{
  "data": {
    "a": {
      "slug": "minicpm-v-4-7-35b-a3b",
      "name": "MiniCPM-V 4.7 35B-A3B",
      "vendor": "OpenBMB",
      "vendorUrl": "https://openbmb.cn",
      "kind": "weights",
      "category": "open-weights",
      "summary": "MiniCPM-V 4.7 35B-A3B is OpenBMB's open-weight vision-language model, a 35B mixture of experts with about 3B active per token that reads images and video, calls tools, and ships in BF16, FP8, 4-bit and GGUF builds under Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/minicpm-v-4-7-35b-a3b",
      "markdownUrl": "https://www.anchorterminal.com/tools/minicpm-v-4-7-35b-a3b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/minicpm-v-4-7-35b-a3b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/minicpm-v-4-7-35b-a3b.json",
      "dns": "minicpm-v-4-7-35b-a3b.grade.anchorterminal.com",
      "repo": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "openbmb/MiniCPM-V-4.7-35B-A3B"
        },
        {
          "registry": "huggingface",
          "name": "openbmb/MiniCPM-V-4.7-35B-A3B-gguf"
        },
        {
          "registry": "huggingface",
          "name": "openbmb/MiniCPM-V-4.7-35B-A3B-FP8"
        },
        {
          "registry": "huggingface",
          "name": "openbmb/MiniCPM-V-4.7-35B-A3B-AWQ"
        },
        {
          "registry": "huggingface",
          "name": "openbmb/MiniCPM-V-4.7-35B-A3B-GPTQ"
        },
        {
          "registry": "huggingface",
          "name": "openbmb/MiniCPM-V-4.7-35B-A3B-int4"
        },
        {
          "registry": "ollama",
          "name": "openbmb/minicpm-v4.7:35b-a3b"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The main repository and OpenBMB's GGUF, FP8, AWQ, GPTQ and int4 repositories are not gated (checked 2026-11-10).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0, named in the card's metadata and carried as a LICENSE file in OpenBMB's GitHub repository. Running it yourself costs the hardware, about 70 GB for the BF16 files, 22.7 GB for the 4-bit safetensors builds or 18.7 GB for the IQ4_XS GGUF (checked 2026-11-10).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-11-10).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-11-10"
      },
      "docsUrl": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "multimodal",
        "vision",
        "video",
        "on-device",
        "tool-calling",
        "apache-2.0",
        "safetensors",
        "gguf",
        "fp8",
        "awq",
        "gptq",
        "self-hosted"
      ],
      "lastRelease": "2026-11-03",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 61,
        "grade": "C",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1518,
        "categoryRank": 61,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 70,
          "reach": 90,
          "risk": 55,
          "upkeep": 68
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-10"
        },
        "negative": 0,
        "verdict": "A vision-language cousin for images and video that fits a 24 to 32 GB machine from OpenBMB's own GGUF, under plain Apache 2.0, with a publisher that answers. The card says little about limits, training data or how tool calls come back, so test the tool format in your runtime before relying on it.",
        "bestFor": "Agents that need to read screenshots, documents and video on a local machine with 24 to 32 GB for the model, under a permissive licence",
        "strengths": [
          "Fits about 24 GB from OpenBMB's IQ4_XS GGUF and 32 GB from its Q4_K_M, 4-bit safetensors or Ollama builds",
          "Runs in llama.cpp, Ollama, vLLM, SGLang and Transformers from their released versions, with llama.cpp's own guide",
          "Plain Apache 2.0, ungated, safetensors and GGUF only, no remote code",
          "OpenBMB answered 8 of 12 discussions and dates its releases",
          "Images, several images and video in one model, with thinking off by default"
        ],
        "weaknesses": [
          "No limitations section, safety results, agent-use guidance or security contact",
          "Training data isn't described, and the card gives no context length, languages or knowledge cut-off",
          "The card shows a tool-calling request but not the reply, and names no parser",
          "OpenBMB's cookbook is behind the runtimes on SGLang and Ollama support",
          "No inference provider is mapped on Hugging Face"
        ],
        "agentNotes": [
          "Start it with `llama-server -hf openbmb/MiniCPM-V-4.7-35B-A3B-gguf:Q4_K_M --jinja`, or `ollama run openbmb/minicpm-v4.7:35b-a3b`",
          "For image input in llama.cpp add `mmproj-model-f16.gguf`, or the 4x projector for more visual detail at a higher cost",
          "The GGUF files at the repository root have thinking off. Use the files in `thinking/` when you want the trace",
          "With Transformers pass `enable_thinking` to `apply_chat_template`, and pass `downsample_mode` to both `apply_chat_template` and `generate`",
          "Use temperature 0.7, top_p 0.95 and top_k 100 in instruct mode, as the card recommends",
          "Check tool calls in your runtime first, since the card names no parser. The template uses the Qwen3.5 function-block format",
          "Validate JSON output yourself, since structured output isn't documented"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 0,
        "avgRating": 0,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "C",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 61
          }
        ],
        "editorialScores": {
          "interface": 70,
          "reach": 90,
          "risk": 55,
          "upkeep": 68
        },
        "provenanceScore": 58,
        "checked": {
          "categories": {
            "interface": "2026-11-10",
            "reach": "2026-11-10",
            "risk": "2026-11-10",
            "upkeep": "2026-11-10"
          },
          "last": "2026-11-10",
          "oldest": "2026-11-10",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "ollama run openbmb/minicpm-v4.7:35b-a3b\n# or, with llama.cpp and OpenBMB's own GGUF:\nllama-server -hf openbmb/MiniCPM-V-4.7-35B-A3B-gguf:Q4_K_M --jinja\n# or, with Transformers' server, as the card gives it:\npip install \"transformers[serving]\u003e=5.18.0\"\ntransformers serve openbmb/MiniCPM-V-4.7-35B-A3B --port 8000 --host 0.0.0.0",
        "http": "curl http://localhost:11434/api/chat \\\n  -d '{\"model\": \"openbmb/minicpm-v4.7:35b-a3b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/minicpm-v-4-7-35b-a3b"
      },
      "sameCompany": [
        "minicpm-v-4-7-1b",
        "minicpm-o-4-5",
        "minicpm5-2b"
      ],
      "weights": {
        "trainedBy": "OpenBMB (ModelBest and Tsinghua NLP Lab)",
        "builtOn": "Qwen3.5 and Qwen3.6 as the language backbone and SigLIP2 as the vision module, per the card's acknowledgements. Not declared in the metadata. The text config is qwen3_5_moe_text with 40 layers and 256 experts",
        "params": "35.2B (3B active)",
        "context": 262144,
        "goodFor": "Vision-language mixture-of-experts model for images, several images and video, with tool calling and thinking off by default, fast for its size with about 3B active parameters.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "MiniCPM-V-4_7-35B-A3B-IQ4_XS",
            "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B-gguf/blob/main/MiniCPM-V-4_7-35B-A3B-IQ4_XS.gguf",
            "who": "publisher",
            "precision": "iq4_xs",
            "sizeGB": 18.7,
            "memoryGB": 24,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ],
            "note": "The smallest file at 4 bits. IQ3_M and IQ3_XS (15.4 and 14.5 GB) are below the 4-bit line and are not listed"
          },
          {
            "name": "MiniCPM-V-4.7-35B-A3B-AWQ",
            "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B-AWQ",
            "who": "publisher",
            "precision": "w4a16",
            "sizeGB": 22.7,
            "memoryGB": 32,
            "format": "safetensors",
            "runsOn": [
              "vLLM"
            ],
            "note": "AWQ, 4-bit weights with 16-bit activations"
          },
          {
            "name": "MiniCPM-V-4.7-35B-A3B-GPTQ",
            "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B-GPTQ",
            "who": "publisher",
            "precision": "w4a16",
            "sizeGB": 22.7,
            "memoryGB": 32,
            "format": "safetensors",
            "runsOn": [
              "vLLM"
            ],
            "note": "GPTQ, 4-bit weights with 16-bit activations"
          },
          {
            "name": "MiniCPM-V-4.7-35B-A3B-int4",
            "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B-int4",
            "who": "publisher",
            "precision": "int4",
            "sizeGB": 22.7,
            "memoryGB": 32,
            "format": "safetensors",
            "runsOn": [
              "vLLM"
            ],
            "note": "Listed on the card with the FP8 build under llm-compressor's FP8 and INT4 guide"
          },
          {
            "name": "MiniCPM-V-4_7-35B-A3B-Q5_K_M",
            "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B-gguf/blob/main/MiniCPM-V-4_7-35B-A3B-Q5_K_M.gguf",
            "who": "publisher",
            "precision": "q5_k_m",
            "sizeGB": 24.7,
            "memoryGB": 32,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ]
          },
          {
            "name": "MiniCPM-V-4_7-35B-A3B-Q4_K_M",
            "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B-gguf/blob/main/MiniCPM-V-4_7-35B-A3B-Q4_K_M.gguf",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 21.2,
            "memoryGB": 32,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ],
            "note": "Add mmproj-model-f16.gguf (1.1 GB) or mmproj-model-f16-4x.gguf (0.9 GB) for image input. The same file with thinking on is in the thinking/ folder"
          },
          {
            "name": "ollama openbmb/minicpm-v4.7:35b-a3b",
            "url": "https://ollama.com/openbmb/minicpm-v4.7",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 22,
            "memoryGB": 32,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "OpenBMB's own namespace on Ollama, not Ollama's library. Q4_K_M, 22 GB as the tags page shows it, with q8_0 (38 GB) and fp16 (70 GB) tags beside it"
          },
          {
            "name": "MiniCPM-V-4.7-35B-A3B-FP8",
            "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B-FP8",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 38,
            "memoryGB": 48,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang"
            ]
          },
          {
            "name": "MiniCPM-V-4_7-35B-A3B-Q8_0",
            "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B-gguf/blob/main/MiniCPM-V-4_7-35B-A3B-Q8_0.gguf",
            "who": "publisher",
            "precision": "q8_0",
            "sizeGB": 36.9,
            "memoryGB": 48,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ]
          },
          {
            "name": "MiniCPM-V-4_7-35B-A3B-Q6_K",
            "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B-gguf/blob/main/MiniCPM-V-4_7-35B-A3B-Q6_K.gguf",
            "who": "publisher",
            "precision": "q6_k",
            "sizeGB": 28.5,
            "memoryGB": 48,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ]
          },
          {
            "name": "MiniCPM-V-4.7-35B-A3B",
            "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 70.4,
            "memoryGB": 96,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)",
              "Transformers"
            ]
          },
          {
            "name": "MiniCPM-V-4_7-35B-A3B-F16",
            "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B-gguf/blob/main/MiniCPM-V-4_7-35B-A3B-F16.gguf",
            "who": "publisher",
            "precision": "f16",
            "sizeGB": 69.4,
            "memoryGB": 96,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ]
          }
        ],
        "run": "llama-server -hf openbmb/MiniCPM-V-4.7-35B-A3B-gguf:Q4_K_M --jinja",
        "runBuild": "MiniCPM-V-4_7-35B-A3B-Q4_K_M",
        "checked": "2026-11-10",
        "minMemoryGB": 24
      },
      "family": {
        "lab": "OpenBMB (MiniCPM)",
        "family": "MiniCPM-V 4",
        "url": "https://www.anchorterminal.com/open-weights/#openbmb",
        "others": [
          {
            "name": "MiniCPM-V 4.7 1B",
            "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-1B",
            "params": "1.55B (1,554,476,272 per the safetensors count)",
            "listing": "https://www.anchorterminal.com/tools/minicpm-v-4-7-1b"
          }
        ],
        "olderCount": 22
      },
      "area": "models",
      "provenance": {
        "legalEntity": "OpenBMB (named in the GitHub LICENSE's copyright line)",
        "domain": "openbmb.cn",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://github.com/OpenBMB/MiniCPM-V",
        "securityTxt": "none",
        "checked": "2026-11-10",
        "notes": [
          "No legal entity is named on the card. OpenBMB is the open-source organisation of ModelBest and Tsinghua University's NLP lab, as the MiniCPM5-2B listing records.",
          "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.",
          "The changelog is the GitHub README's dated news list (the release on 12 October 2026, DSpark heads on 15 October) and the repositories' commit messages, which say what changed.",
          "openbmb.cn/.well-known/security.txt returns 404 and github.com/OpenBMB/MiniCPM-V has no SECURITY.md.",
          "unchecked: the domain's registration date."
        ],
        "score": 58
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/minicpm-v-4-7-35b-a3b.json"
    },
    "answer": "MiniCPM-V 4.7 35B-A3B and occamy-1.0 score within a point of each other on agent readiness, 61 (C) and 61 (C). occamy-1.0 leads on documentation and upkeep.",
    "b": {
      "slug": "occamy-1-0",
      "name": "occamy-1.0",
      "vendor": "Accio (Accio-Lab)",
      "vendorUrl": "https://accio-lab.github.io/occamy/",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Occamy-1.0 is Accio's open-weight agent model for long multi-step work with tools, files and code, a fine-tune of Qwen3.6-35B-A3B (35B mixture of experts, 3B active) published on Hugging Face under Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/occamy-1-0",
      "markdownUrl": "https://www.anchorterminal.com/tools/occamy-1-0.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/occamy-1-0.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/occamy-1-0.json",
      "dns": "occamy-1-0.grade.anchorterminal.com",
      "repo": "https://huggingface.co/Accio-Lab/occamy-1.0",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "Accio-Lab/occamy-1.0"
        },
        {
          "registry": "huggingface",
          "name": "Accio-Lab/occamy-1.0-FP8"
        },
        {
          "registry": "huggingface",
          "name": "Accio-Lab/occamy-1.0-NVFP4"
        },
        {
          "registry": "huggingface",
          "name": "Accio-Lab/occamy-1.0-GGUF"
        },
        {
          "registry": "huggingface",
          "name": "Accio-Lab/occamy-1.0-APEX-GGUF"
        },
        {
          "registry": "huggingface",
          "name": "Accio-Lab/occamy-1.0-MLX-4bit"
        },
        {
          "registry": "huggingface",
          "name": "Accio-Lab/occamy-1.0-MLX-mxfp4"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The main repository and all of Accio's build repositories are not gated (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0, the same licence as the Qwen3.6-35B-A3B base. Running it yourself costs the hardware, 70.2 GB for BF16 and 18.4 GB for the smallest 4-bit build (checked 2026-10-21).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-10-21"
      },
      "docsUrl": "https://huggingface.co/Accio-Lab/occamy-1.0",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "multimodal",
        "reasoning",
        "tool-calling",
        "agent",
        "coding",
        "derivative",
        "apache-2.0",
        "safetensors",
        "gguf",
        "mlx",
        "fp8",
        "nvfp4",
        "self-hosted"
      ],
      "lastRelease": "2026-10-02",
      "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": 49,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 70,
          "reach": 90,
          "risk": 55,
          "upkeep": 74
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-03"
        },
        "negative": 0,
        "verdict": "A Qwen3.6-35B-A3B fine-tuned for long agent work with tools, files and code, under plain Apache 2.0, and Accio's 4-bit builds run in about 23 GB. The main gaps are no safety results, agent guidance or security contact, and every benchmark figure is Accio's own.",
        "bestFor": "Agents that want an Apache 2.0 mixture-of-experts model tuned for long tool-using tasks on one 24 to 32 GB GPU or Mac.",
        "strengths": [
          "Apache 2.0 like its base, with nothing gated",
          "Accio publishes FP8, NVFP4, GGUF and MLX builds, the 4-bit ones within 24 GB",
          "Same architecture and template as Qwen3.6-35B-A3B, so released llama.cpp, Ollama, vLLM, SGLang and Transformers run it",
          "A technical report, the supervised data mix with token counts and a published data subset",
          "MD5 and SHA-256 checksums for every file"
        ],
        "weaknesses": [
          "No safety results, prompt-injection guidance or security contact",
          "No hosted API or inference provider",
          "Tool-calling format and structured output aren't documented beyond the template and parser name",
          "Languages and a knowledge cut-off aren't stated",
          "The MLX builds await Apple Metal acceptance, per Accio's own cards"
        ],
        "agentNotes": [
          "Serve with `--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder` on vLLM 0.19.0 or SGLang 0.5.10 and later",
          "BF16 is 70.2 GB, so on a 128 GB machine run it without the card's 8-way tensor parallelism",
          "Keep the whole assistant message, reasoning and tool calls included, in later turns, as the card says",
          "Sampling per the card is temperature 1.0, top_p 0.95, top_k 20 and presence_penalty 1.5",
          "With the GGUFs, NFC-normalise input text and read TOKENIZER.md, since the files store qwen2 pre-tokenizer metadata",
          "The base's MTP layer isn't in this checkpoint, Accio-Lab/occamy-1.0-MTP is an experimental separate head",
          "Ollama has no Occamy library entry, so load Accio's GGUF in Ollama 0.40.14 or later, which builds a llama.cpp with the qwen35moe architecture"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 3,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "C",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 61
          }
        ],
        "editorialScores": {
          "interface": 70,
          "reach": 90,
          "risk": 55,
          "upkeep": 74
        },
        "provenanceScore": 24,
        "checked": {
          "categories": {
            "interface": "2026-11-03",
            "reach": "2026-11-03",
            "risk": "2026-11-03",
            "upkeep": "2026-11-03"
          },
          "last": "2026-11-03",
          "lastMoved": "2026-11-03",
          "oldest": "2026-11-03",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "vllm serve Accio-Lab/occamy-1.0 --max-model-len 262144 --reasoning-parser qwen3 \\\n  --enable-auto-tool-choice --tool-call-parser qwen3_coder\n# or a 4-bit GGUF with llama.cpp:\nllama-server -hf Accio-Lab/occamy-1.0-GGUF:Q4_K_M --jinja -c 131072",
        "http": "curl http://localhost:8000/v1/chat/completions \\\n  -H 'Content-Type: application/json' \\\n  -d '{\"model\": \"Accio-Lab/occamy-1.0\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/occamy-1-0"
      },
      "weights": {
        "trainedBy": "Accio (Accio-Lab)",
        "builtOn": "Qwen/Qwen3.6-35B-A3B (Apache 2.0), declared in the card's metadata (base_model) and text as the post-trained starting checkpoint. Same qwen3_5_moe architecture and config apart from the removed MTP layer, and the same chat template. Occamy is also Apache 2.0",
        "params": "35B (3B active)",
        "active": "3B",
        "context": 262144,
        "goodFor": "Mixture-of-experts agent model for long multi-step work with tools, files, code and office tasks, text and images in, thinking on by default.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "occamy-1.0-MLX-mxfp4",
            "url": "https://huggingface.co/Accio-Lab/occamy-1.0-MLX-mxfp4",
            "who": "publisher",
            "precision": "mxfp4",
            "sizeGB": 18.4,
            "memoryGB": 24,
            "format": "mlx",
            "runsOn": [
              "MLX"
            ],
            "note": "Text only. Apple Metal acceptance pending, per the card"
          },
          {
            "name": "occamy-1.0-MLX-4bit",
            "url": "https://huggingface.co/Accio-Lab/occamy-1.0-MLX-4bit",
            "who": "publisher",
            "precision": "mlx-4bit",
            "sizeGB": 19.5,
            "memoryGB": 24,
            "format": "mlx",
            "runsOn": [
              "MLX"
            ],
            "note": "Text only. Apple Metal acceptance pending, per the card"
          },
          {
            "name": "occamy-1.0-IQ4_XS",
            "url": "https://huggingface.co/Accio-Lab/occamy-1.0-GGUF/blob/main/occamy-1.0-IQ4_XS.gguf",
            "who": "publisher",
            "precision": "iq4_xs",
            "sizeGB": 18.7,
            "memoryGB": 24,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ],
            "note": "Image input needs the 0.90 GB mmproj file. Input should be NFC-normalised, per the card"
          },
          {
            "name": "occamy-1.0-APEX-Compact",
            "url": "https://huggingface.co/Accio-Lab/occamy-1.0-APEX-GGUF/blob/main/occamy-1.0-APEX-Compact.gguf",
            "who": "publisher",
            "precision": "mixed-q3-q4",
            "sizeGB": 16.5,
            "memoryGB": 24,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ],
            "note": "Most routed experts at Q3_K, so it doesn't count as a 4-bit build for the memory line"
          },
          {
            "name": "occamy-1.0-NVFP4",
            "url": "https://huggingface.co/Accio-Lab/occamy-1.0-NVFP4",
            "who": "publisher",
            "precision": "nvfp4",
            "sizeGB": 23.9,
            "memoryGB": 32,
            "format": "safetensors",
            "note": "Expert-only mixed precision, checked on H200 with Marlin W4A16, per the card"
          },
          {
            "name": "occamy-1.0-Q4_K_M",
            "url": "https://huggingface.co/Accio-Lab/occamy-1.0-GGUF/blob/main/occamy-1.0-Q4_K_M.gguf",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 21.2,
            "memoryGB": 32,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ]
          },
          {
            "name": "occamy-1.0-FP8",
            "url": "https://huggingface.co/Accio-Lab/occamy-1.0-FP8",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 36.6,
            "memoryGB": 48,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang"
            ]
          },
          {
            "name": "occamy-1.0-Q8_0",
            "url": "https://huggingface.co/Accio-Lab/occamy-1.0-GGUF/blob/main/occamy-1.0-Q8_0.gguf",
            "who": "publisher",
            "precision": "q8_0",
            "sizeGB": 36.9,
            "memoryGB": 48,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ]
          },
          {
            "name": "occamy-1.0",
            "url": "https://huggingface.co/Accio-Lab/occamy-1.0",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 70.2,
            "memoryGB": 96,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)",
              "Transformers"
            ]
          }
        ],
        "run": "vllm serve Accio-Lab/occamy-1.0 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder",
        "runBuild": "occamy-1.0",
        "checked": "2026-11-03",
        "minMemoryGB": 24
      },
      "family": {
        "lab": "Accio (Accio-Lab)",
        "family": "occamy",
        "url": "https://www.anchorterminal.com/open-weights/#accio-lab",
        "others": [
          {
            "name": "occamy-1.0-MTP",
            "url": "https://huggingface.co/Accio-Lab/occamy-1.0-MTP",
            "params": "not stated (draft head only)"
          }
        ],
        "olderCount": 4,
        "unrestrictedCopies": 5
      },
      "area": "models",
      "provenance": {
        "legalEntity": "",
        "domain": "accio-lab.github.io",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "No legal entity is named on the card, the report or the repository. The report's byline is Accio Team, and the Hugging Face organisation Accio-Lab (fullname Accio, 15 members) isn't verified. The GitHub README and the project website link the organisation.",
          "Nothing is hosted, so the licence stands in for terms of service and no privacy policy is scored.",
          "The project website is a GitHub Pages site drawn by script, so its text couldn't be read beyond the title.",
          "No SECURITY.md in Accio-Lab/occamy. The README sends bug reports and feature requests to GitHub issues."
        ],
        "score": 24
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/occamy-1-0.json",
      "live": {
        "slug": "occamy-1-0",
        "securityTxt": {
          "url": "https://accio-lab.github.io/.well-known/security.txt",
          "state": "none",
          "checkedAt": "2026-11-10T16:08:29.220890734Z"
        },
        "domain": {
          "domain": "github.io",
          "checkedAt": "2026-11-08T15:08:12.013832199Z"
        },
        "updatedAt": "2026-11-10T16:08:29.220890734Z"
      }
    },
    "edges": [
      {
        "by": 6,
        "category": "upkeep",
        "leads": "occamy-1-0",
        "name": "Documentation and upkeep",
        "reasons": {
          "a": "Model card 8, it covers intended use, architecture and parameters but not limitations, context length, languages or a knowledge cut-off. Training data 0, none described. Last change 20, a commit on 3 November 2026. Responsiveness 20, OpenBMB answered 8 of 12 discussions. Version policy 10, earlier versions keep their repositories and the changes are dated and described. File health 10, the files agree. 8 + 0 + 20 + 20 + 10 + 10 = 68.",
          "b": "Model card 14 of 20, languages and a knowledge cut-off are missing. Training data 15, the mix by type with token counts, the method and a published subset, without a cut-off. Last change 20, Accio's GGUF build card changed on 19 October 2026 and the main card on 2 October. Responsiveness 10, Accio answered 4 of 9 questions. Version policy 5, a first release with no files replaced. File health 10, the files agree with the card. 14 + 15 + 20 + 10 + 5 + 10 = 74."
        },
        "scores": {
          "a": 68,
          "b": 74
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "OpenBMB",
        "b": "Accio (Accio-Lab)",
        "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": "2026-11-03",
        "b": "2026-10-02",
        "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": "none",
        "b": "3/5 (4)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "MiniCPM-V 4.7 35B-A3B and occamy-1.0 score within a point of each other on agent readiness, 61 (C) and 61 (C). occamy-1.0 leads on documentation and upkeep.",
        "question": "Which is better for AI agents, MiniCPM-V 4.7 35B-A3B or occamy-1.0?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": null,
        "also": null,
        "goodFor": "Agents that need to read screenshots, documents and video on a local machine with 24 to 32 GB for the model, under a permissive licence",
        "slug": "minicpm-v-4-7-35b-a3b",
        "watchFor": "No limitations section, safety results, agent-use guidance or security contact"
      },
      {
        "aheadOn": [
          "Documentation and upkeep, 74 against 68"
        ],
        "also": null,
        "goodFor": "Agents that want an Apache 2.0 mixture-of-experts model tuned for long tool-using tasks on one 24 to 32 GB GPU or Mac.",
        "slug": "occamy-1-0",
        "watchFor": "No safety results, prompt-injection guidance or security contact"
      }
    ],
    "history": {
      "minicpm-v-4-7-35b-a3b": null,
      "occamy-1-0": [
        {
          "causes": [
            "correction"
          ],
          "date": "2026-11-03",
          "delta": 0,
          "from": 61,
          "fromGrade": "C",
          "to": 61,
          "toGrade": "C"
        }
      ]
    },
    "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-4-7-35b-a3b.json",
        "title": "Agents-A1-4B vs MiniCPM-V 4.7 35B-A3B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-minicpm-v-4-7-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-occamy-1-0.json",
        "title": "Agents-A1-4B vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-occamy-1-0"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minicpm-v-4-7-35b-a3b.json",
        "title": "Apertus v1.5 8B vs MiniCPM-V 4.7 35B-A3B",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minicpm-v-4-7-35b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-occamy-1-0.json",
        "title": "Apertus v1.5 8B vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-occamy-1-0"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minicpm-v-4-7-35b-a3b.json",
        "title": "Apodex-1.1-mini vs MiniCPM-V 4.7 35B-A3B",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minicpm-v-4-7-35b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-occamy-1-0.json",
        "title": "Apodex-1.1-mini vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-occamy-1-0"
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      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-minicpm-v-4-7-35b-a3b.json",
        "title": "Arete-27B vs MiniCPM-V 4.7 35B-A3B",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-minicpm-v-4-7-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-occamy-1-0.json",
        "title": "Arete-27B vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-occamy-1-0"
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      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-minicpm-v-4-7-35b-a3b.json",
        "title": "BigBang-v1 vs MiniCPM-V 4.7 35B-A3B",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-minicpm-v-4-7-35b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-occamy-1-0.json",
        "title": "BigBang-v1 vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-occamy-1-0"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-minicpm-v-4-7-35b-a3b.json",
        "title": "Carbon-X vs MiniCPM-V 4.7 35B-A3B",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-minicpm-v-4-7-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-occamy-1-0.json",
        "title": "Carbon-X vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-occamy-1-0"
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      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-minicpm-v-4-7-35b-a3b.json",
        "title": "Cola2 vs MiniCPM-V 4.7 35B-A3B",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-minicpm-v-4-7-35b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-occamy-1-0.json",
        "title": "Cola2 vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-occamy-1-0"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-minicpm-v-4-7-35b-a3b.json",
        "title": "Cosmos-Reason2-2B vs MiniCPM-V 4.7 35B-A3B",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-minicpm-v-4-7-35b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-occamy-1-0.json",
        "title": "Cosmos-Reason2-2B vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-occamy-1-0"
      },
      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-minicpm-v-4-7-35b-a3b.json",
        "title": "Darwin-36B-Opus vs MiniCPM-V 4.7 35B-A3B",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-minicpm-v-4-7-35b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-occamy-1-0.json",
        "title": "Darwin-36B-Opus vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-occamy-1-0"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-minicpm-v-4-7-35b-a3b.json",
        "title": "DeepSeek-V3.2 vs MiniCPM-V 4.7 35B-A3B",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-minicpm-v-4-7-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-occamy-1-0.json",
        "title": "DeepSeek-V3.2 vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-occamy-1-0"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-minicpm-v-4-7-35b-a3b.json",
        "title": "DeepSeek V4.1 Flash vs MiniCPM-V 4.7 35B-A3B",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-minicpm-v-4-7-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-occamy-1-0.json",
        "title": "DeepSeek V4.1 Flash vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-occamy-1-0"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-minicpm-v-4-7-35b-a3b.json",
        "title": "DeepSeek-V4-Flash-0731 vs MiniCPM-V 4.7 35B-A3B",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-minicpm-v-4-7-35b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-occamy-1-0.json",
        "title": "DeepSeek-V4-Flash-0731 vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-occamy-1-0"
      },
      {
        "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-minicpm-v-4-7-35b-a3b.json",
        "title": "Devstral Small 2 vs MiniCPM-V 4.7 35B-A3B",
        "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-minicpm-v-4-7-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-occamy-1-0.json",
        "title": "Devstral Small 2 vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-occamy-1-0"
      },
      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-minicpm-v-4-7-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-minicpm-v-4-7-35b-a3b"
      },
      {
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        "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-occamy-1-0.json",
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        "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-minicpm-v-4-7-35b-a3b.json",
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        "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-occamy-1-0.json",
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        "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-minicpm-v-4-7-35b-a3b.json",
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        "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-occamy-1-0.json",
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-minicpm-v-4-7-35b-a3b.json",
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        "edge": "",
        "key": "reach",
        "minicpm-v-4-7-35b-a3b": 90,
        "name": "Reach",
        "occamy-1-0": 90,
        "weight": 25
      },
      {
        "by": 0,
        "edge": "",
        "key": "risk",
        "minicpm-v-4-7-35b-a3b": 55,
        "name": "Risk",
        "occamy-1-0": 55,
        "weight": 20
      },
      {
        "by": 0,
        "edge": "",
        "key": "interface",
        "minicpm-v-4-7-35b-a3b": 70,
        "name": "Agent interface",
        "occamy-1-0": 70,
        "weight": 10
      },
      {
        "by": 6,
        "edge": "occamy-1-0",
        "key": "upkeep",
        "minicpm-v-4-7-35b-a3b": 68,
        "name": "Documentation and upkeep",
        "occamy-1-0": 74,
        "weight": 10
      }
    ],
    "summary": "MiniCPM-V 4.7 35B-A3B and occamy-1.0 score within a point of each other on agent readiness, 61 (C) and 61 (C). occamy-1.0 leads on documentation and upkeep. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "minicpm-v-4-7-35b-a3b": "A vision-language cousin for images and video that fits a 24 to 32 GB machine from OpenBMB's own GGUF, under plain Apache 2.0, with a publisher that answers. The card says little about limits, training data or how tool calls come back, so test the tool format in your runtime before relying on it.",
      "occamy-1-0": "A Qwen3.6-35B-A3B fine-tuned for long agent work with tools, files and code, under plain Apache 2.0, and Accio's 4-bit builds run in about 23 GB. The main gaps are no safety results, agent guidance or security contact, and every benchmark figure is Accio's own."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-occamy-1-0",
    "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-occamy-1-0.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-occamy-1-0.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-occamy-1-0.min.md"
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  "markdown": "MiniCPM-V 4.7 35B-A3B and occamy-1.0 score within a point of each other on agent readiness, 61 (C) and 61 (C). occamy-1.0 leads on documentation and upkeep. Both do open-weight models you run yourself.\n\n- MiniCPM-V 4.7 35B-A3B: grade C, 61/100, rank #77 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/minicpm-v-4-7-35b-a3b.md · JSON https://www.anchorterminal.com/api/v1/tools/minicpm-v-4-7-35b-a3b.json\n- occamy-1.0: grade C, 61/100, rank #59 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/occamy-1-0.md · JSON https://www.anchorterminal.com/api/v1/tools/occamy-1-0.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 35B-A3B (C)\n\nGood for: Agents that need to read screenshots, documents and video on a local machine with 24 to 32 GB for the model, under a permissive licence\n\nWatch for: No limitations section, safety results, agent-use guidance or security contact\n\n### occamy-1.0 (C)\n\nGood for: Agents that want an Apache 2.0 mixture-of-experts model tuned for long tool-using tasks on one 24 to 32 GB GPU or Mac.\n\nAhead on:\n- Documentation and upkeep, 74 against 68\n\nWatch for: No safety results, prompt-injection guidance or security contact\n\n\n## Why the edge\n\n### Documentation and upkeep: occamy-1.0 +6\n\n- MiniCPM-V 4.7 35B-A3B (68): Model card 8, it covers intended use, architecture and parameters but not limitations, context length, languages or a knowledge cut-off. Training data 0, none described. Last change 20, a commit on 3 November 2026. Responsiveness 20, OpenBMB answered 8 of 12 discussions. Version policy 10, earlier versions keep their repositories and the changes are dated and described. File health 10, the files agree. 8 + 0 + 20 + 20 + 10 + 10 = 68.\n- occamy-1.0 (74): Model card 14 of 20, languages and a knowledge cut-off are missing. Training data 15, the mix by type with token counts, the method and a published subset, without a cut-off. Last change 20, Accio's GGUF build card changed on 19 October 2026 and the main card on 2 October. Responsiveness 10, Accio answered 4 of 9 questions. Version policy 5, a first release with no files replaced. File health 10, the files agree with the card. 14 + 15 + 20 + 10 + 5 + 10 = 74.\n\n## Score by category\n\n| Category | Weight | MiniCPM-V 4.7 35B-A3B | occamy-1.0 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 90 | 90 | even |\n| Risk | 20% (20 this run) | 55 | 55 | even |\n| Agent interface | 10% (10 this run) | 70 | 70 | even |\n| Documentation and upkeep | 10% (10 this run) | 68 | 74 | occamy-1.0 +6 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | MiniCPM-V 4.7 35B-A3B | occamy-1.0 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | OpenBMB | Accio (Accio-Lab) |\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 | 2026-11-03 | 2026-10-02 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | none | 3/5 (4) |\n\n## How the scores have moved\n\n### MiniCPM-V 4.7 35B-A3B\n\nNo score change since it was listed.\n\n### occamy-1.0\n\n- no change on 3 November 2026: correction\n\n## Verdicts\n\n**MiniCPM-V 4.7 35B-A3B.** A vision-language cousin for images and video that fits a 24 to 32 GB machine from OpenBMB's own GGUF, under plain Apache 2.0, with a publisher that answers. The card says little about limits, training data or how tool calls come back, so test the tool format in your runtime before relying on it.\n\n**occamy-1.0.** A Qwen3.6-35B-A3B fine-tuned for long agent work with tools, files and code, under plain Apache 2.0, and Accio's 4-bit builds run in about 23 GB. The main gaps are no safety results, agent guidance or security contact, and every benchmark figure is Accio's own.\n\n## Before you call either\n\n### MiniCPM-V 4.7 35B-A3B\n\n1. Start it with `llama-server -hf openbmb/MiniCPM-V-4.7-35B-A3B-gguf:Q4_K_M --jinja`, or `ollama run openbmb/minicpm-v4.7:35b-a3b`\n2. For image input in llama.cpp add `mmproj-model-f16.gguf`, or the 4x projector for more visual detail at a higher cost\n3. The GGUF files at the repository root have thinking off. Use the files in `thinking/` when you want the trace\n4. With Transformers pass `enable_thinking` to `apply_chat_template`, and pass `downsample_mode` to both `apply_chat_template` and `generate`\n5. Use temperature 0.7, top_p 0.95 and top_k 100 in instruct mode, as the card recommends\n6. Check tool calls in your runtime first, since the card names no parser. The template uses the Qwen3.5 function-block format\n7. Validate JSON output yourself, since structured output isn't documented\n\n### occamy-1.0\n\n1. Serve with `--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder` on vLLM 0.19.0 or SGLang 0.5.10 and later\n2. BF16 is 70.2 GB, so on a 128 GB machine run it without the card's 8-way tensor parallelism\n3. Keep the whole assistant message, reasoning and tool calls included, in later turns, as the card says\n4. Sampling per the card is temperature 1.0, top_p 0.95, top_k 20 and presence_penalty 1.5\n5. With the GGUFs, NFC-normalise input text and read TOKENIZER.md, since the files store qwen2 pre-tokenizer metadata\n6. The base's MTP layer isn't in this checkpoint, Accio-Lab/occamy-1.0-MTP is an experimental separate head\n7. Ollama has no Occamy library entry, so load Accio's GGUF in Ollama 0.40.14 or later, which builds a llama.cpp with the qwen35moe architecture\n\n## Questions\n\n### Which is better for AI agents, MiniCPM-V 4.7 35B-A3B or occamy-1.0?\n\nMiniCPM-V 4.7 35B-A3B and occamy-1.0 score within a point of each other on agent readiness, 61 (C) and 61 (C). occamy-1.0 leads on documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-occamy-1-0.json, and with the fewest tokens: https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-occamy-1-0.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"minicpm-v-4-7-35b-a3b\", \"b\": \"occamy-1-0\"}`. From a terminal: `anchor compare minicpm-v-4-7-35b-a3b occamy-1-0`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/minicpm-v-4-7-35b-a3b.json and https://www.anchorterminal.com/api/v1/tools/occamy-1-0.json\n\n## Other comparisons with MiniCPM-V 4.7 35B-A3B or occamy-1.0\n\n- [Agents-A1-4B vs MiniCPM-V 4.7 35B-A3B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-minicpm-v-4-7-35b-a3b.md)\n- [Agents-A1-4B vs occamy-1.0](https://www.anchorterminal.com/compare/agents-a1-4b-vs-occamy-1-0.md)\n- [Apertus v1.5 8B vs MiniCPM-V 4.7 35B-A3B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minicpm-v-4-7-35b-a3b.md)\n- [Apertus v1.5 8B vs occamy-1.0](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-occamy-1-0.md)\n- [Apodex-1.1-mini vs MiniCPM-V 4.7 35B-A3B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minicpm-v-4-7-35b-a3b.md)\n- [Apodex-1.1-mini vs occamy-1.0](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-occamy-1-0.md)\n- 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