{
  "data": {
    "a": {
      "slug": "deepseek-v3-2",
      "name": "DeepSeek-V3.2",
      "vendor": "DeepSeek",
      "vendorUrl": "https://www.deepseek.com",
      "kind": "weights",
      "category": "open-weights",
      "summary": "DeepSeek-V3.2 is DeepSeek's open-weight mixture-of-experts reasoning model with DeepSeek Sparse Attention, trained for tool use with a 128K-token context, published in FP8 under MIT. DeepSeek's API stopped serving it on 24 April 2026, moving to V4 Flash.",
      "url": "https://www.anchorterminal.com/tools/deepseek-v3-2",
      "markdownUrl": "https://www.anchorterminal.com/tools/deepseek-v3-2.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/deepseek-v3-2.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/deepseek-v3-2.json",
      "dns": "deepseek-v3-2.grade.anchorterminal.com",
      "repo": "https://huggingface.co/deepseek-ai/DeepSeek-V3.2",
      "license": "MIT",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "deepseek-ai/DeepSeek-V3.2"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under the MIT licence. DeepSeek's own API served V3.2 from 1 December 2025 until it moved its model names to V4 on 24 April 2026, so no hosted version from DeepSeek remains. Running it yourself costs the hardware, 689.5 GB of files (checked 2026-10-21).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-11-04"
      },
      "docsUrl": "https://huggingface.co/deepseek-ai/DeepSeek-V3.2",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "reasoning",
        "tool-calling",
        "mit",
        "safetensors",
        "fp8",
        "self-hosted"
      ],
      "lastRelease": "2025-12-01",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 58.6,
        "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": 78,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "capability": 69,
          "interface": 60,
          "reach": 60,
          "risk": 50,
          "upkeep": 34
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-11-04"
        },
        "negative": 0,
        "verdict": "DeepSeek-V3.2 is an MIT-licensed reasoning model trained for parallel tool calls, with thinking chosen per request, for long agent work. It needs about 690 GB of FP8 files, eight 80 GB GPUs in vLLM's recipe and prompts built from DeepSeek's Python. The main risk is neglect, with no file changes since December 2025 and no safety evaluation.",
        "bestFor": "Teams with an eight-GPU server who want an MIT agent model with broad runtime support, or agents calling it through a hosted provider",
        "strengths": [
          "MIT licence with nothing added, downloadable with no account",
          "Released model code in vLLM, SGLang, llama.cpp and Transformers, and Ollama through its llama.cpp pin",
          "Trained for several tool calls in one turn, with thinking or chat mode per request",
          "Safetensors only, in DeepSeek's own organisation with a SHA-256 on every shard",
          "Served by DeepInfra (live on Hugging Face's provider list) for agents without the hardware"
        ],
        "weaknesses": [
          "689.5 GB of FP8 files and no smaller build from DeepSeek or a runtime library, far beyond 107.4 GB",
          "No Jinja template, so prompts come from running DeepSeek's encoding code",
          "DeepSeek names no runtime tool parser and documents no structured output",
          "No change since 1 December 2025 and no DeepSeek reply in its discussions",
          "No safety evaluation, agent-use guidance, languages or knowledge cut-off published"
        ],
        "agentNotes": [
          "Start it with vLLM's recipe, vllm serve deepseek-ai/DeepSeek-V3.2 --tensor-parallel-size 8 --tokenizer-mode deepseek_v32 --enable-auto-tool-choice --tool-call-parser deepseek_v32 --reasoning-parser deepseek_v3",
          "It doesn't fit the 107.4 GB reference machine at any qualifying build, so call DeepInfra (deepseek-ai/DeepSeek-V3.2) or deploy it on Fireworks instead",
          "Outside vLLM, build prompts with encoding/encoding_dsv32.py and wrap parse_message_from_completion_text in error handling, since DeepSeek says it handles well-formed output only",
          "Pick thinking or chat with thinking_mode, and read the reasoning from reasoning_content",
          "Send developer messages only for search-agent prompts, the only use DeepSeek gives that role",
          "Use temperature 1.0 and top_p 0.95 and keep prompts within 128K tokens"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 2.3,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "high",
            "grade": "C",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 58.6
          }
        ],
        "editorialScores": {
          "capability": 69,
          "interface": 60,
          "reach": 60,
          "risk": 50,
          "upkeep": 34
        },
        "provenanceScore": 63,
        "checked": {
          "categories": {
            "capability": "2026-11-04",
            "interface": "2026-11-04",
            "reach": "2026-11-04",
            "risk": "2026-11-04",
            "upkeep": "2026-11-04"
          },
          "last": "2026-11-04",
          "oldest": "2026-11-04",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "# vLLM's own recipe for DeepSeek-V3.2 (8 GPUs)\nvllm serve deepseek-ai/DeepSeek-V3.2 --tensor-parallel-size 8 \\\n  --tokenizer-mode deepseek_v32 --enable-auto-tool-choice \\\n  --tool-call-parser deepseek_v32 --reasoning-parser deepseek_v3\n# or DeepSeek's reference demo, from the inference folder\npython convert.py --hf-ckpt-path \"$HF_CKPT_PATH\" --save-path \"$SAVE_PATH\" --n-experts 256 --model-parallel 8\ntorchrun --nproc-per-node 8 generate.py --ckpt-path \"$SAVE_PATH\" --config config_671B_v3.2.json --interactive\n# or, with llama.cpp and Unsloth's GGUF, a third party's conversion:\nllama-server -hf unsloth/DeepSeek-V3.2-GGUF:Q4_K_M --jinja"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/deepseek-v3-2"
      },
      "sameCompany": [
        "deepseek-api",
        "deepseek-search-api",
        "deepseek-ocr-2",
        "deepseek-v4-1-flash",
        "deepseek-v4-flash-0731"
      ],
      "weights": {
        "trainedBy": "DeepSeek",
        "builtOn": "deepseek-ai/DeepSeek-V3.2-Exp-Base (continued from DeepSeek-V3.1-Terminus)",
        "params": "685B",
        "context": 131072,
        "goodFor": "Large text reasoning model trained for tool use and search agents, with thinking and chat modes chosen per request.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "unsloth DeepSeek-V3.2-GGUF (Q4_K_M)",
            "url": "https://huggingface.co/unsloth/DeepSeek-V3.2-GGUF",
            "who": "unsloth",
            "precision": "q4_k_m",
            "sizeGB": 405.4,
            "memoryGB": 512,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "From Unsloth, a third party's conversion and not DeepSeek's. 405.40 GB of Q4_K_M weights, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it"
          },
          {
            "name": "DeepSeek-V3.2",
            "url": "https://huggingface.co/deepseek-ai/DeepSeek-V3.2",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 689.5,
            "memoryGB": 1024,
            "format": "safetensors",
            "note": "Native FP8 (E4M3) with BF16 and F32 for the rest; the only build DeepSeek publishes"
          }
        ],
        "run": "vllm serve deepseek-ai/DeepSeek-V3.2 --tensor-parallel-size 8 --tokenizer-mode deepseek_v32 --enable-auto-tool-choice --tool-call-parser deepseek_v32 --reasoning-parser deepseek_v3",
        "runBuild": "DeepSeek-V3.2",
        "checked": "2026-11-04",
        "minMemoryGB": 512
      },
      "family": {
        "lab": "DeepSeek",
        "family": "DeepSeek V3",
        "url": "https://www.anchorterminal.com/open-weights/#deepseek",
        "others": [
          {
            "name": "DeepSeek-V3-Base",
            "url": "https://huggingface.co/deepseek-ai/DeepSeek-V3-Base",
            "params": "671B total, 37B activated"
          },
          {
            "name": "DeepSeek-V3",
            "url": "https://huggingface.co/deepseek-ai/DeepSeek-V3",
            "params": "671B total, 37B activated"
          },
          {
            "name": "DeepSeek-V3-0324",
            "url": "https://huggingface.co/deepseek-ai/DeepSeek-V3-0324",
            "params": "not stated on card"
          },
          {
            "name": "DeepSeek-V3.1-Base",
            "url": "https://huggingface.co/deepseek-ai/DeepSeek-V3.1-Base",
            "params": "671B total, 37B activated"
          },
          {
            "name": "DeepSeek-V3.1",
            "url": "https://huggingface.co/deepseek-ai/DeepSeek-V3.1",
            "params": "671B total, 37B activated"
          },
          {
            "name": "DeepSeek-V3.1-Terminus",
            "url": "https://huggingface.co/deepseek-ai/DeepSeek-V3.1-Terminus",
            "params": "not stated on card"
          },
          {
            "name": "DeepSeek-V3.2-Exp-Base",
            "url": "https://huggingface.co/deepseek-ai/DeepSeek-V3.2-Exp-Base",
            "params": "not stated on card"
          },
          {
            "name": "DeepSeek-V3.2-Exp",
            "url": "https://huggingface.co/deepseek-ai/DeepSeek-V3.2-Exp",
            "params": "not stated on card (inference config named config_671B_v3.2.json)"
          },
          {
            "name": "DeepSeek-V3.2-Speciale",
            "url": "https://huggingface.co/deepseek-ai/DeepSeek-V3.2-Speciale",
            "params": "not stated on card"
          }
        ],
        "olderCount": 72
      },
      "area": "models",
      "provenance": {
        "legalEntity": "DeepSeek (the MIT licence names DeepSeek, and DeepSeek's platform terms name Hangzhou DeepSeek Artificial Intelligence Co., Ltd.)",
        "domain": "deepseek.com",
        "domainRegistered": "2000-08-13",
        "domainNote": "deepseek.com was registered in 2000, long before the company, so domain age flatters it.",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "The legal entity is the one DeepSeek's platform terms name, read for the deepseek-api listing. The weights' licence names only DeepSeek.",
          "Nothing is hosted, so the MIT licence stands in for terms of service and no privacy policy is scored.",
          "No changelog for the weights. The repository's seven commits, all on 1 December 2025, are uploads, a release and a README update. The DeepSeek-V3.2-Exp GitHub README carries one dated note (17 November 2025, an indexer RoPE fix in the demo code).",
          "RDAP gives 13 August 2000 as the registration date of deepseek.com (read 21 October 2026).",
          "deepseek.com/.well-known/security.txt and www.deepseek.com/.well-known/security.txt answered 404 on 21 October 2026.",
          "DeepSeek's site footer has a Report Security Issue link to security@deepseek.com, a company-wide address, read from https://www.deepseek.com/en/transparency/ on 21 October 2026."
        ],
        "score": 63
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/deepseek-v3-2.json",
      "live": {
        "slug": "deepseek-v3-2",
        "securityTxt": {
          "url": "https://deepseek.com/.well-known/security.txt",
          "state": "none",
          "checkedAt": "2026-11-10T16:07:51.938889615Z"
        },
        "domain": {
          "domain": "deepseek.com",
          "registered": "2000-08-13",
          "source": "https://rdap.verisign.com/com/v1/domain/deepseek.com",
          "checkedAt": "2026-11-08T14:52:49.217816183Z"
        },
        "updatedAt": "2026-11-10T16:07:51.938889615Z"
      }
    },
    "answer": "MiniCPM-V 4.7 1B scores 61 (C) on agent readiness against DeepSeek-V3.2's 58.6 (C), and leads in every scored category.",
    "b": {
      "slug": "minicpm-v-4-7-1b",
      "name": "MiniCPM-V 4.7 1B",
      "vendor": "OpenBMB",
      "vendorUrl": "https://openbmb.cn",
      "kind": "weights",
      "category": "open-weights",
      "summary": "MiniCPM-V 4.7 1B is OpenBMB's small open-weight vision-language model, 1.55B parameters, that reads images and video on a phone or laptop, calls tools, and ships in BF16, 4-bit and GGUF builds under Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/minicpm-v-4-7-1b",
      "markdownUrl": "https://www.anchorterminal.com/tools/minicpm-v-4-7-1b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/minicpm-v-4-7-1b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/minicpm-v-4-7-1b.json",
      "dns": "minicpm-v-4-7-1b.grade.anchorterminal.com",
      "repo": "https://huggingface.co/openbmb/MiniCPM-V-4.7-1B",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "openbmb/MiniCPM-V-4.7-1B"
        },
        {
          "registry": "huggingface",
          "name": "openbmb/MiniCPM-V-4.7-1B-gguf"
        },
        {
          "registry": "huggingface",
          "name": "openbmb/MiniCPM-V-4.7-1B-AWQ"
        },
        {
          "registry": "huggingface",
          "name": "openbmb/MiniCPM-V-4.7-1B-GPTQ"
        },
        {
          "registry": "huggingface",
          "name": "openbmb/MiniCPM-V-4.7-1B-int4"
        },
        {
          "registry": "ollama",
          "name": "openbmb/minicpm-v4.7:1b"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The main repository and OpenBMB's GGUF, 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, 3.1 GB for the BF16 file or about 1.5 GB for the IQ4_XS GGUF with its projector (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-4.7-1B",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "multimodal",
        "vision",
        "video",
        "on-device",
        "small",
        "tool-calling",
        "apache-2.0",
        "safetensors",
        "gguf",
        "awq",
        "gptq",
        "self-hosted"
      ],
      "lastRelease": "2026-10-30",
      "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": 60,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 70,
          "reach": 95,
          "risk": 55,
          "upkeep": 48
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-10"
        },
        "negative": 0,
        "verdict": "A vision-language cousin small enough for a phone, under plain Apache 2.0, that every runtime we count can load. The projector is bigger than the language model, so plan for about 2 GB. The card says nothing about limits or training data and doesn't show how a tool call comes back.",
        "bestFor": "Agents that need a cheap local model to read screenshots, photos and short video on a phone, a laptop or beside a bigger model",
        "strengths": [
          "Runs in about 2 GB from OpenBMB's own GGUF with its projector",
          "Runs in llama.cpp, Ollama, vLLM, SGLang and Transformers from their released versions",
          "Plain Apache 2.0, ungated, safetensors and GGUF only, no remote code",
          "Images, several images and video in one small model, with thinking off by default",
          "OpenBMB publishes 4-bit builds, a GGUF repository and a speculative-decoding head itself"
        ],
        "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",
          "Nobody has asked a question on the repository yet, so there's no record of OpenBMB answering for this size",
          "No inference provider is mapped on Hugging Face"
        ],
        "agentNotes": [
          "Start it with `llama-server -hf openbmb/MiniCPM-V-4.7-1B-gguf:Q4_K_M --jinja`, or `ollama run openbmb/minicpm-v4.7:1b`",
          "For image input in llama.cpp add `mmproj-model-f16.gguf`, or the smaller 4x projector",
          "The GGUF files at the repository root have thinking off. Use the files in `thinking/` when you want the trace",
          "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",
          "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": 95,
          "risk": 55,
          "upkeep": 48
        },
        "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-v4.7:1b\n# or, with llama.cpp and OpenBMB's own GGUF:\nllama-server -hf openbmb/MiniCPM-V-4.7-1B-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-4.7-1B --port 8000 --host 0.0.0.0",
        "http": "curl http://localhost:11434/api/chat \\\n  -d '{\"model\": \"openbmb/minicpm-v4.7:1b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/minicpm-v-4-7-1b"
      },
      "sameCompany": [
        "minicpm-v-4-7-35b-a3b",
        "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_text with 24 layers",
        "params": "1.55B",
        "context": 262144,
        "goodFor": "Small vision-language model for images, several images and video on a phone or a laptop, with tool calling and thinking off by default.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "MiniCPM-V-4.7-1B",
            "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-1B",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 3.11,
            "memoryGB": 8,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)",
              "Transformers"
            ]
          },
          {
            "name": "MiniCPM-V-4.7-1B-AWQ",
            "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-1B-AWQ",
            "who": "publisher",
            "precision": "w4a16",
            "sizeGB": 2.37,
            "memoryGB": 8,
            "format": "safetensors",
            "runsOn": [
              "vLLM"
            ],
            "note": "AWQ, 4-bit weights with 16-bit activations"
          },
          {
            "name": "MiniCPM-V-4.7-1B-GPTQ",
            "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-1B-GPTQ",
            "who": "publisher",
            "precision": "w4a16",
            "sizeGB": 1.87,
            "memoryGB": 8,
            "format": "safetensors",
            "runsOn": [
              "vLLM"
            ],
            "note": "GPTQ, 4-bit weights with 16-bit activations"
          },
          {
            "name": "MiniCPM-V-4.7-1B-int4",
            "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-1B-int4",
            "who": "publisher",
            "precision": "int4",
            "sizeGB": 1.57,
            "memoryGB": 8,
            "format": "safetensors",
            "runsOn": [
              "vLLM"
            ]
          },
          {
            "name": "MiniCPM-V-4_7-1B-F16",
            "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-1B-gguf/blob/main/MiniCPM-V-4_7-1B-F16.gguf",
            "who": "publisher",
            "precision": "f16",
            "sizeGB": 3.13,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ],
            "note": "2.02 GB of weights plus the 1.11 GB projector"
          },
          {
            "name": "MiniCPM-V-4_7-1B-Q8_0",
            "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-1B-gguf/blob/main/MiniCPM-V-4_7-1B-Q8_0.gguf",
            "who": "publisher",
            "precision": "q8_0",
            "sizeGB": 2.19,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ],
            "note": "1.08 GB of weights plus the 1.11 GB projector"
          },
          {
            "name": "MiniCPM-V-4_7-1B-Q6_K",
            "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-1B-gguf/blob/main/MiniCPM-V-4_7-1B-Q6_K.gguf",
            "who": "publisher",
            "precision": "q6_k",
            "sizeGB": 1.95,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ],
            "note": "0.84 GB of weights plus the 1.11 GB projector"
          },
          {
            "name": "MiniCPM-V-4_7-1B-Q4_K_M",
            "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-1B-gguf/blob/main/MiniCPM-V-4_7-1B-Q4_K_M.gguf",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 1.78,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ],
            "note": "0.67 GB of weights plus mmproj-model-f16.gguf (1.11 GB), or 1.57 GB with the 4x projector (0.90 GB). The same file with thinking on is in the thinking/ folder"
          },
          {
            "name": "MiniCPM-V-4_7-1B-IQ4_XS",
            "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-1B-gguf/blob/main/MiniCPM-V-4_7-1B-IQ4_XS.gguf",
            "who": "publisher",
            "precision": "iq4_xs",
            "sizeGB": 1.54,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ],
            "note": "0.64 GB of weights plus the 0.90 GB 4x projector, the smallest at 4 bits. The IQ3 files are below the 4-bit line and are not listed"
          },
          {
            "name": "ollama openbmb/minicpm-v4.7:1b",
            "url": "https://ollama.com/openbmb/minicpm-v4.7",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 1.8,
            "memoryGB": 8,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "OpenBMB's own namespace on Ollama, not Ollama's library. 1.8 GB as the tags page shows it, with q8_0 (2.2 GB) and fp16 (3.1 GB) tags beside it"
          }
        ],
        "run": "llama-server -hf openbmb/MiniCPM-V-4.7-1B-gguf:Q4_K_M --jinja",
        "runBuild": "MiniCPM-V-4_7-1B-Q4_K_M",
        "checked": "2026-11-10",
        "minMemoryGB": 8
      },
      "family": {
        "lab": "OpenBMB (MiniCPM)",
        "family": "MiniCPM-V 4",
        "url": "https://www.anchorterminal.com/open-weights/#openbmb",
        "others": [
          {
            "name": "MiniCPM-V 4.7 35B-A3B",
            "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B",
            "params": "35.2B total, 3B active (MoE; 35,212,875,824 per the safetensors count)",
            "listing": "https://www.anchorterminal.com/tools/minicpm-v-4-7-35b-a3b"
          }
        ],
        "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-4-7-1b.json"
    },
    "edges": [
      {
        "by": 35,
        "category": "reach",
        "leads": "minicpm-v-4-7-1b",
        "name": "Reach",
        "reasons": {
          "a": "Memory earns nothing, because the only build from DeepSeek or a runtime's library is the 689.5 GB FP8 release, which needs about 827 GB with the fifth added. Runtimes earn the full 25 for released model code in vLLM, SGLang, llama.cpp and Transformers, and for Ollama 0.40.14, whose llama.cpp pin (b11963) carries the deepseek32 architecture with no gate in Ollama's server code, though its library entry stays retired; nothing from LM Studio. Builds earn 10 for the single FP8 precision DeepSeek publishes. Terms earn the full 15 for MIT with nothing added, and download the full 10 since the repository isn't gated. Hosted earns nothing, since DeepSeek's own API moved to V4 on 24 April 2026 and Hugging Face maps only DeepInfra as live for it on 4 November (Novita and Featherless in error), where the line needs two providers. Fireworks' model page says Serverless Not Supported and lists it only for on-demand deployment on dedicated GPUs, which is renting the hardware rather than calling a served model.",
          "b": "Memory 30, OpenBMB's own IQ4_XS GGUF is 0.64 GB, 1.54 GB with the 0.90 GB vision projector, and 1.54 GB × 1.2 = 1.9 GB, which fits 16 GB with room to spare. Runtimes 25, MiniCPMV4_7ForConditionalGeneration in vLLM's registry at v0.33.0, the minicpmv4_7 model type in SGLang's minicpmv.py at v0.5.23, the minicpmv4_7 model in Transformers at v5.22.2, a MiniCPM-V 4.7 guide and conversion code in llama.cpp at b12242, and an openbmb/minicpm-v4.7:1b tag in OpenBMB's namespace on Ollama, read as the 35B-A3B listing's is. Builds 15, OpenBMB publishes AWQ, GPTQ and int4 safetensors builds and a GGUF repository from IQ3_XS to F16, so quantised builds at two precisions or more. Terms 15, Apache 2.0 in the card's metadata and as the LICENSE of OpenBMB's MiniCPM-V repository, an OSI licence. Download 10, gated is false on the main repository and on the GGUF, AWQ, GPTQ and int4 repositories, so the files download with no account or terms. Hosted 0, Hugging Face lists no inference provider for the model (an empty provider mapping on 10 November 2026), and no provider's own list was read. 30 + 25 + 15 + 15 + 10 + 0 = 95."
        },
        "scores": {
          "a": 60,
          "b": 95
        }
      },
      {
        "by": 14,
        "category": "upkeep",
        "leads": "minicpm-v-4-7-1b",
        "name": "Documentation and upkeep",
        "reasons": {
          "a": "The model card earns 14 when read with DeepSeek's own V3.2 model card PDF, which adds intended use and a parameter count, leaving languages and a knowledge cut-off missing. Training data earns 5, a general description with no cut-off. Last change earns 5, as nothing in the repository has changed since 1 December 2025 (338 days on 4 November) and DeepSeek's deepseek-recipe has no V3.2 code. Responsiveness earns nothing, with no reply from a deepseek-ai member in any of the 40 most recent discussions. Version policy earns 5 for earlier versions kept apart without dated notes. File health earns 5 because loading and parsing reports wait without a reply.",
          "b": "Model card 8, Intended use (image, multi-image and video understanding on device), architecture (early visual-token compression inside the vision encoder) and parameters (1B) are there. Training data 0, no description of the training data on the card. Last change 20, the last commit, on 30 October 2026, updated the card and removed an unused asset folder, eleven days before this check. Responsiveness 0, the repository has no discussions that aren't pull requests (two pull requests in all), so nobody has asked anything yet and the line reads as none answered, as settled in calibration. Version policy 10, MiniCPM-V 4.5 and 4.6 stay in their own repositories and the GitHub README dates each release. File health 10, Config, template and card agree on the architecture and the thinking switch, and no report waits without a reply, since there are no discussions. 8 + 0 + 20 + 0 + 10 + 10 = 48."
        },
        "scores": {
          "a": 34,
          "b": 48
        }
      },
      {
        "by": 10,
        "category": "interface",
        "leads": "minicpm-v-4-7-1b",
        "name": "Agent interface",
        "reasons": {
          "a": "The chat template earns 10, as the format exists only as Python code to run. The tool-calling format earns 10 because DeepSeek documents the DSML format in code but names no runtime parser, which only vLLM's own recipe does. Trained tool calling earns the full 20 for several calls in one block. Structured output earns nothing, as JSON output isn't documented. Reasoning control earns 10 for thinking or chat mode per request with reasoning_content returned. Context earns 10 for 128K trained tokens.",
          "b": "Chat template 20, chat_template.jinja in the repository, 7,249 characters. Tool-calling format 10, the card shows tools declared in the OpenAI tools array in a request to Transformers' server, but not how a call comes back, and names no parser for vLLM or SGLang. Trained tool calling 20, the template loops over tool_calls, so it renders several calls in one assistant turn, and the card gives a tool-calling request as a supported use. Structured output 0, Neither JSON nor JSON Schema output is documented or claimed for the model on the card. Reasoning control 10, the card passes enable_thinking per request in its examples, the template has been off by default since 19 October 2026, and the GGUF repository keeps thinking builds in their own folder. Context 10, 262,144 tokens, max_position_embeddings in the text config. 20 + 10 + 20 + 0 + 10 + 10 = 70."
        },
        "scores": {
          "a": 60,
          "b": 70
        }
      },
      {
        "by": 5,
        "category": "risk",
        "leads": "minicpm-v-4-7-1b",
        "name": "Risk",
        "reasons": {
          "a": "File safety earns 15, safetensors only but 10 off because prompts are built by running DeepSeek's Python from the repository. Provenance earns the full 15 for the deepseek-ai organisation and a SHA-256 on every shard. Safety evaluation earns nothing, since no results are published anywhere. Agent guidance earns nothing, since the notes about the parser and the developer role are usage notes, read alike for all three DeepSeek listings. Licence risk keeps the full 15 for plain MIT. The security programme earns 5 for the security@deepseek.com reporting address on DeepSeek's site, with no public advisories.",
          "b": "File safety 25, safetensors in the main repository and the AWQ, GPTQ and int4 builds, GGUF in the GGUF build, with no .bin, .pt, .pth or .py files in the main repository. Provenance 15, files in the openbmb organisation that OpenBMB's GitHub README links to (10), with a SHA-256 for both large files in the main repository (5). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations or disclaimer section, and no prompt-injection guidance, instruction hierarchy or statement of trained refusals. Licence risk 15, Apache 2.0 has no use policy, field-of-use limit, threshold or change clause, nothing is added on the card, and the LICENSE in OpenBMB's GitHub repository resolved on 10 November 2026. Security programme 0, no SECURITY.md in OpenBMB/MiniCPM-V and no security.txt on openbmb.cn (both 404 on 10 November 2026), and no advisories found. 25 + 15 + 0 + 0 + 15 + 0 = 55."
        },
        "scores": {
          "a": 50,
          "b": 55
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "DeepSeek",
        "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": "2025-12-01",
        "b": "2026-10-30",
        "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": "2.3/5 (4)",
        "b": "none",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "MiniCPM-V 4.7 1B scores 61 (C) on agent readiness against DeepSeek-V3.2's 58.6 (C), and leads in every scored category.",
        "question": "Which is better for AI agents, DeepSeek-V3.2 or MiniCPM-V 4.7 1B?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": null,
        "also": null,
        "goodFor": "Teams with an eight-GPU server who want an MIT agent model with broad runtime support, or agents calling it through a hosted provider",
        "slug": "deepseek-v3-2",
        "watchFor": "689.5 GB of FP8 files and no smaller build from DeepSeek or a runtime library, far beyond 107.4 GB"
      },
      {
        "aheadOn": [
          "Reach, 95 against 60",
          "Risk, 55 against 50",
          "Agent interface, 70 against 60",
          "Documentation and upkeep, 48 against 34"
        ],
        "also": null,
        "goodFor": "Agents that need a cheap local model to read screenshots, photos and short video on a phone, a laptop or beside a bigger model",
        "slug": "minicpm-v-4-7-1b",
        "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-deepseek-v3-2.json",
        "title": "Agents-A1-4B vs DeepSeek-V3.2",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-deepseek-v3-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-minicpm-v-4-7-1b.json",
        "title": "Agents-A1-4B vs MiniCPM-V 4.7 1B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-minicpm-v-4-7-1b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-deepseek-v3-2.json",
        "title": "Apertus v1.5 8B vs DeepSeek-V3.2",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-deepseek-v3-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minicpm-v-4-7-1b.json",
        "title": "Apertus v1.5 8B vs MiniCPM-V 4.7 1B",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minicpm-v-4-7-1b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-deepseek-v3-2.json",
        "title": "Apodex-1.1-mini vs DeepSeek-V3.2",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-deepseek-v3-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minicpm-v-4-7-1b.json",
        "title": "Apodex-1.1-mini vs MiniCPM-V 4.7 1B",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minicpm-v-4-7-1b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-deepseek-v3-2.json",
        "title": "Arete-27B vs DeepSeek-V3.2",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-deepseek-v3-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-minicpm-v-4-7-1b.json",
        "title": "Arete-27B vs MiniCPM-V 4.7 1B",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-minicpm-v-4-7-1b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-deepseek-v3-2.json",
        "title": "BigBang-v1 vs DeepSeek-V3.2",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-deepseek-v3-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-minicpm-v-4-7-1b.json",
        "title": "BigBang-v1 vs MiniCPM-V 4.7 1B",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-minicpm-v-4-7-1b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-deepseek-v3-2.json",
        "title": "Carbon-X vs DeepSeek-V3.2",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-deepseek-v3-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-minicpm-v-4-7-1b.json",
        "title": "Carbon-X vs MiniCPM-V 4.7 1B",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-minicpm-v-4-7-1b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-deepseek-v3-2.json",
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        "url": "https://www.anchorterminal.com/compare/cola2-vs-deepseek-v3-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-minicpm-v-4-7-1b.json",
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        "url": "https://www.anchorterminal.com/compare/cola2-vs-minicpm-v-4-7-1b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-deepseek-v3-2.json",
        "title": "Cosmos-Reason2-2B vs DeepSeek-V3.2",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-deepseek-v3-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-minicpm-v-4-7-1b.json",
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        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-minicpm-v-4-7-1b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-deepseek-v3-2.json",
        "title": "Darwin-36B-Opus vs DeepSeek-V3.2",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-deepseek-v3-2"
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      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-minicpm-v-4-7-1b.json",
        "title": "Darwin-36B-Opus vs MiniCPM-V 4.7 1B",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-minicpm-v-4-7-1b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-deepseek-v4-1-flash.json",
        "title": "DeepSeek-V3.2 vs DeepSeek V4.1 Flash",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-deepseek-v4-1-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-deepseek-v4-flash-0731.json",
        "title": "DeepSeek-V3.2 vs DeepSeek-V4-Flash-0731",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-deepseek-v4-flash-0731"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-devstral-small-2.json",
        "title": "DeepSeek-V3.2 vs Devstral Small 2",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-devstral-small-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-diffusiongemma-26b-a4b-it.json",
        "title": "DeepSeek-V3.2 vs DiffusionGemma 26B A4B IT",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-diffusiongemma-26b-a4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-exaone-4-5-33b.json",
        "title": "DeepSeek-V3.2 vs EXAONE 4.5 33B",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-exaone-4-5-33b"
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        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gemma-4-12b-it.json",
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        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gemma-4-12b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gemma-4-26b-a4b-it.json",
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        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gemma-4-26b-a4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gemma-4-31b-it.json",
        "title": "DeepSeek-V3.2 vs Gemma 4 31B IT",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gemma-4-31b-it"
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      {
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    "scores": [
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        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
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      {
        "by": 35,
        "deepseek-v3-2": 60,
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        "key": "reach",
        "minicpm-v-4-7-1b": 95,
        "name": "Reach",
        "weight": 25
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        "by": 5,
        "deepseek-v3-2": 50,
        "edge": "minicpm-v-4-7-1b",
        "key": "risk",
        "minicpm-v-4-7-1b": 55,
        "name": "Risk",
        "weight": 20
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        "by": 10,
        "deepseek-v3-2": 60,
        "edge": "minicpm-v-4-7-1b",
        "key": "interface",
        "minicpm-v-4-7-1b": 70,
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        "weight": 10
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        "deepseek-v3-2": 34,
        "edge": "minicpm-v-4-7-1b",
        "key": "upkeep",
        "minicpm-v-4-7-1b": 48,
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        "weight": 10
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    "tasks": null,
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    "verdicts": {
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      "minicpm-v-4-7-1b": "A vision-language cousin small enough for a phone, under plain Apache 2.0, that every runtime we count can load. The projector is bigger than the language model, so plan for about 2 GB. The card says nothing about limits or training data and doesn't show how a tool call comes back."
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    "markdown": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-minicpm-v-4-7-1b.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-minicpm-v-4-7-1b.min.md"
  },
  "markdown": "MiniCPM-V 4.7 1B scores 61 (C) on agent readiness against DeepSeek-V3.2's 58.6 (C), and leads in every scored category. Both do open-weight models you run yourself.\n\n- DeepSeek-V3.2: grade C, 58.6/100, rank #102 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/deepseek-v3-2.md · JSON https://www.anchorterminal.com/api/v1/tools/deepseek-v3-2.json\n- MiniCPM-V 4.7 1B: grade C, 61/100, rank #76 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/minicpm-v-4-7-1b.md · JSON https://www.anchorterminal.com/api/v1/tools/minicpm-v-4-7-1b.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### DeepSeek-V3.2 (C)\n\nGood for: Teams with an eight-GPU server who want an MIT agent model with broad runtime support, or agents calling it through a hosted provider\n\nWatch for: 689.5 GB of FP8 files and no smaller build from DeepSeek or a runtime library, far beyond 107.4 GB\n\n### MiniCPM-V 4.7 1B (C)\n\nGood for: Agents that need a cheap local model to read screenshots, photos and short video on a phone, a laptop or beside a bigger model\n\nAhead on:\n- Reach, 95 against 60\n- Risk, 55 against 50\n- Agent interface, 70 against 60\n- Documentation and upkeep, 48 against 34\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 1B +35\n\n- DeepSeek-V3.2 (60): Memory earns nothing, because the only build from DeepSeek or a runtime's library is the 689.5 GB FP8 release, which needs about 827 GB with the fifth added. Runtimes earn the full 25 for released model code in vLLM, SGLang, llama.cpp and Transformers, and for Ollama 0.40.14, whose llama.cpp pin (b11963) carries the deepseek32 architecture with no gate in Ollama's server code, though its library entry stays retired; nothing from LM Studio. Builds earn 10 for the single FP8 precision DeepSeek publishes. Terms earn the full 15 for MIT with nothing added, and download the full 10 since the repository isn't gated. Hosted earns nothing, since DeepSeek's own API moved to V4 on 24 April 2026 and Hugging Face maps only DeepInfra as live for it on 4 November (Novita and Featherless in error), where the line needs two providers. Fireworks' model page says Serverless Not Supported and lists it only for on-demand deployment on dedicated GPUs, which is renting the hardware rather than calling a served model.\n- MiniCPM-V 4.7 1B (95): Memory 30, OpenBMB's own IQ4_XS GGUF is 0.64 GB, 1.54 GB with the 0.90 GB vision projector, and 1.54 GB × 1.2 = 1.9 GB, which fits 16 GB with room to spare. Runtimes 25, MiniCPMV4_7ForConditionalGeneration in vLLM's registry at v0.33.0, the minicpmv4_7 model type in SGLang's minicpmv.py at v0.5.23, the minicpmv4_7 model in Transformers at v5.22.2, a MiniCPM-V 4.7 guide and conversion code in llama.cpp at b12242, and an openbmb/minicpm-v4.7:1b tag in OpenBMB's namespace on Ollama, read as the 35B-A3B listing's is. Builds 15, OpenBMB publishes AWQ, GPTQ and int4 safetensors builds and a GGUF repository from IQ3_XS to F16, so quantised builds at two precisions or more. Terms 15, Apache 2.0 in the card's metadata and as the LICENSE of OpenBMB's MiniCPM-V repository, an OSI licence. Download 10, gated is false on the main repository and on the GGUF, AWQ, GPTQ and int4 repositories, so the files download with no account or terms. Hosted 0, Hugging Face lists no inference provider for the model (an empty provider mapping on 10 November 2026), and no provider's own list was read. 30 + 25 + 15 + 15 + 10 + 0 = 95.\n\n### Documentation and upkeep: MiniCPM-V 4.7 1B +14\n\n- DeepSeek-V3.2 (34): The model card earns 14 when read with DeepSeek's own V3.2 model card PDF, which adds intended use and a parameter count, leaving languages and a knowledge cut-off missing. Training data earns 5, a general description with no cut-off. Last change earns 5, as nothing in the repository has changed since 1 December 2025 (338 days on 4 November) and DeepSeek's deepseek-recipe has no V3.2 code. Responsiveness earns nothing, with no reply from a deepseek-ai member in any of the 40 most recent discussions. Version policy earns 5 for earlier versions kept apart without dated notes. File health earns 5 because loading and parsing reports wait without a reply.\n- MiniCPM-V 4.7 1B (48): Model card 8, Intended use (image, multi-image and video understanding on device), architecture (early visual-token compression inside the vision encoder) and parameters (1B) are there. Training data 0, no description of the training data on the card. Last change 20, the last commit, on 30 October 2026, updated the card and removed an unused asset folder, eleven days before this check. Responsiveness 0, the repository has no discussions that aren't pull requests (two pull requests in all), so nobody has asked anything yet and the line reads as none answered, as settled in calibration. Version policy 10, MiniCPM-V 4.5 and 4.6 stay in their own repositories and the GitHub README dates each release. File health 10, Config, template and card agree on the architecture and the thinking switch, and no report waits without a reply, since there are no discussions. 8 + 0 + 20 + 0 + 10 + 10 = 48.\n\n### Agent interface: MiniCPM-V 4.7 1B +10\n\n- DeepSeek-V3.2 (60): The chat template earns 10, as the format exists only as Python code to run. The tool-calling format earns 10 because DeepSeek documents the DSML format in code but names no runtime parser, which only vLLM's own recipe does. Trained tool calling earns the full 20 for several calls in one block. Structured output earns nothing, as JSON output isn't documented. Reasoning control earns 10 for thinking or chat mode per request with reasoning_content returned. Context earns 10 for 128K trained tokens.\n- MiniCPM-V 4.7 1B (70): Chat template 20, chat_template.jinja in the repository, 7,249 characters. Tool-calling format 10, the card shows tools declared in the OpenAI tools array in a request to Transformers' server, but not how a call comes back, and names no parser for vLLM or SGLang. Trained tool calling 20, the template loops over tool_calls, so it renders several calls in one assistant turn, and the card gives a tool-calling request as a supported use. Structured output 0, Neither JSON nor JSON Schema output is documented or claimed for the model on the card. Reasoning control 10, the card passes enable_thinking per request in its examples, the template has been off by default since 19 October 2026, and the GGUF repository keeps thinking builds in their own folder. Context 10, 262,144 tokens, max_position_embeddings in the text config. 20 + 10 + 20 + 0 + 10 + 10 = 70.\n\n### Risk: MiniCPM-V 4.7 1B +5\n\n- DeepSeek-V3.2 (50): File safety earns 15, safetensors only but 10 off because prompts are built by running DeepSeek's Python from the repository. Provenance earns the full 15 for the deepseek-ai organisation and a SHA-256 on every shard. Safety evaluation earns nothing, since no results are published anywhere. Agent guidance earns nothing, since the notes about the parser and the developer role are usage notes, read alike for all three DeepSeek listings. Licence risk keeps the full 15 for plain MIT. The security programme earns 5 for the security@deepseek.com reporting address on DeepSeek's site, with no public advisories.\n- MiniCPM-V 4.7 1B (55): File safety 25, safetensors in the main repository and the AWQ, GPTQ and int4 builds, GGUF in the GGUF build, with no .bin, .pt, .pth or .py files in the main repository. Provenance 15, files in the openbmb organisation that OpenBMB's GitHub README links to (10), with a SHA-256 for both large files in the main repository (5). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations or disclaimer section, and no prompt-injection guidance, instruction hierarchy or statement of trained refusals. Licence risk 15, Apache 2.0 has no use policy, field-of-use limit, threshold or change clause, nothing is added on the card, and the LICENSE in OpenBMB's GitHub repository resolved on 10 November 2026. Security programme 0, no SECURITY.md in OpenBMB/MiniCPM-V and no security.txt on openbmb.cn (both 404 on 10 November 2026), and no advisories found. 25 + 15 + 0 + 0 + 15 + 0 = 55.\n\n## Score by category\n\n| Category | Weight | DeepSeek-V3.2 | MiniCPM-V 4.7 1B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 60 | 95 | MiniCPM-V 4.7 1B +35 |\n| Risk | 20% (20 this run) | 50 | 55 | MiniCPM-V 4.7 1B +5 |\n| Agent interface | 10% (10 this run) | 60 | 70 | MiniCPM-V 4.7 1B +10 |\n| Documentation and upkeep | 10% (10 this run) | 34 | 48 | MiniCPM-V 4.7 1B +14 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **58.6 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | DeepSeek-V3.2 | MiniCPM-V 4.7 1B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | DeepSeek | 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 | 2025-12-01 | 2026-10-30 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | 2.3/5 (4) | none |\n\n## Verdicts\n\n**DeepSeek-V3.2.** DeepSeek-V3.2 is an MIT-licensed reasoning model trained for parallel tool calls, with thinking chosen per request, for long agent work. It needs about 690 GB of FP8 files, eight 80 GB GPUs in vLLM's recipe and prompts built from DeepSeek's Python. The main risk is neglect, with no file changes since December 2025 and no safety evaluation.\n\n**MiniCPM-V 4.7 1B.** A vision-language cousin small enough for a phone, under plain Apache 2.0, that every runtime we count can load. The projector is bigger than the language model, so plan for about 2 GB. The card says nothing about limits or training data and doesn't show how a tool call comes back.\n\n## Before you call either\n\n### DeepSeek-V3.2\n\n1. Start it with vLLM's recipe, vllm serve deepseek-ai/DeepSeek-V3.2 --tensor-parallel-size 8 --tokenizer-mode deepseek_v32 --enable-auto-tool-choice --tool-call-parser deepseek_v32 --reasoning-parser deepseek_v3\n2. It doesn't fit the 107.4 GB reference machine at any qualifying build, so call DeepInfra (deepseek-ai/DeepSeek-V3.2) or deploy it on Fireworks instead\n3. Outside vLLM, build prompts with encoding/encoding_dsv32.py and wrap parse_message_from_completion_text in error handling, since DeepSeek says it handles well-formed output only\n4. Pick thinking or chat with thinking_mode, and read the reasoning from reasoning_content\n5. Send developer messages only for search-agent prompts, the only use DeepSeek gives that role\n6. Use temperature 1.0 and top_p 0.95 and keep prompts within 128K tokens\n\n### MiniCPM-V 4.7 1B\n\n1. Start it with `llama-server -hf openbmb/MiniCPM-V-4.7-1B-gguf:Q4_K_M --jinja`, or `ollama run openbmb/minicpm-v4.7:1b`\n2. For image input in llama.cpp add `mmproj-model-f16.gguf`, or the smaller 4x projector\n3. The GGUF files at the repository root have thinking off. Use the files in `thinking/` when you want the trace\n4. Use temperature 0.7, top_p 0.95 and top_k 100 in instruct mode, as the card recommends\n5. Check tool calls in your runtime first, since the card names no parser\n6. Validate JSON output yourself, since structured output isn't documented\n\n## Questions\n\n### Which is better for AI agents, DeepSeek-V3.2 or MiniCPM-V 4.7 1B?\n\nMiniCPM-V 4.7 1B scores 61 (C) on agent readiness against DeepSeek-V3.2's 58.6 (C), and leads in every scored category.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/deepseek-v3-2-vs-minicpm-v-4-7-1b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/deepseek-v3-2-vs-minicpm-v-4-7-1b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"deepseek-v3-2\", \"b\": \"minicpm-v-4-7-1b\"}`. 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