{
  "data": {
    "a": {
      "slug": "mimo-v2-6-flash-rl",
      "name": "MiMo-V2.6-Flash-RL",
      "vendor": "Xiaomi (MiMo team)",
      "vendorUrl": "https://mimo.xiaomi.com",
      "kind": "weights",
      "category": "open-weights",
      "summary": "MiMo-V2.6-Flash-RL is Xiaomi's open-weight agent model, a mixture of experts of 309B parameters with 15B active, taking text, images, video and audio with a 1M context. MIT. The weights are 178 GB at 4 bits, so it needs a server.",
      "url": "https://www.anchorterminal.com/tools/mimo-v2-6-flash-rl",
      "markdownUrl": "https://www.anchorterminal.com/tools/mimo-v2-6-flash-rl.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/mimo-v2-6-flash-rl.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/mimo-v2-6-flash-rl.json",
      "dns": "mimo-v2-6-flash-rl.grade.anchorterminal.com",
      "repo": "https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-RL",
      "license": "MIT (metadata only, no LICENSE file)",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "XiaomiMiMo/MiMo-V2.6-Flash-RL"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-11-10).",
      "pricing": "free",
      "pricingNotes": "Free to download and use under MIT, per the card's metadata. Running it yourself needs a multi-GPU server: the files are 177.7 GB with most weights at 4 bits. Xiaomi's API platform and Novita serve it for those without the hardware (checked 2026-11-10).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-11-10).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-11-10"
      },
      "docsUrl": "https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-RL",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "multimodal",
        "vision",
        "audio",
        "video",
        "reasoning",
        "tool-calling",
        "agentic",
        "long-context",
        "mit",
        "safetensors",
        "fp4",
        "custom-code",
        "self-hosted"
      ],
      "lastRelease": "2026-10-28",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 61,
        "grade": "C",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1518,
        "categoryRank": 59,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 85,
          "reach": 65,
          "risk": 45,
          "upkeep": 59
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-10"
        },
        "negative": 0,
        "verdict": "A large agent model with an open licence: text, images, video and audio, a 1M context, tool calling it was trained for, and code in four released runtimes. It's a server model, 178 GB at 4 bits, and the card's commands run the repository's code. No safety results, little said about data, and few questions answered.",
        "bestFor": "Teams with a GPU server, or a provider account, that want an open agent model with a very long context",
        "strengths": [
          "MIT, ungated, with hosted options",
          "Four released runtimes carry it, with tool and reasoning parsers in vLLM and SGLang",
          "A 1M context and four input types",
          "Trained with reinforcement learning across agent harnesses",
          "A GGUF from the llama.cpp project's own organisation"
        ],
        "weaknesses": [
          "178 GB at 4 bits, so it needs about 200 GB of memory",
          "The card's serve commands pass trust-remote-code",
          "A pickle file sits in the draft model's folder",
          "No safety results, limitations or data sources on the card",
          "Xiaomi replied in a few of the discussions only"
        ],
        "agentNotes": [
          "Plan for a provider unless you have a multi-GPU server: Xiaomi's API platform, OpenRouter or Novita",
          "On SGLang or vLLM pass `--tool-call-parser mimo --reasoning-parser mimo`",
          "Use temperature 1.0 and top_p 0.95, as the card recommends",
          "The card's commands pass trust-remote-code. Read the repository's three Python files first, or use Transformers 5.22.2, which has the model built in",
          "Skip the dflash folder unless you need speculative decoding. Its mask_embedding.pt is a pickle",
          "The Use Restrictions section asks for human oversight of high-risk actions"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 0,
        "avgRating": 0,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "C",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 61
          }
        ],
        "editorialScores": {
          "interface": 85,
          "reach": 65,
          "risk": 45,
          "upkeep": 59
        },
        "provenanceScore": 58,
        "checked": {
          "categories": {
            "interface": "2026-11-10",
            "reach": "2026-11-10",
            "risk": "2026-11-10",
            "upkeep": "2026-11-10"
          },
          "last": "2026-11-10",
          "oldest": "2026-11-10",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "pip install \"sglang\u003e=0.5.23\"\nsglang serve --trust-remote-code --model-path XiaomiMiMo/MiMo-V2.6-Flash-RL --tp 8 --reasoning-parser mimo --tool-call-parser mimo\n# the card's full command adds data-parallel attention and speculative decoding flags",
        "http": "# SGLang and vLLM serve an OpenAI-compatible API. The card recommends temperature 1.0 and top_p 0.95,\n# and names the mimo tool-call and reasoning parsers for both"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/mimo-v2-6-flash-rl"
      },
      "sameCompany": [
        "mimo-v2-6-distill-qwen-9b"
      ],
      "weights": {
        "trainedBy": "Xiaomi (MiMo team)",
        "params": "309B",
        "active": "15B",
        "context": 1048576,
        "goodFor": "Long agent runs over code, tools, screens and audio on a multi-GPU server, or through a provider.",
        "runtimes": [
          "llama.cpp",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "MiMo-V2.6-Flash-RL",
            "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-RL",
            "who": "publisher",
            "precision": "fp4",
            "sizeGB": 177.74,
            "memoryGB": 256,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang",
              "Transformers"
            ],
            "note": "The only build Xiaomi publishes, with most weights at 4 bits"
          },
          {
            "name": "ggml-org MiMo-V2.6-Flash-RL-GGUF (MXFP4)",
            "url": "https://huggingface.co/ggml-org/MiMo-V2.6-Flash-RL-GGUF",
            "who": "llama.cpp",
            "precision": "mxfp4",
            "sizeGB": 168.93,
            "memoryGB": 256,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "From ggml-org, the llama.cpp project's organisation. 167.37 GB of MXFP4 weights plus the 1.56 GB Q8_0 projector, from the Hub's model information on 10 November 2026"
          }
        ],
        "run": "sglang serve --trust-remote-code --model-path XiaomiMiMo/MiMo-V2.6-Flash-RL --tp 8 --reasoning-parser mimo --tool-call-parser mimo",
        "runBuild": "MiMo-V2.6-Flash-RL",
        "checked": "2026-11-10",
        "minMemoryGB": 256
      },
      "family": {
        "lab": "Xiaomi (MiMo)",
        "family": "MiMo-V2 (V2, V2.5, V2.6)",
        "url": "https://www.anchorterminal.com/open-weights/#xiaomi",
        "others": [
          {
            "name": "MiMo-V2.6-Distill-Qwen-9B",
            "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B",
            "params": "9B (Hub safetensors count 9.41B)",
            "listing": "https://www.anchorterminal.com/tools/mimo-v2-6-distill-qwen-9b"
          },
          {
            "name": "MiMo-V2.6-Pro-MOPD",
            "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Pro-MOPD",
            "params": "1024.22B (Hub safetensors count; the name states none)"
          },
          {
            "name": "MiMo-V2.6-Flash-MOPD",
            "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-MOPD",
            "params": "310.76B (Hub safetensors count; the name states none)"
          },
          {
            "name": "MiMo-V2.6-Pro-RL",
            "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Pro-RL",
            "params": "1024.22B (Hub safetensors count; the name states none)"
          },
          {
            "name": "MiMo-V2.5-Pro-Base",
            "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.5-Pro-Base",
            "params": "1023.18B (Hub safetensors count; the name states none)"
          },
          {
            "name": "MiMo-V2.5-Pro",
            "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.5-Pro",
            "params": "1023.18B (Hub safetensors count; the name states none)"
          },
          {
            "name": "MiMo-V2.5-Base",
            "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.5-Base",
            "params": "310.76B (Hub safetensors count; the name states none)"
          },
          {
            "name": "MiMo-V2.5",
            "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.5",
            "params": "310.76B (Hub safetensors count; the name states none)"
          },
          {
            "name": "MiMo-V2.5-ASR",
            "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.5-ASR",
            "params": "7.62B (Hub safetensors count; the name states none)"
          },
          {
            "name": "MiMo-V2-Flash-Base",
            "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2-Flash-Base",
            "params": "309.77B (Hub safetensors count; the name states none)"
          },
          {
            "name": "MiMo-V2-Flash",
            "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2-Flash",
            "params": "309.77B (Hub safetensors count; the name states none)"
          }
        ],
        "olderCount": 12
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Xiaomi (the card and report name Xiaomi MiMo; no LICENSE file names a copyright holder)",
        "domain": "mimo.xiaomi.com",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-RL/commits/main",
        "securityTxt": "none",
        "checked": "2026-11-10",
        "notes": [
          "Xiaomi's legal entity, domain and security facts are carried from our MiMo-V2.6 Distill listing.",
          "Nothing is hosted under this listing, so MIT stands in for terms of service and no privacy policy is scored.",
          "The changelog is the commit history, 13 commits from 21 September to 28 October 2026."
        ],
        "score": 58
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/mimo-v2-6-flash-rl.json"
    },
    "answer": "MiMo-V2.6-Flash-RL scores 61 (C) on agent readiness against Baidu Unlimited-OCR-2's 54.2 (C), and leads in 3 of 4 scored categories.",
    "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",
      "dns": "unlimited-ocr-2.grade.anchorterminal.com",
      "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": 54.2,
        "grade": "C",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1518,
        "categoryRank": 99,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 75,
          "reach": 55,
          "risk": 45,
          "upkeep": 50
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-04"
        },
        "negative": 0,
        "verdict": "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.",
        "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",
          "ParseBench's maintainer measured content faithfulness 89.16, visual grounding 73.2 and tables 72.74 in his own run on 27 October 2026",
          "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 Baidu publishes no quantised build (the 16 on the Hub are by others)",
          "The card states no parameters, languages, context length or limitations, and no safety results or security contact were found",
          "ParseBench scores charts at 1.06 and semantic formatting at 49.57, and marks the row unverified because its outputs aren't published",
          "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.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",
          "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": "C",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 54.2
          }
        ],
        "editorialScores": {
          "interface": 75,
          "reach": 55,
          "risk": 45,
          "upkeep": 50
        },
        "provenanceScore": 73,
        "checked": {
          "categories": {
            "interface": "2026-11-04",
            "reach": "2026-11-04",
            "risk": "2026-11-04",
            "upkeep": "2026-11-04"
          },
          "last": "2026-11-04",
          "lastMoved": "2026-11-04",
          "oldest": "2026-11-04",
          "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.dev13+g75b9b06b02-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; the 16 quantised copies on the Hub (GGUF, MLX, NVFP4, MXFP4 and others, from 27 October 2026) are by others"
          }
        ],
        "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.dev13+g75b9b06b02-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": "2026-11-04",
        "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": "2026-11-04",
        "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, 26 commits from 26 October to 2 November 2026 by one account (WEI), the weights at 04:38 UTC on 26 October, the SGLang wheel replaced on 29 October and the last README change at 06:55 UTC on 2 November. 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": "2026-11-10T16:07:34.784845531Z"
        },
        "domain": {
          "domain": "baidu.com",
          "registered": "1999-10-11",
          "source": "https://rdap.verisign.com/com/v1/domain/baidu.com",
          "checkedAt": "2026-11-08T15:09:53.94742006Z"
        },
        "pages": [
          {
            "url": "https://huggingface.co/baidu/Unlimited-OCR-2/commits/main",
            "kind": "changelog",
            "status": 200,
            "checkedAt": "2026-11-10T16:22:24.520993905Z",
            "changedAt": "2026-11-10T16:22:24.520993905Z",
            "fingerprint": "7ff63623adec"
          }
        ],
        "updatedAt": "2026-11-10T16:22:24.520993905Z"
      }
    },
    "edges": [
      {
        "by": 10,
        "category": "reach",
        "leads": "mimo-v2-6-flash-rl",
        "name": "Reach",
        "reasons": {
          "a": "Memory 5, the smallest build at 4 bits or better is ggml-org's MXFP4 GGUF, 168.93 GB with its projector, and 168.93 GB × 1.2 = 202.7 GB, within 256 GB. Xiaomi's own files are 177.74 GB. ggml-org's Q2_K is below 4 bits and isn't read. Runtimes 20, Released vLLM 0.33.0 and SGLang 0.5.23 have the mimo_v2 model and the mimo parsers, Transformers 5.22.2 has a mimo_v2_flash model, and llama.cpp b12242 has the architecture, with a GGUF from its own organisation. Builds 10, one precision from Xiaomi, the 4-bit files it releases. Terms 15, MIT in the card's metadata, an OSI licence. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, the card says it is on Xiaomi's MiMo Open Platform API and on OpenRouter, and Novita is live for it on Hugging Face. 5 + 20 + 10 + 15 + 10 + 5 = 65.",
          "b": "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."
        },
        "scores": {
          "a": 65,
          "b": 55
        }
      },
      {
        "by": 10,
        "category": "interface",
        "leads": "mimo-v2-6-flash-rl",
        "name": "Agent interface",
        "reasons": {
          "a": "Chat template 20, chat_template.jinja in the repository (3,868 characters), with a copy in tokenizer_config.json. Tool-calling format 25, the card names the parser to use in two runtimes: the mimo tool-call parser in SGLang and in vLLM, with auto tool choice, and both ship it in a release. Trained tool calling 20, the card describes reinforcement learning on coding and general agent tasks across several harnesses, and the template renders every call in a turn. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, the template has an enable_thinking switch and keeps reasoning_content, and the card names the mimo reasoning parser for SGLang and vLLM, which returns the reasoning apart from the answer. Context 10, 1M tokens per the card's model summary, and 1,048,576 positions in config.json. 20 + 25 + 20 + 0 + 10 + 10 = 85.",
          "b": "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."
        },
        "scores": {
          "a": 85,
          "b": 75
        }
      },
      {
        "by": 9,
        "category": "upkeep",
        "leads": "mimo-v2-6-flash-rl",
        "name": "Documentation and upkeep",
        "reasons": {
          "a": "Model card 14, Intended use, the architecture, the parameters, the context and the languages (in the metadata) are on the card. Training data 5, A description of the training method with no data sources, sizes or cut-off. Last change 20, the last commits, on 28 October 2026, updated the card, 13 days before this check. Responsiveness 5, Of the 13 most recent discussions that aren't pull requests (21 September to 6 November 2026), 3 have a reply from a XiaomiMiMo member, which is a few. Version policy 5, MiMo-V2.5 stays in its own repositories and V2.6 came out in new ones, with no dated note on the older cards that we read. File health 10, Config, template and card agree on the architecture and the 1M context, and two early faults (invalid JSON in the draft model's config, preprocessor values) were fixed in commits that say so. 14 + 5 + 20 + 5 + 5 + 10 = 59.",
          "b": "The card covers intended use and the new attention, and misses limitations, parameters, context length, languages and a cut-off (5). Training data is described by mix and labelling with no datasets named and no cut-off (5). The card changed on 2 November 2026 and the SGLang wheel on 29 October (20). Baidu's uploader replied in 1 of the 5 Hub discussions, a few (5). The first model stays in its own repository without a note pointing here (5), and the files agree with the card (10)."
        },
        "scores": {
          "a": 59,
          "b": 50
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Xiaomi (MiMo team)",
        "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": "MIT (metadata only, no LICENSE file)",
        "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": "2026-10-28",
        "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"
      }
    ],
    "faq": [
      {
        "answer": "MiMo-V2.6-Flash-RL scores 61 (C) on agent readiness against Baidu Unlimited-OCR-2's 54.2 (C), and leads in 3 of 4 scored categories.",
        "question": "Which is better for AI agents, MiMo-V2.6-Flash-RL or Baidu Unlimited-OCR-2?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 65 against 55",
          "Agent interface, 85 against 75",
          "Documentation and upkeep, 59 against 50"
        ],
        "also": null,
        "goodFor": "Teams with a GPU server, or a provider account, that want an open agent model with a very long context",
        "slug": "mimo-v2-6-flash-rl",
        "watchFor": "178 GB at 4 bits, so it needs about 200 GB of memory"
      },
      {
        "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"
      }
    ],
    "history": {
      "mimo-v2-6-flash-rl": null,
      "unlimited-ocr-2": [
        {
          "causes": [
            "company"
          ],
          "date": "2026-11-04",
          "delta": 0.7,
          "from": 53.5,
          "fromGrade": "D",
          "to": 54.2,
          "toGrade": "C"
        }
      ]
    },
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-mimo-v2-6-flash-rl.json",
        "title": "Gemma 4 E4B IT vs MiMo-V2.6-Flash-RL",
        "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-mimo-v2-6-flash-rl"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-unlimited-ocr-2.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-unlimited-ocr-2"
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      {
        "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-mimo-v2-6-flash-rl.json",
        "title": "GLM-4.5-Air vs MiMo-V2.6-Flash-RL",
        "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-mimo-v2-6-flash-rl"
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      {
        "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-unlimited-ocr-2.json",
        "title": "GLM-4.5-Air vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-unlimited-ocr-2"
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      {
        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-mimo-v2-6-flash-rl.json",
        "title": "GLM-4.7-Flash vs MiMo-V2.6-Flash-RL",
        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-mimo-v2-6-flash-rl"
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      {
        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-unlimited-ocr-2.json",
        "title": "GLM-4.7-Flash vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-unlimited-ocr-2"
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      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-mimo-v2-6-flash-rl.json",
        "title": "GLM-5.3-Flash vs MiMo-V2.6-Flash-RL",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-mimo-v2-6-flash-rl"
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      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-unlimited-ocr-2.json",
        "title": "GLM-5.3-Flash vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-unlimited-ocr-2"
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      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-mimo-v2-6-flash-rl.json",
        "title": "GLM-5.3 vs MiMo-V2.6-Flash-RL",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-mimo-v2-6-flash-rl"
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      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-unlimited-ocr-2.json",
        "title": "GLM-5.3 vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-unlimited-ocr-2"
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      {
        "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-mimo-v2-6-flash-rl.json",
        "title": "gpt-oss-120b vs MiMo-V2.6-Flash-RL",
        "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-mimo-v2-6-flash-rl"
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      {
        "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-unlimited-ocr-2.json",
        "title": "gpt-oss-120b vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-unlimited-ocr-2"
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      {
        "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-mimo-v2-6-flash-rl.json",
        "title": "gpt-oss-20b vs MiMo-V2.6-Flash-RL",
        "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-mimo-v2-6-flash-rl"
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      {
        "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-unlimited-ocr-2.json",
        "title": "gpt-oss-20b vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-unlimited-ocr-2"
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      {
        "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-mimo-v2-6-flash-rl.json",
        "title": "Granite 4.2 30B vs MiMo-V2.6-Flash-RL",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-mimo-v2-6-flash-rl"
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      {
        "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-unlimited-ocr-2.json",
        "title": "Granite 4.2 30B vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-unlimited-ocr-2"
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      {
        "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-mimo-v2-6-flash-rl.json",
        "title": "Granite 4.2 3B vs MiMo-V2.6-Flash-RL",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-mimo-v2-6-flash-rl"
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      {
        "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-unlimited-ocr-2.json",
        "title": "Granite 4.2 3B vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-unlimited-ocr-2"
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      {
        "json": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-mimo-v2-6-flash-rl.json",
        "title": "Granite 4.2 8B vs MiMo-V2.6-Flash-RL",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-mimo-v2-6-flash-rl"
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      {
        "json": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-unlimited-ocr-2.json",
        "title": "Granite 4.2 8B vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-unlimited-ocr-2"
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      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-mimo-v2-6-flash-rl.json",
        "title": "Hy-Memory-30B-A3B vs MiMo-V2.6-Flash-RL",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-mimo-v2-6-flash-rl"
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      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-unlimited-ocr-2.json",
        "title": "Hy-Memory-30B-A3B vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-unlimited-ocr-2"
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      {
        "json": "https://www.anchorterminal.com/compare/hy3-vs-mimo-v2-6-flash-rl.json",
        "title": "Hy3 vs MiMo-V2.6-Flash-RL",
        "url": "https://www.anchorterminal.com/compare/hy3-vs-mimo-v2-6-flash-rl"
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      {
        "json": "https://www.anchorterminal.com/compare/hy3-vs-unlimited-ocr-2.json",
        "title": "Hy3 vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/hy3-vs-unlimited-ocr-2"
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      {
        "json": "https://www.anchorterminal.com/compare/inkling-small-vs-mimo-v2-6-flash-rl.json",
        "title": "Inkling-Small vs MiMo-V2.6-Flash-RL",
        "url": "https://www.anchorterminal.com/compare/inkling-small-vs-mimo-v2-6-flash-rl"
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      {
        "json": "https://www.anchorterminal.com/compare/inkling-small-vs-unlimited-ocr-2.json",
        "title": "Inkling-Small vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/inkling-small-vs-unlimited-ocr-2"
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      {
        "json": "https://www.anchorterminal.com/compare/inkling-vs-mimo-v2-6-flash-rl.json",
        "title": "Inkling vs MiMo-V2.6-Flash-RL",
        "url": "https://www.anchorterminal.com/compare/inkling-vs-mimo-v2-6-flash-rl"
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      {
        "json": "https://www.anchorterminal.com/compare/inkling-vs-unlimited-ocr-2.json",
        "title": "Inkling vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/inkling-vs-unlimited-ocr-2"
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      {
        "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-mimo-v2-6-flash-rl.json",
        "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs MiMo-V2.6-Flash-RL",
        "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-mimo-v2-6-flash-rl"
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      {
        "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-unlimited-ocr-2.json",
        "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-unlimited-ocr-2"
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      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-mimo-v2-6-flash-rl.json",
        "title": "Kimi K3.1 Pro vs MiMo-V2.6-Flash-RL",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-mimo-v2-6-flash-rl"
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      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-unlimited-ocr-2.json",
        "title": "Kimi K3.1 Pro vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-unlimited-ocr-2"
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      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-mimo-v2-6-flash-rl.json",
        "title": "Kimi K3 vs MiMo-V2.6-Flash-RL",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-mimo-v2-6-flash-rl"
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      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-unlimited-ocr-2.json",
        "title": "Kimi K3 vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-unlimited-ocr-2"
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      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-mimo-v2-6-flash-rl.json",
        "title": "Kolibri-1 vs MiMo-V2.6-Flash-RL",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-mimo-v2-6-flash-rl"
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      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-unlimited-ocr-2.json",
        "title": "Kolibri-1 vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-unlimited-ocr-2"
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      {
        "json": "https://www.anchorterminal.com/compare/kronos-vs-mimo-v2-6-flash-rl.json",
        "title": "Kronos vs MiMo-V2.6-Flash-RL",
        "url": "https://www.anchorterminal.com/compare/kronos-vs-mimo-v2-6-flash-rl"
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      {
        "json": "https://www.anchorterminal.com/compare/kronos-vs-unlimited-ocr-2.json",
        "title": "Kronos vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/kronos-vs-unlimited-ocr-2"
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      {
        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-mimo-v2-6-flash-rl.json",
        "title": "Laguna M.1 vs MiMo-V2.6-Flash-RL",
        "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-mimo-v2-6-flash-rl"
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      {
        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-unlimited-ocr-2.json",
        "title": "Laguna M.1 vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-mimo-v2-6-flash-rl.json",
        "title": "Laguna S 2.1 vs MiMo-V2.6-Flash-RL",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-mimo-v2-6-flash-rl"
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      {
        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-unlimited-ocr-2.json",
        "title": "Laguna S 2.1 vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-unlimited-ocr-2"
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      {
        "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-mimo-v2-6-flash-rl.json",
        "title": "Laguna XS 2.1 vs MiMo-V2.6-Flash-RL",
        "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-mimo-v2-6-flash-rl"
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      {
        "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-unlimited-ocr-2.json",
        "title": "Laguna XS 2.1 vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-unlimited-ocr-2"
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      {
        "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-mimo-v2-6-flash-rl.json",
        "title": "LBS-27B vs MiMo-V2.6-Flash-RL",
        "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-mimo-v2-6-flash-rl"
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      {
        "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-unlimited-ocr-2.json",
        "title": "LBS-27B vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-unlimited-ocr-2"
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      {
        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-mimo-v2-6-flash-rl.json",
        "title": "LFM2.5-2.6B vs MiMo-V2.6-Flash-RL",
        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-mimo-v2-6-flash-rl"
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      {
        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-unlimited-ocr-2.json",
        "title": "LFM2.5-2.6B vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-unlimited-ocr-2"
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      {
        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-mimo-v2-6-flash-rl.json",
        "title": "LFM3-1.7B vs MiMo-V2.6-Flash-RL",
        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-mimo-v2-6-flash-rl"
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      {
        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-unlimited-ocr-2.json",
        "title": "LFM3-1.7B vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-unlimited-ocr-2"
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      {
        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-mimo-v2-6-flash-rl.json",
        "title": "LFM3-Audio-2B vs MiMo-V2.6-Flash-RL",
        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-mimo-v2-6-flash-rl"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-unlimited-ocr-2.json",
        "title": "LFM3-Audio-2B vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-unlimited-ocr-2"
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      {
        "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-mimo-v2-6-flash-rl.json",
        "title": "LFM3-Omni-2B vs MiMo-V2.6-Flash-RL",
        "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-mimo-v2-6-flash-rl"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-unlimited-ocr-2.json",
        "title": "LFM3-Omni-2B vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-mimo-v2-6-flash-rl.json",
        "title": "LFM3-VL-2B vs MiMo-V2.6-Flash-RL",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-mimo-v2-6-flash-rl"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-unlimited-ocr-2.json",
        "title": "LFM3-VL-2B vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-mimo-v2-6-flash-rl.json",
        "title": "Ling-3.0-tiny vs MiMo-V2.6-Flash-RL",
        "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-mimo-v2-6-flash-rl"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-unlimited-ocr-2.json",
        "title": "Ling-3.0-tiny vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-mimo-v2-6-flash-rl.json",
        "title": "Ling-3.1-flash-1019 vs MiMo-V2.6-Flash-RL",
        "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-mimo-v2-6-flash-rl"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-unlimited-ocr-2.json",
        "title": "Ling-3.1-flash-1019 vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-mimo-v2-6-flash-rl.json",
        "title": "LongCat-2.5 vs MiMo-V2.6-Flash-RL",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-mimo-v2-6-flash-rl"
      },
      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-unlimited-ocr-2.json",
        "title": "LongCat-2.5 vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-mimo-v2-6-flash-rl.json",
        "title": "MiMo-V2.6 Distill Qwen 9B vs MiMo-V2.6-Flash-RL",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-mimo-v2-6-flash-rl"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-unlimited-ocr-2.json",
        "title": "MiMo-V2.6 Distill Qwen 9B vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-minicpm-o-4-5.json",
        "title": "MiMo-V2.6-Flash-RL vs MiniCPM-o 4.5",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-minicpm-o-4-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-minicpm-v-4-7-1b.json",
        "title": "MiMo-V2.6-Flash-RL vs MiniCPM-V 4.7 1B",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-minicpm-v-4-7-1b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-minicpm-v-4-7-35b-a3b.json",
        "title": "MiMo-V2.6-Flash-RL vs MiniCPM-V 4.7 35B-A3B",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-minicpm-v-4-7-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-minicpm5-2b.json",
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        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-minicpm5-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-minimax-m2-5.json",
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        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-minimax-m2-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-minimax-m2-7.json",
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        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-minimax-m2-7"
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        "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-unlimited-ocr-2.json",
        "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-unlimited-ocr-2.json",
        "title": "Nemotron 3.5 Lightning 30B A3B vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-unlimited-ocr-2"
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      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-unlimited-ocr-2.json",
        "title": "Nemotron 3 Nano 30B A3B vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-unlimited-ocr-2.json",
        "title": "Nemotron 3 Nano 4B vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-unlimited-ocr-2"
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      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-unlimited-ocr-2.json",
        "title": "Nemotron 3 Super 120B A12B vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-unlimited-ocr-2.json",
        "title": "Nemotron 3 Ultra 550B A55B vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-unlimited-ocr-2.json",
        "title": "North Mini Code 1.0 vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-unlimited-ocr-2.json",
        "title": "occamy-1.0 vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-unlimited-ocr-2.json",
        "title": "Olmo 3 7B Think vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-unlimited-ocr-2.json",
        "title": "Ornith-1.5-122B vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-unlimited-ocr-2"
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      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-unlimited-ocr-2.json",
        "title": "Ornith-1.5-35B-A3B vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-unlimited-ocr-2.json",
        "title": "Ornith-1.5-397B vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-unlimited-ocr-2.json",
        "title": "Ornith-1.5-9B vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-unlimited-ocr-2.json",
        "title": "OTel-2.0-LLM-31B-IT vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-unlimited-ocr-2"
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      {
        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-unlimited-ocr-2.json",
        "title": "Qwen-AgentWorld-35B-A3B vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-unlimited-ocr-2.json",
        "title": "Qwen3-30B-A3B-Instruct-2507 vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-unlimited-ocr-2"
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      {
        "json": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-unlimited-ocr-2.json",
        "title": "Qwen3-4B-Instruct-2507 vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-unlimited-ocr-2.json",
        "title": "Qwen3.5-0.8B vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-unlimited-ocr-2"
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      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-unlimited-ocr-2.json",
        "title": "Qwen3.5-122B-A10B vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-unlimited-ocr-2.json",
        "title": "Qwen3.5-2B vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-unlimited-ocr-2"
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      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-unlimited-ocr-2.json",
        "title": "Qwen3.5-397B-A17B vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-unlimited-ocr-2"
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      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-unlimited-ocr-2.json",
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        "url": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-unlimited-ocr-2"
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      {
        "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"
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      {
        "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"
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      {
        "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"
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      {
        "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-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-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-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-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"
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      {
        "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"
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      {
        "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-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-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"
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      {
        "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-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/ripple-65b-preview-vs-unlimited-ocr-2.json",
        "title": "Ripple-65B Preview vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-unlimited-ocr-2.json",
        "title": "SenseNova-Loop-9B-Preview vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/smollm3-3b-vs-unlimited-ocr-2.json",
        "title": "SmolLM3-3B vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/smollm3-3b-vs-unlimited-ocr-2"
      },
      {
        "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/step-3-7-flash-vs-unlimited-ocr-2.json",
        "title": "Step 3.7 Flash vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/step-3-7-flash-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"
      },
      {
        "json": "https://www.anchorterminal.com/compare/unlimited-ocr-2-vs-zaya1-8b.json",
        "title": "Baidu Unlimited-OCR-2 vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/unlimited-ocr-2-vs-zaya1-8b"
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      {
        "json": "https://www.anchorterminal.com/compare/chandra-ocr-2-vs-unlimited-ocr-2.json",
        "title": "Chandra OCR 2 vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/chandra-ocr-2-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-ocr-2-vs-unlimited-ocr-2.json",
        "title": "DeepSeek-OCR 2 vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/deepseek-ocr-2-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hunyuanocr-vs-unlimited-ocr-2.json",
        "title": "HunyuanOCR 1.5 vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/hunyuanocr-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lightonocr-3-4b-vs-unlimited-ocr-2.json",
        "title": "LightOnOCR-3-4B vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/lightonocr-3-4b-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanonets-ocr2-3b-vs-unlimited-ocr-2.json",
        "title": "Nanonets-OCR2-3B vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/nanonets-ocr2-3b-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/surya-ocr-2-5-vs-unlimited-ocr-2.json",
        "title": "Surya OCR 2.5 vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/surya-ocr-2-5-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/unlimited-ocr-2-vs-wevisone-vl-2b.json",
        "title": "Baidu Unlimited-OCR-2 vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/unlimited-ocr-2-vs-wevisone-vl-2b"
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      {
        "json": "https://www.anchorterminal.com/compare/unlimited-ocr-2-vs-wevisone-vl-4b.json",
        "title": "Baidu Unlimited-OCR-2 vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/unlimited-ocr-2-vs-wevisone-vl-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/unlimited-ocr-2-vs-xiaomi-ocr-0.json",
        "title": "Baidu Unlimited-OCR-2 vs Xiaomi-OCR-0",
        "url": "https://www.anchorterminal.com/compare/unlimited-ocr-2-vs-xiaomi-ocr-0"
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    ],
    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 10,
        "edge": "mimo-v2-6-flash-rl",
        "key": "reach",
        "mimo-v2-6-flash-rl": 65,
        "name": "Reach",
        "unlimited-ocr-2": 55,
        "weight": 25
      },
      {
        "by": 0,
        "edge": "",
        "key": "risk",
        "mimo-v2-6-flash-rl": 45,
        "name": "Risk",
        "unlimited-ocr-2": 45,
        "weight": 20
      },
      {
        "by": 10,
        "edge": "mimo-v2-6-flash-rl",
        "key": "interface",
        "mimo-v2-6-flash-rl": 85,
        "name": "Agent interface",
        "unlimited-ocr-2": 75,
        "weight": 10
      },
      {
        "by": 9,
        "edge": "mimo-v2-6-flash-rl",
        "key": "upkeep",
        "mimo-v2-6-flash-rl": 59,
        "name": "Documentation and upkeep",
        "unlimited-ocr-2": 50,
        "weight": 10
      }
    ],
    "summary": "MiMo-V2.6-Flash-RL scores 61 (C) on agent readiness against Baidu Unlimited-OCR-2's 54.2 (C), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "mimo-v2-6-flash-rl": "A large agent model with an open licence: text, images, video and audio, a 1M context, tool calling it was trained for, and code in four released runtimes. It's a server model, 178 GB at 4 bits, and the card's commands run the repository's code. No safety results, little said about data, and few questions answered.",
      "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."
    }
  },
  "kind": "anchor.page",
  "links": {
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  "markdown": "MiMo-V2.6-Flash-RL scores 61 (C) on agent readiness against Baidu Unlimited-OCR-2's 54.2 (C), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself.\n\n- MiMo-V2.6-Flash-RL: grade C, 61/100, rank #75 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/mimo-v2-6-flash-rl.md · JSON https://www.anchorterminal.com/api/v1/tools/mimo-v2-6-flash-rl.json\n- 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\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### MiMo-V2.6-Flash-RL (C)\n\nGood for: Teams with a GPU server, or a provider account, that want an open agent model with a very long context\n\nAhead on:\n- Reach, 65 against 55\n- Agent interface, 85 against 75\n- Documentation and upkeep, 59 against 50\n\nWatch for: 178 GB at 4 bits, so it needs about 200 GB of memory\n\n### Baidu Unlimited-OCR-2 (C)\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## Why the edge\n\n### Reach: MiMo-V2.6-Flash-RL +10\n\n- MiMo-V2.6-Flash-RL (65): Memory 5, the smallest build at 4 bits or better is ggml-org's MXFP4 GGUF, 168.93 GB with its projector, and 168.93 GB × 1.2 = 202.7 GB, within 256 GB. Xiaomi's own files are 177.74 GB. ggml-org's Q2_K is below 4 bits and isn't read. Runtimes 20, Released vLLM 0.33.0 and SGLang 0.5.23 have the mimo_v2 model and the mimo parsers, Transformers 5.22.2 has a mimo_v2_flash model, and llama.cpp b12242 has the architecture, with a GGUF from its own organisation. Builds 10, one precision from Xiaomi, the 4-bit files it releases. Terms 15, MIT in the card's metadata, an OSI licence. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, the card says it is on Xiaomi's MiMo Open Platform API and on OpenRouter, and Novita is live for it on Hugging Face. 5 + 20 + 10 + 15 + 10 + 5 = 65.\n- 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.\n\n### Agent interface: MiMo-V2.6-Flash-RL +10\n\n- MiMo-V2.6-Flash-RL (85): Chat template 20, chat_template.jinja in the repository (3,868 characters), with a copy in tokenizer_config.json. Tool-calling format 25, the card names the parser to use in two runtimes: the mimo tool-call parser in SGLang and in vLLM, with auto tool choice, and both ship it in a release. Trained tool calling 20, the card describes reinforcement learning on coding and general agent tasks across several harnesses, and the template renders every call in a turn. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, the template has an enable_thinking switch and keeps reasoning_content, and the card names the mimo reasoning parser for SGLang and vLLM, which returns the reasoning apart from the answer. Context 10, 1M tokens per the card's model summary, and 1,048,576 positions in config.json. 20 + 25 + 20 + 0 + 10 + 10 = 85.\n- 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.\n\n### Documentation and upkeep: MiMo-V2.6-Flash-RL +9\n\n- MiMo-V2.6-Flash-RL (59): Model card 14, Intended use, the architecture, the parameters, the context and the languages (in the metadata) are on the card. Training data 5, A description of the training method with no data sources, sizes or cut-off. Last change 20, the last commits, on 28 October 2026, updated the card, 13 days before this check. Responsiveness 5, Of the 13 most recent discussions that aren't pull requests (21 September to 6 November 2026), 3 have a reply from a XiaomiMiMo member, which is a few. Version policy 5, MiMo-V2.5 stays in its own repositories and V2.6 came out in new ones, with no dated note on the older cards that we read. File health 10, Config, template and card agree on the architecture and the 1M context, and two early faults (invalid JSON in the draft model's config, preprocessor values) were fixed in commits that say so. 14 + 5 + 20 + 5 + 5 + 10 = 59.\n- Baidu Unlimited-OCR-2 (50): The card covers intended use and the new attention, and misses limitations, parameters, context length, languages and a cut-off (5). Training data is described by mix and labelling with no datasets named and no cut-off (5). The card changed on 2 November 2026 and the SGLang wheel on 29 October (20). Baidu's uploader replied in 1 of the 5 Hub discussions, a few (5). The first model stays in its own repository without a note pointing here (5), and the files agree with the card (10).\n\n## Score by category\n\n| Category | Weight | MiMo-V2.6-Flash-RL | Baidu Unlimited-OCR-2 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 65 | 55 | MiMo-V2.6-Flash-RL +10 |\n| Risk | 20% (20 this run) | 45 | 45 | even |\n| Agent interface | 10% (10 this run) | 85 | 75 | MiMo-V2.6-Flash-RL +10 |\n| Documentation and upkeep | 10% (10 this run) | 59 | 50 | MiMo-V2.6-Flash-RL +9 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **54.2 · C** | |\n\n## Facts side by side\n\n| Fact | MiMo-V2.6-Flash-RL | Baidu Unlimited-OCR-2 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Xiaomi (MiMo team) | 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 | MIT (metadata only, no LICENSE file) | 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 | 2026-10-28 | 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\n## How the scores have moved\n\n### MiMo-V2.6-Flash-RL\n\nNo score change since it was listed.\n\n### Baidu Unlimited-OCR-2\n\n- +0.7 on 4 November 2026 (D to C): company\n\n## Verdicts\n\n**MiMo-V2.6-Flash-RL.** A large agent model with an open licence: text, images, video and audio, a 1M context, tool calling it was trained for, and code in four released runtimes. It's a server model, 178 GB at 4 bits, and the card's commands run the repository's code. No safety results, little said about data, and few questions answered.\n\n**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.\n\n## Before you call either\n\n### MiMo-V2.6-Flash-RL\n\n1. Plan for a provider unless you have a multi-GPU server: Xiaomi's API platform, OpenRouter or Novita\n2. On SGLang or vLLM pass `--tool-call-parser mimo --reasoning-parser mimo`\n3. Use temperature 1.0 and top_p 0.95, as the card recommends\n4. The card's commands pass trust-remote-code. Read the repository's three Python files first, or use Transformers 5.22.2, which has the model built in\n5. Skip the dflash folder unless you need speculative decoding. Its mask_embedding.pt is a pickle\n6. The Use Restrictions section asks for human oversight of high-risk actions\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.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\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, MiMo-V2.6-Flash-RL or Baidu Unlimited-OCR-2?\n\nMiMo-V2.6-Flash-RL scores 61 (C) on agent readiness against Baidu Unlimited-OCR-2's 54.2 (C), and leads in 3 of 4 scored categories.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-unlimited-ocr-2.json, and with the fewest tokens: https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-unlimited-ocr-2.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"mimo-v2-6-flash-rl\", \"b\": \"unlimited-ocr-2\"}`. From a terminal: `anchor compare mimo-v2-6-flash-rl unlimited-ocr-2`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/mimo-v2-6-flash-rl.json and https://www.anchorterminal.com/api/v1/tools/unlimited-ocr-2.json\n\n## Other comparisons with MiMo-V2.6-Flash-RL or Baidu Unlimited-OCR-2\n\n- [Agents-A1-4B vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/agents-a1-4b-vs-mimo-v2-6-flash-rl.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 MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-mimo-v2-6-flash-rl.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 MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-mimo-v2-6-flash-rl.md)\n- [Apodex-1.1-mini vs Baidu 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