingface.co/HuggingFaceTB/SmolLM3-3B-ONNX/blob/main/onnx/model_q4.onnx_data",
            "who": "publisher",
            "precision": "q4",
            "sizeGB": 2.8,
            "memoryGB": 8,
            "format": "onnx",
            "note": "ONNX for Transformers.js and ONNX Runtime, none of the runtimes listed here"
          },
          {
            "name": "SmolLM3-3B-ONNX bnb4",
            "url": "https://huggingface.co/HuggingFaceTB/SmolLM3-3B-ONNX/blob/main/onnx/model_bnb4.onnx_data",
            "who": "publisher",
            "precision": "bnb4",
            "sizeGB": 2.7,
            "memoryGB": 8,
            "format": "onnx",
            "note": "ONNX for Transformers.js and ONNX Runtime, none of the runtimes listed here"
          },
          {
            "name": "SmolLM3-3B-ONNX q4f16",
            "url": "https://huggingface.co/HuggingFaceTB/SmolLM3-3B-ONNX/blob/main/onnx/model_q4f16.onnx_data",
            "who": "publisher",
            "precision": "q4f16",
            "sizeGB": 2.1,
            "memoryGB": 8,
            "format": "onnx",
            "note": "ONNX for Transformers.js and ONNX Runtime, none of the runtimes listed here"
          }
        ],
        "run": "vllm serve HuggingFaceTB/SmolLM3-3B --enable-auto-tool-choice --tool-call-parser=hermes",
        "runBuild": "SmolLM3-3B",
        "checked": "2026-10-21",
        "minMemoryGB": 8
      },
      "family": {
        "lab": "Hugging Face (SmolLM)",
        "family": "SmolLM3",
        "url": "https://www.anchorterminal.com/open-weights/#huggingface",
        "others": [
          {
            "name": "SmolLM3-3B-Base",
            "url": "https://huggingface.co/HuggingFaceTB/SmolLM3-3B-Base",
            "params": "3B"
          },
          {
            "name": "SmolLM3-3B-checkpoints",
            "url": "https://huggingface.co/HuggingFaceTB/SmolLM3-3B-checkpoints",
            "params": "3B"
          },
          {
            "name": "SmolLM3-3B-GSM8K-SFT",
            "url": "https://huggingface.co/HuggingFaceTB/SmolLM3-3B-GSM8K-SFT",
            "params": "3B"
          }
        ],
        "olderCount": 20,
        "unrestrictedCopies": 5
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Hugging Face, Inc.",
        "domain": "huggingface.co",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/HuggingFaceTB/SmolLM3-3B/commits/main",
        "securityTxt": "valid",
        "checked": "2026-10-21",
        "notes": [
          "The model is published in HuggingFaceTB (Hugging Face Smol Models Research), the organisation Hugging Face's own launch post at huggingface.co/blog/smollm3 links to. Hugging Face's Terms of Service name Hugging Face, Inc.",
          "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.",
          "The changelog is the repository's commit history. A few messages say what changed (an EOS token ID update on 8 July 2025, a template fix for vLLM on 9 July 2025); most read Update README.md.",
          "huggingface.co/.well-known/security.txt is valid, with a contact and an expiry of 1 July 2030.",
          "rdap.org has no RDAP service for .co domains, so the registration date wasn't checked."
        ],
        "score": 67
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/smollm3-3b.json",
      "live": {
        "slug": "smollm3-3b",
        "securityTxt": {
          "url": "https://huggingface.co/.well-known/security.txt",
          "state": "valid",
          "expires": "2030-07-01T08:42:00.000Z",
          "checkedAt": "2026-10-27T15:31:26.533299918Z"
        },
        "domain": {
          "domain": "huggingface.co",
          "checkedAt": "2026-10-25T13:59:29.030902536Z"
        },
        "pages": [
          {
            "url": "https://huggingface.co/HuggingFaceTB/SmolLM3-3B/commits/main",
            "kind": "changelog",
            "status": 200,
            "checkedAt": "2026-10-27T03:47:51.632575855Z",
            "changedAt": "2026-10-27T03:47:51.632575855Z",
            "fingerprint": "ec918c0ee58f"
          }
        ],
        "updatedAt": "2026-10-27T15:31:26.533299918Z"
      }
    },
    "answer": "SmolLM3-3B scores 61 (C) on agent readiness against Baidu Unlimited-OCR-2's 53.5 (D), and leads in every scored category.",
    "b": {
      "slug": "unlimited-ocr-2",
      "name": "Baidu Unlimited-OCR-2",
      "vendor": "Baidu",
      "vendorUrl": "https://github.com/baidu",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Unlimited-OCR-2 is Baidu's open-weight document OCR model, about 3.3B parameters, that parses page images, from one page to documents of hundreds of pages in one request, into layout blocks with boxes and HTML tables, under MIT.",
      "url": "https://www.anchorterminal.com/tools/unlimited-ocr-2",
      "markdownUrl": "https://www.anchorterminal.com/tools/unlimited-ocr-2.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/unlimited-ocr-2.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/unlimited-ocr-2.json",
      "repo": "https://huggingface.co/baidu/Unlimited-OCR-2",
      "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)",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "baidu/Unlimited-OCR-2"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository isn't gated (checked 2026-10-27).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under MIT by the repository's metadata and the LICENSE file in Baidu's GitHub code repository. Baidu sells no per-call access to these weights in this listing. Running them costs the hardware, 6.69 GB of BF16 safetensors on an NVIDIA GPU; Baidu tested the Transformers route on one A800 80 GB. No inference provider serves it on Hugging Face (checked 2026-10-27).",
      "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 2026-10-27).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-10-27"
      },
      "docsUrl": "https://huggingface.co/baidu/Unlimited-OCR-2",
      "capabilities": [
        "docs.ocr",
        "docs.parse",
        "docs.tables",
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "ocr",
        "document-parsing",
        "multi-page",
        "long-documents",
        "tables",
        "layout",
        "bounding-boxes",
        "vision",
        "mixture-of-experts",
        "safetensors",
        "self-hosted",
        "new"
      ],
      "lastRelease": "2026-10-26",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 53.5,
        "grade": "D",
        "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": 1217,
        "categoryRank": 74,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "interface": 75,
          "reach": 55,
          "risk": 45,
          "upkeep": 45
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-10-27"
        },
        "negative": 0,
        "verdict": "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.",
        "bestFor": "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",
        "strengths": [
          "Small, 3.3B parameters with about 0.5B active per token, in 6.69 GB of BF16 safetensors",
          "Reads many pages in one request with cross-page context, merging tables and text that continue across pages",
          "MIT with nothing added, ungated, files hashed in Baidu's verified organisation",
          "An OpenAI-compatible server through SGLang with page-window scheduling for long documents, and a documented converter from its block output to Markdown"
        ],
        "weaknesses": [
          "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",
          "No released runtime runs its sparse attention yet, and no quantised build",
          "The card states no parameters, languages, context length or limitations, and no safety results or security contact were found",
          "Every accuracy figure is Baidu's own, one of its two long-document benchmarks is Baidu's own, and no independent result exists for this checkpoint",
          "Charts and figures come back as boxes to crop, not as data, and the report says heading hierarchy is weak over long documents"
        ],
        "agentNotes": [
          "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\")`",
          "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",
          "Send all pages of a document in one request, images first, then the text `\u003cPAGE\u003e\u003cimage\u003e` 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)",
          "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",
          "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"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 0,
        "avgRating": 0,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "D",
            "methodology": "0.6",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 53.5
          }
        ],
        "editorialScores": {
          "interface": 75,
          "reach": 55,
          "risk": 45,
          "upkeep": 45
        },
        "provenanceScore": 73,
        "checked": {
          "categories": {
            "interface": "2026-10-27",
            "reach": "2026-10-27",
            "risk": "2026-10-27",
            "upkeep": "2026-10-27"
          },
          "last": "2026-10-27",
          "oldest": "2026-10-27",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "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\n# then AutoModel.from_pretrained(\"baidu/Unlimited-OCR-2\", trust_remote_code=True, use_safetensors=True, torch_dtype=torch.bfloat16).eval().cuda()\n# and model.infer_multi(tokenizer, image_files=[\"page_1.jpg\", \"page_2.jpg\"], output_path=\"out\")",
        "http": "# after: hf download baidu/Unlimited-OCR-2 --local-dir uocr2 \u0026\u0026 uv venv --python 3.10 \u0026\u0026 . .venv/bin/activate\n#        uv pip install uocr2/wheel/sglang-0.5.20.dev10+g8d62f5fb9c-py3-none-any.whl pymupdf==1.27.2.2 fastokens==0.3.2\n#        SGLANG_UOCR_EANABLE_DOC_LIMIT=1 python -m sglang.launch_server --model-path baidu/Unlimited-OCR-2 --served-model-name Unlimited-OCR-2 --chat-template unlimited-ocr --tokenizer-backend fastokens --mem-fraction-static 0.7 --max-prefill-tokens 2048 --enable-custom-logit-processor --enable-streaming-session --disable-radix-cache --port 10000\ncurl http://127.0.0.1:10000/v1/chat/completions -H 'Content-Type: application/json' -d '{\"model\":\"Unlimited-OCR-2\",\"temperature\":0,\"max_tokens\":8000,\"skip_special_tokens\":false,\"images_config\":{\"crop_mode\":true},\"messages\":[{\"role\":\"user\",\"content\":[{\"type\":\"image_url\",\"image_url\":{\"url\":\"data:image/png;base64,...\"}},{\"type\":\"text\",\"text\":\"\u003cPAGE\u003e\u003cimage\u003eMulti page parsing.\"}]}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/docs.ocr",
        "tool": "https://letme.dev/unlimited-ocr-2"
      },
      "weights": {
        "trainedBy": "Baidu (ERNIE Team)",
        "builtOn": "baidu/Unlimited-OCR (post-trained from its checkpoint per the report, MIT by its metadata), whose backbone is deepseek-ai/DeepSeek-OCR (MIT by its metadata); not declared in the card's metadata",
        "params": "3.3B (3B-A0.5B mixture-of-experts decoder per the report)",
        "context": 131072,
        "goodFor": "Parsing long scanned or rendered documents, tens to hundreds of pages, into ordered layout blocks with boxes and HTML tables in one request, on an NVIDIA GPU, if Baidu's own results hold up.",
        "runtimes": [],
        "builds": [
          {
            "name": "Unlimited-OCR-2 (BF16 safetensors)",
            "url": "https://huggingface.co/baidu/Unlimited-OCR-2",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 6.69,
            "memoryGB": 12,
            "format": "safetensors",
            "runsOn": [
              "Transformers (remote code)",
              "SGLang (Baidu's customised wheel)"
            ],
            "note": "Two shards, 6,688,305,576 bytes, so 6.69 GB × 1.2 = 8.03 GB, within 12 GB. The whole repository is 6.87 GB with the report, the wheel and a 156 MB GIF, which comes to the same band. The only build from Baidu; no copy by anyone else exists yet"
          }
        ],
        "run": "hf download baidu/Unlimited-OCR-2 --local-dir uocr2 \u0026\u0026 uv venv --python 3.10 \u0026\u0026 . .venv/bin/activate \u0026\u0026 uv pip install uocr2/wheel/sglang-0.5.20.dev10+g8d62f5fb9c-py3-none-any.whl pymupdf==1.27.2.2 fastokens==0.3.2 \u0026\u0026 SGLANG_UOCR_EANABLE_DOC_LIMIT=1 SGLANG_UOCR_DOC_GUNDAM_PAGE_WINDOW=10 SGLANG_UOCR_DOC_BASE_PAGE_WINDOW=10 SGLANG_UOCR_MAX_PAGE_TOKENS=20000 python -m sglang.launch_server --model-path baidu/Unlimited-OCR-2 --served-model-name Unlimited-OCR-2 --chat-template unlimited-ocr --tokenizer-backend fastokens --mem-fraction-static 0.7 --max-prefill-tokens 2048 --enable-custom-logit-processor --enable-streaming-session --allow-auto-truncate --disable-radix-cache --port 10000",
        "runBuild": "Unlimited-OCR-2 (BF16 safetensors)",
        "checked": "2026-10-27",
        "minMemoryGB": 12
      },
      "family": {
        "lab": "Baidu",
        "family": "Unlimited OCR 2",
        "url": "https://www.anchorterminal.com/open-weights/#baidu",
        "olderCount": 1
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Baidu, Inc.",
        "domain": "baidu.com",
        "domainRegistered": "1999-10-11",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/baidu/Unlimited-OCR-2/commits/main",
        "securityTxt": "unknown",
        "checked": "2026-10-27",
        "notes": [
          "The files sit in Baidu's verified baidu organisation on Hugging Face, which Baidu's GitHub organisation profile links to, and the GitHub code repository's README links this repository. The report's byline is the Baidu ERNIE Team. The legal entity is the card's Baidu Inc., not checked against a registry. The domain registration date is from Verisign's RDAP record for baidu.com, read 2026-10-27.",
          "Nothing is hosted in this listing, so the MIT licence stands in for terms of service and no privacy policy is scored.",
          "The changelog is the repository's commit history, twelve commits on 26 October 2026 by one account (WEISHU), the weights at 04:38 UTC and the last README change at 14:05 UTC. The card's release line dates the release 27 October 2026.",
          "www.baidu.com's robots.txt disallows every path to all agents not named in it, so its security.txt is not read for this listing, and bsrc.baidu.com answered Access denied to its robots.txt. No security contact is named on the card or in the GitHub repository."
        ],
        "score": 73
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/unlimited-ocr-2.json",
      "live": {
        "slug": "unlimited-ocr-2",
        "securityTxt": {
          "url": "https://baidu.com/.well-known/security.txt",
          "state": "none",
          "checkedAt": "2026-10-27T15:32:20.71581076Z"
        },
        "updatedAt": "2026-10-27T15:32:20.71581076Z"
      }
    },
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Hugging Face",
        "b": "Baidu",
        "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": "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)",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2025-09-10",
        "b": "2026-10-26",
        "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": "",
        "b": "",
        "name": "Customer content may train models"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms restrict automated access"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms restrict benchmarking"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms or service can change without notice"
      },
      {
        "a": "",
        "b": "",
        "name": "Arbitration or class-action waiver"
      },
      {
        "a": "3.3/5 (4)",
        "b": "none",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "SmolLM3-3B scores 61 (C) on agent readiness against Baidu Unlimited-OCR-2's 53.5 (D), and leads in every scored category.",
        "question": "Which is better for AI agents, SmolLM3-3B or Baidu Unlimited-OCR-2?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 85 against 55",
          "Risk, 65 against 45",
          "Agent interface, 80 against 75",
          "Documentation and upkeep, 54 against 45"
        ],
        "also": null,
        "goodFor": "Agents that want a small, fully transparent tool-calling cousin on a laptop, CPU or edge device",
        "slug": "smollm3-3b",
        "watchFor": "65,536 tokens trained; 131,072 needs a YaRN edit to config.json"
      },
      {
        "aheadOn": null,
        "also": null,
        "goodFor": "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",
        "slug": "unlimited-ocr-2",
        "watchFor": "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"
      }
    ],
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-smollm3-3b.json",
        "title": "Agents-A1-4B vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-unlimited-ocr-2.json",
        "title": "Agents-A1-4B vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-smollm3-3b.json",
        "title": "Apertus v1.5 8B vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-unlimited-ocr-2.json",
        "title": "Apertus v1.5 8B vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-smollm3-3b.json",
        "title": "Apodex-1.1-mini vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-unlimited-ocr-2.json",
        "title": "Apodex-1.1-mini vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-smollm3-3b.json",
        "title": "BigBang-v1 vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-unlimited-ocr-2.json",
        "title": "BigBang-v1 vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-smollm3-3b.json",
        "title": "Carbon-X vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-unlimited-ocr-2.json",
        "title": "Carbon-X vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-smollm3-3b.json",
        "title": "Cosmos-Reason2-2B vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-unlimited-ocr-2.json",
        "title": "Cosmos-Reason2-2B vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-smollm3-3b.json",
        "title": "Darwin-36B-Opus vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-unlimited-ocr-2.json",
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        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-smollm3-3b.json",
        "title": "DeepSeek-V3.2 vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-unlimited-ocr-2.json",
        "title": "DeepSeek-V3.2 vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-smollm3-3b.json",
        "title": "DeepSeek V4.1 Flash vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-smollm3-3b"
      },
      {
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        "title": "DeepSeek V4.1 Flash vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-smollm3-3b.json",
        "title": "DeepSeek-V4-Flash-0731 vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-unlimited-ocr-2.json",
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        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-smollm3-3b.json",
        "title": "DiffusionGemma 26B A4B IT vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-smollm3-3b"
      },
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        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-smollm3-3b.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-smollm3-3b"
      },
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        "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-unlimited-ocr-2"
      },
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        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-smollm3-3b"
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      },
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      },
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      },
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        "url": "https://www.anchorterminal.com/compare/inkling-small-vs-smollm3-3b"
      },
      {
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      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-smollm3-3b.json",
        "title": "Qwen3.5-4B vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-unlimited-ocr-2.json",
        "title": "Qwen3.5-4B vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-smollm3-3b.json",
        "title": "Qwen3.5-9B vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-unlimited-ocr-2.json",
        "title": "Qwen3.5-9B vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-smollm3-3b.json",
        "title": "Qwen3.6-27B vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-unlimited-ocr-2.json",
        "title": "Qwen3.6-27B vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-smollm3-3b.json",
        "title": "Qwen3.6-35B-A3B vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-unlimited-ocr-2.json",
        "title": "Qwen3.6-35B-A3B vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-smollm3-3b.json",
        "title": "Qwen3.8-27B vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-unlimited-ocr-2.json",
        "title": "Qwen3.8-27B vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-smollm3-3b.json",
        "title": "Qwen3.8-Flash-Next vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-unlimited-ocr-2.json",
        "title": "Qwen3.8-Flash-Next vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-smollm3-3b.json",
        "title": "Qwen3-Coder-30B-A3B-Instruct vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-unlimited-ocr-2.json",
        "title": "Qwen3-Coder-30B-A3B-Instruct vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-smollm3-3b.json",
        "title": "Qwen3-Coder-Next vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-unlimited-ocr-2.json",
        "title": "Qwen3-Coder-Next vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-smollm3-3b.json",
        "title": "Qwen3-Omni-30B-A3B-Instruct vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-smollm3-3b.json",
        "title": "Qwen3-VL-2B-Instruct vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-unlimited-ocr-2.json",
        "title": "Qwen3-VL-2B-Instruct vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-smollm3-3b.json",
        "title": "Qwen3-VL-30B-A3B-Instruct vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-unlimited-ocr-2.json",
        "title": "Qwen3-VL-30B-A3B-Instruct vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-smollm3-3b.json",
        "title": "Qwen3-VL-32B-Instruct vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-unlimited-ocr-2.json",
        "title": "Qwen3-VL-32B-Instruct vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-smollm3-3b.json",
        "title": "Qwen3-VL-4B-Instruct vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-unlimited-ocr-2.json",
        "title": "Qwen3-VL-4B-Instruct vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-smollm3-3b.json",
        "title": "Qwen3-VL-8B-Instruct vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-unlimited-ocr-2.json",
        "title": "Qwen3-VL-8B-Instruct vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-smollm3-3b.json",
        "title": "Qwopus3.8-27B-Flash-V2 vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-unlimited-ocr-2.json",
        "title": "Qwopus3.8-27B-Flash-V2 vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/recursive-flint-vs-smollm3-3b.json",
        "title": "Recursive Flint vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/recursive-flint-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/recursive-flint-vs-unlimited-ocr-2.json",
        "title": "Recursive Flint vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/recursive-flint-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/smollm3-3b-vs-spark-x2-6-4b.json",
        "title": "SmolLM3-3B vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/smollm3-3b-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/smollm3-3b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "SmolLM3-3B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/smollm3-3b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/smollm3-3b-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "SmolLM3-3B vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/smollm3-3b-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/smollm3-3b-vs-thinkingcap-qwen3-8-27b.json",
        "title": "SmolLM3-3B vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/smollm3-3b-vs-thinkingcap-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-unlimited-ocr-2.json",
        "title": "Spark-X2.6-4B vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-27b-vs-unlimited-ocr-2.json",
        "title": "Swift 1.5 Qwen3.8-27B vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-27b-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-flash-next-vs-unlimited-ocr-2.json",
        "title": "Swift 1.5 Qwen3.8-Flash-Next vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-flash-next-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/thinkingcap-qwen3-8-27b-vs-unlimited-ocr-2.json",
        "title": "ThinkingCap (Qwen3.8 27B) vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/thinkingcap-qwen3-8-27b-vs-unlimited-ocr-2"
      }
    ],
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 30,
        "edge": "smollm3-3b",
        "key": "reach",
        "name": "Reach",
        "smollm3-3b": 85,
        "unlimited-ocr-2": 55,
        "weight": 25
      },
      {
        "by": 20,
        "edge": "smollm3-3b",
        "key": "risk",
        "name": "Risk",
        "smollm3-3b": 65,
        "unlimited-ocr-2": 45,
        "weight": 20
      },
      {
        "by": 5,
        "edge": "smollm3-3b",
        "key": "interface",
        "name": "Agent interface",
        "smollm3-3b": 80,
        "unlimited-ocr-2": 75,
        "weight": 10
      },
      {
        "by": 9,
        "edge": "smollm3-3b",
        "key": "upkeep",
        "name": "Documentation and upkeep",
        "smollm3-3b": 54,
        "unlimited-ocr-2": 45,
        "weight": 10
      }
    ],
    "summary": "SmolLM3-3B scores 61 (C) 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.",
    "tutorials": [],
    "verdicts": {
      "smollm3-3b": "A tiny, fully open cousin with documented tool calling and switchable thinking, for local subtasks and routing on laptops and CPUs. It needs about 8 GB in BF16, or under 3 GB in Hugging Face's 4-bit ONNX build for ONNX Runtime. The main risks are a 64K native context, no safety results and no repository changes since September 2025.",
      "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."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/smollm3-3b-vs-unlimited-ocr-2",
    "json": "https://www.anchorterminal.com/compare/smollm3-3b-vs-unlimited-ocr-2.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/smollm3-3b-vs-unlimited-ocr-2.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/smollm3-3b-vs-unlimited-ocr-2.min.md"
  },
  "markdown": "SmolLM3-3B scores 61 (C) 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.\n\n- SmolLM3-3B: grade C, 61/100, rank #51 of 113 open-weight models. Markdown https://www.anchorterminal.com/tools/smollm3-3b.md · JSON https://www.anchorterminal.com/api/v1/tools/smollm3-3b.json\n- 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\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 3504 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### SmolLM3-3B (C)\n\nGood for: Agents that want a small, fully transparent tool-calling cousin on a laptop, CPU or edge device\n\nAhead on:\n- Reach, 85 against 55\n- Risk, 65 against 45\n- Agent interface, 80 against 75\n- Documentation and upkeep, 54 against 45\n\nWatch for: 65,536 tokens trained; 131,072 needs a YaRN edit to config.json\n\n### Baidu Unlimited-OCR-2 (D)\n\nGood 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\n\nWatch 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\n\n\n## Score by category\n\n| Category | Weight | SmolLM3-3B | Baidu Unlimited-OCR-2 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 85 | 55 | SmolLM3-3B +30 |\n| Risk | 20% (20 this run) | 65 | 45 | SmolLM3-3B +20 |\n| Agent interface | 10% (10 this run) | 80 | 75 | SmolLM3-3B +5 |\n| Documentation and upkeep | 10% (10 this run) | 54 | 45 | SmolLM3-3B +9 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **53.5 · D** | |\n\n## Facts side by side\n\n| Fact | SmolLM3-3B | Baidu Unlimited-OCR-2 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Hugging Face | Baidu |\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 | 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) |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2025-09-10 | 2026-10-26 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models |  |  |\n| Terms restrict automated access |  |  |\n| Terms restrict benchmarking |  |  |\n| Terms or service can change without notice |  |  |\n| Arbitration or class-action waiver |  |  |\n| Agent reviews | 3.3/5 (4) | none |\n\n## Verdicts\n\n**SmolLM3-3B.** A tiny, fully open cousin with documented tool calling and switchable thinking, for local subtasks and routing on laptops and CPUs. It needs about 8 GB in BF16, or under 3 GB in Hugging Face's 4-bit ONNX build for ONNX Runtime. The main risks are a 64K native context, no safety results and no repository changes since September 2025.\n\n**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.\n\n## Before you call either\n\n### SmolLM3-3B\n\n1. Start it with `vllm serve HuggingFaceTB/SmolLM3-3B --enable-auto-tool-choice --tool-call-parser=hermes`, or tool calls come back as text\n2. Pass `chat_template_kwargs` with `enable_thinking` false for short tasks, since thinking is on by default; a `/think` or `/no_think` flag in the system prompt overrides the kwarg\n3. Put earlier tool calls in the assistant message content, since the template ignores a `tool_calls` field when it renders history\n4. Add the YaRN `rope_scaling` block (factor 2.0, original 65536) to config.json before sending more than 65,536 tokens\n5. Use temperature 0.6 and top_p 0.95, the card's recommended sampling\n6. Needs Transformers v4.53.0 or later; for llama.cpp use a GGUF from ggml-org or another converter, since Hugging Face publishes none\n7. Validate JSON output yourself or use the runtime's guided decoding\n\n### Baidu Unlimited-OCR-2\n\n1. 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\")`\n2. 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\n3. Send all pages of a document in one request, images first, then the text `\u003cPAGE\u003e\u003cimage\u003e` 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)\n4. 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\n5. 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\n\n## Questions\n\n### Which is better for AI agents, SmolLM3-3B or Baidu Unlimited-OCR-2?\n\nSmolLM3-3B scores 61 (C) on agent readiness against Baidu Unlimited-OCR-2's 53.5 (D), and leads in every scored category.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/smollm3-3b-vs-unlimited-ocr-2.json, and with the fewest tokens: https://www.anchorterminal.com/compare/smollm3-3b-vs-unlimited-ocr-2.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"smollm3-3b\", \"b\": \"unlimited-ocr-2\"}`. From a terminal: `anchor compare smollm3-3b unlimited-ocr-2`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/smollm3-3b.json and https://www.anchorterminal.com/api/v1/tools/unlimited-ocr-2.json\n\n## Other comparisons with SmolLM3-3B or Baidu Unlimited-OCR-2\n\n- [Agents-A1-4B vs SmolLM3-3B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-smollm3-3b.md)\n- [Agents-A1-4B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/agents-a1-4b-vs-unlimited-ocr-2.md)\n- [Apertus v1.5 8B vs SmolLM3-3B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-smollm3-3b.md)\n- [Apertus v1.5 8B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-unlimited-ocr-2.md)\n- [Apodex-1.1-mini vs SmolLM3-3B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-smollm3-3b.md)\n- [Apodex-1.1-mini vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-unlimited-ocr-2.md)\n- [BigBang-v1 vs SmolLM3-3B](https://www.anchorterminal.com/compare/bigbang-v1-vs-smollm3-3b.md)\n- [BigBang-v1 vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/bigbang-v1-vs-unlimited-ocr-2.md)\n- [Carbon-X vs SmolLM3-3B](https://www.anchorterminal.com/compare/carbon-x-vs-smollm3-3b.md)\n- [Carbon-X vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/carbon-x-vs-unlimited-ocr-2.md)\n- [Cosmos-Reason2-2B vs SmolLM3-3B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-smollm3-3b.md)\n- [Cosmos-Reason2-2B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-unlimited-ocr-2.md)\n- [Darwin-36B-Opus vs SmolLM3-3B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-smollm3-3b.md)\n- [Darwin-36B-Opus vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-unlimited-ocr-2.md)\n- [DeepSeek-V3.2 vs SmolLM3-3B](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-smollm3-3b.md)\n- [DeepSeek-V3.2 vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-unlimited-ocr-2.md)\n- [DeepSeek V4.1 Flash vs SmolLM3-3B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-smollm3-3b.md)\n- [DeepSeek V4.1 Flash vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-unlimited-ocr-2.md)\n- [DeepSeek-V4-Flash-0731 vs SmolLM3-3B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-smollm3-3b.md)\n- [DeepSeek-V4-Flash-0731 vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-unlimited-ocr-2.md)\n- [DiffusionGemma 26B A4B IT vs SmolLM3-3B](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-smollm3-3b.md)\n- [DiffusionGemma 26B A4B IT vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-unlimited-ocr-2.md)\n- [Gemma 4 12B IT vs SmolLM3-3B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-smollm3-3b.md)\n- [Gemma 4 12B IT vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-unlimited-ocr-2.md)\n- [Gemma 4 26B A4B IT vs SmolLM3-3B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-smollm3-3b.md)\n- [Gemma 4 26B A4B IT vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-unlimited-ocr-2.md)\n- [Gemma 4 31B IT vs SmolLM3-3B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-smollm3-3b.md)\n- [Gemma 4 31B IT vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-unlimited-ocr-2.md)\n- [Gemma 4 E2B IT vs SmolLM3-3B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-smollm3-3b.md)\n- [Gemma 4 E2B IT vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-unlimited-ocr-2.md)\n- [Gemma 4 E4B IT vs SmolLM3-3B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-smollm3-3b.md)\n- [Gemma 4 E4B IT vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-unlimited-ocr-2.md)\n- [GLM-4.5-Air vs SmolLM3-3B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-smollm3-3b.md)\n- [GLM-4.5-Air vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/glm-4-5-air-vs-unlimited-ocr-2.md)\n- [GLM-4.7-Flash vs SmolLM3-3B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-smollm3-3b.md)\n- [GLM-4.7-Flash vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-unlimited-ocr-2.md)\n- [GLM-5.3-Flash vs SmolLM3-3B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-smollm3-3b.md)\n- [GLM-5.3-Flash vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-unlimited-ocr-2.md)\n- [GLM-5.3 vs SmolLM3-3B](https://www.anchorterminal.com/compare/glm-5-3-vs-smollm3-3b.md)\n- [GLM-5.3 vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/glm-5-3-vs-unlimited-ocr-2.md)\n- [gpt-oss-120b vs SmolLM3-3B](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-smollm3-3b.md)\n- [gpt-oss-120b vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-unlimited-ocr-2.md)\n- [gpt-oss-20b vs SmolLM3-3B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-smollm3-3b.md)\n- [gpt-oss-20b vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-unlimited-ocr-2.md)\n- [Granite 4.2 3B vs SmolLM3-3B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-smollm3-3b.md)\n- [Granite 4.2 3B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-unlimited-ocr-2.md)\n- [Hy3 vs SmolLM3-3B](https://www.anchorterminal.com/compare/hy3-vs-smollm3-3b.md)\n- [Hy3 vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/hy3-vs-unlimited-ocr-2.md)\n- [Inkling-Small vs SmolLM3-3B](https://www.anchorterminal.com/compare/inkling-small-vs-smollm3-3b.md)\n- [Inkling-Small vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/inkling-small-vs-unlimited-ocr-2.md)\n- [Inkling vs SmolLM3-3B](https://www.anchorterminal.com/compare/inkling-vs-smollm3-3b.md)\n- [Inkling vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/inkling-vs-unlimited-ocr-2.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs SmolLM3-3B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-smollm3-3b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-unlimited-ocr-2.md)\n- [Kimi K3 vs SmolLM3-3B](https://www.anchorterminal.com/compare/kimi-k3-vs-smollm3-3b.md)\n- [Kimi K3 vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/kimi-k3-vs-unlimited-ocr-2.md)\n- [Kolibri-1 vs SmolLM3-3B](https://www.anchorterminal.com/compare/kolibri-1-vs-smollm3-3b.md)\n- [Kolibri-1 vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/kolibri-1-vs-unlimited-ocr-2.md)\n- [Kronos vs SmolLM3-3B](https://www.anchorterminal.com/compare/kronos-vs-smollm3-3b.md)\n- [Kronos vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/kronos-vs-unlimited-ocr-2.md)\n- [Laguna M.1 vs SmolLM3-3B](https://www.anchorterminal.com/compare/laguna-m-1-vs-smollm3-3b.md)\n- [Laguna M.1 vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/laguna-m-1-vs-unlimited-ocr-2.md)\n- [Laguna S 2.1 vs SmolLM3-3B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-smollm3-3b.md)\n- [Laguna S 2.1 vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-unlimited-ocr-2.md)\n- [Laguna XS 2.1 vs SmolLM3-3B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-smollm3-3b.md)\n- [Laguna XS 2.1 vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-unlimited-ocr-2.md)\n- [LBS-27B vs SmolLM3-3B](https://www.anchorterminal.com/compare/lbs-27b-vs-smollm3-3b.md)\n- [LBS-27B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/lbs-27b-vs-unlimited-ocr-2.md)\n- [LFM2.5-2.6B vs SmolLM3-3B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-smollm3-3b.md)\n- [LFM2.5-2.6B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-unlimited-ocr-2.md)\n- [Ling-3.0-tiny vs SmolLM3-3B](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-smollm3-3b.md)\n- [Ling-3.0-tiny vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-unlimited-ocr-2.md)\n- [MiMo-V2.6 Distill Qwen 9B vs SmolLM3-3B](https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-smollm3-3b.md)\n- [MiMo-V2.6 Distill Qwen 9B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-unlimited-ocr-2.md)\n- [MiniCPM-o 4.5 vs SmolLM3-3B](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-smollm3-3b.md)\n- [MiniCPM-o 4.5 vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-unlimited-ocr-2.md)\n- [MiniCPM5-2B vs SmolLM3-3B](https://www.anchorterminal.com/compare/minicpm5-2b-vs-smollm3-3b.md)\n- [MiniCPM5-2B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/minicpm5-2b-vs-unlimited-ocr-2.md)\n- [MiniMax-M2.5 vs SmolLM3-3B](https://www.anchorterminal.com/compare/minimax-m2-5-vs-smollm3-3b.md)\n- [MiniMax-M2.5 vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/minimax-m2-5-vs-unlimited-ocr-2.md)\n- [MiniMax-M2.7 vs SmolLM3-3B](https://www.anchorterminal.com/compare/minimax-m2-7-vs-smollm3-3b.md)\n- [MiniMax-M2.7 vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/minimax-m2-7-vs-unlimited-ocr-2.md)\n- [MiniMax-M3.1-Flash vs SmolLM3-3B](https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-smollm3-3b.md)\n- [MiniMax-M3.1-Flash vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-unlimited-ocr-2.md)\n- [Muse Glimmer 30B vs SmolLM3-3B](https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-smollm3-3b.md)\n- [Muse Glimmer 30B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-unlimited-ocr-2.md)\n- [Nanbeige4.3-33B vs SmolLM3-3B](https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-smollm3-3b.md)\n- [Nanbeige4.3-33B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-unlimited-ocr-2.md)\n- [NVIDIA Nemotron 3.5 Bolt E4B 4GB vs SmolLM3-3B](https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-smollm3-3b.md)\n- [NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-unlimited-ocr-2.md)\n- [Nemotron 3.5 Lightning 30B A3B vs SmolLM3-3B](https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-smollm3-3b.md)\n- [Nemotron 3.5 Lightning 30B A3B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-unlimited-ocr-2.md)\n- [Nemotron 3 Nano 30B A3B vs SmolLM3-3B](https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-smollm3-3b.md)\n- [Nemotron 3 Nano 30B A3B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-unlimited-ocr-2.md)\n- [Nemotron 3 Nano 4B vs SmolLM3-3B](https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-smollm3-3b.md)\n- [Nemotron 3 Nano 4B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-unlimited-ocr-2.md)\n- [Nemotron 3 Nano Omni 30B A3B Reasoning vs SmolLM3-3B](https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-smollm3-3b.md)\n- [Nemotron 3 Super 120B A12B vs SmolLM3-3B](https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-smollm3-3b.md)\n- [Nemotron 3 Super 120B A12B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-unlimited-ocr-2.md)\n- [Nemotron 3 Ultra 550B A55B vs SmolLM3-3B](https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-smollm3-3b.md)\n- [Nemotron 3 Ultra 550B A55B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-unlimited-ocr-2.md)\n- [occamy-1.0 vs SmolLM3-3B](https://www.anchorterminal.com/compare/occamy-1-0-vs-smollm3-3b.md)\n- [occamy-1.0 vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/occamy-1-0-vs-unlimited-ocr-2.md)\n- [Olmo 3 7B Think vs SmolLM3-3B](https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-smollm3-3b.md)\n- [Olmo 3 7B Think vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-unlimited-ocr-2.md)\n- [Ornith-1.5-35B-A3B vs SmolLM3-3B](https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-smollm3-3b.md)\n- [Ornith-1.5-35B-A3B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-unlimited-ocr-2.md)\n- [Ornith-1.5-397B vs SmolLM3-3B](https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-smollm3-3b.md)\n- [Ornith-1.5-397B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-unlimited-ocr-2.md)\n- [Ornith-1.5-9B vs SmolLM3-3B](https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-smollm3-3b.md)\n- [Ornith-1.5-9B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-unlimited-ocr-2.md)\n- [Qwen3-30B-A3B-Instruct-2507 vs SmolLM3-3B](https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-smollm3-3b.md)\n- [Qwen3-30B-A3B-Instruct-2507 vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-unlimited-ocr-2.md)\n- [Qwen3-4B-Instruct-2507 vs SmolLM3-3B](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-smollm3-3b.md)\n- [Qwen3-4B-Instruct-2507 vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-unlimited-ocr-2.md)\n- [Qwen3.5-0.8B vs SmolLM3-3B](https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-smollm3-3b.md)\n- [Qwen3.5-0.8B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-unlimited-ocr-2.md)\n- [Qwen3.5-122B-A10B vs SmolLM3-3B](https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-smollm3-3b.md)\n- [Qwen3.5-122B-A10B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-unlimited-ocr-2.md)\n- [Qwen3.5-2B vs SmolLM3-3B](https://www.anchorterminal.com/compare/qwen3-5-2b-vs-smollm3-3b.md)\n- [Qwen3.5-2B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/qwen3-5-2b-vs-unlimited-ocr-2.md)\n- [Qwen3.5-397B-A17B vs SmolLM3-3B](https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-smollm3-3b.md)\n- [Qwen3.5-397B-A17B vs Baidu 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SmolLM3-3B](https://www.anchorterminal.com/compare/qwen3-8-27b-vs-smollm3-3b.md)\n- [Qwen3.8-27B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/qwen3-8-27b-vs-unlimited-ocr-2.md)\n- [Qwen3.8-Flash-Next vs SmolLM3-3B](https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-smollm3-3b.md)\n- [Qwen3.8-Flash-Next vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-unlimited-ocr-2.md)\n- [Qwen3-Coder-30B-A3B-Instruct vs SmolLM3-3B](https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-smollm3-3b.md)\n- [Qwen3-Coder-30B-A3B-Instruct vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-unlimited-ocr-2.md)\n- [Qwen3-Coder-Next vs SmolLM3-3B](https://www.anchorterminal.com/compare/qwen3-coder-next-vs-smollm3-3b.md)\n- [Qwen3-Coder-Next vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/qwen3-coder-next-vs-unlimited-ocr-2.md)\n- [Qwen3-Omni-30B-A3B-Instruct vs 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