{
  "data": {
    "a": {
      "slug": "glm-4-5-air",
      "name": "GLM-4.5-Air",
      "vendor": "Z.ai (Zhipu AI)",
      "vendorUrl": "https://z.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "GLM-4.5-Air is Z.ai's open-weight mixture-of-experts model for English and Chinese, 106B total and 12B active parameters, with thinking that turns off per request, tool calling and a 128K context, published in BF16 and FP8 under MIT.",
      "url": "https://www.anchorterminal.com/tools/glm-4-5-air",
      "markdownUrl": "https://www.anchorterminal.com/tools/glm-4-5-air.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/glm-4-5-air.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/glm-4-5-air.json",
      "dns": "glm-4-5-air.grade.anchorterminal.com",
      "repo": "https://huggingface.co/zai-org/GLM-4.5-Air",
      "license": "MIT",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "zai-org/GLM-4.5-Air"
        },
        {
          "registry": "huggingface",
          "name": "zai-org/GLM-4.5-Air-FP8"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Neither repository is gated (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under the MIT licence named on the card. Running it yourself costs the hardware, 221.0 GB for the BF16 files and 112.6 GB for Z.ai's FP8 build. Z.ai's own API also serves GLM-4.5-Air (checked 2026-10-21).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-11-04"
      },
      "docsUrl": "https://github.com/zai-org/GLM-4.5",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "reasoning",
        "tool-calling",
        "chinese",
        "mit",
        "safetensors",
        "fp8",
        "self-hosted"
      ],
      "lastRelease": "2025-08-11",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 60.3,
        "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": 71,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "capability": 50,
          "interface": 85,
          "reach": 70,
          "risk": 65,
          "upkeep": 38
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-11-04"
        },
        "negative": 0,
        "verdict": "An English and Chinese mixture-of-experts model for agent, coding and tool-calling work, with switchable thinking and released support in llama.cpp, vLLM, SGLang and Transformers. It needs about 135 GB for Z.ai's FP8 build, two to four H100s by Z.ai's figures, and Z.ai and Novita host it. Its files date from August 2025, with no Z.ai reply since July 2025.",
        "bestFor": "Agents with two to four datacentre GPUs, or a provider account, who want a permissively licensed English and Chinese tool-calling model with wide runtime support.",
        "strengths": [
          "Released support in llama.cpp, vLLM, SGLang and Transformers",
          "Plain MIT with no gate, no account and no use policy",
          "Safetensors only, with a checksum on every shard in the publisher's own organisation",
          "Z.ai documents the glm45 tool and reasoning parsers, and thinking turns off per request",
          "Hosted by Z.ai and Novita for agents without the hardware"
        ],
        "weaknesses": [
          "Z.ai's smallest build is FP8 at about 135 GB in use, with no 4-bit build of its own",
          "No limitations, architecture, context or knowledge cut-off on the card, and no training cut-off anywhere",
          "No agent guidance or security contact",
          "No reply from Z.ai in its discussions since 30 July 2025",
          "Structured output isn't documented for the weights"
        ],
        "agentNotes": [
          "Z.ai's FP8 recipe is `python3 -m sglang.launch_server --model-path zai-org/GLM-4.5-Air-FP8 --tp-size 4 --tool-call-parser glm45 --reasoning-parser glm45`, and 2 H100s are enough below the full 128K context",
          "Use the glm45 tool and reasoning parsers for this model, the glm47 parser in Z.ai's current README is for GLM-4.7",
          "Thinking is on by default, pass `chat_template_kwargs` with `enable_thinking` false for short tasks, which makes the template add /nothink and an empty think block",
          "Declare tools in OpenAI style, and the template renders arguments as arg_key and arg_value pairs that the parser turns back into JSON",
          "Keep prompts under 128,000 tokens, the tokenizer config's limit, though config.json allows 131,072",
          "Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented for the weights"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 2.8,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "high",
            "grade": "C",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 60.3
          }
        ],
        "editorialScores": {
          "capability": 50,
          "interface": 85,
          "reach": 70,
          "risk": 65,
          "upkeep": 38
        },
        "provenanceScore": 73,
        "checked": {
          "categories": {
            "capability": "2026-11-04",
            "interface": "2026-11-04",
            "reach": "2026-11-04",
            "risk": "2026-11-04",
            "upkeep": "2026-11-04"
          },
          "last": "2026-11-04",
          "lastMoved": "2026-11-04",
          "oldest": "2026-11-04",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "# per Z.ai's FP8 card (4 H100 for the full 128K context):\npython3 -m sglang.launch_server --model-path zai-org/GLM-4.5-Air-FP8 --tp-size 4 \\\n  --tool-call-parser glm45 --reasoning-parser glm45 --served-model-name glm-4.5-air --port 8000\n# or, with llama.cpp and llama.cpp's own GGUF:\nllama-server -hf ggml-org/GLM-4.5-Air-GGUF:Q4_K_M --jinja",
        "http": "curl http://localhost:8000/v1/chat/completions \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\n    \"model\": \"glm-4.5-air\",\n    \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}],\n    \"chat_template_kwargs\": {\"enable_thinking\": false}\n  }'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/glm-4-5-air"
      },
      "sameCompany": [
        "glm-4-7-flash",
        "glm-5-3",
        "glm-5-3-flash"
      ],
      "weights": {
        "trainedBy": "Z.ai",
        "params": "106B",
        "active": "12B",
        "context": 131072,
        "goodFor": "Mid-size mixture-of-experts reasoning model for English and Chinese agent, coding and tool-calling work, with thinking that turns off per request.",
        "runtimes": [
          "llama.cpp",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "ggml-org GLM-4.5-Air-GGUF (Q4_K_M)",
            "url": "https://huggingface.co/ggml-org/GLM-4.5-Air-GGUF",
            "who": "llama.cpp",
            "precision": "q4_k_m",
            "sizeGB": 63.63,
            "memoryGB": 96,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "From ggml-org, the llama.cpp project's organisation. 63.63 GB of Q4_K_M weights, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it yet"
          },
          {
            "name": "GLM-4.5-Air-FP8",
            "url": "https://huggingface.co/zai-org/GLM-4.5-Air-FP8",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 112.6,
            "memoryGB": 192,
            "format": "safetensors",
            "note": "112.6 GB × 1.2 = 135.1 GB, more than the reference machine's 107.4 GB GPU share"
          },
          {
            "name": "ggml-org GLM-4.5-Air-GGUF (Q8_0)",
            "url": "https://huggingface.co/ggml-org/GLM-4.5-Air-GGUF",
            "who": "llama.cpp",
            "precision": "q8_0",
            "sizeGB": 113.61,
            "memoryGB": 192,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "From ggml-org, the llama.cpp project's organisation. 113.61 GB of Q8_0 weights, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it yet"
          },
          {
            "name": "GLM-4.5-Air",
            "url": "https://huggingface.co/zai-org/GLM-4.5-Air",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 221,
            "memoryGB": 512,
            "format": "safetensors"
          }
        ],
        "run": "python3 -m sglang.launch_server --model-path zai-org/GLM-4.5-Air-FP8 --tp-size 4 --tool-call-parser glm45 --reasoning-parser glm45 --served-model-name glm-4.5-air --port 8000",
        "runBuild": "GLM-4.5-Air-FP8",
        "checked": "2026-10-21",
        "minMemoryGB": 96
      },
      "family": {
        "lab": "Z.ai (Zhipu AI)",
        "family": "GLM-4",
        "url": "https://www.anchorterminal.com/open-weights/#zai",
        "others": [
          {
            "name": "GLM-4-9B",
            "url": "https://huggingface.co/zai-org/glm-4-9b",
            "params": "9B"
          },
          {
            "name": "GLM-4-9B-Chat",
            "url": "https://huggingface.co/zai-org/glm-4-9b-chat",
            "params": "9B"
          },
          {
            "name": "GLM-4-9B-Chat-1M",
            "url": "https://huggingface.co/zai-org/glm-4-9b-chat-1m",
            "params": "9B"
          },
          {
            "name": "GLM-4V-9B",
            "url": "https://huggingface.co/zai-org/glm-4v-9b",
            "params": "9B"
          },
          {
            "name": "GLM-4-9B-0414",
            "url": "https://huggingface.co/zai-org/GLM-4-9B-0414",
            "params": "9B"
          },
          {
            "name": "GLM-4-32B-Base-0414",
            "url": "https://huggingface.co/zai-org/GLM-4-32B-Base-0414",
            "params": "32B"
          },
          {
            "name": "GLM-4-32B-0414",
            "url": "https://huggingface.co/zai-org/GLM-4-32B-0414",
            "params": "32B"
          },
          {
            "name": "GLM-Z1-9B-0414",
            "url": "https://huggingface.co/zai-org/GLM-Z1-9B-0414",
            "params": "9B"
          },
          {
            "name": "GLM-Z1-32B-0414",
            "url": "https://huggingface.co/zai-org/GLM-Z1-32B-0414",
            "params": "32B"
          },
          {
            "name": "GLM-Z1-Rumination-32B-0414",
            "url": "https://huggingface.co/zai-org/GLM-Z1-Rumination-32B-0414",
            "params": "32B"
          },
          {
            "name": "GLM-4.1V-9B-Base",
            "url": "https://huggingface.co/zai-org/GLM-4.1V-9B-Base",
            "params": "9B"
          },
          {
            "name": "GLM-4.1V-9B-Thinking",
            "url": "https://huggingface.co/zai-org/GLM-4.1V-9B-Thinking",
            "params": "9B"
          },
          {
            "name": "GLM-4.5-Base",
            "url": "https://huggingface.co/zai-org/GLM-4.5-Base",
            "params": "355B-A32B"
          },
          {
            "name": "GLM-4.5",
            "url": "https://huggingface.co/zai-org/GLM-4.5",
            "params": "355B-A32B"
          },
          {
            "name": "GLM-4.5-Air-Base",
            "url": "https://huggingface.co/zai-org/GLM-4.5-Air-Base",
            "params": "106B-A12B"
          },
          {
            "name": "GLM-4.5V",
            "url": "https://huggingface.co/zai-org/GLM-4.5V",
            "params": "106B-A12B"
          },
          {
            "name": "GLM-4.6",
            "url": "https://huggingface.co/zai-org/GLM-4.6",
            "params": "355B-A32B"
          },
          {
            "name": "GLM-4.6V",
            "url": "https://huggingface.co/zai-org/GLM-4.6V",
            "params": "106B"
          },
          {
            "name": "GLM-4.6V-Flash",
            "url": "https://huggingface.co/zai-org/GLM-4.6V-Flash",
            "params": "9B"
          },
          {
            "name": "GLM-4.7",
            "url": "https://huggingface.co/zai-org/GLM-4.7",
            "params": "355B-A32B"
          },
          {
            "name": "GLM-4.7-Flash",
            "url": "https://huggingface.co/zai-org/GLM-4.7-Flash",
            "params": "30B-A3B",
            "listing": "https://www.anchorterminal.com/tools/glm-4-7-flash"
          }
        ],
        "olderCount": 19,
        "unrestrictedCopies": 5
      },
      "area": "models",
      "provenance": {
        "legalEntity": "JINGSHENG HENGXING TECHNOLOGY PTE.LTD (operator of Z.ai per its terms of use, and registrant of z.ai, per the first batch's check on 21 October 2026)",
        "domain": "z.ai",
        "domainRegistered": "2017-12-15",
        "domainNote": "z.ai was registered in 2017 and transferred in October 2024, so its age says little about the company's.",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/zai-org/GLM-4.5-Air/commits/main",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "Z.ai's GitHub README links the zai-org organisation on Hugging Face for every GLM-4.5 checkpoint. Hugging Face hasn't verified the organisation.",
          "Nothing is hosted for this listing, so the MIT licence stands in for terms of service and no privacy policy is scored.",
          "The changelog is the repository's commit history, 10 commits from 20 July to 11 August 2025, the upload and card updates.",
          "z.ai/.well-known/security.txt, docs.z.ai/.well-known/security.txt and www.zhipuai.cn/.well-known/security.txt answered 404 on 21 October 2026, and bigmodel.cn answers that path with its web app."
        ],
        "score": 73
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/glm-4-5-air.json",
      "live": {
        "slug": "glm-4-5-air",
        "securityTxt": {
          "url": "https://z.ai/.well-known/security.txt",
          "state": "none",
          "checkedAt": "2026-11-10T16:09:36.154640571Z"
        },
        "domain": {
          "domain": "z.ai",
          "registered": "2017-12-15",
          "source": "https://rdap.identitydigital.services/rdap/domain/z.ai",
          "checkedAt": "2026-11-08T15:05:18.801433245Z"
        },
        "pages": [
          {
            "url": "https://huggingface.co/zai-org/GLM-4.5-Air/commits/main",
            "kind": "changelog",
            "status": 200,
            "checkedAt": "2026-11-10T16:23:58.528767364Z",
            "changedAt": "2026-11-10T16:23:58.528767364Z",
            "fingerprint": "ed03a7a53ba5"
          }
        ],
        "updatedAt": "2026-11-10T16:23:58.528767364Z"
      }
    },
    "answer": "MiniCPM-V 4.7 35B-A3B and GLM-4.5-Air score within a point of each other on agent readiness, 61 (C) and 60.3 (C). GLM-4.5-Air leads on risk and agent interface.",
    "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": 30,
        "category": "upkeep",
        "leads": "minicpm-v-4-7-35b-a3b",
        "name": "Documentation and upkeep",
        "reasons": {
          "a": "Model card 8 of 20, intended use, parameters and languages only. Training data 5, the technical report describes the sources and filtering with no cut-off. Last change 5, the weights' files date from August 2025 (Hub head a24ceef6, 11 August 2025, re-read on 4 November) and Z.ai's GitHub repository last changed on 1 February 2026, 276 days before this check, so within a year. Responsiveness 10, Z.ai answered 4 of the 14 questions, all in the first three days, recounted by script on 4 November. Version policy 5, a first release. File health 5, the config and tokenizer disagree on the context and a tokenisation report has no reply.",
          "b": "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."
        },
        "scores": {
          "a": 38,
          "b": 68
        }
      },
      {
        "by": 20,
        "category": "reach",
        "leads": "minicpm-v-4-7-35b-a3b",
        "name": "Reach",
        "reasons": {
          "a": "Memory 5 of 30, Z.ai's smallest build is FP8 at 112.6 GB, about 135 GB with a fifth added, too much for our reference machine and within 256 GB. Runtimes 25 (the cap), released llama.cpp, vLLM, SGLang and Transformers run it, and Ollama 0.40.14 (2 November 2026) builds llama.cpp b11963, which has the glm4moe architecture, as kolibri-1 and occamy-1-0 are credited for Ollama's pin. Ollama and LM Studio have no library entry. Builds 10, one quantised build (FP8). Terms 15, MIT with nothing added. Download 10, no gate. Hosted 5, Z.ai and Novita serve it on Hugging Face.",
          "b": "Memory 25, since OpenBMB's IQ4_XS GGUF with its vision projector is 19.6 GB and needs about 23.6 GB with the fifth added, which fits 24 GB, while its Q4_K_M GGUF, 4-bit safetensors builds and Ollama entry need 32 GB. Runtimes 25, from vLLM 0.33.0, SGLang 0.5.23, Transformers 5.18.0 and later, llama.cpp's own MiniCPM-V 4.7 support and OpenBMB's entry on Ollama. Builds 15, OpenBMB publishes FP8, three 4-bit safetensors builds and GGUF. Terms 15, Apache 2.0 with nothing added. Download 10, nothing is gated. Hosted 0, no inference provider is mapped. 25 + 25 + 15 + 15 + 10 + 0 = 90."
        },
        "scores": {
          "a": 70,
          "b": 90
        }
      },
      {
        "by": 15,
        "category": "interface",
        "leads": "glm-4-5-air",
        "name": "Agent interface",
        "reasons": {
          "a": "Chat template 20, a Jinja file in the repository. Tool-calling format 25, Z.ai names the glm45 parser for vLLM and SGLang, says to declare tools in OpenAI style and shows calls coming back. Trained tool calling 20, one or more calls per turn. Structured output 0, documented only for Z.ai's hosted API. Reasoning control 10, enable_thinking false turns thinking off per request and the glm45 reasoning parser returns it separately. Context 10, 128K 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
        }
      },
      {
        "by": 10,
        "category": "risk",
        "leads": "glm-4-5-air",
        "name": "Risk",
        "reasons": {
          "a": "File safety 25, safetensors only and nothing from the repository runs. Provenance 15, the files are in the organisation Z.ai's GitHub links to, with a SHA-256 on every LFS file. Safety evaluation 10, Z.ai's own SafetyBench results in the technical report. Agent guidance 0, nothing on prompt injection, refusals or safeguards. Licence risk 15, plain MIT. Security programme 0, no security.txt, disclosure policy or SECURITY file was found, and public advisories 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": 65,
          "b": 55
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Z.ai (Zhipu AI)",
        "b": "OpenBMB",
        "name": "Vendor"
      },
      {
        "a": "no (local only)",
        "b": "no (local only)",
        "name": "Hosted endpoint"
      },
      {
        "a": "",
        "b": "",
        "name": "Transports"
      },
      {
        "a": "None",
        "b": "None",
        "name": "Auth"
      },
      {
        "a": "Free",
        "b": "Free",
        "name": "Pricing"
      },
      {
        "a": "no",
        "b": "no",
        "name": "x402"
      },
      {
        "a": "MIT",
        "b": "Apache-2.0",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2025-08-11",
        "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": "2.8/5 (4)",
        "b": "none",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "MiniCPM-V 4.7 35B-A3B and GLM-4.5-Air score within a point of each other on agent readiness, 61 (C) and 60.3 (C). GLM-4.5-Air leads on risk and agent interface.",
        "question": "Which is better for AI agents, GLM-4.5-Air or MiniCPM-V 4.7 35B-A3B?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Risk, 65 against 55",
          "Agent interface, 85 against 70"
        ],
        "also": null,
        "goodFor": "Agents with two to four datacentre GPUs, or a provider account, who want a permissively licensed English and Chinese tool-calling model with wide runtime support.",
        "slug": "glm-4-5-air",
        "watchFor": "Z.ai's smallest build is FP8 at about 135 GB in use, with no 4-bit build of its own"
      },
      {
        "aheadOn": [
          "Reach, 90 against 70",
          "Documentation and upkeep, 68 against 38"
        ],
        "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": {
      "glm-4-5-air": [
        {
          "causes": [
            "correction"
          ],
          "date": "2026-11-04",
          "delta": 1.2,
          "from": 59.1,
          "fromGrade": "C",
          "to": 60.3,
          "toGrade": "C"
        }
      ],
      "minicpm-v-4-7-35b-a3b": null
    },
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-4-5-air.json",
        "title": "Agents-A1-4B vs GLM-4.5-Air",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-4-5-air"
      },
      {
        "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-glm-4-5-air.json",
        "title": "Apertus v1.5 8B vs GLM-4.5-Air",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-4-5-air"
      },
      {
        "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-glm-4-5-air.json",
        "title": "Apodex-1.1-mini vs GLM-4.5-Air",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-glm-4-5-air"
      },
      {
        "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-glm-4-5-air.json",
        "title": "Arete-27B vs GLM-4.5-Air",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-glm-4-5-air"
      },
      {
        "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-glm-4-5-air.json",
        "title": "BigBang-v1 vs GLM-4.5-Air",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-glm-4-5-air"
      },
      {
        "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-glm-4-5-air.json",
        "title": "Carbon-X vs GLM-4.5-Air",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-glm-4-5-air"
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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"
      }
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    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 20,
        "edge": "minicpm-v-4-7-35b-a3b",
        "glm-4-5-air": 70,
        "key": "reach",
        "minicpm-v-4-7-35b-a3b": 90,
        "name": "Reach",
        "weight": 25
      },
      {
        "by": 10,
        "edge": "glm-4-5-air",
        "glm-4-5-air": 65,
        "key": "risk",
        "minicpm-v-4-7-35b-a3b": 55,
        "name": "Risk",
        "weight": 20
      },
      {
        "by": 15,
        "edge": "glm-4-5-air",
        "glm-4-5-air": 85,
        "key": "interface",
        "minicpm-v-4-7-35b-a3b": 70,
        "name": "Agent interface",
        "weight": 10
      },
      {
        "by": 30,
        "edge": "minicpm-v-4-7-35b-a3b",
        "glm-4-5-air": 38,
        "key": "upkeep",
        "minicpm-v-4-7-35b-a3b": 68,
        "name": "Documentation and upkeep",
        "weight": 10
      }
    ],
    "summary": "MiniCPM-V 4.7 35B-A3B and GLM-4.5-Air score within a point of each other on agent readiness, 61 (C) and 60.3 (C). GLM-4.5-Air leads on risk and agent interface. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "glm-4-5-air": "An English and Chinese mixture-of-experts model for agent, coding and tool-calling work, with switchable thinking and released support in llama.cpp, vLLM, SGLang and Transformers. It needs about 135 GB for Z.ai's FP8 build, two to four H100s by Z.ai's figures, and Z.ai and Novita host it. Its files date from August 2025, with no Z.ai reply since July 2025.",
      "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."
    }
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  "kind": "anchor.page",
  "links": {
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    "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-minicpm-v-4-7-35b-a3b.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-minicpm-v-4-7-35b-a3b.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-minicpm-v-4-7-35b-a3b.min.md"
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  "markdown": "MiniCPM-V 4.7 35B-A3B and GLM-4.5-Air score within a point of each other on agent readiness, 61 (C) and 60.3 (C). GLM-4.5-Air leads on risk and agent interface. Both do open-weight models you run yourself.\n\n- GLM-4.5-Air: grade C, 60.3/100, rank #90 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/glm-4-5-air.md · JSON https://www.anchorterminal.com/api/v1/tools/glm-4-5-air.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### GLM-4.5-Air (C)\n\nGood for: Agents with two to four datacentre GPUs, or a provider account, who want a permissively licensed English and Chinese tool-calling model with wide runtime support.\n\nAhead on:\n- Risk, 65 against 55\n- Agent interface, 85 against 70\n\nWatch for: Z.ai's smallest build is FP8 at about 135 GB in use, with no 4-bit build of its own\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\nAhead on:\n- Reach, 90 against 70\n- Documentation and upkeep, 68 against 38\n\nWatch for: No limitations section, safety results, agent-use guidance or security contact\n\n\n## Why the edge\n\n### Documentation and upkeep: MiniCPM-V 4.7 35B-A3B +30\n\n- GLM-4.5-Air (38): Model card 8 of 20, intended use, parameters and languages only. Training data 5, the technical report describes the sources and filtering with no cut-off. Last change 5, the weights' files date from August 2025 (Hub head a24ceef6, 11 August 2025, re-read on 4 November) and Z.ai's GitHub repository last changed on 1 February 2026, 276 days before this check, so within a year. Responsiveness 10, Z.ai answered 4 of the 14 questions, all in the first three days, recounted by script on 4 November. Version policy 5, a first release. File health 5, the config and tokenizer disagree on the context and a tokenisation report has no reply.\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\n### Reach: MiniCPM-V 4.7 35B-A3B +20\n\n- GLM-4.5-Air (70): Memory 5 of 30, Z.ai's smallest build is FP8 at 112.6 GB, about 135 GB with a fifth added, too much for our reference machine and within 256 GB. Runtimes 25 (the cap), released llama.cpp, vLLM, SGLang and Transformers run it, and Ollama 0.40.14 (2 November 2026) builds llama.cpp b11963, which has the glm4moe architecture, as kolibri-1 and occamy-1-0 are credited for Ollama's pin. Ollama and LM Studio have no library entry. Builds 10, one quantised build (FP8). Terms 15, MIT with nothing added. Download 10, no gate. Hosted 5, Z.ai and Novita serve it on Hugging Face.\n- MiniCPM-V 4.7 35B-A3B (90): Memory 25, since OpenBMB's IQ4_XS GGUF with its vision projector is 19.6 GB and needs about 23.6 GB with the fifth added, which fits 24 GB, while its Q4_K_M GGUF, 4-bit safetensors builds and Ollama entry need 32 GB. Runtimes 25, from vLLM 0.33.0, SGLang 0.5.23, Transformers 5.18.0 and later, llama.cpp's own MiniCPM-V 4.7 support and OpenBMB's entry on Ollama. Builds 15, OpenBMB publishes FP8, three 4-bit safetensors builds and GGUF. Terms 15, Apache 2.0 with nothing added. Download 10, nothing is gated. Hosted 0, no inference provider is mapped. 25 + 25 + 15 + 15 + 10 + 0 = 90.\n\n### Agent interface: GLM-4.5-Air +15\n\n- GLM-4.5-Air (85): Chat template 20, a Jinja file in the repository. Tool-calling format 25, Z.ai names the glm45 parser for vLLM and SGLang, says to declare tools in OpenAI style and shows calls coming back. Trained tool calling 20, one or more calls per turn. Structured output 0, documented only for Z.ai's hosted API. Reasoning control 10, enable_thinking false turns thinking off per request and the glm45 reasoning parser returns it separately. Context 10, 128K 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### Risk: GLM-4.5-Air +10\n\n- GLM-4.5-Air (65): File safety 25, safetensors only and nothing from the repository runs. Provenance 15, the files are in the organisation Z.ai's GitHub links to, with a SHA-256 on every LFS file. Safety evaluation 10, Z.ai's own SafetyBench results in the technical report. Agent guidance 0, nothing on prompt injection, refusals or safeguards. Licence risk 15, plain MIT. Security programme 0, no security.txt, disclosure policy or SECURITY file was found, and public advisories 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## Score by category\n\n| Category | Weight | GLM-4.5-Air | 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) | 70 | 90 | MiniCPM-V 4.7 35B-A3B +20 |\n| Risk | 20% (20 this run) | 65 | 55 | GLM-4.5-Air +10 |\n| Agent interface | 10% (10 this run) | 85 | 70 | GLM-4.5-Air +15 |\n| Documentation and upkeep | 10% (10 this run) | 38 | 68 | MiniCPM-V 4.7 35B-A3B +30 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **60.3 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | GLM-4.5-Air | MiniCPM-V 4.7 35B-A3B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Z.ai (Zhipu AI) | OpenBMB |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports |  |  |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | MIT | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2025-08-11 | 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 | 2.8/5 (4) | none |\n\n## How the scores have moved\n\n### GLM-4.5-Air\n\n- +1.2 on 4 November 2026: correction\n\n### MiniCPM-V 4.7 35B-A3B\n\nNo score change since it was listed.\n\n## Verdicts\n\n**GLM-4.5-Air.** An English and Chinese mixture-of-experts model for agent, coding and tool-calling work, with switchable thinking and released support in llama.cpp, vLLM, SGLang and Transformers. It needs about 135 GB for Z.ai's FP8 build, two to four H100s by Z.ai's figures, and Z.ai and Novita host it. Its files date from August 2025, with no Z.ai reply since July 2025.\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### GLM-4.5-Air\n\n1. Z.ai's FP8 recipe is `python3 -m sglang.launch_server --model-path zai-org/GLM-4.5-Air-FP8 --tp-size 4 --tool-call-parser glm45 --reasoning-parser glm45`, and 2 H100s are enough below the full 128K context\n2. Use the glm45 tool and reasoning parsers for this model, the glm47 parser in Z.ai's current README is for GLM-4.7\n3. Thinking is on by default, pass `chat_template_kwargs` with `enable_thinking` false for short tasks, which makes the template add /nothink and an empty think block\n4. Declare tools in OpenAI style, and the template renders arguments as arg_key and arg_value pairs that the parser turns back into JSON\n5. Keep prompts under 128,000 tokens, the tokenizer config's limit, though config.json allows 131,072\n6. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented for the weights\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, GLM-4.5-Air or MiniCPM-V 4.7 35B-A3B?\n\nMiniCPM-V 4.7 35B-A3B and GLM-4.5-Air score within a point of each other on agent readiness, 61 (C) and 60.3 (C). GLM-4.5-Air leads on risk and agent interface.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/glm-4-5-air-vs-minicpm-v-4-7-35b-a3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/glm-4-5-air-vs-minicpm-v-4-7-35b-a3b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"glm-4-5-air\", \"b\": \"minicpm-v-4-7-35b-a3b\"}`. From a terminal: `anchor compare glm-4-5-air minicpm-v-4-7-35b-a3b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/glm-4-5-air.json and https://www.anchorterminal.com/api/v1/tools/minicpm-v-4-7-35b-a3b.json\n\n## Other comparisons with GLM-4.5-Air or MiniCPM-V 4.7 35B-A3B\n\n- [Agents-A1-4B vs GLM-4.5-Air](https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-4-5-air.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 GLM-4.5-Air](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-4-5-air.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 GLM-4.5-Air](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-glm-4-5-air.md)\n- [Apodex-1.1-mini vs MiniCPM-V 4.7 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