{
  "data": {
    "a": {
      "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"
    },
    "answer": "MiniCPM-V 4.7 1B scores 61 (C) on agent readiness against SenseNova-Loop-9B-Preview's 46.8 (D), and leads in every scored category.",
    "b": {
      "slug": "sensenova-loop-9b-preview",
      "name": "SenseNova-Loop-9B-Preview",
      "vendor": "SenseTime (SenseNova)",
      "vendorUrl": "https://www.sensetime.com/en",
      "kind": "weights",
      "category": "open-weights",
      "summary": "SenseNova-Loop-9B-Preview is SenseTime's open-weight multimodal model of about 9B parameters. It runs 24 of its 32 decoder layers twice, with text and images in, tool calling, a thinking switch and a 256K context. Apache 2.0. No released runtime loads it.",
      "url": "https://www.anchorterminal.com/tools/sensenova-loop-9b-preview",
      "markdownUrl": "https://www.anchorterminal.com/tools/sensenova-loop-9b-preview.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/sensenova-loop-9b-preview.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/sensenova-loop-9b-preview.json",
      "dns": "sensenova-loop-9b-preview.grade.anchorterminal.com",
      "repo": "https://huggingface.co/sensenova/SenseNova-Loop-9B-Preview",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "sensenova/SenseNova-Loop-9B-Preview"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-11-10).",
      "pricing": "free",
      "pricingNotes": "Free to download and use under Apache 2.0. Running it yourself costs the hardware: the files are 38.1 GB because most weights are stored in 32-bit floats, and the card's example loads them as BF16 (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://github.com/OpenSenseNova/SenseNova-Loop",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "multimodal",
        "vision",
        "looped-transformer",
        "tool-calling",
        "reasoning",
        "long-context",
        "preview",
        "apache-2.0",
        "safetensors",
        "custom-code",
        "self-hosted"
      ],
      "lastRelease": "2026-11-02",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 46.8,
        "grade": "D",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1518,
        "categoryRank": 110,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 60,
          "reach": 40,
          "risk": 50,
          "upkeep": 44
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-10"
        },
        "negative": 0,
        "verdict": "An unusual architecture under Apache 2.0, with a 256K training context, a thinking switch and tool calling in the template. It's a preview in practice too: no released runtime runs it, loading it runs the repository's code, the files are twice the size the card implies, and nobody from SenseNova has answered a question yet.",
        "bestFor": "Researchers who want to study or fine-tune a looped transformer, more than agents that need a model to call today",
        "strengths": [
          "Apache 2.0, ungated",
          "A 256K training context, stated on the card",
          "Thinking can be switched per request",
          "The architecture and parameter counts are documented",
          "Changed in the last ten days"
        ],
        "weaknesses": [
          "No released runtime runs it. vLLM and SGLang need unmerged pull requests",
          "Loading it runs Python from the repository",
          "The files are 38.1 GB, mostly F32, though the card says BF16",
          "No quantised build",
          "No tool-calling documentation beyond the template",
          "No limitations, safety results or data sources, and the technical report isn't out",
          "None of 5 questions answered"
        ],
        "agentNotes": [
          "Read modeling_sensenova_loop.py before you load it. trust_remote_code runs it",
          "Pin transformers 5.18.0, the version the card checked",
          "Load with dtype=torch.bfloat16 as the card does, or the 38 GB of F32 weights stay at full size",
          "Pass enable_thinking=False to apply_chat_template for a direct answer",
          "Tool calls come back as tool_call blocks with function and parameter tags. Parse them yourself, since no runtime ships a parser",
          "Wait for the vLLM or SGLang pull request to land in a release before putting it behind an API"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 0,
        "avgRating": 0,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "D",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 46.8
          }
        ],
        "editorialScores": {
          "interface": 60,
          "reach": 40,
          "risk": 50,
          "upkeep": 44
        },
        "provenanceScore": 51,
        "checked": {
          "categories": {
            "interface": "2026-11-10",
            "reach": "2026-11-10",
            "risk": "2026-11-10",
            "upkeep": "2026-11-10"
          },
          "last": "2026-11-10",
          "oldest": "2026-11-10",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "pip install \"transformers==5.18.0\" accelerate safetensors\n# then AutoModelForCausalLM.from_pretrained(\"sensenova/SenseNova-Loop-9B-Preview\", trust_remote_code=True, dtype=torch.bfloat16)\n# trust_remote_code runs modeling_sensenova_loop.py from the repository. Read it first",
        "http": "# No released server runs it. The card's vLLM and SGLang commands build from open pull requests,\n# and its LightLLM example serves an OpenAI-compatible API at http://localhost:8000/v1 on four GPUs"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/sensenova-loop-9b-preview"
      },
      "weights": {
        "trainedBy": "SenseTime (SenseNova)",
        "params": "9B",
        "context": 262144,
        "goodFor": "Trying a looped transformer for long-context, tool-using work, if you can run the repository's own code on a large GPU.",
        "runtimes": [],
        "builds": [
          {
            "name": "SenseNova-Loop-9B-Preview (safetensors, mostly F32)",
            "url": "https://huggingface.co/sensenova/SenseNova-Loop-9B-Preview/tree/main",
            "who": "publisher",
            "precision": "f32",
            "sizeGB": 38.14,
            "memoryGB": 48,
            "format": "safetensors",
            "note": "Eight shards and the MTP head. Stored mostly as 32-bit floats; the card's example loads it as BF16, about half the size in memory"
          }
        ],
        "run": "python -c \"from transformers import AutoModelForCausalLM; AutoModelForCausalLM.from_pretrained('sensenova/SenseNova-Loop-9B-Preview', trust_remote_code=True)\"",
        "runBuild": "SenseNova-Loop-9B-Preview (safetensors, mostly F32)",
        "checked": "2026-11-10",
        "minMemoryGB": 48
      },
      "family": {
        "lab": "SenseTime (SenseNova)",
        "family": "SenseNova-Loop",
        "url": "https://www.anchorterminal.com/open-weights/#sensenova"
      },
      "area": "models",
      "provenance": {
        "legalEntity": "SenseTime, listed on the Stock Exchange of Hong Kong (00020) per its investor page. SenseNova is its foundation model series, per its site",
        "domain": "sensetime.com",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "https://github.com/OpenSenseNova/SenseNova-Loop/blob/main/LICENSE",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/sensenova/SenseNova-Loop-9B-Preview/commits/main",
        "securityTxt": "none",
        "checked": "2026-11-10",
        "notes": [
          "The card names SenseNova only. SenseTime's site presents SenseNova as its foundation model series, and that is the tie between the two; the site wasn't read for a link to this repository.",
          "The domain's registration date wasn't looked up.",
          "Nothing is hosted under this listing, so Apache 2.0 stands in for terms of service and no privacy policy is scored.",
          "www.sensetime.com has no security.txt (404 on 10 November 2026). Its about page gives a security contact.",
          "The changelog is the commit history, 39 commits from 28 October to 2 November 2026."
        ],
        "score": 51
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/sensenova-loop-9b-preview.json"
    },
    "edges": [
      {
        "by": 55,
        "category": "reach",
        "leads": "minicpm-v-4-7-1b",
        "name": "Reach",
        "reasons": {
          "a": "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.",
          "b": "Memory 15, the only build is the safetensors set, 38.14 GB with its MTP head, since the Hub counts 9.41B of its parameters in F32. Runtimes 0, none of the six runs it in a released version. Builds 0, SenseNova publishes one set of weights and no quantised build. Terms 15, Apache 2.0 in the card's metadata and on the card, which links the LICENSE in the GitHub repository, and nothing added. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it, and the card names no API that serves it. 15 + 0 + 0 + 15 + 10 + 0 = 40."
        },
        "scores": {
          "a": 95,
          "b": 40
        }
      },
      {
        "by": 10,
        "category": "interface",
        "leads": "minicpm-v-4-7-1b",
        "name": "Agent interface",
        "reasons": {
          "a": "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.",
          "b": "Chat template 20, chat_template.jinja in the repository (7,756 characters). Tool-calling format 10, only the template says it: a tools block, and calls written as tool_call blocks with function and parameter tags. Trained tool calling 10, the card says the training recipes include tool trajectories and that the model is for agentic workflows. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, the card documents enable_thinking in the chat template call, on or off per request, and the template puts the reasoning in think tags ahead of the answer. Context 10, 262,144 tokens in training, per the card's specification table, and config.json gives the language model 262,144 positions. 20 + 10 + 10 + 0 + 10 + 10 = 60."
        },
        "scores": {
          "a": 70,
          "b": 60
        }
      },
      {
        "by": 5,
        "category": "risk",
        "leads": "minicpm-v-4-7-1b",
        "name": "Risk",
        "reasons": {
          "a": "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.",
          "b": "File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the sensenova organisation, which the GitHub repository for the model links to (10), and the Hub gives a SHA-256 for the large files (5). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations or safety section, and nothing on instructions inside tool results or images. Licence risk 15, Apache 2.0 with no use policy, no threshold and no right to change the terms. Security programme 5, SenseTime's about page gives a security contact (5). 15 + 15 + 0 + 0 + 15 + 5 = 50."
        },
        "scores": {
          "a": 55,
          "b": 50
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "OpenBMB",
        "b": "SenseTime (SenseNova)",
        "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": "Apache-2.0",
        "b": "Apache-2.0",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-10-30",
        "b": "2026-11-02",
        "name": "Last release"
      },
      {
        "a": "no document linked",
        "b": "not read yet",
        "name": "Terms last updated"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Privacy policy last updated"
      }
    ],
    "faq": [
      {
        "answer": "MiniCPM-V 4.7 1B scores 61 (C) on agent readiness against SenseNova-Loop-9B-Preview's 46.8 (D), and leads in every scored category.",
        "question": "Which is better for AI agents, MiniCPM-V 4.7 1B or SenseNova-Loop-9B-Preview?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 95 against 40",
          "Risk, 55 against 50",
          "Agent interface, 70 against 60"
        ],
        "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"
      },
      {
        "aheadOn": null,
        "also": null,
        "goodFor": "Researchers who want to study or fine-tune a looped transformer, more than agents that need a model to call today",
        "slug": "sensenova-loop-9b-preview",
        "watchFor": "No released runtime runs it. vLLM and SGLang need unmerged pull requests"
      }
    ],
    "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-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/agents-a1-4b-vs-sensenova-loop-9b-preview.json",
        "title": "Agents-A1-4B vs SenseNova-Loop-9B-Preview",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-sensenova-loop-9b-preview"
      },
      {
        "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/apertus-v1-5-8b-vs-sensenova-loop-9b-preview.json",
        "title": "Apertus v1.5 8B vs SenseNova-Loop-9B-Preview",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-sensenova-loop-9b-preview"
      },
      {
        "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/apodex-1-1-mini-vs-sensenova-loop-9b-preview.json",
        "title": "Apodex-1.1-mini vs SenseNova-Loop-9B-Preview",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-sensenova-loop-9b-preview"
      },
      {
        "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/arete-27b-vs-sensenova-loop-9b-preview.json",
        "title": "Arete-27B vs SenseNova-Loop-9B-Preview",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-sensenova-loop-9b-preview"
      },
      {
        "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/bigbang-v1-vs-sensenova-loop-9b-preview.json",
        "title": "BigBang-v1 vs SenseNova-Loop-9B-Preview",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-sensenova-loop-9b-preview"
      },
      {
        "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/carbon-x-vs-sensenova-loop-9b-preview.json",
        "title": "Carbon-X vs SenseNova-Loop-9B-Preview",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-sensenova-loop-9b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-minicpm-v-4-7-1b.json",
        "title": "Cola2 vs MiniCPM-V 4.7 1B",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-minicpm-v-4-7-1b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-sensenova-loop-9b-preview.json",
        "title": "Cola2 vs SenseNova-Loop-9B-Preview",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-sensenova-loop-9b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-minicpm-v-4-7-1b.json",
        "title": "Cosmos-Reason2-2B vs MiniCPM-V 4.7 1B",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-minicpm-v-4-7-1b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-sensenova-loop-9b-preview.json",
        "title": "Cosmos-Reason2-2B vs SenseNova-Loop-9B-Preview",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-sensenova-loop-9b-preview"
      },
      {
        "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/darwin-36b-opus-vs-sensenova-loop-9b-preview.json",
        "title": "Darwin-36B-Opus vs SenseNova-Loop-9B-Preview",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-sensenova-loop-9b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-minicpm-v-4-7-1b.json",
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        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-minicpm-v-4-7-1b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-sensenova-loop-9b-preview.json",
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        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-sensenova-loop-9b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-minicpm-v-4-7-1b.json",
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        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-minicpm-v-4-7-1b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-sensenova-loop-9b-preview.json",
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        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-sensenova-loop-9b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-minicpm-v-4-7-1b.json",
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      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-sensenova-loop-9b-preview.json",
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        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-sensenova-loop-9b-preview"
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        "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-sensenova-loop-9b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-minicpm-v-4-7-1b.json",
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      {
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        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-sensenova-loop-9b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-minicpm-v-4-7-1b.json",
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        "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-minicpm-v-4-7-1b"
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      {
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        "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-sensenova-loop-9b-preview"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-minicpm-v-4-7-1b.json",
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-sensenova-loop-9b-preview.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-sensenova-loop-9b-preview"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-minicpm-v-4-7-1b.json",
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      {
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      {
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        "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-sensenova-loop-9b-preview.json",
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        "title": "Qwen3.5-9B vs SenseNova-Loop-9B-Preview",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-sensenova-loop-9b-preview"
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        "json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-sensenova-loop-9b-preview.json",
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        "json": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-sensenova-loop-9b-preview.json",
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        "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-smollm3-3b.json",
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        "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-zaya1-8b.json",
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    ],
    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 55,
        "edge": "minicpm-v-4-7-1b",
        "key": "reach",
        "minicpm-v-4-7-1b": 95,
        "name": "Reach",
        "sensenova-loop-9b-preview": 40,
        "weight": 25
      },
      {
        "by": 5,
        "edge": "minicpm-v-4-7-1b",
        "key": "risk",
        "minicpm-v-4-7-1b": 55,
        "name": "Risk",
        "sensenova-loop-9b-preview": 50,
        "weight": 20
      },
      {
        "by": 10,
        "edge": "minicpm-v-4-7-1b",
        "key": "interface",
        "minicpm-v-4-7-1b": 70,
        "name": "Agent interface",
        "sensenova-loop-9b-preview": 60,
        "weight": 10
      },
      {
        "by": 4,
        "edge": "minicpm-v-4-7-1b",
        "key": "upkeep",
        "minicpm-v-4-7-1b": 48,
        "name": "Documentation and upkeep",
        "sensenova-loop-9b-preview": 44,
        "weight": 10
      }
    ],
    "summary": "MiniCPM-V 4.7 1B scores 61 (C) on agent readiness against SenseNova-Loop-9B-Preview's 46.8 (D), and leads in every scored category. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "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.",
      "sensenova-loop-9b-preview": "An unusual architecture under Apache 2.0, with a 256K training context, a thinking switch and tool calling in the template. It's a preview in practice too: no released runtime runs it, loading it runs the repository's code, the files are twice the size the card implies, and nobody from SenseNova has answered a question yet."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
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    "markdown": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-sensenova-loop-9b-preview.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-sensenova-loop-9b-preview.min.md"
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  "markdown": "MiniCPM-V 4.7 1B scores 61 (C) on agent readiness against SenseNova-Loop-9B-Preview's 46.8 (D), and leads in every scored category. Both do open-weight models you run yourself.\n\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- SenseNova-Loop-9B-Preview: grade D, 46.8/100, rank #155 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/sensenova-loop-9b-preview.md · JSON https://www.anchorterminal.com/api/v1/tools/sensenova-loop-9b-preview.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### 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 40\n- Risk, 55 against 50\n- Agent interface, 70 against 60\n\nWatch for: No limitations section, safety results, agent-use guidance or security contact\n\n### SenseNova-Loop-9B-Preview (D)\n\nGood for: Researchers who want to study or fine-tune a looped transformer, more than agents that need a model to call today\n\nWatch for: No released runtime runs it. vLLM and SGLang need unmerged pull requests\n\n\n## Why the edge\n\n### Reach: MiniCPM-V 4.7 1B +55\n\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- SenseNova-Loop-9B-Preview (40): Memory 15, the only build is the safetensors set, 38.14 GB with its MTP head, since the Hub counts 9.41B of its parameters in F32. Runtimes 0, none of the six runs it in a released version. Builds 0, SenseNova publishes one set of weights and no quantised build. Terms 15, Apache 2.0 in the card's metadata and on the card, which links the LICENSE in the GitHub repository, and nothing added. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it, and the card names no API that serves it. 15 + 0 + 0 + 15 + 10 + 0 = 40.\n\n### Agent interface: MiniCPM-V 4.7 1B +10\n\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- SenseNova-Loop-9B-Preview (60): Chat template 20, chat_template.jinja in the repository (7,756 characters). Tool-calling format 10, only the template says it: a tools block, and calls written as tool_call blocks with function and parameter tags. Trained tool calling 10, the card says the training recipes include tool trajectories and that the model is for agentic workflows. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, the card documents enable_thinking in the chat template call, on or off per request, and the template puts the reasoning in think tags ahead of the answer. Context 10, 262,144 tokens in training, per the card's specification table, and config.json gives the language model 262,144 positions. 20 + 10 + 10 + 0 + 10 + 10 = 60.\n\n### Risk: MiniCPM-V 4.7 1B +5\n\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- SenseNova-Loop-9B-Preview (50): File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the sensenova organisation, which the GitHub repository for the model links to (10), and the Hub gives a SHA-256 for the large files (5). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations or safety section, and nothing on instructions inside tool results or images. Licence risk 15, Apache 2.0 with no use policy, no threshold and no right to change the terms. Security programme 5, SenseTime's about page gives a security contact (5). 15 + 15 + 0 + 0 + 15 + 5 = 50.\n\n## Score by category\n\n| Category | Weight | MiniCPM-V 4.7 1B | SenseNova-Loop-9B-Preview | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 95 | 40 | MiniCPM-V 4.7 1B +55 |\n| Risk | 20% (20 this run) | 55 | 50 | MiniCPM-V 4.7 1B +5 |\n| Agent interface | 10% (10 this run) | 70 | 60 | MiniCPM-V 4.7 1B +10 |\n| Documentation and upkeep | 10% (10 this run) | 48 | 44 | MiniCPM-V 4.7 1B +4 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **46.8 · D** | |\n\n## Facts side by side\n\n| Fact | MiniCPM-V 4.7 1B | SenseNova-Loop-9B-Preview |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | OpenBMB | SenseTime (SenseNova) |\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 | Apache-2.0 | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-30 | 2026-11-02 |\n| Terms last updated | no document linked | not read yet |\n| Privacy policy last updated | no document linked | no document linked |\n\n## Verdicts\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**SenseNova-Loop-9B-Preview.** An unusual architecture under Apache 2.0, with a 256K training context, a thinking switch and tool calling in the template. It's a preview in practice too: no released runtime runs it, loading it runs the repository's code, the files are twice the size the card implies, and nobody from SenseNova has answered a question yet.\n\n## Before you call either\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### SenseNova-Loop-9B-Preview\n\n1. Read modeling_sensenova_loop.py before you load it. trust_remote_code runs it\n2. Pin transformers 5.18.0, the version the card checked\n3. Load with dtype=torch.bfloat16 as the card does, or the 38 GB of F32 weights stay at full size\n4. Pass enable_thinking=False to apply_chat_template for a direct answer\n5. Tool calls come back as tool_call blocks with function and parameter tags. Parse them yourself, since no runtime ships a parser\n6. Wait for the vLLM or SGLang pull request to land in a release before putting it behind an API\n\n## Questions\n\n### Which is better for AI agents, MiniCPM-V 4.7 1B or SenseNova-Loop-9B-Preview?\n\nMiniCPM-V 4.7 1B scores 61 (C) on agent readiness against SenseNova-Loop-9B-Preview's 46.8 (D), and leads in every scored category.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-sensenova-loop-9b-preview.json, and with the fewest tokens: https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-sensenova-loop-9b-preview.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"minicpm-v-4-7-1b\", \"b\": \"sensenova-loop-9b-preview\"}`. 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