{
  "data": {
    "a": {
      "slug": "minimax-m2-5",
      "name": "MiniMax-M2.5",
      "vendor": "MiniMax",
      "vendorUrl": "https://www.minimax.io",
      "kind": "weights",
      "category": "open-weights",
      "summary": "MiniMax-M2.5 is MiniMax's open-weight mixture-of-experts model for coding and agent work, about 229B parameters in FP8 with a 196,608-token context and thinking always on, published on Hugging Face on 12 February 2026.",
      "url": "https://www.anchorterminal.com/tools/minimax-m2-5",
      "markdownUrl": "https://www.anchorterminal.com/tools/minimax-m2-5.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/minimax-m2-5.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/minimax-m2-5.json",
      "repo": "https://huggingface.co/MiniMaxAI/MiniMax-M2.5",
      "license": "MiniMax Model License (model) and modified MIT (code)",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "MiniMaxAI/MiniMax-M2.5"
        }
      ],
      "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 and royalty-free, under the MiniMax Model License for the weights and a modified MIT licence for the code, both with attribution duties and a Prohibited Uses Policy for the model. Running it yourself costs the hardware, 230.2 GB of FP8 files. MiniMax's API closed M2.5 to new users on 19 October 2026 and retires it on 18 November 2026 (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/MiniMaxAI/MiniMax-M2.5",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "reasoning",
        "tool-calling",
        "coding",
        "custom-licence",
        "safetensors",
        "fp8",
        "self-hosted"
      ],
      "lastRelease": "2026-02-12",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 57.5,
        "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": 61,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "capability": 73,
          "interface": 75,
          "reach": 65,
          "risk": 30,
          "upkeep": 22
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "MiniMax-M2.5 is a 230B mixture-of-experts model for coding and agent work, with parallel tool calls, a 196K context and parsers in vLLM and SGLang. It needs a 4-GPU server for FP8 or about 155 GB for a 4-bit build. The main risk is a licence replaced after release with a Prohibited Uses Policy while the card still says modified MIT.",
        "bestFor": "Teams with a 4 to 8 GPU server who want an open agent and coding model with parallel tool calls and can live with its use policy",
        "strengths": [
          "Runs in released vLLM, SGLang, llama.cpp, Transformers and LM Studio",
          "Tool-call format documented by MiniMax with named vLLM and SGLang parsers and a parsing function",
          "Several tool calls in one block, and 196,608 tokens of context",
          "Safetensors only, in the publisher's organisation with a SHA-256 for every shard",
          "No gate or account, and served by two inference providers on Hugging Face"
        ],
        "weaknesses": [
          "230 GB in FP8, and about 155 GB for the smallest 4-bit library build, more than a 128 GB machine",
          "Thinking is always on, with no documented way to turn it off or budget it",
          "The card says modified MIT and links to a deleted file, while the licence in the repository adds a Prohibited Uses Policy and termination for breach",
          "No safety evaluation, agent guidance or security contact",
          "No MiniMax reply in discussions since April 2026, and the card leaves out limitations, architecture, parameters and a cut-off"
        ],
        "agentNotes": [
          "Start it with vLLM on 4 GPUs of 96 GB, SAFETENSORS_FAST_GPU=1 vllm serve MiniMaxAI/MiniMax-M2.5 --trust-remote-code --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser minimax_m2 --reasoning-parser minimax_m2_append_think",
          "In SGLang use --tool-call-parser minimax-m2 and --reasoning-parser minimax-append-think, with --trust-remote-code",
          "MiniMax's guides budget 220 GB for the weights and 240 GB per 1M tokens of KV cache",
          "On a single large-memory machine, LM Studio's catalogue entry minimax/minimax-m2.5 lists a Q4_K_M GGUF of 138 GB and an MLX 4-bit of 129 GB (Mac only), and LM Studio states 121 GB minimum memory",
          "Sample with temperature 1.0, top_p 0.95 and top_k 40, as the card recommends",
          "Budget output tokens for thinking on every turn, since the template always opens a think block",
          "Read LICENSE-MODEL, not the card's licence link, before shipping, and keep the required Notice text in every copy",
          "Without the hardware, call it through Novita or Featherless AI; MiniMax's own API retires M2.5 on 18 November 2026"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 1.8,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "high",
            "grade": "C",
            "methodology": "0.6",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 57.5
          }
        ],
        "editorialScores": {
          "capability": 73,
          "interface": 75,
          "reach": 65,
          "risk": 30,
          "upkeep": 22
        },
        "provenanceScore": 55,
        "checked": {
          "categories": {
            "capability": "2026-10-21",
            "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": "# MiniMax's vLLM guide, 4 GPUs\nuv pip install vllm --torch-backend=auto\nSAFETENSORS_FAST_GPU=1 vllm serve MiniMaxAI/MiniMax-M2.5 --trust-remote-code \\\n  --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser minimax_m2 \\\n  --reasoning-parser minimax_m2_append_think",
        "http": "curl http://localhost:8000/v1/chat/completions \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\"model\": \"MiniMaxAI/MiniMax-M2.5\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}], \"temperature\": 1.0, \"top_p\": 0.95, \"top_k\": 40}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/minimax-m2-5"
      },
      "sameCompany": [
        "minimax-api",
        "minimax-video",
        "minimax-voice-cloning",
        "minimax-code",
        "minimax-m2-7",
        "minimax-m3-1-flash"
      ],
      "weights": {
        "trainedBy": "MiniMax",
        "params": "228.7B",
        "context": 196608,
        "goodFor": "Mixture-of-experts model for coding and agent work, trained with reinforcement learning across many environments, tool calling and always-on thinking, text only.",
        "runtimes": [
          "llama.cpp",
          "vLLM",
          "SGLang",
          "LM Studio",
          "Transformers"
        ],
        "builds": [
          {
            "name": "MiniMax-M2.5",
            "url": "https://huggingface.co/MiniMaxAI/MiniMax-M2.5",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 230.1,
            "memoryGB": 512,
            "format": "safetensors",
            "note": "Native FP8; the only build MiniMax publishes"
          }
        ],
        "run": "SAFETENSORS_FAST_GPU=1 vllm serve MiniMaxAI/MiniMax-M2.5 --trust-remote-code --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser minimax_m2 --reasoning-parser minimax_m2_append_think",
        "runBuild": "MiniMax-M2.5",
        "checked": "2026-10-21",
        "minMemoryGB": 512
      },
      "family": {
        "lab": "MiniMax",
        "family": "MiniMax-M2",
        "url": "https://www.anchorterminal.com/open-weights/#minimax",
        "others": [
          {
            "name": "MiniMax-M2",
            "url": "https://huggingface.co/MiniMaxAI/MiniMax-M2",
            "params": "230B total, 10B active"
          },
          {
            "name": "MiniMax-M2.1",
            "url": "https://huggingface.co/MiniMaxAI/MiniMax-M2.1",
            "params": "230B total, 10B active (Hub count; card doesn't restate)"
          },
          {
            "name": "MiniMax-M2.7",
            "url": "https://huggingface.co/MiniMaxAI/MiniMax-M2.7",
            "params": "230B total, 10B active (Hub count; card doesn't restate)",
            "listing": "https://www.anchorterminal.com/tools/minimax-m2-7"
          }
        ],
        "olderCount": 7,
        "unrestrictedCopies": 5
      },
      "area": "models",
      "provenance": {
        "legalEntity": "MiniMax AI (named in the licences; MiniMax's platform terms name Nanonoble Pte. Ltd., Singapore)",
        "domain": "minimax.io",
        "domainRegistered": "2021-03-27",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "The MiniMax Model License and the MIT code licence name MiniMax AI. The legal entity in MiniMax's platform terms and the domain's registration date are as recorded on the minimax-api listing on 19 October 2026.",
          "Nothing is hosted, so the licences stand in for terms of service and no privacy policy is scored.",
          "No changelog. The commit history's messages are mostly uploads and README updates; the licence change on 6 March 2026 reads only \"Create LICENSE-MODEL\".",
          "www.minimax.io/.well-known/security.txt redirects to the home page on 21 October 2026, so there is no security.txt.",
          "MiniMax's site (www.minimax.io/models/text) links to huggingface.co/MiniMaxAI/MiniMax-M2.5, so the repository is the publisher's own."
        ],
        "score": 55
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/minimax-m2-5.json",
      "live": {
        "slug": "minimax-m2-5",
        "securityTxt": {
          "url": "https://minimax.io/.well-known/security.txt",
          "state": "none",
          "checkedAt": "2026-10-27T15:31:11.2669296Z"
        },
        "domain": {
          "domain": "minimax.io",
          "checkedAt": "2026-10-25T13:52:12.446610874Z"
        },
        "updatedAt": "2026-10-27T15:31:11.2669296Z"
      }
    },
    "answer": "MiniMax-M2.5 scores 57.5 (C) on agent readiness against Baidu Unlimited-OCR-2's 53.5 (D), and leads in 1 of 4 scored categories. Baidu Unlimited-OCR-2 leads on risk and documentation and upkeep.",
    "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": "MiniMax",
        "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": "MiniMax Model License (model) and modified MIT (code)",
        "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": "2026-02-12",
        "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": "1.8/5 (4)",
        "b": "none",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "MiniMax-M2.5 scores 57.5 (C) on agent readiness against Baidu Unlimited-OCR-2's 53.5 (D), and leads in 1 of 4 scored categories. Baidu Unlimited-OCR-2 leads on risk and documentation and upkeep.",
        "question": "Which is better for AI agents, MiniMax-M2.5 or Baidu Unlimited-OCR-2?"
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    ],
    "goodFor": [
      {
        "aheadOn": [
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        ],
        "also": null,
        "goodFor": "Teams with a 4 to 8 GPU server who want an open agent and coding model with parallel tool calls and can live with its use policy",
        "slug": "minimax-m2-5",
        "watchFor": "230 GB in FP8, and about 155 GB for the smallest 4-bit library build, more than a 128 GB machine"
      },
      {
        "aheadOn": [
          "Risk, 45 against 30",
          "Documentation and upkeep, 45 against 22"
        ],
        "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"
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      "capability": "model.open-weights",
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        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-minimax-m2-5"
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      {
        "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"
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      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minimax-m2-5.json",
        "title": "Apertus v1.5 8B vs MiniMax-M2.5",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minimax-m2-5"
      },
      {
        "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-minimax-m2-5.json",
        "title": "Apodex-1.1-mini vs MiniMax-M2.5",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minimax-m2-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-unlimited-ocr-2.json",
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        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-unlimited-ocr-2"
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      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-minimax-m2-5.json",
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        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-minimax-m2-5"
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      {
        "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"
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      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-minimax-m2-5.json",
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        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-minimax-m2-5"
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      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-unlimited-ocr-2.json",
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        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-minimax-m2-5.json",
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        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-minimax-m2-5"
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        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-unlimited-ocr-2"
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      {
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        "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"
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      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-minimax-m2-5.json",
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        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-unlimited-ocr-2"
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        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-unlimited-ocr-2"
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        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-minimax-m2-5"
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        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-minimax-m2-5.json",
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        "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-unlimited-ocr-2.json",
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        "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-unlimited-ocr-2.json",
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        "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"
      },
      {
        "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/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-1-5-35b-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/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"
      },
      {
        "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"
      },
      {
        "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"
      },
      {
        "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"
      },
      {
        "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-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-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-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-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"
      },
      {
        "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-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"
      },
      {
        "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/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/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": 10,
        "edge": "minimax-m2-5",
        "key": "reach",
        "minimax-m2-5": 65,
        "name": "Reach",
        "unlimited-ocr-2": 55,
        "weight": 25
      },
      {
        "by": 15,
        "edge": "unlimited-ocr-2",
        "key": "risk",
        "minimax-m2-5": 30,
        "name": "Risk",
        "unlimited-ocr-2": 45,
        "weight": 20
      },
      {
        "by": 0,
        "edge": "",
        "key": "interface",
        "minimax-m2-5": 75,
        "name": "Agent interface",
        "unlimited-ocr-2": 75,
        "weight": 10
      },
      {
        "by": 23,
        "edge": "unlimited-ocr-2",
        "key": "upkeep",
        "minimax-m2-5": 22,
        "name": "Documentation and upkeep",
        "unlimited-ocr-2": 45,
        "weight": 10
      }
    ],
    "summary": "MiniMax-M2.5 scores 57.5 (C) on agent readiness against Baidu Unlimited-OCR-2's 53.5 (D), and leads in 1 of 4 scored categories. Baidu Unlimited-OCR-2 leads on risk and documentation and upkeep. Both do open-weight models you run yourself.",
    "tutorials": [],
    "verdicts": {
      "minimax-m2-5": "MiniMax-M2.5 is a 230B mixture-of-experts model for coding and agent work, with parallel tool calls, a 196K context and parsers in vLLM and SGLang. It needs a 4-GPU server for FP8 or about 155 GB for a 4-bit build. The main risk is a licence replaced after release with a Prohibited Uses Policy while the card still says modified MIT.",
      "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/minimax-m2-5-vs-unlimited-ocr-2",
    "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-unlimited-ocr-2.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-unlimited-ocr-2.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-unlimited-ocr-2.min.md"
  },
  "markdown": "MiniMax-M2.5 scores 57.5 (C) on agent readiness against Baidu Unlimited-OCR-2's 53.5 (D), and leads in 1 of 4 scored categories. Baidu Unlimited-OCR-2 leads on risk and documentation and upkeep. Both do open-weight models you run yourself.\n\n- MiniMax-M2.5: grade C, 57.5/100, rank #76 of 113 open-weight models. Markdown https://www.anchorterminal.com/tools/minimax-m2-5.md · JSON https://www.anchorterminal.com/api/v1/tools/minimax-m2-5.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### MiniMax-M2.5 (C)\n\nGood for: Teams with a 4 to 8 GPU server who want an open agent and coding model with parallel tool calls and can live with its use policy\n\nAhead on:\n- Reach, 65 against 55\n\nWatch for: 230 GB in FP8, and about 155 GB for the smallest 4-bit library build, more than a 128 GB machine\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\nAhead on:\n- Risk, 45 against 30\n- Documentation and upkeep, 45 against 22\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 | MiniMax-M2.5 | 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 | MiniMax-M2.5 +10 |\n| Risk | 20% (20 this run) | 30 | 45 | Baidu Unlimited-OCR-2 +15 |\n| Agent interface | 10% (10 this run) | 75 | 75 | even |\n| Documentation and upkeep | 10% (10 this run) | 22 | 45 | Baidu Unlimited-OCR-2 +23 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **57.5 · C** | **53.5 · D** | |\n\n## Facts side by side\n\n| Fact | MiniMax-M2.5 | Baidu Unlimited-OCR-2 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | MiniMax | 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 | MiniMax Model License (model) and modified MIT (code) | 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 | 2026-02-12 | 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 | 1.8/5 (4) | none |\n\n## Verdicts\n\n**MiniMax-M2.5.** MiniMax-M2.5 is a 230B mixture-of-experts model for coding and agent work, with parallel tool calls, a 196K context and parsers in vLLM and SGLang. It needs a 4-GPU server for FP8 or about 155 GB for a 4-bit build. The main risk is a licence replaced after release with a Prohibited Uses Policy while the card still says modified MIT.\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### MiniMax-M2.5\n\n1. Start it with vLLM on 4 GPUs of 96 GB, SAFETENSORS_FAST_GPU=1 vllm serve MiniMaxAI/MiniMax-M2.5 --trust-remote-code --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser minimax_m2 --reasoning-parser minimax_m2_append_think\n2. In SGLang use --tool-call-parser minimax-m2 and --reasoning-parser minimax-append-think, with --trust-remote-code\n3. MiniMax's guides budget 220 GB for the weights and 240 GB per 1M tokens of KV cache\n4. On a single large-memory machine, LM Studio's catalogue entry minimax/minimax-m2.5 lists a Q4_K_M GGUF of 138 GB and an MLX 4-bit of 129 GB (Mac only), and LM Studio states 121 GB minimum memory\n5. Sample with temperature 1.0, top_p 0.95 and top_k 40, as the card recommends\n6. Budget output tokens for thinking on every turn, since the template always opens a think block\n7. Read LICENSE-MODEL, not the card's licence link, before shipping, and keep the required Notice text in every copy\n8. Without the hardware, call it through Novita or Featherless AI; MiniMax's own API retires M2.5 on 18 November 2026\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, MiniMax-M2.5 or Baidu Unlimited-OCR-2?\n\nMiniMax-M2.5 scores 57.5 (C) on agent readiness against Baidu Unlimited-OCR-2's 53.5 (D), and leads in 1 of 4 scored categories. Baidu Unlimited-OCR-2 leads on risk and documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/minimax-m2-5-vs-unlimited-ocr-2.json, and with the fewest tokens: https://www.anchorterminal.com/compare/minimax-m2-5-vs-unlimited-ocr-2.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"minimax-m2-5\", \"b\": \"unlimited-ocr-2\"}`. From a terminal: `anchor compare minimax-m2-5 unlimited-ocr-2`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/minimax-m2-5.json and https://www.anchorterminal.com/api/v1/tools/unlimited-ocr-2.json\n\n## Other comparisons with MiniMax-M2.5 or Baidu Unlimited-OCR-2\n\n- [Agents-A1-4B vs MiniMax-M2.5](https://www.anchorterminal.com/compare/agents-a1-4b-vs-minimax-m2-5.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 MiniMax-M2.5](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minimax-m2-5.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 MiniMax-M2.5](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minimax-m2-5.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 MiniMax-M2.5](https://www.anchorterminal.com/compare/bigbang-v1-vs-minimax-m2-5.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 MiniMax-M2.5](https://www.anchorterminal.com/compare/carbon-x-vs-minimax-m2-5.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 MiniMax-M2.5](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-minimax-m2-5.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 MiniMax-M2.5](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-minimax-m2-5.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 MiniMax-M2.5](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-minimax-m2-5.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 MiniMax-M2.5](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-minimax-m2-5.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 MiniMax-M2.5](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-minimax-m2-5.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 MiniMax-M2.5](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-minimax-m2-5.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 MiniMax-M2.5](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-minimax-m2-5.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 MiniMax-M2.5](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-minimax-m2-5.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 MiniMax-M2.5](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-minimax-m2-5.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 MiniMax-M2.5](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-minimax-m2-5.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 MiniMax-M2.5](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-minimax-m2-5.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 MiniMax-M2.5](https://www.anchorterminal.com/compare/glm-4-5-air-vs-minimax-m2-5.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 MiniMax-M2.5](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-minimax-m2-5.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 MiniMax-M2.5](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-minimax-m2-5.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 MiniMax-M2.5](https://www.anchorterminal.com/compare/glm-5-3-vs-minimax-m2-5.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 MiniMax-M2.5](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-minimax-m2-5.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 MiniMax-M2.5](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-minimax-m2-5.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 MiniMax-M2.5](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-minimax-m2-5.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 MiniMax-M2.5](https://www.anchorterminal.com/compare/hy3-vs-minimax-m2-5.md)\n- [Hy3 vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/hy3-vs-unlimited-ocr-2.md)\n- [Inkling-Small vs MiniMax-M2.5](https://www.anchorterminal.com/compare/inkling-small-vs-minimax-m2-5.md)\n- [Inkling-Small vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/inkling-small-vs-unlimited-ocr-2.md)\n- [Inkling vs MiniMax-M2.5](https://www.anchorterminal.com/compare/inkling-vs-minimax-m2-5.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 MiniMax-M2.5](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-minimax-m2-5.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 MiniMax-M2.5](https://www.anchorterminal.com/compare/kimi-k3-vs-minimax-m2-5.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 MiniMax-M2.5](https://www.anchorterminal.com/compare/kolibri-1-vs-minimax-m2-5.md)\n- [Kolibri-1 vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/kolibri-1-vs-unlimited-ocr-2.md)\n- [Kronos vs MiniMax-M2.5](https://www.anchorterminal.com/compare/kronos-vs-minimax-m2-5.md)\n- [Kronos vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/kronos-vs-unlimited-ocr-2.md)\n- [Laguna M.1 vs MiniMax-M2.5](https://www.anchorterminal.com/compare/laguna-m-1-vs-minimax-m2-5.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 MiniMax-M2.5](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-minimax-m2-5.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 MiniMax-M2.5](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-minimax-m2-5.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 MiniMax-M2.5](https://www.anchorterminal.com/compare/lbs-27b-vs-minimax-m2-5.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 MiniMax-M2.5](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minimax-m2-5.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 MiniMax-M2.5](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-minimax-m2-5.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 MiniMax-M2.5](https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-minimax-m2-5.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 MiniMax-M2.5](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-minimax-m2-5.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 MiniMax-M2.5](https://www.anchorterminal.com/compare/minicpm5-2b-vs-minimax-m2-5.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 MiniMax-M2.7](https://www.anchorterminal.com/compare/minimax-m2-5-vs-minimax-m2-7.md)\n- [MiniMax-M2.5 vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/minimax-m2-5-vs-minimax-m3-1-flash.md)\n- [MiniMax-M2.5 vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/minimax-m2-5-vs-muse-glimmer-30b.md)\n- [MiniMax-M2.5 vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/minimax-m2-5-vs-nanbeige-4-3-33b.md)\n- [MiniMax-M2.5 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/minimax-m2-5-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [MiniMax-M2.5 vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/minimax-m2-5-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [MiniMax-M2.5 vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/minimax-m2-5-vs-nemotron-3-nano-30b-a3b.md)\n- [MiniMax-M2.5 vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/minimax-m2-5-vs-nemotron-3-nano-4b.md)\n- [MiniMax-M2.5 vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/minimax-m2-5-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [MiniMax-M2.5 vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/minimax-m2-5-vs-nemotron-3-super-120b-a12b.md)\n- [MiniMax-M2.5 vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/minimax-m2-5-vs-nemotron-3-ultra-550b-a55b.md)\n- [MiniMax-M2.5 vs occamy-1.0](https://www.anchorterminal.com/compare/minimax-m2-5-vs-occamy-1-0.md)\n- [MiniMax-M2.5 vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/minimax-m2-5-vs-olmo-3-7b-think.md)\n- [MiniMax-M2.5 vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/minimax-m2-5-vs-ornith-1-5-35b-a3b.md)\n- [MiniMax-M2.5 vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/minimax-m2-5-vs-ornith-1-5-397b.md)\n- [MiniMax-M2.5 vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/minimax-m2-5-vs-ornith-1-5-9b.md)\n- [MiniMax-M2.5 vs Qwen3-30B-A3B-Instruct-2507](https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-30b-a3b-instruct-2507.md)\n- [MiniMax-M2.5 vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-4b-instruct-2507.md)\n- [MiniMax-M2.5 vs Qwen3.5-0.8B](https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-5-0-8b.md)\n- [MiniMax-M2.5 vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-5-122b-a10b.md)\n- [MiniMax-M2.5 vs Qwen3.5-2B](https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-5-2b.md)\n- [MiniMax-M2.5 vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-5-397b-a17b.md)\n- [MiniMax-M2.5 vs Qwen3.5-4B](https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-5-4b.md)\n- [MiniMax-M2.5 vs Qwen3.5-9B](https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-5-9b.md)\n- [MiniMax-M2.5 vs Qwen3.6-27B](https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-6-27b.md)\n- [MiniMax-M2.5 vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-6-35b-a3b.md)\n- [MiniMax-M2.5 vs Qwen3.8-27B](https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-8-27b.md)\n- [MiniMax-M2.5 vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-8-flash-next.md)\n- [MiniMax-M2.5 vs Qwen3-Coder-30B-A3B-Instruct](https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-coder-30b-a3b-instruct.md)\n- [MiniMax-M2.5 vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-coder-next.md)\n- [MiniMax-M2.5 vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-omni-30b-a3b-instruct.md)\n- [MiniMax-M2.5 vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-vl-2b-instruct.md)\n- [MiniMax-M2.5 vs Qwen3-VL-30B-A3B-Instruct](https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-vl-30b-a3b-instruct.md)\n- [MiniMax-M2.5 vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-vl-32b-instruct.md)\n- [MiniMax-M2.5 vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-vl-4b-instruct.md)\n- [MiniMax-M2.5 vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-vl-8b-instruct.md)\n- [MiniMax-M2.5 vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwopus3-8-27b-flash-v2.md)\n- [MiniMax-M2.5 vs Recursive Flint](https://www.anchorterminal.com/compare/minimax-m2-5-vs-recursive-flint.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 Spark-X2.6-4B](https://www.anchorterminal.com/compare/minimax-m2-5-vs-spark-x2-6-4b.md)\n- [MiniMax-M2.5 vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/minimax-m2-5-vs-swift-1-5-qwen3-8-27b.md)\n- [MiniMax-M2.5 vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/minimax-m2-5-vs-swift-1-5-qwen3-8-flash-next.md)\n- [MiniMax-M2.5 vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/minimax-m2-5-vs-thinkingcap-qwen3-8-27b.md)\n- [MiniMax-M2.7 vs Baidu 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