{
  "data": {
    "a": {
      "slug": "longcat-2-5",
      "name": "LongCat-2.5",
      "vendor": "Meituan (LongCat)",
      "vendorUrl": "https://longcat.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "LongCat-2.5 is Meituan's open-weight flagship, a multimodal mixture-of-experts model of 1.6T parameters with about 48B active per token, built for long agentic coding work. MIT licence. The weights are 3,562 GB, and the card documents deployment on NPU hardware only.",
      "url": "https://www.anchorterminal.com/tools/longcat-2-5",
      "markdownUrl": "https://www.anchorterminal.com/tools/longcat-2-5.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/longcat-2-5.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/longcat-2-5.json",
      "dns": "longcat-2-5.grade.anchorterminal.com",
      "repo": "https://huggingface.co/meituan-longcat/LongCat-2.5",
      "license": "MIT",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "meituan-longcat/LongCat-2.5"
        },
        {
          "registry": "huggingface",
          "name": "meituan-longcat/LongCat-2.5-W4A4"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Neither repository is gated (checked 2026-11-10).",
      "pricing": "free",
      "pricingNotes": "Free to download and use under the MIT licence. Running it yourself is a data-centre job: 3,562 GB in BF16, or 1,336 GB for Meituan's 4-bit build, which the card says is for INT4 computation on NPU accelerators (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/meituan-longcat/LongCat-2.5",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "multimodal",
        "reasoning",
        "tool-calling",
        "agentic-coding",
        "mit",
        "safetensors",
        "int4",
        "npu",
        "self-hosted"
      ],
      "lastRelease": "2026-11-10",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 50.5,
        "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": 1518,
        "categoryRank": 107,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 75,
          "reach": 35,
          "risk": 60,
          "upkeep": 46
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-10"
        },
        "negative": 0,
        "verdict": "Meituan's flagship is MIT-licensed and documented for tool calling and switchable reasoning, with strong numbers of its own on agentic coding. Almost nobody can run it: 3,562 GB of weights, a 1,336 GB 4-bit build for NPU accelerators, and no released runtime with its architecture. A model to watch until a runtime or a provider picks it up.",
        "bestFor": "Operators with NPU clusters, and anyone tracking what the open flagships can do",
        "strengths": [
          "MIT licence, ungated",
          "Tool calling documented with a worked example",
          "Reasoning can be switched per request and comes back as reasoning_content",
          "Meituan publishes its own 4-bit build",
          "Updated on the day we checked"
        ],
        "weaknesses": [
          "3,562 GB in BF16 and 1,336 GB at 4 bits",
          "No released runtime has its architecture",
          "The only documented deployment is on NPU hardware through Meituan's fork",
          "config.json names model code that isn't in the repository",
          "No stated context length, languages, safety results or data sources",
          "No inference provider serves it"
        ],
        "agentNotes": [
          "You can't run this on a workstation or a single GPU server. Plan for a provider, and none serves it yet",
          "If an operator serves it, call it as an OpenAI-compatible chat API with tools in the function format",
          "Pass `chat_template_kwargs` with `enable_thinking` true or false",
          "With thinking on, keep earlier reasoning in the history (`save_history_reasoning_content` true), as the card recommends",
          "The card's results use temperature 1.0 and top_p 0.95",
          "Its benchmark table is Meituan's own. The unmarked scores are in-house"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 0,
        "avgRating": 0,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "D",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 50.5
          }
        ],
        "editorialScores": {
          "interface": 75,
          "reach": 35,
          "risk": 60,
          "upkeep": 46
        },
        "provenanceScore": 49,
        "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": "# No released runtime loads it. Meituan's NPU instructions are in its SGLang fork:\n# github.com/meituan-longcat/SGLang-FluentLLM, branch longcat-2.5-npu, npu_test/ReadMe.md",
        "http": "# The card's example calls an OpenAI-compatible chat completions API with tools, and passes\n# extra_body={\"chat_template_kwargs\": {\"enable_thinking\": true, \"save_history_reasoning_content\": true}}"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/longcat-2-5"
      },
      "weights": {
        "trainedBy": "Meituan (LongCat team)",
        "builtOn": "LongCat-2.0 and LongCat-Next, per the card",
        "params": "1.6T",
        "active": "48B",
        "context": 524288,
        "goodFor": "Long agentic coding and research runs with images, for an operator with NPU clusters. Nobody else can run it yet.",
        "runtimes": [],
        "builds": [
          {
            "name": "LongCat-2.5-W4A4",
            "url": "https://huggingface.co/meituan-longcat/LongCat-2.5-W4A4",
            "who": "publisher",
            "precision": "int4",
            "sizeGB": 1336,
            "memoryGB": 2048,
            "format": "safetensors",
            "note": "Meituan's 4-bit build of the experts' weights and activations, for INT4 computation on NPU accelerators"
          },
          {
            "name": "LongCat-2.5 (BF16 safetensors)",
            "url": "https://huggingface.co/meituan-longcat/LongCat-2.5",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 3562,
            "memoryGB": 8192,
            "format": "safetensors",
            "note": "210 files"
          }
        ],
        "run": "See github.com/meituan-longcat/SGLang-FluentLLM, branch longcat-2.5-npu",
        "runBuild": "LongCat-2.5-W4A4",
        "checked": "2026-11-10",
        "minMemoryGB": 2048
      },
      "family": {
        "lab": "Meituan (LongCat)",
        "family": "LongCat-2",
        "url": "https://www.anchorterminal.com/open-weights/#meituan-longcat",
        "others": [
          {
            "name": "LongCat-2.5-W4A4",
            "url": "https://huggingface.co/meituan-longcat/LongCat-2.5-W4A4"
          },
          {
            "name": "LongCat-2.0",
            "url": "https://huggingface.co/meituan-longcat/LongCat-2.0"
          }
        ]
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Meituan, named in the card's licence section. The legal entity wasn't looked up",
        "domain": "longcat.ai",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "https://huggingface.co/meituan-longcat/LongCat-2.5/blob/main/LICENSE",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/meituan-longcat/LongCat-2.5/commits/main",
        "securityTxt": "unknown",
        "checked": "2026-11-10",
        "notes": [
          "longcat.ai has no robots.txt (the request returns a not-found message) and its pages are JavaScript applications that gave no text on 10 November 2026, so the site's own statements weren't read.",
          "The domain's registration date wasn't looked up.",
          "Nothing is hosted under this listing, so the MIT licence stands in for terms of service and no privacy policy is scored.",
          "The changelog is the commit history, 50 commits or more since 4 November 2026."
        ],
        "score": 49
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/longcat-2-5.json"
    },
    "answer": "MiniCPM-V 4.7 35B-A3B scores 61 (C) on agent readiness against LongCat-2.5's 50.5 (D), and leads in 2 of 4 scored categories. LongCat-2.5 leads on risk and agent interface.",
    "b": {
      "slug": "minicpm-v-4-7-35b-a3b",
      "name": "MiniCPM-V 4.7 35B-A3B",
      "vendor": "OpenBMB",
      "vendorUrl": "https://openbmb.cn",
      "kind": "weights",
      "category": "open-weights",
      "summary": "MiniCPM-V 4.7 35B-A3B is OpenBMB's open-weight vision-language model, a 35B mixture of experts with about 3B active per token that reads images and video, calls tools, and ships in BF16, FP8, 4-bit and GGUF builds under Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/minicpm-v-4-7-35b-a3b",
      "markdownUrl": "https://www.anchorterminal.com/tools/minicpm-v-4-7-35b-a3b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/minicpm-v-4-7-35b-a3b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/minicpm-v-4-7-35b-a3b.json",
      "dns": "minicpm-v-4-7-35b-a3b.grade.anchorterminal.com",
      "repo": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "openbmb/MiniCPM-V-4.7-35B-A3B"
        },
        {
          "registry": "huggingface",
          "name": "openbmb/MiniCPM-V-4.7-35B-A3B-gguf"
        },
        {
          "registry": "huggingface",
          "name": "openbmb/MiniCPM-V-4.7-35B-A3B-FP8"
        },
        {
          "registry": "huggingface",
          "name": "openbmb/MiniCPM-V-4.7-35B-A3B-AWQ"
        },
        {
          "registry": "huggingface",
          "name": "openbmb/MiniCPM-V-4.7-35B-A3B-GPTQ"
        },
        {
          "registry": "huggingface",
          "name": "openbmb/MiniCPM-V-4.7-35B-A3B-int4"
        },
        {
          "registry": "ollama",
          "name": "openbmb/minicpm-v4.7:35b-a3b"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The main repository and OpenBMB's GGUF, FP8, AWQ, GPTQ and int4 repositories are not gated (checked 2026-11-10).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0, named in the card's metadata and carried as a LICENSE file in OpenBMB's GitHub repository. Running it yourself costs the hardware, about 70 GB for the BF16 files, 22.7 GB for the 4-bit safetensors builds or 18.7 GB for the IQ4_XS GGUF (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/openbmb/MiniCPM-V-4.7-35B-A3B",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "multimodal",
        "vision",
        "video",
        "on-device",
        "tool-calling",
        "apache-2.0",
        "safetensors",
        "gguf",
        "fp8",
        "awq",
        "gptq",
        "self-hosted"
      ],
      "lastRelease": "2026-11-03",
      "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": 61,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 70,
          "reach": 90,
          "risk": 55,
          "upkeep": 68
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-10"
        },
        "negative": 0,
        "verdict": "A vision-language cousin for images and video that fits a 24 to 32 GB machine from OpenBMB's own GGUF, under plain Apache 2.0, with a publisher that answers. The card says little about limits, training data or how tool calls come back, so test the tool format in your runtime before relying on it.",
        "bestFor": "Agents that need to read screenshots, documents and video on a local machine with 24 to 32 GB for the model, under a permissive licence",
        "strengths": [
          "Fits about 24 GB from OpenBMB's IQ4_XS GGUF and 32 GB from its Q4_K_M, 4-bit safetensors or Ollama builds",
          "Runs in llama.cpp, Ollama, vLLM, SGLang and Transformers from their released versions, with llama.cpp's own guide",
          "Plain Apache 2.0, ungated, safetensors and GGUF only, no remote code",
          "OpenBMB answered 8 of 12 discussions and dates its releases",
          "Images, several images and video in one model, with thinking off by default"
        ],
        "weaknesses": [
          "No limitations section, safety results, agent-use guidance or security contact",
          "Training data isn't described, and the card gives no context length, languages or knowledge cut-off",
          "The card shows a tool-calling request but not the reply, and names no parser",
          "OpenBMB's cookbook is behind the runtimes on SGLang and Ollama support",
          "No inference provider is mapped on Hugging Face"
        ],
        "agentNotes": [
          "Start it with `llama-server -hf openbmb/MiniCPM-V-4.7-35B-A3B-gguf:Q4_K_M --jinja`, or `ollama run openbmb/minicpm-v4.7:35b-a3b`",
          "For image input in llama.cpp add `mmproj-model-f16.gguf`, or the 4x projector for more visual detail at a higher cost",
          "The GGUF files at the repository root have thinking off. Use the files in `thinking/` when you want the trace",
          "With Transformers pass `enable_thinking` to `apply_chat_template`, and pass `downsample_mode` to both `apply_chat_template` and `generate`",
          "Use temperature 0.7, top_p 0.95 and top_k 100 in instruct mode, as the card recommends",
          "Check tool calls in your runtime first, since the card names no parser. The template uses the Qwen3.5 function-block format",
          "Validate JSON output yourself, since structured output isn't documented"
        ],
        "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": 70,
          "reach": 90,
          "risk": 55,
          "upkeep": 68
        },
        "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": "ollama run openbmb/minicpm-v4.7:35b-a3b\n# or, with llama.cpp and OpenBMB's own GGUF:\nllama-server -hf openbmb/MiniCPM-V-4.7-35B-A3B-gguf:Q4_K_M --jinja\n# or, with Transformers' server, as the card gives it:\npip install \"transformers[serving]\u003e=5.18.0\"\ntransformers serve openbmb/MiniCPM-V-4.7-35B-A3B --port 8000 --host 0.0.0.0",
        "http": "curl http://localhost:11434/api/chat \\\n  -d '{\"model\": \"openbmb/minicpm-v4.7:35b-a3b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/minicpm-v-4-7-35b-a3b"
      },
      "sameCompany": [
        "minicpm-v-4-7-1b",
        "minicpm-o-4-5",
        "minicpm5-2b"
      ],
      "weights": {
        "trainedBy": "OpenBMB (ModelBest and Tsinghua NLP Lab)",
        "builtOn": "Qwen3.5 and Qwen3.6 as the language backbone and SigLIP2 as the vision module, per the card's acknowledgements. Not declared in the metadata. The text config is qwen3_5_moe_text with 40 layers and 256 experts",
        "params": "35.2B (3B active)",
        "context": 262144,
        "goodFor": "Vision-language mixture-of-experts model for images, several images and video, with tool calling and thinking off by default, fast for its size with about 3B active parameters.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "MiniCPM-V-4_7-35B-A3B-IQ4_XS",
            "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B-gguf/blob/main/MiniCPM-V-4_7-35B-A3B-IQ4_XS.gguf",
            "who": "publisher",
            "precision": "iq4_xs",
            "sizeGB": 18.7,
            "memoryGB": 24,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ],
            "note": "The smallest file at 4 bits. IQ3_M and IQ3_XS (15.4 and 14.5 GB) are below the 4-bit line and are not listed"
          },
          {
            "name": "MiniCPM-V-4.7-35B-A3B-AWQ",
            "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B-AWQ",
            "who": "publisher",
            "precision": "w4a16",
            "sizeGB": 22.7,
            "memoryGB": 32,
            "format": "safetensors",
            "runsOn": [
              "vLLM"
            ],
            "note": "AWQ, 4-bit weights with 16-bit activations"
          },
          {
            "name": "MiniCPM-V-4.7-35B-A3B-GPTQ",
            "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B-GPTQ",
            "who": "publisher",
            "precision": "w4a16",
            "sizeGB": 22.7,
            "memoryGB": 32,
            "format": "safetensors",
            "runsOn": [
              "vLLM"
            ],
            "note": "GPTQ, 4-bit weights with 16-bit activations"
          },
          {
            "name": "MiniCPM-V-4.7-35B-A3B-int4",
            "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B-int4",
            "who": "publisher",
            "precision": "int4",
            "sizeGB": 22.7,
            "memoryGB": 32,
            "format": "safetensors",
            "runsOn": [
              "vLLM"
            ],
            "note": "Listed on the card with the FP8 build under llm-compressor's FP8 and INT4 guide"
          },
          {
            "name": "MiniCPM-V-4_7-35B-A3B-Q5_K_M",
            "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B-gguf/blob/main/MiniCPM-V-4_7-35B-A3B-Q5_K_M.gguf",
            "who": "publisher",
            "precision": "q5_k_m",
            "sizeGB": 24.7,
            "memoryGB": 32,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ]
          },
          {
            "name": "MiniCPM-V-4_7-35B-A3B-Q4_K_M",
            "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B-gguf/blob/main/MiniCPM-V-4_7-35B-A3B-Q4_K_M.gguf",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 21.2,
            "memoryGB": 32,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ],
            "note": "Add mmproj-model-f16.gguf (1.1 GB) or mmproj-model-f16-4x.gguf (0.9 GB) for image input. The same file with thinking on is in the thinking/ folder"
          },
          {
            "name": "ollama openbmb/minicpm-v4.7:35b-a3b",
            "url": "https://ollama.com/openbmb/minicpm-v4.7",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 22,
            "memoryGB": 32,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "OpenBMB's own namespace on Ollama, not Ollama's library. Q4_K_M, 22 GB as the tags page shows it, with q8_0 (38 GB) and fp16 (70 GB) tags beside it"
          },
          {
            "name": "MiniCPM-V-4.7-35B-A3B-FP8",
            "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B-FP8",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 38,
            "memoryGB": 48,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang"
            ]
          },
          {
            "name": "MiniCPM-V-4_7-35B-A3B-Q8_0",
            "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B-gguf/blob/main/MiniCPM-V-4_7-35B-A3B-Q8_0.gguf",
            "who": "publisher",
            "precision": "q8_0",
            "sizeGB": 36.9,
            "memoryGB": 48,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ]
          },
          {
            "name": "MiniCPM-V-4_7-35B-A3B-Q6_K",
            "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B-gguf/blob/main/MiniCPM-V-4_7-35B-A3B-Q6_K.gguf",
            "who": "publisher",
            "precision": "q6_k",
            "sizeGB": 28.5,
            "memoryGB": 48,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ]
          },
          {
            "name": "MiniCPM-V-4.7-35B-A3B",
            "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 70.4,
            "memoryGB": 96,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)",
              "Transformers"
            ]
          },
          {
            "name": "MiniCPM-V-4_7-35B-A3B-F16",
            "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B-gguf/blob/main/MiniCPM-V-4_7-35B-A3B-F16.gguf",
            "who": "publisher",
            "precision": "f16",
            "sizeGB": 69.4,
            "memoryGB": 96,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ]
          }
        ],
        "run": "llama-server -hf openbmb/MiniCPM-V-4.7-35B-A3B-gguf:Q4_K_M --jinja",
        "runBuild": "MiniCPM-V-4_7-35B-A3B-Q4_K_M",
        "checked": "2026-11-10",
        "minMemoryGB": 24
      },
      "family": {
        "lab": "OpenBMB (MiniCPM)",
        "family": "MiniCPM-V 4",
        "url": "https://www.anchorterminal.com/open-weights/#openbmb",
        "others": [
          {
            "name": "MiniCPM-V 4.7 1B",
            "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-1B",
            "params": "1.55B (1,554,476,272 per the safetensors count)",
            "listing": "https://www.anchorterminal.com/tools/minicpm-v-4-7-1b"
          }
        ],
        "olderCount": 22
      },
      "area": "models",
      "provenance": {
        "legalEntity": "OpenBMB (named in the GitHub LICENSE's copyright line)",
        "domain": "openbmb.cn",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://github.com/OpenBMB/MiniCPM-V",
        "securityTxt": "none",
        "checked": "2026-11-10",
        "notes": [
          "No legal entity is named on the card. OpenBMB is the open-source organisation of ModelBest and Tsinghua University's NLP lab, as the MiniCPM5-2B listing records.",
          "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.",
          "The changelog is the GitHub README's dated news list (the release on 12 October 2026, DSpark heads on 15 October) and the repositories' commit messages, which say what changed.",
          "openbmb.cn/.well-known/security.txt returns 404 and github.com/OpenBMB/MiniCPM-V has no SECURITY.md.",
          "unchecked: the domain's registration date."
        ],
        "score": 58
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/minicpm-v-4-7-35b-a3b.json"
    },
    "edges": [
      {
        "by": 55,
        "category": "reach",
        "leads": "minicpm-v-4-7-35b-a3b",
        "name": "Reach",
        "reasons": {
          "a": "Memory 0, the smallest build is Meituan's W4A4 repository, 1,336 GB, and 1,336 GB × 1.2 = 1,603 GB, far above 256 GB. The BF16 files are 3,562 GB. Runtimes 0, none of the six runs it in a released version: vLLM 0.33.0, SGLang 0.5.23, Transformers 5.22.2 and llama.cpp b12242 have no LongcatCausalLM or pro_v code, and the model code config.json names isn't in the repository. Builds 10, one quantised build from Meituan, the W4A4 repository (4-bit experts, 1,336 GB). Terms 15, MIT in the metadata, the LICENSE file and the card, with nothing added. Download 10, the Hub API reports gated false for both repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it. 0 + 0 + 10 + 15 + 10 + 0 = 35.",
          "b": "Memory 25, since OpenBMB's IQ4_XS GGUF with its vision projector is 19.6 GB and needs about 23.6 GB with the fifth added, which fits 24 GB, while its Q4_K_M GGUF, 4-bit safetensors builds and Ollama entry need 32 GB. Runtimes 25, from vLLM 0.33.0, SGLang 0.5.23, Transformers 5.18.0 and later, llama.cpp's own MiniCPM-V 4.7 support and OpenBMB's entry on Ollama. Builds 15, OpenBMB publishes FP8, three 4-bit safetensors builds and GGUF. Terms 15, Apache 2.0 with nothing added. Download 10, nothing is gated. Hosted 0, no inference provider is mapped. 25 + 25 + 15 + 15 + 10 + 0 = 90."
        },
        "scores": {
          "a": 35,
          "b": 90
        }
      },
      {
        "by": 22,
        "category": "upkeep",
        "leads": "minicpm-v-4-7-35b-a3b",
        "name": "Documentation and upkeep",
        "reasons": {
          "a": "Model card 11, Intended use, limitations (the usage considerations), the architecture and the parameter counts are on the card. Training data 5, A general description of the training stages and where the reinforcement learning tasks came from, with no sources, sizes or cut-off. Last change 20, the last commit, on 10 November 2026, updated the card, on the day of this check. Responsiveness 0, Neither of the 2 discussions that aren't pull requests (9 and 10 November 2026) has a reply from a meituan-longcat member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, LongCat-2.0 stays in its own repositories and 2.5 came out in new ones, with no dated note on the older ones that we read. File health 5, the template is in both places and agrees, but config.json's auto_map names configuration_longcat_oe and modeling_longcat_oe, which aren't in the repository, and the tokenizer config's 131,072-token length is a quarter of the config's 524,288 positions. 11 + 5 + 20 + 0 + 5 + 5 = 46.",
          "b": "Model card 8, it covers intended use, architecture and parameters but not limitations, context length, languages or a knowledge cut-off. Training data 0, none described. Last change 20, a commit on 3 November 2026. Responsiveness 20, OpenBMB answered 8 of 12 discussions. Version policy 10, earlier versions keep their repositories and the changes are dated and described. File health 10, the files agree. 8 + 0 + 20 + 20 + 10 + 10 = 68."
        },
        "scores": {
          "a": 46,
          "b": 68
        }
      },
      {
        "by": 5,
        "category": "risk",
        "leads": "longcat-2-5",
        "name": "Risk",
        "reasons": {
          "a": "File safety 25, safetensors only, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, the files sit in the meituan-longcat organisation, the name of Meituan's GitHub organisation that the card links for deployment, and the card's licence names Meituan (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions under Usage Considerations: not evaluated for every use, assess accuracy, safety and fairness before sensitive use, and follow the law. Licence risk 15, MIT with no use policy, no threshold and no right to change the terms. Security programme 0, no way to report a flaw on the card beyond a general contact, and Meituan's own security pages weren't looked up, so this scores as absent and is an open question. 25 + 15 + 0 + 5 + 15 + 0 = 60.",
          "b": "File safety 25, safetensors and GGUF only, no Python in the main repository and no remote code needed. Provenance 15, the files come from the openbmb organisation OpenBMB's GitHub README links to, with a SHA-256 for every weight file. Safety evaluation 0 and agent guidance 0, nothing published on either, and the card has no limitations section. Licence risk 15, plain Apache 2.0. Security programme 0, no security.txt or SECURITY.md."
        },
        "scores": {
          "a": 60,
          "b": 55
        }
      },
      {
        "by": 5,
        "category": "interface",
        "leads": "longcat-2-5",
        "name": "Agent interface",
        "reasons": {
          "a": "Chat template 20, chat_template.jinja in the repository (11,823 characters), with a copy in tokenizer_config.json. Tool-calling format 25, the card documents how tools are declared (the OpenAI function format) and how calls come back (tool_calls with an id, a name and JSON arguments, answered by a tool message), in a worked example against an OpenAI-compatible server. Trained tool calling 10, the card describes reinforcement learning on long agentic tasks and shows one tool call in a turn. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, enable_thinking on or off per request through chat_template_kwargs, with the reasoning returned as reasoning_content, both shown on the card. Context 10, the card states no context length, but config.json gives 524,288 positions and the card's own evaluations ran at 512K and 1M sequence lengths after what it calls long-context training, so 128K or more is taken as trained. 20 + 25 + 10 + 0 + 10 + 10 = 75.",
          "b": "Chat template 20, a Jinja file in the repository. Tool-calling format 10, the card shows the tools array going in but not the call coming back, and names no parser. Trained tool calling 20, the template renders several calls in one turn. Structured output 0, not documented or claimed. Reasoning control 10, enable_thinking per request, off by default, with the trace between markers. Context 10, 262,144 tokens native."
        },
        "scores": {
          "a": 75,
          "b": 70
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Meituan (LongCat)",
        "b": "OpenBMB",
        "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",
        "b": "Apache-2.0",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-11-10",
        "b": "2026-11-03",
        "name": "Last release"
      },
      {
        "a": "not read yet",
        "b": "no document linked",
        "name": "Terms last updated"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Privacy policy last updated"
      }
    ],
    "faq": [
      {
        "answer": "MiniCPM-V 4.7 35B-A3B scores 61 (C) on agent readiness against LongCat-2.5's 50.5 (D), and leads in 2 of 4 scored categories. LongCat-2.5 leads on risk and agent interface.",
        "question": "Which is better for AI agents, LongCat-2.5 or MiniCPM-V 4.7 35B-A3B?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Risk, 60 against 55",
          "Agent interface, 75 against 70"
        ],
        "also": null,
        "goodFor": "Operators with NPU clusters, and anyone tracking what the open flagships can do",
        "slug": "longcat-2-5",
        "watchFor": "3,562 GB in BF16 and 1,336 GB at 4 bits"
      },
      {
        "aheadOn": [
          "Reach, 90 against 35",
          "Documentation and upkeep, 68 against 46"
        ],
        "also": null,
        "goodFor": "Agents that need to read screenshots, documents and video on a local machine with 24 to 32 GB for the model, under a permissive licence",
        "slug": "minicpm-v-4-7-35b-a3b",
        "watchFor": "No limitations section, safety results, agent-use guidance or security contact"
      }
    ],
    "history": null,
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-longcat-2-5.json",
        "title": "Agents-A1-4B vs LongCat-2.5",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-longcat-2-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-minicpm-v-4-7-35b-a3b.json",
        "title": "Agents-A1-4B vs MiniCPM-V 4.7 35B-A3B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-minicpm-v-4-7-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-longcat-2-5.json",
        "title": "Apertus v1.5 8B vs LongCat-2.5",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-longcat-2-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minicpm-v-4-7-35b-a3b.json",
        "title": "Apertus v1.5 8B vs MiniCPM-V 4.7 35B-A3B",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minicpm-v-4-7-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-longcat-2-5.json",
        "title": "Apodex-1.1-mini vs LongCat-2.5",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-longcat-2-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minicpm-v-4-7-35b-a3b.json",
        "title": "Apodex-1.1-mini vs MiniCPM-V 4.7 35B-A3B",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minicpm-v-4-7-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-longcat-2-5.json",
        "title": "Arete-27B vs LongCat-2.5",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-longcat-2-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-minicpm-v-4-7-35b-a3b.json",
        "title": "Arete-27B vs MiniCPM-V 4.7 35B-A3B",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-minicpm-v-4-7-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-longcat-2-5.json",
        "title": "BigBang-v1 vs LongCat-2.5",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-longcat-2-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-minicpm-v-4-7-35b-a3b.json",
        "title": "BigBang-v1 vs MiniCPM-V 4.7 35B-A3B",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-minicpm-v-4-7-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-longcat-2-5.json",
        "title": "Carbon-X vs LongCat-2.5",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-longcat-2-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-minicpm-v-4-7-35b-a3b.json",
        "title": "Carbon-X vs MiniCPM-V 4.7 35B-A3B",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-minicpm-v-4-7-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-longcat-2-5.json",
        "title": "Cola2 vs LongCat-2.5",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-longcat-2-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-minicpm-v-4-7-35b-a3b.json",
        "title": "Cola2 vs MiniCPM-V 4.7 35B-A3B",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-minicpm-v-4-7-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-longcat-2-5.json",
        "title": "Cosmos-Reason2-2B vs LongCat-2.5",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-longcat-2-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-minicpm-v-4-7-35b-a3b.json",
        "title": "Cosmos-Reason2-2B vs MiniCPM-V 4.7 35B-A3B",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-minicpm-v-4-7-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-longcat-2-5.json",
        "title": "Darwin-36B-Opus vs LongCat-2.5",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-longcat-2-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-minicpm-v-4-7-35b-a3b.json",
        "title": "Darwin-36B-Opus vs MiniCPM-V 4.7 35B-A3B",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-minicpm-v-4-7-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-longcat-2-5.json",
        "title": "DeepSeek-V3.2 vs LongCat-2.5",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-longcat-2-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-minicpm-v-4-7-35b-a3b.json",
        "title": "DeepSeek-V3.2 vs MiniCPM-V 4.7 35B-A3B",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-minicpm-v-4-7-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-longcat-2-5.json",
        "title": "DeepSeek V4.1 Flash vs LongCat-2.5",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-longcat-2-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-minicpm-v-4-7-35b-a3b.json",
        "title": "DeepSeek V4.1 Flash vs MiniCPM-V 4.7 35B-A3B",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-minicpm-v-4-7-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-longcat-2-5.json",
        "title": "DeepSeek-V4-Flash-0731 vs LongCat-2.5",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-longcat-2-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-minicpm-v-4-7-35b-a3b.json",
        "title": "DeepSeek-V4-Flash-0731 vs MiniCPM-V 4.7 35B-A3B",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-minicpm-v-4-7-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-longcat-2-5.json",
        "title": "Devstral Small 2 vs LongCat-2.5",
        "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-longcat-2-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-minicpm-v-4-7-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-minicpm-v-4-7-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-longcat-2-5.json",
        "title": "DiffusionGemma 26B A4B IT vs LongCat-2.5",
        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-longcat-2-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-minicpm-v-4-7-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-minicpm-v-4-7-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-longcat-2-5.json",
        "title": "EXAONE 4.5 33B vs LongCat-2.5",
        "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-longcat-2-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-minicpm-v-4-7-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-minicpm-v-4-7-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-longcat-2-5.json",
        "title": "Gemma 4 12B IT vs LongCat-2.5",
        "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-longcat-2-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-minicpm-v-4-7-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-minicpm-v-4-7-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-longcat-2-5.json",
        "title": "Gemma 4 26B A4B IT vs LongCat-2.5",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-longcat-2-5"
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        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-nemotron-3-5-bolt-e4b-4gb.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-nemotron-3-5-bolt-e4b-4gb"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-nemotron-3-5-lightning-30b-a3b.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs Nemotron 3.5 Lightning 30B A3B",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-nemotron-3-5-lightning-30b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-nemotron-3-nano-30b-a3b.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs Nemotron 3 Nano 30B A3B",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-nemotron-3-nano-30b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-nemotron-3-nano-4b.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs Nemotron 3 Nano 4B",
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      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs Nemotron 3 Nano Omni 30B A3B Reasoning",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-nemotron-3-super-120b-a12b.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs Nemotron 3 Super 120B A12B",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-nemotron-3-super-120b-a12b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-nemotron-3-ultra-550b-a55b.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs Nemotron 3 Ultra 550B A55B",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-nemotron-3-ultra-550b-a55b"
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      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-north-mini-code-1-0.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs North Mini Code 1.0",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-north-mini-code-1-0"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-occamy-1-0.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-occamy-1-0"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-olmo-3-7b-think.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs Olmo 3 7B Think",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-olmo-3-7b-think"
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      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-ornith-1-5-122b.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-ornith-1-5-35b-a3b.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs Ornith-1.5-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-ornith-1-5-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-ornith-1-5-397b.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-ornith-1-5-397b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-ornith-1-5-9b.json",
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        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-ornith-1-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-otel-2-0-llm-31b-it.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs OTel-2.0-LLM-31B-IT",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-otel-2-0-llm-31b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen-agentworld-35b-a3b.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen-agentworld-35b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-30b-a3b-instruct-2507.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs Qwen3-30B-A3B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-30b-a3b-instruct-2507"
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      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-4b-instruct-2507.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs Qwen3-4B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-4b-instruct-2507"
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      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-5-0-8b.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs Qwen3.5-0.8B",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-5-0-8b"
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      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-5-122b-a10b.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-5-122b-a10b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-5-2b.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs Qwen3.5-2B",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-5-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-5-397b-a17b.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs Qwen3.5-397B-A17B",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-5-397b-a17b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-5-4b.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs Qwen3.5-4B",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-5-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-5-9b.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-6-27b.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs Qwen3.6-27B",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-6-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-6-35b-a3b.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs Qwen3.6-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-6-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-8-27b.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-8-flash-next.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-coder-30b-a3b-instruct.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs Qwen3-Coder-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-coder-30b-a3b-instruct"
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      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-coder-next.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-coder-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-omni-30b-a3b-instruct.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs Qwen3-Omni-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-omni-30b-a3b-instruct"
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      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-vl-2b-instruct.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs Qwen3-VL-2B-Instruct",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-vl-2b-instruct"
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      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-vl-30b-a3b-instruct"
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      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-vl-32b-instruct.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs Qwen3-VL-32B-Instruct",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-vl-32b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-vl-4b-instruct.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-vl-8b-instruct.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-vl-8b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwopus3-8-27b-flash-v2.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-recursive-flint.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-ripple-65b-preview.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-sensenova-loop-9b-preview.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs SenseNova-Loop-9B-Preview",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-sensenova-loop-9b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-smollm3-3b.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-spark-x2-6-4b.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-spark-x2-6-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-step-3-7-flash.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-step-3-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-thinkingcap-qwen3-8-27b.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-thinkingcap-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-unlimited-ocr-2.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-wevisone-vl-2b.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-wevisone-vl-4b.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-zaya1-8b.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-zaya1-8b"
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    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 55,
        "edge": "minicpm-v-4-7-35b-a3b",
        "key": "reach",
        "longcat-2-5": 35,
        "minicpm-v-4-7-35b-a3b": 90,
        "name": "Reach",
        "weight": 25
      },
      {
        "by": 5,
        "edge": "longcat-2-5",
        "key": "risk",
        "longcat-2-5": 60,
        "minicpm-v-4-7-35b-a3b": 55,
        "name": "Risk",
        "weight": 20
      },
      {
        "by": 5,
        "edge": "longcat-2-5",
        "key": "interface",
        "longcat-2-5": 75,
        "minicpm-v-4-7-35b-a3b": 70,
        "name": "Agent interface",
        "weight": 10
      },
      {
        "by": 22,
        "edge": "minicpm-v-4-7-35b-a3b",
        "key": "upkeep",
        "longcat-2-5": 46,
        "minicpm-v-4-7-35b-a3b": 68,
        "name": "Documentation and upkeep",
        "weight": 10
      }
    ],
    "summary": "MiniCPM-V 4.7 35B-A3B scores 61 (C) on agent readiness against LongCat-2.5's 50.5 (D), and leads in 2 of 4 scored categories. LongCat-2.5 leads on risk and agent interface. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "longcat-2-5": "Meituan's flagship is MIT-licensed and documented for tool calling and switchable reasoning, with strong numbers of its own on agentic coding. Almost nobody can run it: 3,562 GB of weights, a 1,336 GB 4-bit build for NPU accelerators, and no released runtime with its architecture. A model to watch until a runtime or a provider picks it up.",
      "minicpm-v-4-7-35b-a3b": "A vision-language cousin for images and video that fits a 24 to 32 GB machine from OpenBMB's own GGUF, under plain Apache 2.0, with a publisher that answers. The card says little about limits, training data or how tool calls come back, so test the tool format in your runtime before relying on it."
    }
  },
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    "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-minicpm-v-4-7-35b-a3b.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/longcat-2-5-vs-minicpm-v-4-7-35b-a3b.md",
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    "slim": "https://www.anchorterminal.com/compare/longcat-2-5-vs-minicpm-v-4-7-35b-a3b.min.md"
  },
  "markdown": "MiniCPM-V 4.7 35B-A3B scores 61 (C) on agent readiness against LongCat-2.5's 50.5 (D), and leads in 2 of 4 scored categories. LongCat-2.5 leads on risk and agent interface. Both do open-weight models you run yourself.\n\n- LongCat-2.5: grade D, 50.5/100, rank #145 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/longcat-2-5.md · JSON https://www.anchorterminal.com/api/v1/tools/longcat-2-5.json\n- MiniCPM-V 4.7 35B-A3B: grade C, 61/100, rank #77 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/minicpm-v-4-7-35b-a3b.md · JSON https://www.anchorterminal.com/api/v1/tools/minicpm-v-4-7-35b-a3b.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### LongCat-2.5 (D)\n\nGood for: Operators with NPU clusters, and anyone tracking what the open flagships can do\n\nAhead on:\n- Risk, 60 against 55\n- Agent interface, 75 against 70\n\nWatch for: 3,562 GB in BF16 and 1,336 GB at 4 bits\n\n### MiniCPM-V 4.7 35B-A3B (C)\n\nGood for: Agents that need to read screenshots, documents and video on a local machine with 24 to 32 GB for the model, under a permissive licence\n\nAhead on:\n- Reach, 90 against 35\n- Documentation and upkeep, 68 against 46\n\nWatch for: No limitations section, safety results, agent-use guidance or security contact\n\n\n## Why the edge\n\n### Reach: MiniCPM-V 4.7 35B-A3B +55\n\n- LongCat-2.5 (35): Memory 0, the smallest build is Meituan's W4A4 repository, 1,336 GB, and 1,336 GB × 1.2 = 1,603 GB, far above 256 GB. The BF16 files are 3,562 GB. Runtimes 0, none of the six runs it in a released version: vLLM 0.33.0, SGLang 0.5.23, Transformers 5.22.2 and llama.cpp b12242 have no LongcatCausalLM or pro_v code, and the model code config.json names isn't in the repository. Builds 10, one quantised build from Meituan, the W4A4 repository (4-bit experts, 1,336 GB). Terms 15, MIT in the metadata, the LICENSE file and the card, with nothing added. Download 10, the Hub API reports gated false for both repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it. 0 + 0 + 10 + 15 + 10 + 0 = 35.\n- MiniCPM-V 4.7 35B-A3B (90): Memory 25, since OpenBMB's IQ4_XS GGUF with its vision projector is 19.6 GB and needs about 23.6 GB with the fifth added, which fits 24 GB, while its Q4_K_M GGUF, 4-bit safetensors builds and Ollama entry need 32 GB. Runtimes 25, from vLLM 0.33.0, SGLang 0.5.23, Transformers 5.18.0 and later, llama.cpp's own MiniCPM-V 4.7 support and OpenBMB's entry on Ollama. Builds 15, OpenBMB publishes FP8, three 4-bit safetensors builds and GGUF. Terms 15, Apache 2.0 with nothing added. Download 10, nothing is gated. Hosted 0, no inference provider is mapped. 25 + 25 + 15 + 15 + 10 + 0 = 90.\n\n### Documentation and upkeep: MiniCPM-V 4.7 35B-A3B +22\n\n- LongCat-2.5 (46): Model card 11, Intended use, limitations (the usage considerations), the architecture and the parameter counts are on the card. Training data 5, A general description of the training stages and where the reinforcement learning tasks came from, with no sources, sizes or cut-off. Last change 20, the last commit, on 10 November 2026, updated the card, on the day of this check. Responsiveness 0, Neither of the 2 discussions that aren't pull requests (9 and 10 November 2026) has a reply from a meituan-longcat member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, LongCat-2.0 stays in its own repositories and 2.5 came out in new ones, with no dated note on the older ones that we read. File health 5, the template is in both places and agrees, but config.json's auto_map names configuration_longcat_oe and modeling_longcat_oe, which aren't in the repository, and the tokenizer config's 131,072-token length is a quarter of the config's 524,288 positions. 11 + 5 + 20 + 0 + 5 + 5 = 46.\n- MiniCPM-V 4.7 35B-A3B (68): Model card 8, it covers intended use, architecture and parameters but not limitations, context length, languages or a knowledge cut-off. Training data 0, none described. Last change 20, a commit on 3 November 2026. Responsiveness 20, OpenBMB answered 8 of 12 discussions. Version policy 10, earlier versions keep their repositories and the changes are dated and described. File health 10, the files agree. 8 + 0 + 20 + 20 + 10 + 10 = 68.\n\n### Risk: LongCat-2.5 +5\n\n- LongCat-2.5 (60): File safety 25, safetensors only, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, the files sit in the meituan-longcat organisation, the name of Meituan's GitHub organisation that the card links for deployment, and the card's licence names Meituan (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions under Usage Considerations: not evaluated for every use, assess accuracy, safety and fairness before sensitive use, and follow the law. Licence risk 15, MIT with no use policy, no threshold and no right to change the terms. Security programme 0, no way to report a flaw on the card beyond a general contact, and Meituan's own security pages weren't looked up, so this scores as absent and is an open question. 25 + 15 + 0 + 5 + 15 + 0 = 60.\n- MiniCPM-V 4.7 35B-A3B (55): File safety 25, safetensors and GGUF only, no Python in the main repository and no remote code needed. Provenance 15, the files come from the openbmb organisation OpenBMB's GitHub README links to, with a SHA-256 for every weight file. Safety evaluation 0 and agent guidance 0, nothing published on either, and the card has no limitations section. Licence risk 15, plain Apache 2.0. Security programme 0, no security.txt or SECURITY.md.\n\n### Agent interface: LongCat-2.5 +5\n\n- LongCat-2.5 (75): Chat template 20, chat_template.jinja in the repository (11,823 characters), with a copy in tokenizer_config.json. Tool-calling format 25, the card documents how tools are declared (the OpenAI function format) and how calls come back (tool_calls with an id, a name and JSON arguments, answered by a tool message), in a worked example against an OpenAI-compatible server. Trained tool calling 10, the card describes reinforcement learning on long agentic tasks and shows one tool call in a turn. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, enable_thinking on or off per request through chat_template_kwargs, with the reasoning returned as reasoning_content, both shown on the card. Context 10, the card states no context length, but config.json gives 524,288 positions and the card's own evaluations ran at 512K and 1M sequence lengths after what it calls long-context training, so 128K or more is taken as trained. 20 + 25 + 10 + 0 + 10 + 10 = 75.\n- MiniCPM-V 4.7 35B-A3B (70): Chat template 20, a Jinja file in the repository. Tool-calling format 10, the card shows the tools array going in but not the call coming back, and names no parser. Trained tool calling 20, the template renders several calls in one turn. Structured output 0, not documented or claimed. Reasoning control 10, enable_thinking per request, off by default, with the trace between markers. Context 10, 262,144 tokens native.\n\n## Score by category\n\n| Category | Weight | LongCat-2.5 | MiniCPM-V 4.7 35B-A3B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 35 | 90 | MiniCPM-V 4.7 35B-A3B +55 |\n| Risk | 20% (20 this run) | 60 | 55 | LongCat-2.5 +5 |\n| Agent interface | 10% (10 this run) | 75 | 70 | LongCat-2.5 +5 |\n| Documentation and upkeep | 10% (10 this run) | 46 | 68 | MiniCPM-V 4.7 35B-A3B +22 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **50.5 · D** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | LongCat-2.5 | MiniCPM-V 4.7 35B-A3B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Meituan (LongCat) | OpenBMB |\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 | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-11-10 | 2026-11-03 |\n| Terms last updated | not read yet | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n\n## Verdicts\n\n**LongCat-2.5.** Meituan's flagship is MIT-licensed and documented for tool calling and switchable reasoning, with strong numbers of its own on agentic coding. Almost nobody can run it: 3,562 GB of weights, a 1,336 GB 4-bit build for NPU accelerators, and no released runtime with its architecture. A model to watch until a runtime or a provider picks it up.\n\n**MiniCPM-V 4.7 35B-A3B.** A vision-language cousin for images and video that fits a 24 to 32 GB machine from OpenBMB's own GGUF, under plain Apache 2.0, with a publisher that answers. The card says little about limits, training data or how tool calls come back, so test the tool format in your runtime before relying on it.\n\n## Before you call either\n\n### LongCat-2.5\n\n1. You can't run this on a workstation or a single GPU server. Plan for a provider, and none serves it yet\n2. If an operator serves it, call it as an OpenAI-compatible chat API with tools in the function format\n3. Pass `chat_template_kwargs` with `enable_thinking` true or false\n4. With thinking on, keep earlier reasoning in the history (`save_history_reasoning_content` true), as the card recommends\n5. The card's results use temperature 1.0 and top_p 0.95\n6. Its benchmark table is Meituan's own. The unmarked scores are in-house\n\n### MiniCPM-V 4.7 35B-A3B\n\n1. Start it with `llama-server -hf openbmb/MiniCPM-V-4.7-35B-A3B-gguf:Q4_K_M --jinja`, or `ollama run openbmb/minicpm-v4.7:35b-a3b`\n2. For image input in llama.cpp add `mmproj-model-f16.gguf`, or the 4x projector for more visual detail at a higher cost\n3. The GGUF files at the repository root have thinking off. Use the files in `thinking/` when you want the trace\n4. With Transformers pass `enable_thinking` to `apply_chat_template`, and pass `downsample_mode` to both `apply_chat_template` and `generate`\n5. Use temperature 0.7, top_p 0.95 and top_k 100 in instruct mode, as the card recommends\n6. Check tool calls in your runtime first, since the card names no parser. The template uses the Qwen3.5 function-block format\n7. Validate JSON output yourself, since structured output isn't documented\n\n## Questions\n\n### Which is better for AI agents, LongCat-2.5 or MiniCPM-V 4.7 35B-A3B?\n\nMiniCPM-V 4.7 35B-A3B scores 61 (C) on agent readiness against LongCat-2.5's 50.5 (D), and leads in 2 of 4 scored categories. LongCat-2.5 leads on risk and agent interface.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/longcat-2-5-vs-minicpm-v-4-7-35b-a3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/longcat-2-5-vs-minicpm-v-4-7-35b-a3b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"longcat-2-5\", \"b\": \"minicpm-v-4-7-35b-a3b\"}`. 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