{
  "data": {
    "a": {
      "slug": "kimi-k3",
      "name": "Kimi K3",
      "vendor": "Moonshot AI",
      "vendorUrl": "https://www.moonshot.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Kimi K3 is Moonshot AI's open-weight multimodal mixture-of-experts model, 2.8T parameters with 104B active and a 1M-token context, trained in MXFP4 and published on Hugging Face under the modified-MIT Kimi K3 Licence.",
      "url": "https://www.anchorterminal.com/tools/kimi-k3",
      "markdownUrl": "https://www.anchorterminal.com/tools/kimi-k3.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/kimi-k3.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/kimi-k3.json",
      "dns": "kimi-k3.grade.anchorterminal.com",
      "repo": "https://huggingface.co/moonshotai/Kimi-K3",
      "license": "Kimi K3 License (modified MIT)",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "moonshotai/Kimi-K3"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-10-24).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under the Kimi K3 Licence. A company running a model-as-a-service business with more than $20 million in revenue over 12 months needs a separate agreement with Moonshot AI, at a price not published. Running it yourself costs the hardware, 1,561 GB of files (checked 2026-10-24).",
      "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 2026-10-24).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-11-02"
      },
      "docsUrl": "https://huggingface.co/moonshotai/Kimi-K3",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "multimodal",
        "reasoning",
        "tool-calling",
        "modified-mit",
        "safetensors",
        "mxfp4",
        "self-hosted"
      ],
      "lastRelease": "2026-09-02",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 62.4,
        "grade": "B",
        "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": 23,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "capability": 75,
          "interface": 65,
          "reach": 50,
          "risk": 65,
          "upkeep": 41
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-11-02"
        },
        "negative": 0,
        "verdict": "Kimi K3 is a 2.8T multimodal agent model with a 1M-token context, parallel tool calls and per-request effort, for long coding and research sessions. Its 1,561 GB of files need a multi-node GPU cluster, so most agents will call a hosted copy. The main risks are Moonshot's Python running at load and prompt time, and no answers in its repository.",
        "bestFor": "Teams with a multi-node GPU cluster who want an open 1M-context agent model for long coding and research work",
        "strengths": [
          "1,048,576 tokens of context trained in, with indexed parallel tool calls in one turn",
          "Model code in released vLLM, SGLang and llama.cpp, with recipes linked from the card",
          "A third-party cyber evaluation by the UK AI Security Institute and CAISI",
          "Safetensors only, with a SHA-256 for every shard, downloadable with no account",
          "Served by Moonshot's API and five inference providers for agents without the hardware"
        ],
        "weaknesses": [
          "1,561 GB of files at its smallest publisher build, far beyond any single machine",
          "No Jinja template, so prompts, tokeniser and model load through Moonshot's Python with trust_remote_code",
          "No Moonshot reply in the 40 most recent discussions, and a tokeniser report waits unanswered",
          "No disclosure route for flaws and no prompt-injection guidance",
          "No training sources, sizes or knowledge cut-off, and the card leaves out limitations and languages"
        ],
        "agentNotes": [
          "Start it with SGLang on a node of 8 B300s, sglang serve --trust-remote-code --model-path moonshotai/Kimi-K3 --tp-size 8 --context-length 65536 --mem-fraction-static 0.82 --reasoning-parser kimi_k3 --tool-call-parser kimi_k3 --port 30000",
          "Use the kimi_k3 tool-call and reasoning parsers in vLLM or SGLang, and validate tool-call arguments against the schema, since vLLM's recipe says K3 sometimes emits a format its parser doesn't expect",
          "Pass the whole assistant message back every turn, reasoning_content and tool_calls included, or output becomes unstable",
          "Thinking is always on; set reasoning_effort to low or high when max is too slow",
          "Sampling per the card is temperature 1.0, with top-p 0.95 for single-step tasks and 1.0 for agentic ones",
          "Don't hand an ongoing session from another model to K3, and state boundaries in the system prompt, since Moonshot warns it can act on its own when intent is unclear",
          "Without the hardware, call it through Moonshot's API or an inference provider listed on its Hugging Face page"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 2.3,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "high",
            "grade": "B",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 62.4
          }
        ],
        "editorialScores": {
          "capability": 75,
          "interface": 65,
          "reach": 50,
          "risk": 65,
          "upkeep": 41
        },
        "provenanceScore": 70,
        "checked": {
          "categories": {
            "capability": "2026-11-02",
            "interface": "2026-11-02",
            "reach": "2026-11-02",
            "risk": "2026-11-02",
            "upkeep": "2026-11-02"
          },
          "last": "2026-11-02",
          "oldest": "2026-11-02",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "# SGLang, per its Kimi-K3 cookbook linked from the card (one node of 8 B300 GPUs)\nsglang serve --trust-remote-code --model-path moonshotai/Kimi-K3 --tp-size 8 \\\n  --context-length 65536 --mem-fraction-static 0.82 \\\n  --reasoning-parser kimi_k3 --tool-call-parser kimi_k3 --port 30000\n# or, with llama.cpp and Unsloth's GGUF, a third party's conversion:\nllama-server -hf unsloth/Kimi-K3-GGUF:UD-Q4_K_XL --jinja",
        "http": "curl http://localhost:30000/v1/chat/completions \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\n    \"model\": \"moonshotai/Kimi-K3\",\n    \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}],\n    \"reasoning_effort\": \"high\"\n  }'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/kimi-k3"
      },
      "sameCompany": [
        "kimi-k3-1-pro"
      ],
      "weights": {
        "trainedBy": "Moonshot AI",
        "params": "2.8T",
        "active": "104B",
        "context": 1048576,
        "goodFor": "Large multimodal agent model for coding and long tool-calling sessions, text and images in, with always-on reasoning and JSON Schema output.",
        "runtimes": [
          "llama.cpp",
          "vLLM",
          "SGLang"
        ],
        "builds": [
          {
            "name": "Kimi-K3",
            "url": "https://huggingface.co/moonshotai/Kimi-K3",
            "who": "publisher",
            "precision": "mxfp4",
            "sizeGB": 1560.9,
            "memoryGB": 2048,
            "format": "safetensors",
            "note": "Trained in MXFP4; the only build Moonshot publishes"
          },
          {
            "name": "unsloth Kimi-K3-GGUF (UD-Q4_K_XL)",
            "url": "https://huggingface.co/unsloth/Kimi-K3-GGUF",
            "who": "unsloth",
            "precision": "q4_k_xl",
            "sizeGB": 1509.57,
            "memoryGB": 2048,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "From Unsloth, a third party's conversion and not Moonshot's. 1508.67 GB of UD-Q4_K_XL weights plus the 0.90 GB F16 projector, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it"
          }
        ],
        "run": "sglang serve --trust-remote-code --model-path moonshotai/Kimi-K3 --tp-size 8 --context-length 65536 --mem-fraction-static 0.82 --reasoning-parser kimi_k3 --tool-call-parser kimi_k3 --port 30000",
        "runBuild": "Kimi-K3",
        "checked": "2026-10-24",
        "minMemoryGB": 2048
      },
      "family": {
        "lab": "Moonshot AI (Kimi)",
        "family": "Kimi K3",
        "url": "https://www.anchorterminal.com/open-weights/#moonshot",
        "olderCount": 18,
        "unrestrictedCopies": 5
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Moonshot AI (the licence names Moonshot AI; the Kimi Platform terms name Moonshot AI PTE. LTD.)",
        "domain": "moonshot.ai",
        "domainRegistered": "2017-12-15",
        "domainNote": "The .ai registry's RDAP server (Identity Digital) gives moonshot.ai a registration date of 15 December 2017, read 24 October 2026; rdap.org didn't answer on 21 October.",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/moonshotai/Kimi-K3/commits/main",
        "securityTxt": "none",
        "checked": "2026-10-24",
        "notes": [
          "The licence names Moonshot AI. The Kimi Platform's terms (platform.kimi.ai/docs/agreement/modeluse) name Moonshot AI PTE. LTD. as the party for its API.",
          "Nothing is hosted, so the Kimi K3 Licence stands in for terms of service and no privacy policy is scored.",
          "The changelog is the repository's commit history. Its messages mostly say \"Update README.md\" or \"Update encoding_k3.py\" without saying what changed; the last change, on 2 September 2026, added an evaluation result.",
          "moonshot.ai, www.moonshot.ai, kimi.com and www.kimi.com answered /.well-known/security.txt with an HTML page and platform.kimi.ai with 404 on 21 and 24 October 2026, so no security.txt was found. The Kimi-K3 GitHub repository has no SECURITY.md."
        ],
        "score": 70
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/kimi-k3.json",
      "live": {
        "slug": "kimi-k3",
        "securityTxt": {
          "url": "https://moonshot.ai/.well-known/security.txt",
          "state": "none",
          "checkedAt": "2026-11-10T16:08:03.282831769Z"
        },
        "domain": {
          "domain": "moonshot.ai",
          "registered": "2017-12-15",
          "source": "https://rdap.identitydigital.services/rdap/domain/moonshot.ai",
          "checkedAt": "2026-11-08T14:52:29.297019771Z"
        },
        "pages": [
          {
            "url": "https://huggingface.co/moonshotai/Kimi-K3/commits/main",
            "kind": "changelog",
            "status": 200,
            "checkedAt": "2026-11-10T16:23:14.587006924Z",
            "changedAt": "2026-11-10T16:23:14.587006924Z",
            "fingerprint": "f16ede6bc278"
          }
        ],
        "updatedAt": "2026-11-10T16:23:14.587006924Z"
      }
    },
    "answer": "Kimi K3 scores 62.4 (B) on agent readiness against MiniCPM-V 4.7 1B's 61 (C), and leads in 1 of 4 scored categories. MiniCPM-V 4.7 1B leads on reach, agent interface and documentation and upkeep.",
    "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": 45,
        "category": "reach",
        "leads": "minicpm-v-4-7-1b",
        "name": "Reach",
        "reasons": {
          "a": "Memory earns nothing, because the only build of 4-bit precision or better from Moonshot or a runtime's own library is Moonshot's MXFP4 release of 1,561 GB, about 1,873 GB with the fifth added, far above 256 GB. Ollama and LM Studio list it only as a cloud model. Runtimes earn 15, for model code in released vLLM, SGLang and llama.cpp; Transformers has no kimi_k3 code of its own and loads Moonshot's remote code, so it doesn't count. Builds earn 10 for one publisher precision, the MXFP4 files the model was trained for. Terms earn 10, a custom MIT-based licence that is free for commercial use below a revenue threshold. Download earns 10, with no gate and no account. Hosted earns 5, since Moonshot's API serves it and five inference providers are live for it on Hugging Face.",
          "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": 50,
          "b": 95
        }
      },
      {
        "by": 10,
        "category": "risk",
        "leads": "kimi-k3",
        "name": "Risk",
        "reasons": {
          "a": "File safety earns 15, safetensors only with no pickle files, less 10 because the documented loading path runs Moonshot's Python with trust_remote_code and builds prompts with encoding_k3.py. Provenance earns the full 15, files in the organisation Moonshot links to with a SHA-256 for every shard. Safety evaluation earns 20 for the cyber assessment the UK AI Security Institute and CAISI ran and published, which found its safeguards didn't stop exploit development. Agent guidance earns 5 for the blog's general cautions about proactiveness and passing back thinking. Licence risk earns 10, losing 5 for the model-as-a-service revenue threshold and nothing else. The security programme earns nothing, with no security.txt, disclosure policy, SECURITY.md or public advisories found.",
          "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": 65,
          "b": 55
        }
      },
      {
        "by": 7,
        "category": "upkeep",
        "leads": "minicpm-v-4-7-1b",
        "name": "Documentation and upkeep",
        "reasons": {
          "a": "The model card earns 11, covering intended use, architecture, parameters and context, but not limitations, languages or a knowledge cut-off. Training data earns 5 for a general description with no cut-off. The last change earns 15, an encoding_k3.py update on 20 August, 74 days before 2 November and within 90; the GitHub repository's last commit is 6 August. Responsiveness earns nothing, with no Moonshot reply in the 40 most recent discussions. Version policy earns 5 for a first release. File health earns 5, because a report that the tokeniser goes past the declared vocabulary waits 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": 41,
          "b": 48
        }
      },
      {
        "by": 5,
        "category": "interface",
        "leads": "minicpm-v-4-7-1b",
        "name": "Agent interface",
        "reasons": {
          "a": "The chat template earns 10, because there is no Jinja template and the prompt format exists only as encoding_k3.py, code you run. The tool-calling format earns 10, since Moonshot documents the tools channel but the parser name comes only from vLLM's and SGLang's own recipes. Trained tool calling earns 20 for indexed parallel calls in one turn. Structured output earns 5, as the card names it among its linked guides without saying how to ask the weights for it, and the how sits in hosted-API docs. Reasoning control earns 10, with per-request effort documented on the card and reasoning returned as reasoning_content. Context earns 10 for 1,048,576 tokens trained in.",
          "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": 65,
          "b": 70
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Moonshot AI",
        "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": "Kimi K3 License (modified MIT)",
        "b": "Apache-2.0",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-09-02",
        "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": "Kimi K3 scores 62.4 (B) on agent readiness against MiniCPM-V 4.7 1B's 61 (C), and leads in 1 of 4 scored categories. MiniCPM-V 4.7 1B leads on reach, agent interface and documentation and upkeep.",
        "question": "Which is better for AI agents, Kimi K3 or MiniCPM-V 4.7 1B?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Risk, 65 against 55"
        ],
        "also": null,
        "goodFor": "Teams with a multi-node GPU cluster who want an open 1M-context agent model for long coding and research work",
        "slug": "kimi-k3",
        "watchFor": "1,561 GB of files at its smallest publisher build, far beyond any single machine"
      },
      {
        "aheadOn": [
          "Reach, 95 against 50",
          "Agent interface, 70 against 65",
          "Documentation and upkeep, 48 against 41"
        ],
        "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-kimi-k3.json",
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        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-kimi-k3"
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      {
        "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-kimi-k3.json",
        "title": "Apertus v1.5 8B vs Kimi K3",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-kimi-k3"
      },
      {
        "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"
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      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-kimi-k3.json",
        "title": "Apodex-1.1-mini vs Kimi K3",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-kimi-k3"
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      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minicpm-v-4-7-1b.json",
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        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minicpm-v-4-7-1b"
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      {
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        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-kimi-k3"
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        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-minicpm-v-4-7-1b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-kimi-k3.json",
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        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-kimi-k3"
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        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-minicpm-v-4-7-1b.json",
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        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-minicpm-v-4-7-1b"
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      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-kimi-k3.json",
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        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-kimi-k3"
      },
      {
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        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-minicpm-v-4-7-1b"
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        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-kimi-k3"
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        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
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      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-nemotron-3-super-120b-a12b.json",
        "title": "MiniCPM-V 4.7 1B vs Nemotron 3 Super 120B A12B",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-nemotron-3-super-120b-a12b"
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        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-nemotron-3-ultra-550b-a55b.json",
        "title": "MiniCPM-V 4.7 1B vs Nemotron 3 Ultra 550B A55B",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-nemotron-3-ultra-550b-a55b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-north-mini-code-1-0.json",
        "title": "MiniCPM-V 4.7 1B vs North Mini Code 1.0",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-north-mini-code-1-0"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-occamy-1-0.json",
        "title": "MiniCPM-V 4.7 1B vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-occamy-1-0"
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      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-olmo-3-7b-think.json",
        "title": "MiniCPM-V 4.7 1B vs Olmo 3 7B Think",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-olmo-3-7b-think"
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        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-ornith-1-5-122b.json",
        "title": "MiniCPM-V 4.7 1B vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-ornith-1-5-35b-a3b.json",
        "title": "MiniCPM-V 4.7 1B vs Ornith-1.5-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-ornith-1-5-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-ornith-1-5-397b.json",
        "title": "MiniCPM-V 4.7 1B vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-ornith-1-5-397b"
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      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-ornith-1-5-9b.json",
        "title": "MiniCPM-V 4.7 1B vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-ornith-1-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-otel-2-0-llm-31b-it.json",
        "title": "MiniCPM-V 4.7 1B vs OTel-2.0-LLM-31B-IT",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-otel-2-0-llm-31b-it"
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      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen-agentworld-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-30b-a3b-instruct-2507.json",
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      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-4b-instruct-2507.json",
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        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-5-0-8b.json",
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        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-5-0-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-5-122b-a10b.json",
        "title": "MiniCPM-V 4.7 1B vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-5-122b-a10b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-5-2b.json",
        "title": "MiniCPM-V 4.7 1B vs Qwen3.5-2B",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-5-2b"
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      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-5-397b-a17b.json",
        "title": "MiniCPM-V 4.7 1B vs Qwen3.5-397B-A17B",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-5-397b-a17b"
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      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-5-4b.json",
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        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-5-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-5-9b.json",
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        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-5-9b"
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      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-6-27b.json",
        "title": "MiniCPM-V 4.7 1B vs Qwen3.6-27B",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-6-27b"
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      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-6-35b-a3b.json",
        "title": "MiniCPM-V 4.7 1B vs Qwen3.6-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-6-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-8-27b.json",
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        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-coder-30b-a3b-instruct.json",
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        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-vl-2b-instruct.json",
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        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-vl-2b-instruct"
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      {
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        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-vl-32b-instruct.json",
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        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-vl-32b-instruct"
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        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwopus3-8-27b-flash-v2"
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        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-recursive-flint.json",
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        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-recursive-flint"
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      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-ripple-65b-preview.json",
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        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-ripple-65b-preview"
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        "title": "MiniCPM-V 4.7 1B vs SenseNova-Loop-9B-Preview",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-sensenova-loop-9b-preview"
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        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-smollm3-3b.json",
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        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-smollm3-3b"
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        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-spark-x2-6-4b.json",
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        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-spark-x2-6-4b"
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        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-thinkingcap-qwen3-8-27b.json",
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        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-unlimited-ocr-2.json",
        "title": "MiniCPM-V 4.7 1B vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-wevisone-vl-2b.json",
        "title": "MiniCPM-V 4.7 1B vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-wevisone-vl-2b"
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      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-wevisone-vl-4b.json",
        "title": "MiniCPM-V 4.7 1B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-zaya1-8b.json",
        "title": "MiniCPM-V 4.7 1B vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-zaya1-8b"
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    ],
    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 45,
        "edge": "minicpm-v-4-7-1b",
        "key": "reach",
        "kimi-k3": 50,
        "minicpm-v-4-7-1b": 95,
        "name": "Reach",
        "weight": 25
      },
      {
        "by": 10,
        "edge": "kimi-k3",
        "key": "risk",
        "kimi-k3": 65,
        "minicpm-v-4-7-1b": 55,
        "name": "Risk",
        "weight": 20
      },
      {
        "by": 5,
        "edge": "minicpm-v-4-7-1b",
        "key": "interface",
        "kimi-k3": 65,
        "minicpm-v-4-7-1b": 70,
        "name": "Agent interface",
        "weight": 10
      },
      {
        "by": 7,
        "edge": "minicpm-v-4-7-1b",
        "key": "upkeep",
        "kimi-k3": 41,
        "minicpm-v-4-7-1b": 48,
        "name": "Documentation and upkeep",
        "weight": 10
      }
    ],
    "summary": "Kimi K3 scores 62.4 (B) on agent readiness against MiniCPM-V 4.7 1B's 61 (C), and leads in 1 of 4 scored categories. MiniCPM-V 4.7 1B leads on reach, agent interface and documentation and upkeep. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "kimi-k3": "Kimi K3 is a 2.8T multimodal agent model with a 1M-token context, parallel tool calls and per-request effort, for long coding and research sessions. Its 1,561 GB of files need a multi-node GPU cluster, so most agents will call a hosted copy. The main risks are Moonshot's Python running at load and prompt time, and no answers in its repository.",
      "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."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/kimi-k3-vs-minicpm-v-4-7-1b",
    "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-minicpm-v-4-7-1b.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/kimi-k3-vs-minicpm-v-4-7-1b.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/kimi-k3-vs-minicpm-v-4-7-1b.min.md"
  },
  "markdown": "Kimi K3 scores 62.4 (B) on agent readiness against MiniCPM-V 4.7 1B's 61 (C), and leads in 1 of 4 scored categories. MiniCPM-V 4.7 1B leads on reach, agent interface and documentation and upkeep. Both do open-weight models you run yourself.\n\n- Kimi K3: grade B, 62.4/100, rank #30 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/kimi-k3.md · JSON https://www.anchorterminal.com/api/v1/tools/kimi-k3.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### Kimi K3 (B)\n\nGood for: Teams with a multi-node GPU cluster who want an open 1M-context agent model for long coding and research work\n\nAhead on:\n- Risk, 65 against 55\n\nWatch for: 1,561 GB of files at its smallest publisher build, far beyond any single machine\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 50\n- Agent interface, 70 against 65\n- Documentation and upkeep, 48 against 41\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 +45\n\n- Kimi K3 (50): Memory earns nothing, because the only build of 4-bit precision or better from Moonshot or a runtime's own library is Moonshot's MXFP4 release of 1,561 GB, about 1,873 GB with the fifth added, far above 256 GB. Ollama and LM Studio list it only as a cloud model. Runtimes earn 15, for model code in released vLLM, SGLang and llama.cpp; Transformers has no kimi_k3 code of its own and loads Moonshot's remote code, so it doesn't count. Builds earn 10 for one publisher precision, the MXFP4 files the model was trained for. Terms earn 10, a custom MIT-based licence that is free for commercial use below a revenue threshold. Download earns 10, with no gate and no account. Hosted earns 5, since Moonshot's API serves it and five inference providers are live for it on Hugging Face.\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### Risk: Kimi K3 +10\n\n- Kimi K3 (65): File safety earns 15, safetensors only with no pickle files, less 10 because the documented loading path runs Moonshot's Python with trust_remote_code and builds prompts with encoding_k3.py. Provenance earns the full 15, files in the organisation Moonshot links to with a SHA-256 for every shard. Safety evaluation earns 20 for the cyber assessment the UK AI Security Institute and CAISI ran and published, which found its safeguards didn't stop exploit development. Agent guidance earns 5 for the blog's general cautions about proactiveness and passing back thinking. Licence risk earns 10, losing 5 for the model-as-a-service revenue threshold and nothing else. The security programme earns nothing, with no security.txt, disclosure policy, SECURITY.md or public advisories found.\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### Documentation and upkeep: MiniCPM-V 4.7 1B +7\n\n- Kimi K3 (41): The model card earns 11, covering intended use, architecture, parameters and context, but not limitations, languages or a knowledge cut-off. Training data earns 5 for a general description with no cut-off. The last change earns 15, an encoding_k3.py update on 20 August, 74 days before 2 November and within 90; the GitHub repository's last commit is 6 August. Responsiveness earns nothing, with no Moonshot reply in the 40 most recent discussions. Version policy earns 5 for a first release. File health earns 5, because a report that the tokeniser goes past the declared vocabulary waits 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 +5\n\n- Kimi K3 (65): The chat template earns 10, because there is no Jinja template and the prompt format exists only as encoding_k3.py, code you run. The tool-calling format earns 10, since Moonshot documents the tools channel but the parser name comes only from vLLM's and SGLang's own recipes. Trained tool calling earns 20 for indexed parallel calls in one turn. Structured output earns 5, as the card names it among its linked guides without saying how to ask the weights for it, and the how sits in hosted-API docs. Reasoning control earns 10, with per-request effort documented on the card and reasoning returned as reasoning_content. Context earns 10 for 1,048,576 tokens trained in.\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## Score by category\n\n| Category | Weight | Kimi K3 | MiniCPM-V 4.7 1B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 50 | 95 | MiniCPM-V 4.7 1B +45 |\n| Risk | 20% (20 this run) | 65 | 55 | Kimi K3 +10 |\n| Agent interface | 10% (10 this run) | 65 | 70 | MiniCPM-V 4.7 1B +5 |\n| Documentation and upkeep | 10% (10 this run) | 41 | 48 | MiniCPM-V 4.7 1B +7 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **62.4 · B** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Kimi K3 | MiniCPM-V 4.7 1B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Moonshot AI | 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 | Kimi K3 License (modified MIT) | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-09-02 | 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**Kimi K3.** Kimi K3 is a 2.8T multimodal agent model with a 1M-token context, parallel tool calls and per-request effort, for long coding and research sessions. Its 1,561 GB of files need a multi-node GPU cluster, so most agents will call a hosted copy. The main risks are Moonshot's Python running at load and prompt time, and no answers in its repository.\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### Kimi K3\n\n1. Start it with SGLang on a node of 8 B300s, sglang serve --trust-remote-code --model-path moonshotai/Kimi-K3 --tp-size 8 --context-length 65536 --mem-fraction-static 0.82 --reasoning-parser kimi_k3 --tool-call-parser kimi_k3 --port 30000\n2. Use the kimi_k3 tool-call and reasoning parsers in vLLM or SGLang, and validate tool-call arguments against the schema, since vLLM's recipe says K3 sometimes emits a format its parser doesn't expect\n3. Pass the whole assistant message back every turn, reasoning_content and tool_calls included, or output becomes unstable\n4. Thinking is always on; set reasoning_effort to low or high when max is too slow\n5. Sampling per the card is temperature 1.0, with top-p 0.95 for single-step tasks and 1.0 for agentic ones\n6. Don't hand an ongoing session from another model to K3, and state boundaries in the system prompt, since Moonshot warns it can act on its own when intent is unclear\n7. Without the hardware, call it through Moonshot's API or an inference provider listed on its Hugging Face page\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, Kimi K3 or MiniCPM-V 4.7 1B?\n\nKimi K3 scores 62.4 (B) on agent readiness against MiniCPM-V 4.7 1B's 61 (C), and leads in 1 of 4 scored categories. MiniCPM-V 4.7 1B leads on reach, agent interface and documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/kimi-k3-vs-minicpm-v-4-7-1b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/kimi-k3-vs-minicpm-v-4-7-1b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"kimi-k3\", \"b\": \"minicpm-v-4-7-1b\"}`. 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