{
  "data": {
    "a": {
      "slug": "gemma-4-26b-a4b-it",
      "name": "Gemma 4 26B A4B IT",
      "vendor": "Google DeepMind",
      "vendorUrl": "https://gemma.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Gemma 4 26B A4B IT is Google DeepMind's open-weight instruction-tuned mixture-of-experts model, 25.2B parameters with 3.8B active, taking text and images with a 262,144-token context, published on Hugging Face under Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/gemma-4-26b-a4b-it",
      "markdownUrl": "https://www.anchorterminal.com/tools/gemma-4-26b-a4b-it.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/gemma-4-26b-a4b-it.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/gemma-4-26b-a4b-it.json",
      "dns": "gemma-4-26b-a4b-it.grade.anchorterminal.com",
      "repo": "https://huggingface.co/google/gemma-4-26B-A4B-it",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "google/gemma-4-26B-A4B-it"
        },
        {
          "registry": "huggingface",
          "name": "google/gemma-4-26B-A4B-it-qat-q4_0-gguf"
        },
        {
          "registry": "ollama",
          "name": "gemma4:26b"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Google also hosts it as gemma-4-26b-a4b-it on the Gemini API, which needs an API key. Running it yourself costs the hardware, 57.7 GB at BF16 and 14.4 GB at Q4_0 per Google (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://gemma.ai/models/gemma-4/",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "multimodal",
        "reasoning",
        "tool-calling",
        "apache-2.0",
        "safetensors",
        "gguf",
        "self-hosted"
      ],
      "lastRelease": "2026-07-20",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 72.2,
        "grade": "BB",
        "agentReady": true,
        "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": 3,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "capability": 59,
          "interface": 85,
          "reach": 90,
          "risk": 70,
          "upkeep": 65
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-11-01"
        },
        "negative": 0,
        "verdict": "A fast mixture-of-experts cousin for general agent work with tools, optional thinking and image input, under Apache 2.0. Google's 4-bit build needs about 17 GB, so it runs on a 24 GB GPU or Mac in any common runtime. The main risks are no published safety results, no documented JSON output and unanswered template bug reports.",
        "bestFor": "Agents on a 24 GB or larger GPU or Mac that want a fast, permissively licensed tool-calling model with images and long context",
        "strengths": [
          "Plain Apache 2.0, no account or gate, safetensors and GGUF only with a checksum on every file",
          "Google's own 4-bit QAT GGUF needs about 17 GB with working space",
          "Released support in vLLM, SGLang, llama.cpp, Ollama, LM Studio and Transformers",
          "Tool declaration and call format documented by Google, with parallel calls and named parsers",
          "262,144 tokens of native context and thinking that turns on or off per request",
          "Also served by the Gemini API and four inference providers"
        ],
        "weaknesses": [
          "No safety evaluation figures published",
          "No documented way to ask for JSON or schema output",
          "Only one quantised precision from Google, no FP8",
          "Files unchanged since 20 July 2026, with chat-template bug reports unanswered",
          "Training data described only by type and cut-off"
        ],
        "agentNotes": [
          "On the reference machine run `llama-server -hf google/gemma-4-26B-A4B-it-qat-q4_0-gguf --hf-file gemma-4-26B_q4_0-it.gguf --jinja -c 65536`, and add the mmproj file for images",
          "With Ollama run `ollama run gemma4:26b` and raise num_ctx or OLLAMA_CONTEXT_LENGTH, since Google's guide says it defaults to 4K below 24 GiB of VRAM and agents want at least 64K",
          "In vLLM or SGLang pass `--tool-call-parser gemma4 --reasoning-parser gemma4` (vLLM also `--enable-auto-tool-choice`), or tool calls come back as text",
          "Thinking is off by default; pass enable_thinking=True or start the system prompt with \u003c|think|\u003e, and drop earlier turns' thinking from history except on tool-call turns",
          "Validate tool names and arguments before running them, and check any JSON yourself"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 3.8,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "high",
            "grade": "BB",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 72.2
          }
        ],
        "editorialScores": {
          "capability": 59,
          "interface": 85,
          "reach": 90,
          "risk": 70,
          "upkeep": 65
        },
        "provenanceScore": 72,
        "checked": {
          "categories": {
            "capability": "2026-11-01",
            "interface": "2026-11-01",
            "reach": "2026-11-01",
            "risk": "2026-11-01",
            "upkeep": "2026-11-01"
          },
          "last": "2026-11-01",
          "oldest": "2026-11-01",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "ollama run gemma4:26b\n# or, with vLLM:\nvllm serve google/gemma-4-26B-A4B-it --max-model-len 262144 --reasoning-parser gemma4 \\\n  --enable-auto-tool-choice --tool-call-parser gemma4",
        "http": "curl http://localhost:11434/api/chat \\\n  -d '{\"model\": \"gemma4:26b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}], \"options\": {\"num_ctx\": 65536}}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/gemma-4-26b-a4b-it"
      },
      "sameCompany": [
        "embeddinggemma-2",
        "embeddinggemma-300m",
        "shieldgemma",
        "diffusiongemma-26b-a4b-it",
        "gemma-4-12b-it",
        "gemma-4-31b-it",
        "gemma-4-e2b-it",
        "gemma-4-e4b-it"
      ],
      "sameModel": [
        "gemini-api"
      ],
      "weights": {
        "trainedBy": "Google DeepMind",
        "builtOn": "google/gemma-4-26B-A4B",
        "params": "25.2B",
        "active": "3.8B",
        "context": 262144,
        "goodFor": "General assistant and agent model with tool calling and optional thinking, taking text, images and video frames, fast for its size as a mixture of experts.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "LM Studio",
          "Transformers"
        ],
        "builds": [
          {
            "name": "gemma-4-26B_q4_0-it",
            "url": "https://huggingface.co/google/gemma-4-26B-A4B-it-qat-q4_0-gguf/blob/main/gemma-4-26B_q4_0-it.gguf",
            "who": "publisher",
            "precision": "q4_0",
            "sizeGB": 14.4,
            "memoryGB": 24,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)",
              "LM Studio"
            ],
            "note": "Quantisation-aware trained by Google. Add gemma-4-26B-it-mmproj.gguf (1.2 GB) for image input"
          },
          {
            "name": "ollama gemma4:26b",
            "url": "https://ollama.com/library/gemma4:26b",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 19,
            "memoryGB": 24,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "Q4_K_M weights with a BF16 vision projector and a Q8_0 draft model (digest 001e5dafc3c7). Size as Ollama's tags page shows it, rounded to whole GB above 10 GB; the Linux build is the llama.cpp variant"
          },
          {
            "name": "gemma-4-26B-A4B-it",
            "url": "https://huggingface.co/google/gemma-4-26B-A4B-it",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 51.6,
            "memoryGB": 64,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)"
            ]
          }
        ],
        "run": "llama-server -hf google/gemma-4-26B-A4B-it-qat-q4_0-gguf --hf-file gemma-4-26B_q4_0-it.gguf --jinja -c 65536",
        "runBuild": "gemma-4-26B_q4_0-it",
        "checked": "2026-10-21",
        "minMemoryGB": 24
      },
      "family": {
        "lab": "Google (Gemma)",
        "family": "Gemma 4",
        "url": "https://www.anchorterminal.com/open-weights/#google",
        "others": [
          {
            "name": "gemma-4-26B-A4B",
            "url": "https://huggingface.co/google/gemma-4-26B-A4B",
            "params": "25.2B total, 3.8B active"
          },
          {
            "name": "gemma-4-31B",
            "url": "https://huggingface.co/google/gemma-4-31B",
            "params": "30.7B"
          },
          {
            "name": "gemma-4-31B-it",
            "url": "https://huggingface.co/google/gemma-4-31B-it",
            "params": "30.7B",
            "listing": "https://www.anchorterminal.com/tools/gemma-4-31b-it"
          },
          {
            "name": "gemma-4-E2B",
            "url": "https://huggingface.co/google/gemma-4-E2B",
            "params": "2.3B effective (5.1B with embeddings)"
          },
          {
            "name": "gemma-4-E2B-it",
            "url": "https://huggingface.co/google/gemma-4-E2B-it",
            "params": "2.3B effective (5.1B with embeddings)",
            "listing": "https://www.anchorterminal.com/tools/gemma-4-e2b-it"
          },
          {
            "name": "gemma-4-E4B",
            "url": "https://huggingface.co/google/gemma-4-E4B",
            "params": "4.5B effective (8B with embeddings)"
          },
          {
            "name": "gemma-4-E4B-it",
            "url": "https://huggingface.co/google/gemma-4-E4B-it",
            "params": "4.5B effective (8B with embeddings)",
            "listing": "https://www.anchorterminal.com/tools/gemma-4-e4b-it"
          },
          {
            "name": "gemma-4-26B-A4B-it-assistant",
            "url": "https://huggingface.co/google/gemma-4-26B-A4B-it-assistant",
            "params": "420M (file metadata on Hugging Face; drafter)"
          },
          {
            "name": "gemma-4-31B-it-assistant",
            "url": "https://huggingface.co/google/gemma-4-31B-it-assistant",
            "params": "470M (file metadata on Hugging Face; drafter)"
          },
          {
            "name": "gemma-4-E2B-it-assistant",
            "url": "https://huggingface.co/google/gemma-4-E2B-it-assistant",
            "params": "78M (file metadata on Hugging Face; drafter)"
          },
          {
            "name": "gemma-4-E4B-it-assistant",
            "url": "https://huggingface.co/google/gemma-4-E4B-it-assistant",
            "params": "79M (file metadata on Hugging Face; drafter)"
          },
          {
            "name": "gemma-4-12B",
            "url": "https://huggingface.co/google/gemma-4-12B",
            "params": "11.95B"
          },
          {
            "name": "gemma-4-12B-it",
            "url": "https://huggingface.co/google/gemma-4-12B-it",
            "params": "11.95B",
            "listing": "https://www.anchorterminal.com/tools/gemma-4-12b-it"
          },
          {
            "name": "gemma-4-12B-it-assistant",
            "url": "https://huggingface.co/google/gemma-4-12B-it-assistant",
            "params": "423M (file metadata on Hugging Face; drafter)"
          },
          {
            "name": "diffusiongemma-26B-A4B-it",
            "url": "https://huggingface.co/google/diffusiongemma-26B-A4B-it",
            "params": "25.2B total, 3.8B active",
            "listing": "https://www.anchorterminal.com/tools/diffusiongemma-26b-a4b-it"
          },
          {
            "name": "embeddinggemma-2",
            "url": "https://huggingface.co/google/embeddinggemma-2",
            "params": "740M",
            "listing": "https://www.anchorterminal.com/tools/embeddinggemma-2"
          }
        ],
        "olderCount": 171,
        "unrestrictedCopies": 5
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Google LLC (RDAP registrant of gemma.ai; the card names Google DeepMind as author)",
        "domain": "gemma.ai",
        "domainRegistered": "2017-12-16",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://gemma.ai/models/releases/",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "RDAP names Google LLC as the registrant of gemma.ai, registered 16 December 2017, with MarkMonitor as registrar and Google's name servers. ai.google.dev's Gemma pages say the documentation moved to gemma.ai.",
          "Nothing is hosted under this listing, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.",
          "gemma.ai keeps a dated releases page for the Gemma family (Gemma 4 on 31 March 2026). The Hugging Face commit history says what each change fixed.",
          "gemma.ai/.well-known/security.txt answers 404. Google's company-wide security.txt at google.com points to its vulnerability reward programme."
        ],
        "score": 72
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/gemma-4-26b-a4b-it.json",
      "live": {
        "slug": "gemma-4-26b-a4b-it",
        "securityTxt": {
          "url": "https://gemma.ai/.well-known/security.txt",
          "state": "none",
          "checkedAt": "2026-11-10T16:08:36.94110613Z"
        },
        "domain": {
          "domain": "gemma.ai",
          "registered": "2017-12-16",
          "source": "https://rdap.identitydigital.services/rdap/domain/gemma.ai",
          "checkedAt": "2026-11-08T15:00:32.790630815Z"
        },
        "updatedAt": "2026-11-10T16:08:36.94110613Z"
      }
    },
    "answer": "Gemma 4 26B A4B IT scores 72.2 (BB) on agent readiness against MiniCPM-V 4.7 35B-A3B's 61 (C), and leads in 2 of 4 scored categories.",
    "b": {
      "slug": "minicpm-v-4-7-35b-a3b",
      "name": "MiniCPM-V 4.7 35B-A3B",
      "vendor": "OpenBMB",
      "vendorUrl": "https://openbmb.cn",
      "kind": "weights",
      "category": "open-weights",
      "summary": "MiniCPM-V 4.7 35B-A3B is OpenBMB's open-weight vision-language model, a 35B mixture of experts with about 3B active per token that reads images and video, calls tools, and ships in BF16, FP8, 4-bit and GGUF builds under Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/minicpm-v-4-7-35b-a3b",
      "markdownUrl": "https://www.anchorterminal.com/tools/minicpm-v-4-7-35b-a3b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/minicpm-v-4-7-35b-a3b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/minicpm-v-4-7-35b-a3b.json",
      "dns": "minicpm-v-4-7-35b-a3b.grade.anchorterminal.com",
      "repo": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "openbmb/MiniCPM-V-4.7-35B-A3B"
        },
        {
          "registry": "huggingface",
          "name": "openbmb/MiniCPM-V-4.7-35B-A3B-gguf"
        },
        {
          "registry": "huggingface",
          "name": "openbmb/MiniCPM-V-4.7-35B-A3B-FP8"
        },
        {
          "registry": "huggingface",
          "name": "openbmb/MiniCPM-V-4.7-35B-A3B-AWQ"
        },
        {
          "registry": "huggingface",
          "name": "openbmb/MiniCPM-V-4.7-35B-A3B-GPTQ"
        },
        {
          "registry": "huggingface",
          "name": "openbmb/MiniCPM-V-4.7-35B-A3B-int4"
        },
        {
          "registry": "ollama",
          "name": "openbmb/minicpm-v4.7:35b-a3b"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The main repository and OpenBMB's GGUF, FP8, AWQ, GPTQ and int4 repositories are not gated (checked 2026-11-10).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0, named in the card's metadata and carried as a LICENSE file in OpenBMB's GitHub repository. Running it yourself costs the hardware, about 70 GB for the BF16 files, 22.7 GB for the 4-bit safetensors builds or 18.7 GB for the IQ4_XS GGUF (checked 2026-11-10).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-11-10).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-11-10"
      },
      "docsUrl": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "multimodal",
        "vision",
        "video",
        "on-device",
        "tool-calling",
        "apache-2.0",
        "safetensors",
        "gguf",
        "fp8",
        "awq",
        "gptq",
        "self-hosted"
      ],
      "lastRelease": "2026-11-03",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 61,
        "grade": "C",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1518,
        "categoryRank": 61,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 70,
          "reach": 90,
          "risk": 55,
          "upkeep": 68
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-10"
        },
        "negative": 0,
        "verdict": "A vision-language cousin for images and video that fits a 24 to 32 GB machine from OpenBMB's own GGUF, under plain Apache 2.0, with a publisher that answers. The card says little about limits, training data or how tool calls come back, so test the tool format in your runtime before relying on it.",
        "bestFor": "Agents that need to read screenshots, documents and video on a local machine with 24 to 32 GB for the model, under a permissive licence",
        "strengths": [
          "Fits about 24 GB from OpenBMB's IQ4_XS GGUF and 32 GB from its Q4_K_M, 4-bit safetensors or Ollama builds",
          "Runs in llama.cpp, Ollama, vLLM, SGLang and Transformers from their released versions, with llama.cpp's own guide",
          "Plain Apache 2.0, ungated, safetensors and GGUF only, no remote code",
          "OpenBMB answered 8 of 12 discussions and dates its releases",
          "Images, several images and video in one model, with thinking off by default"
        ],
        "weaknesses": [
          "No limitations section, safety results, agent-use guidance or security contact",
          "Training data isn't described, and the card gives no context length, languages or knowledge cut-off",
          "The card shows a tool-calling request but not the reply, and names no parser",
          "OpenBMB's cookbook is behind the runtimes on SGLang and Ollama support",
          "No inference provider is mapped on Hugging Face"
        ],
        "agentNotes": [
          "Start it with `llama-server -hf openbmb/MiniCPM-V-4.7-35B-A3B-gguf:Q4_K_M --jinja`, or `ollama run openbmb/minicpm-v4.7:35b-a3b`",
          "For image input in llama.cpp add `mmproj-model-f16.gguf`, or the 4x projector for more visual detail at a higher cost",
          "The GGUF files at the repository root have thinking off. Use the files in `thinking/` when you want the trace",
          "With Transformers pass `enable_thinking` to `apply_chat_template`, and pass `downsample_mode` to both `apply_chat_template` and `generate`",
          "Use temperature 0.7, top_p 0.95 and top_k 100 in instruct mode, as the card recommends",
          "Check tool calls in your runtime first, since the card names no parser. The template uses the Qwen3.5 function-block format",
          "Validate JSON output yourself, since structured output isn't documented"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 0,
        "avgRating": 0,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "C",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 61
          }
        ],
        "editorialScores": {
          "interface": 70,
          "reach": 90,
          "risk": 55,
          "upkeep": 68
        },
        "provenanceScore": 58,
        "checked": {
          "categories": {
            "interface": "2026-11-10",
            "reach": "2026-11-10",
            "risk": "2026-11-10",
            "upkeep": "2026-11-10"
          },
          "last": "2026-11-10",
          "oldest": "2026-11-10",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "ollama run openbmb/minicpm-v4.7:35b-a3b\n# or, with llama.cpp and OpenBMB's own GGUF:\nllama-server -hf openbmb/MiniCPM-V-4.7-35B-A3B-gguf:Q4_K_M --jinja\n# or, with Transformers' server, as the card gives it:\npip install \"transformers[serving]\u003e=5.18.0\"\ntransformers serve openbmb/MiniCPM-V-4.7-35B-A3B --port 8000 --host 0.0.0.0",
        "http": "curl http://localhost:11434/api/chat \\\n  -d '{\"model\": \"openbmb/minicpm-v4.7:35b-a3b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/minicpm-v-4-7-35b-a3b"
      },
      "sameCompany": [
        "minicpm-v-4-7-1b",
        "minicpm-o-4-5",
        "minicpm5-2b"
      ],
      "weights": {
        "trainedBy": "OpenBMB (ModelBest and Tsinghua NLP Lab)",
        "builtOn": "Qwen3.5 and Qwen3.6 as the language backbone and SigLIP2 as the vision module, per the card's acknowledgements. Not declared in the metadata. The text config is qwen3_5_moe_text with 40 layers and 256 experts",
        "params": "35.2B (3B active)",
        "context": 262144,
        "goodFor": "Vision-language mixture-of-experts model for images, several images and video, with tool calling and thinking off by default, fast for its size with about 3B active parameters.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "MiniCPM-V-4_7-35B-A3B-IQ4_XS",
            "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B-gguf/blob/main/MiniCPM-V-4_7-35B-A3B-IQ4_XS.gguf",
            "who": "publisher",
            "precision": "iq4_xs",
            "sizeGB": 18.7,
            "memoryGB": 24,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ],
            "note": "The smallest file at 4 bits. IQ3_M and IQ3_XS (15.4 and 14.5 GB) are below the 4-bit line and are not listed"
          },
          {
            "name": "MiniCPM-V-4.7-35B-A3B-AWQ",
            "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B-AWQ",
            "who": "publisher",
            "precision": "w4a16",
            "sizeGB": 22.7,
            "memoryGB": 32,
            "format": "safetensors",
            "runsOn": [
              "vLLM"
            ],
            "note": "AWQ, 4-bit weights with 16-bit activations"
          },
          {
            "name": "MiniCPM-V-4.7-35B-A3B-GPTQ",
            "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B-GPTQ",
            "who": "publisher",
            "precision": "w4a16",
            "sizeGB": 22.7,
            "memoryGB": 32,
            "format": "safetensors",
            "runsOn": [
              "vLLM"
            ],
            "note": "GPTQ, 4-bit weights with 16-bit activations"
          },
          {
            "name": "MiniCPM-V-4.7-35B-A3B-int4",
            "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B-int4",
            "who": "publisher",
            "precision": "int4",
            "sizeGB": 22.7,
            "memoryGB": 32,
            "format": "safetensors",
            "runsOn": [
              "vLLM"
            ],
            "note": "Listed on the card with the FP8 build under llm-compressor's FP8 and INT4 guide"
          },
          {
            "name": "MiniCPM-V-4_7-35B-A3B-Q5_K_M",
            "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B-gguf/blob/main/MiniCPM-V-4_7-35B-A3B-Q5_K_M.gguf",
            "who": "publisher",
            "precision": "q5_k_m",
            "sizeGB": 24.7,
            "memoryGB": 32,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ]
          },
          {
            "name": "MiniCPM-V-4_7-35B-A3B-Q4_K_M",
            "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B-gguf/blob/main/MiniCPM-V-4_7-35B-A3B-Q4_K_M.gguf",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 21.2,
            "memoryGB": 32,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ],
            "note": "Add mmproj-model-f16.gguf (1.1 GB) or mmproj-model-f16-4x.gguf (0.9 GB) for image input. The same file with thinking on is in the thinking/ folder"
          },
          {
            "name": "ollama openbmb/minicpm-v4.7:35b-a3b",
            "url": "https://ollama.com/openbmb/minicpm-v4.7",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 22,
            "memoryGB": 32,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "OpenBMB's own namespace on Ollama, not Ollama's library. Q4_K_M, 22 GB as the tags page shows it, with q8_0 (38 GB) and fp16 (70 GB) tags beside it"
          },
          {
            "name": "MiniCPM-V-4.7-35B-A3B-FP8",
            "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B-FP8",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 38,
            "memoryGB": 48,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang"
            ]
          },
          {
            "name": "MiniCPM-V-4_7-35B-A3B-Q8_0",
            "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B-gguf/blob/main/MiniCPM-V-4_7-35B-A3B-Q8_0.gguf",
            "who": "publisher",
            "precision": "q8_0",
            "sizeGB": 36.9,
            "memoryGB": 48,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ]
          },
          {
            "name": "MiniCPM-V-4_7-35B-A3B-Q6_K",
            "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B-gguf/blob/main/MiniCPM-V-4_7-35B-A3B-Q6_K.gguf",
            "who": "publisher",
            "precision": "q6_k",
            "sizeGB": 28.5,
            "memoryGB": 48,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ]
          },
          {
            "name": "MiniCPM-V-4.7-35B-A3B",
            "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 70.4,
            "memoryGB": 96,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)",
              "Transformers"
            ]
          },
          {
            "name": "MiniCPM-V-4_7-35B-A3B-F16",
            "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B-gguf/blob/main/MiniCPM-V-4_7-35B-A3B-F16.gguf",
            "who": "publisher",
            "precision": "f16",
            "sizeGB": 69.4,
            "memoryGB": 96,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ]
          }
        ],
        "run": "llama-server -hf openbmb/MiniCPM-V-4.7-35B-A3B-gguf:Q4_K_M --jinja",
        "runBuild": "MiniCPM-V-4_7-35B-A3B-Q4_K_M",
        "checked": "2026-11-10",
        "minMemoryGB": 24
      },
      "family": {
        "lab": "OpenBMB (MiniCPM)",
        "family": "MiniCPM-V 4",
        "url": "https://www.anchorterminal.com/open-weights/#openbmb",
        "others": [
          {
            "name": "MiniCPM-V 4.7 1B",
            "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-1B",
            "params": "1.55B (1,554,476,272 per the safetensors count)",
            "listing": "https://www.anchorterminal.com/tools/minicpm-v-4-7-1b"
          }
        ],
        "olderCount": 22
      },
      "area": "models",
      "provenance": {
        "legalEntity": "OpenBMB (named in the GitHub LICENSE's copyright line)",
        "domain": "openbmb.cn",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://github.com/OpenBMB/MiniCPM-V",
        "securityTxt": "none",
        "checked": "2026-11-10",
        "notes": [
          "No legal entity is named on the card. OpenBMB is the open-source organisation of ModelBest and Tsinghua University's NLP lab, as the MiniCPM5-2B listing records.",
          "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.",
          "The changelog is the GitHub README's dated news list (the release on 12 October 2026, DSpark heads on 15 October) and the repositories' commit messages, which say what changed.",
          "openbmb.cn/.well-known/security.txt returns 404 and github.com/OpenBMB/MiniCPM-V has no SECURITY.md.",
          "unchecked: the domain's registration date."
        ],
        "score": 58
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/minicpm-v-4-7-35b-a3b.json"
    },
    "edges": [
      {
        "by": 15,
        "category": "risk",
        "leads": "gemma-4-26b-a4b-it",
        "name": "Risk",
        "reasons": {
          "a": "File safety 25, safetensors and GGUF only, with nothing from the repository run on load. Provenance 15, the files sit in Google's own organisation and every file has a checksum. Safety evaluation 0, the card says testing showed minimal policy violations but gives no figures. Agent guidance 10, the card names the harm categories its safety training covers and asks for content-safety safeguards, and the tool guide says to validate calls, with no prompt-injection guidance. Licence risk 15, Apache 2.0 with no added policy, no threshold and a working link. Security programme 5, Google's security.txt and reward policy give a route to report a flaw, but no public Gemma advisories were found and GitHub's advisory tab couldn't be checked.",
          "b": "File safety 25, safetensors and GGUF only, no Python in the main repository and no remote code needed. Provenance 15, the files come from the openbmb organisation OpenBMB's GitHub README links to, with a SHA-256 for every weight file. Safety evaluation 0 and agent guidance 0, nothing published on either, and the card has no limitations section. Licence risk 15, plain Apache 2.0. Security programme 0, no security.txt or SECURITY.md."
        },
        "scores": {
          "a": 70,
          "b": 55
        }
      },
      {
        "by": 15,
        "category": "interface",
        "leads": "gemma-4-26b-a4b-it",
        "name": "Agent interface",
        "reasons": {
          "a": "Chat template 20, a Jinja file in the repository. Tool-calling format 25, Google documents how tools are declared and how calls come back, ships a response schema for Transformers' parser, and names the gemma4 parser in vLLM and SGLang. Trained tool calling 20, several calls in one turn. Structured output 0, nothing documents how to ask the weights for JSON or a schema. Reasoning control 10, thinking is off by default, turned on per request, and returned in a thought channel. Context 10, 262,144 tokens native.",
          "b": "Chat template 20, a Jinja file in the repository. Tool-calling format 10, the card shows the tools array going in but not the call coming back, and names no parser. Trained tool calling 20, the template renders several calls in one turn. Structured output 0, not documented or claimed. Reasoning control 10, enable_thinking per request, off by default, with the trace between markers. Context 10, 262,144 tokens native."
        },
        "scores": {
          "a": 85,
          "b": 70
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Google DeepMind",
        "b": "OpenBMB",
        "name": "Vendor"
      },
      {
        "a": "no (local only)",
        "b": "no (local only)",
        "name": "Hosted endpoint"
      },
      {
        "a": "",
        "b": "",
        "name": "Transports"
      },
      {
        "a": "None",
        "b": "None",
        "name": "Auth"
      },
      {
        "a": "Free",
        "b": "Free",
        "name": "Pricing"
      },
      {
        "a": "no",
        "b": "no",
        "name": "x402"
      },
      {
        "a": "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": "2026-07-20",
        "b": "2026-11-03",
        "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": "3.8/5 (4)",
        "b": "none",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Gemma 4 26B A4B IT scores 72.2 (BB) on agent readiness against MiniCPM-V 4.7 35B-A3B's 61 (C), and leads in 2 of 4 scored categories.",
        "question": "Which is better for AI agents, Gemma 4 26B A4B IT or MiniCPM-V 4.7 35B-A3B?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Risk, 70 against 55",
          "Agent interface, 85 against 70"
        ],
        "also": [
          "Agent-ready, a grade of BB or better"
        ],
        "goodFor": "Agents on a 24 GB or larger GPU or Mac that want a fast, permissively licensed tool-calling model with images and long context",
        "slug": "gemma-4-26b-a4b-it",
        "watchFor": "No safety evaluation figures published"
      },
      {
        "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"
      }
    ],
    "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-gemma-4-26b-a4b-it.json",
        "title": "Agents-A1-4B vs Gemma 4 26B A4B IT",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-26b-a4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-minicpm-v-4-7-35b-a3b.json",
        "title": "Agents-A1-4B vs MiniCPM-V 4.7 35B-A3B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-minicpm-v-4-7-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-26b-a4b-it.json",
        "title": "Apertus v1.5 8B vs Gemma 4 26B A4B IT",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-26b-a4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minicpm-v-4-7-35b-a3b.json",
        "title": "Apertus v1.5 8B vs MiniCPM-V 4.7 35B-A3B",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minicpm-v-4-7-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gemma-4-26b-a4b-it.json",
        "title": "Apodex-1.1-mini vs Gemma 4 26B A4B IT",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gemma-4-26b-a4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minicpm-v-4-7-35b-a3b.json",
        "title": "Apodex-1.1-mini vs MiniCPM-V 4.7 35B-A3B",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minicpm-v-4-7-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-gemma-4-26b-a4b-it.json",
        "title": "Arete-27B vs Gemma 4 26B A4B IT",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-gemma-4-26b-a4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-minicpm-v-4-7-35b-a3b.json",
        "title": "Arete-27B vs MiniCPM-V 4.7 35B-A3B",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-minicpm-v-4-7-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-gemma-4-26b-a4b-it.json",
        "title": "BigBang-v1 vs Gemma 4 26B A4B IT",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-gemma-4-26b-a4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-minicpm-v-4-7-35b-a3b.json",
        "title": "BigBang-v1 vs MiniCPM-V 4.7 35B-A3B",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-minicpm-v-4-7-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-gemma-4-26b-a4b-it.json",
        "title": "Carbon-X vs Gemma 4 26B A4B IT",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-gemma-4-26b-a4b-it"
      },
      {
        "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"
      },
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        "json": "https://www.anchorterminal.com/compare/cola2-vs-gemma-4-26b-a4b-it.json",
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        "url": "https://www.anchorterminal.com/compare/cola2-vs-gemma-4-26b-a4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-minicpm-v-4-7-35b-a3b.json",
        "title": "Cola2 vs MiniCPM-V 4.7 35B-A3B",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-minicpm-v-4-7-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-gemma-4-26b-a4b-it.json",
        "title": "Cosmos-Reason2-2B vs Gemma 4 26B A4B IT",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-gemma-4-26b-a4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-minicpm-v-4-7-35b-a3b.json",
        "title": "Cosmos-Reason2-2B vs MiniCPM-V 4.7 35B-A3B",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-minicpm-v-4-7-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-gemma-4-26b-a4b-it.json",
        "title": "Darwin-36B-Opus vs Gemma 4 26B A4B IT",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-gemma-4-26b-a4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-minicpm-v-4-7-35b-a3b.json",
        "title": "Darwin-36B-Opus vs MiniCPM-V 4.7 35B-A3B",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-minicpm-v-4-7-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gemma-4-26b-a4b-it.json",
        "title": "DeepSeek-V3.2 vs Gemma 4 26B A4B IT",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gemma-4-26b-a4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-minicpm-v-4-7-35b-a3b.json",
        "title": "DeepSeek-V3.2 vs MiniCPM-V 4.7 35B-A3B",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-minicpm-v-4-7-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gemma-4-26b-a4b-it.json",
        "title": "DeepSeek V4.1 Flash vs Gemma 4 26B A4B IT",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gemma-4-26b-a4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-minicpm-v-4-7-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-minicpm-v-4-7-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-vl-4b-instruct.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-vl-8b-instruct.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-vl-8b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwopus3-8-27b-flash-v2.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-recursive-flint.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-ripple-65b-preview.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-sensenova-loop-9b-preview.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs SenseNova-Loop-9B-Preview",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-sensenova-loop-9b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-smollm3-3b.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-spark-x2-6-4b.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-step-3-7-flash.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-step-3-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-thinkingcap-qwen3-8-27b.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-thinkingcap-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-unlimited-ocr-2.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-wevisone-vl-2b.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-wevisone-vl-4b.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-zaya1-8b.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-zaya1-8b"
      }
    ],
    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 0,
        "edge": "",
        "gemma-4-26b-a4b-it": 90,
        "key": "reach",
        "minicpm-v-4-7-35b-a3b": 90,
        "name": "Reach",
        "weight": 25
      },
      {
        "by": 15,
        "edge": "gemma-4-26b-a4b-it",
        "gemma-4-26b-a4b-it": 70,
        "key": "risk",
        "minicpm-v-4-7-35b-a3b": 55,
        "name": "Risk",
        "weight": 20
      },
      {
        "by": 15,
        "edge": "gemma-4-26b-a4b-it",
        "gemma-4-26b-a4b-it": 85,
        "key": "interface",
        "minicpm-v-4-7-35b-a3b": 70,
        "name": "Agent interface",
        "weight": 10
      },
      {
        "by": 3,
        "edge": "minicpm-v-4-7-35b-a3b",
        "gemma-4-26b-a4b-it": 65,
        "key": "upkeep",
        "minicpm-v-4-7-35b-a3b": 68,
        "name": "Documentation and upkeep",
        "weight": 10
      }
    ],
    "summary": "Gemma 4 26B A4B IT scores 72.2 (BB) on agent readiness against MiniCPM-V 4.7 35B-A3B's 61 (C), and leads in 2 of 4 scored categories. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "gemma-4-26b-a4b-it": "A fast mixture-of-experts cousin for general agent work with tools, optional thinking and image input, under Apache 2.0. Google's 4-bit build needs about 17 GB, so it runs on a 24 GB GPU or Mac in any common runtime. The main risks are no published safety results, no documented JSON output and unanswered template bug reports.",
      "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."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-minicpm-v-4-7-35b-a3b",
    "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-minicpm-v-4-7-35b-a3b.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-minicpm-v-4-7-35b-a3b.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-minicpm-v-4-7-35b-a3b.min.md"
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  "markdown": "Gemma 4 26B A4B IT scores 72.2 (BB) on agent readiness against MiniCPM-V 4.7 35B-A3B's 61 (C), and leads in 2 of 4 scored categories. Both do open-weight models you run yourself.\n\n- Gemma 4 26B A4B IT: grade BB, 72.2/100, rank #5 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/gemma-4-26b-a4b-it.md · JSON https://www.anchorterminal.com/api/v1/tools/gemma-4-26b-a4b-it.json\n- MiniCPM-V 4.7 35B-A3B: grade C, 61/100, rank #77 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/minicpm-v-4-7-35b-a3b.md · JSON https://www.anchorterminal.com/api/v1/tools/minicpm-v-4-7-35b-a3b.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### Gemma 4 26B A4B IT (BB)\n\nGood for: Agents on a 24 GB or larger GPU or Mac that want a fast, permissively licensed tool-calling model with images and long context\n\nAhead on:\n- Risk, 70 against 55\n- Agent interface, 85 against 70\n\nAlso in its favour:\n- Agent-ready, a grade of BB or better\n\nWatch for: No safety evaluation figures published\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\n## Why the edge\n\n### Risk: Gemma 4 26B A4B IT +15\n\n- Gemma 4 26B A4B IT (70): File safety 25, safetensors and GGUF only, with nothing from the repository run on load. Provenance 15, the files sit in Google's own organisation and every file has a checksum. Safety evaluation 0, the card says testing showed minimal policy violations but gives no figures. Agent guidance 10, the card names the harm categories its safety training covers and asks for content-safety safeguards, and the tool guide says to validate calls, with no prompt-injection guidance. Licence risk 15, Apache 2.0 with no added policy, no threshold and a working link. Security programme 5, Google's security.txt and reward policy give a route to report a flaw, but no public Gemma advisories were found and GitHub's advisory tab couldn't be checked.\n- MiniCPM-V 4.7 35B-A3B (55): File safety 25, safetensors and GGUF only, no Python in the main repository and no remote code needed. Provenance 15, the files come from the openbmb organisation OpenBMB's GitHub README links to, with a SHA-256 for every weight file. Safety evaluation 0 and agent guidance 0, nothing published on either, and the card has no limitations section. Licence risk 15, plain Apache 2.0. Security programme 0, no security.txt or SECURITY.md.\n\n### Agent interface: Gemma 4 26B A4B IT +15\n\n- Gemma 4 26B A4B IT (85): Chat template 20, a Jinja file in the repository. Tool-calling format 25, Google documents how tools are declared and how calls come back, ships a response schema for Transformers' parser, and names the gemma4 parser in vLLM and SGLang. Trained tool calling 20, several calls in one turn. Structured output 0, nothing documents how to ask the weights for JSON or a schema. Reasoning control 10, thinking is off by default, turned on per request, and returned in a thought channel. Context 10, 262,144 tokens native.\n- MiniCPM-V 4.7 35B-A3B (70): Chat template 20, a Jinja file in the repository. Tool-calling format 10, the card shows the tools array going in but not the call coming back, and names no parser. Trained tool calling 20, the template renders several calls in one turn. Structured output 0, not documented or claimed. Reasoning control 10, enable_thinking per request, off by default, with the trace between markers. Context 10, 262,144 tokens native.\n\n## Score by category\n\n| Category | Weight | Gemma 4 26B A4B IT | MiniCPM-V 4.7 35B-A3B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 90 | 90 | even |\n| Risk | 20% (20 this run) | 70 | 55 | Gemma 4 26B A4B IT +15 |\n| Agent interface | 10% (10 this run) | 85 | 70 | Gemma 4 26B A4B IT +15 |\n| Documentation and upkeep | 10% (10 this run) | 65 | 68 | MiniCPM-V 4.7 35B-A3B +3 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **72.2 · BB** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Gemma 4 26B A4B IT | MiniCPM-V 4.7 35B-A3B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Google DeepMind | OpenBMB |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports |  |  |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | Apache-2.0 | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-07-20 | 2026-11-03 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | 3.8/5 (4) | none |\n\n## Verdicts\n\n**Gemma 4 26B A4B IT.** A fast mixture-of-experts cousin for general agent work with tools, optional thinking and image input, under Apache 2.0. Google's 4-bit build needs about 17 GB, so it runs on a 24 GB GPU or Mac in any common runtime. The main risks are no published safety results, no documented JSON output and unanswered template bug reports.\n\n**MiniCPM-V 4.7 35B-A3B.** A vision-language cousin for images and video that fits a 24 to 32 GB machine from OpenBMB's own GGUF, under plain Apache 2.0, with a publisher that answers. The card says little about limits, training data or how tool calls come back, so test the tool format in your runtime before relying on it.\n\n## Before you call either\n\n### Gemma 4 26B A4B IT\n\n1. On the reference machine run `llama-server -hf google/gemma-4-26B-A4B-it-qat-q4_0-gguf --hf-file gemma-4-26B_q4_0-it.gguf --jinja -c 65536`, and add the mmproj file for images\n2. With Ollama run `ollama run gemma4:26b` and raise num_ctx or OLLAMA_CONTEXT_LENGTH, since Google's guide says it defaults to 4K below 24 GiB of VRAM and agents want at least 64K\n3. In vLLM or SGLang pass `--tool-call-parser gemma4 --reasoning-parser gemma4` (vLLM also `--enable-auto-tool-choice`), or tool calls come back as text\n4. Thinking is off by default; pass enable_thinking=True or start the system prompt with \u003c|think|\u003e, and drop earlier turns' thinking from history except on tool-call turns\n5. Validate tool names and arguments before running them, and check any JSON yourself\n\n### MiniCPM-V 4.7 35B-A3B\n\n1. Start it with `llama-server -hf openbmb/MiniCPM-V-4.7-35B-A3B-gguf:Q4_K_M --jinja`, or `ollama run openbmb/minicpm-v4.7:35b-a3b`\n2. For image input in llama.cpp add `mmproj-model-f16.gguf`, or the 4x projector for more visual detail at a higher cost\n3. The GGUF files at the repository root have thinking off. Use the files in `thinking/` when you want the trace\n4. With Transformers pass `enable_thinking` to `apply_chat_template`, and pass `downsample_mode` to both `apply_chat_template` and `generate`\n5. Use temperature 0.7, top_p 0.95 and top_k 100 in instruct mode, as the card recommends\n6. Check tool calls in your runtime first, since the card names no parser. The template uses the Qwen3.5 function-block format\n7. Validate JSON output yourself, since structured output isn't documented\n\n## Questions\n\n### Which is better for AI agents, Gemma 4 26B A4B IT or MiniCPM-V 4.7 35B-A3B?\n\nGemma 4 26B A4B IT scores 72.2 (BB) on agent readiness against MiniCPM-V 4.7 35B-A3B's 61 (C), and leads in 2 of 4 scored categories.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-minicpm-v-4-7-35b-a3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-minicpm-v-4-7-35b-a3b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"gemma-4-26b-a4b-it\", \"b\": \"minicpm-v-4-7-35b-a3b\"}`. From a terminal: `anchor compare gemma-4-26b-a4b-it minicpm-v-4-7-35b-a3b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/gemma-4-26b-a4b-it.json and https://www.anchorterminal.com/api/v1/tools/minicpm-v-4-7-35b-a3b.json\n\n## Other comparisons with Gemma 4 26B A4B IT or MiniCPM-V 4.7 35B-A3B\n\n- [Agents-A1-4B vs Gemma 4 26B A4B IT](https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-26b-a4b-it.md)\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- [Apertus v1.5 8B vs Gemma 4 26B A4B IT](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-26b-a4b-it.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- [Apodex-1.1-mini vs Gemma 4 26B A4B IT](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gemma-4-26b-a4b-it.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- [Arete-27B vs Gemma 4 26B A4B IT](https://www.anchorterminal.com/compare/arete-27b-vs-gemma-4-26b-a4b-it.md)\n- [Arete-27B vs MiniCPM-V 4.7 35B-A3B](https://www.anchorterminal.com/compare/arete-27b-vs-minicpm-v-4-7-35b-a3b.md)\n- [BigBang-v1 vs Gemma 4 26B A4B IT](https://www.anchorterminal.com/compare/bigbang-v1-vs-gemma-4-26b-a4b-it.md)\n- [BigBang-v1 vs MiniCPM-V 4.7 35B-A3B](https://www.anchorterminal.com/compare/bigbang-v1-vs-minicpm-v-4-7-35b-a3b.md)\n- [Carbon-X vs Gemma 4 26B A4B IT](https://www.anchorterminal.com/compare/carbon-x-vs-gemma-4-26b-a4b-it.md)\n- [Carbon-X vs MiniCPM-V 4.7 35B-A3B](https://www.anchorterminal.com/compare/carbon-x-vs-minicpm-v-4-7-35b-a3b.md)\n- [Cola2 vs Gemma 4 26B A4B IT](https://www.anchorterminal.com/compare/cola2-vs-gemma-4-26b-a4b-it.md)\n- [Cola2 vs MiniCPM-V 4.7 35B-A3B](https://www.anchorterminal.com/compare/cola2-vs-minicpm-v-4-7-35b-a3b.md)\n- 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