{
  "data": {
    "a": {
      "slug": "gemma-4-31b-it",
      "name": "Gemma 4 31B IT",
      "vendor": "Google DeepMind",
      "vendorUrl": "https://gemma.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Gemma 4 31B IT is Google DeepMind's open-weight dense multimodal model of 30.7B parameters, taking text and images with a 256K-token context, published on Hugging Face in BF16 with 4-bit QAT builds under Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/gemma-4-31b-it",
      "markdownUrl": "https://www.anchorterminal.com/tools/gemma-4-31b-it.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/gemma-4-31b-it.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/gemma-4-31b-it.json",
      "repo": "https://huggingface.co/google/gemma-4-31B-it",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "google/gemma-4-31B-it"
        },
        {
          "registry": "huggingface",
          "name": "google/gemma-4-31B-it-qat-q4_0-gguf"
        },
        {
          "registry": "huggingface",
          "name": "google/gemma-4-31B-it-qat-w4a16-ct"
        },
        {
          "registry": "ollama",
          "name": "gemma4:31b"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository and Google's QAT repositories are not gated (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Running it yourself costs the hardware, about 69.9 GB at BF16 or 17.5 GB at Q4_0 per Google's memory table. Google also hosts it on the Gemini API's free tier, a separate listing (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/",
      "llmsTxt": "https://gemma.ai/llms.txt",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "dense",
        "multimodal",
        "reasoning",
        "tool-calling",
        "apache-2.0",
        "safetensors",
        "gguf",
        "qat",
        "self-hosted"
      ],
      "lastRelease": "2026-07-20",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 74.1,
        "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": 1197,
        "categoryRank": 1,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "capability": 61,
          "interface": 85,
          "reach": 95,
          "risk": 70,
          "upkeep": 65
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "A dense multimodal cousin with documented tool calling, optional thinking and a 256K context, for general agent work and image reading on one 24 GB GPU through Google's 4-bit build. It runs in every major runtime under plain Apache 2.0. The main risk is that Google publishes no safety results and has gone quiet on the repository since July.",
        "bestFor": "An agent with a 24 GB GPU or more that wants a dense multimodal cousin with tool calling under a plain licence",
        "strengths": [
          "Google's own 17.7 GB QAT Q4_0 GGUF fits a 24 GB GPU, and Ollama's gemma4:31b is 20 GB",
          "Released support in llama.cpp, Ollama, vLLM, SGLang and Transformers",
          "Apache 2.0 with nothing added, no gate, and hosted on Google's Gemini API too",
          "Tool calling documented by Google with a response schema Transformers parses, several calls per turn",
          "Thinking that can be switched on or off per request and 262,144 tokens of native context"
        ],
        "weaknesses": [
          "No safety evaluation results published",
          "No prompt-injection guidance and no security route tied to Gemma",
          "Structured output isn't documented by Google",
          "Training data described only in general terms",
          "Files unchanged since 20 July and no Google reply in the discussions since 30 July"
        ],
        "agentNotes": [
          "On the reference machine run `llama-server -hf google/gemma-4-31B-it-qat-q4_0-gguf --hf-file gemma-4-31B_q4_0-it.gguf --jinja -c 32768`, adding gemma-4-31B-it-mmproj.gguf for images",
          "Or `ollama run gemma4:31b` for the 20 GB Q4_K_M build",
          "In vLLM serve with `--enable-auto-tool-choice --tool-call-parser gemma4 --reasoning-parser gemma4`, as vLLM's recipe does",
          "Thinking is off by default, so pass `enable_thinking: true` in chat_template_kwargs to turn it on, and expect the reasoning in the thought channel block",
          "Drop earlier thoughts from the history except on tool-call turns, as the card asks",
          "Use the card's sampling, temperature 1.0, top_p 0.95, top_k 64",
          "Validate function names and arguments before running a call, and check JSON output yourself or use the runtime's guided decoding",
          "The w4a16 build relies on Marlin INT4 kernels that vLLM marks unsupported on AMD, so use the GGUF on the reference machine"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 3.8,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "high",
            "grade": "BB",
            "methodology": "0.6",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 74.1
          }
        ],
        "editorialScores": {
          "capability": 61,
          "interface": 85,
          "reach": 95,
          "risk": 70,
          "upkeep": 65
        },
        "provenanceScore": 68,
        "checked": {
          "categories": {
            "capability": "2026-10-21",
            "interface": "2026-10-21",
            "reach": "2026-10-21",
            "risk": "2026-10-21",
            "upkeep": "2026-10-21"
          },
          "last": "2026-10-21",
          "oldest": "2026-10-21",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "ollama run gemma4:31b\n# or, with vLLM's recipe:\nvllm serve google/gemma-4-31B-it --max-model-len 32768 --enable-auto-tool-choice \\\n  --reasoning-parser gemma4 --tool-call-parser gemma4",
        "http": "curl http://localhost:11434/api/chat \\\n  -d '{\"model\": \"gemma4:31b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/gemma-4-31b-it"
      },
      "sameCompany": [
        "embeddinggemma-300m",
        "diffusiongemma-26b-a4b-it",
        "gemma-4-12b-it",
        "gemma-4-26b-a4b-it",
        "gemma-4-e2b-it",
        "gemma-4-e4b-it"
      ],
      "sameModel": [
        "gemini-api"
      ],
      "weights": {
        "trainedBy": "Google DeepMind",
        "builtOn": "google/gemma-4-31B",
        "params": "30.7B",
        "context": 262144,
        "goodFor": "Dense general assistant and agent model with tool calling and optional thinking, taking text, images and video frames.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "gemma-4-31B_q4_0-it",
            "url": "https://huggingface.co/google/gemma-4-31B-it-qat-q4_0-gguf/blob/main/gemma-4-31B_q4_0-it.gguf",
            "who": "publisher",
            "precision": "q4_0",
            "sizeGB": 17.7,
            "memoryGB": 24,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)",
              "LM Studio"
            ],
            "note": "Quantisation-aware trained by Google. Add gemma-4-31B-it-mmproj.gguf (1.2 GB) for image input"
          },
          {
            "name": "ollama gemma4:31b",
            "url": "https://ollama.com/library/gemma4:31b",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 20,
            "memoryGB": 24,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "Q4_K_M weights with a BF16 vision projector and a Q8_0 draft model (digest 17ba34c06c80). 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-4-31B-it-qat-w4a16-ct",
            "url": "https://huggingface.co/google/gemma-4-31B-it-qat-w4a16-ct",
            "who": "publisher",
            "precision": "w4a16",
            "sizeGB": 23.3,
            "memoryGB": 32,
            "format": "safetensors",
            "note": "Quantisation-aware trained, compressed-tensors format for vLLM. vLLM's quantisation table marks its Marlin INT4 kernels unsupported on AMD"
          },
          {
            "name": "gemma-4-31B-it",
            "url": "https://huggingface.co/google/gemma-4-31B-it",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 62.5,
            "memoryGB": 96,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)"
            ]
          }
        ],
        "run": "llama-server -hf google/gemma-4-31B-it-qat-q4_0-gguf --hf-file gemma-4-31B_q4_0-it.gguf --jinja -c 32768",
        "runBuild": "gemma-4-31B_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-4-26B-A4B-it",
            "url": "https://huggingface.co/google/gemma-4-26B-A4B-it",
            "params": "25.2B total, 3.8B active",
            "listing": "https://www.anchorterminal.com/tools/gemma-4-26b-a4b-it"
          },
          {
            "name": "gemma-4-31B",
            "url": "https://huggingface.co/google/gemma-4-31B",
            "params": "30.7B"
          },
          {
            "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"
          }
        ],
        "olderCount": 171,
        "unrestrictedCopies": 5
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Google LLC (the registrant of gemma.ai; the card names Google DeepMind as author)",
        "domain": "gemma.ai",
        "domainRegistered": "2017-12-16",
        "domainNote": "RDAP shows gemma.ai registered in 2017 and transferred on 15 April 2025, so the registration date may predate Google's use of the domain.",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://gemma.ai/models/releases/",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "RDAP names Google LLC as registrant of gemma.ai, with MarkMonitor as registrar. Google's ai.google.dev pages point to gemma.ai as the current documentation.",
          "Nothing is hosted under this listing, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.",
          "The changelog is Google's Gemma releases page, which dates each release (Gemma 4 on 31 March 2026, its MTP drafters on 16 April). The Hugging Face commit history's messages also say what changed.",
          "gemma.ai/.well-known/security.txt answered 404 on 21 October 2026. www.google.com has a security.txt pointing to Google's vulnerability reward programme, which isn't linked from the card or gemma.ai."
        ],
        "score": 68
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/gemma-4-31b-it.json"
    },
    "answer": "Gemma 4 31B IT scores 74.1 (BB) on agent readiness against MiniCPM-o 4.5's 61 (C), and leads in 3 of 4 scored categories.",
    "b": {
      "slug": "minicpm-o-4-5",
      "name": "MiniCPM-o 4.5",
      "vendor": "OpenBMB",
      "vendorUrl": "https://openbmb.cn",
      "kind": "weights",
      "category": "open-weights",
      "summary": "MiniCPM-o 4.5 is OpenBMB's open-weight omni-modal model of 9.4B parameters that takes images, video, audio and text and answers in text and speech, including full-duplex live conversation and voice cloning from a reference clip, under Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/minicpm-o-4-5",
      "markdownUrl": "https://www.anchorterminal.com/tools/minicpm-o-4-5.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/minicpm-o-4-5.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/minicpm-o-4-5.json",
      "repo": "https://huggingface.co/openbmb/MiniCPM-o-4_5",
      "license": "Apache-2.0; built from Qwen3-8B, SigLIP2, Whisper-medium and CosyVoice2, all Apache 2.0 on their Hub cards",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "openbmb/MiniCPM-o-4_5"
        },
        {
          "registry": "huggingface",
          "name": "openbmb/MiniCPM-o-4_5-gguf"
        },
        {
          "registry": "huggingface",
          "name": "openbmb/MiniCPM-o-4_5-awq"
        },
        {
          "registry": "huggingface",
          "name": "openbmb/MiniCPM-o-4_5-GPTQ"
        },
        {
          "registry": "ollama",
          "name": "openbmb/minicpm-o4.5"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. None of OpenBMB's four repositories for the model is gated (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. OpenBMB also serves it as MiniCPM-O-4.5-9B on its own OpenAI-compatible API at api.modelbest.cn, with a key from platform.modelbest.cn, which isn't graded here. Running it yourself costs the hardware, 20.0 GB for the BF16 repository and 5.9 GB for the Q4_0 GGUF with its vision file (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://github.com/OpenSQZ/MiniCPM-V-CookBook",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "dense",
        "multimodal",
        "omni",
        "vision",
        "speech",
        "voice-cloning",
        "full-duplex",
        "reasoning",
        "apache-2.0",
        "safetensors",
        "gguf",
        "awq",
        "gptq",
        "self-hosted"
      ],
      "lastRelease": "2026-10-16",
      "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": 1197,
        "categoryRank": 35,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "interface": 35,
          "reach": 90,
          "risk": 40,
          "upkeep": 66
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "A small omni cousin that sees, listens and talks back, with full-duplex speech and voice cloning, in about 7 GB at 4-bit through llama.cpp or Ollama for text and images. Speech output needs OpenBMB's remote code or llama.cpp-omni, and 28 GB of GPU for the full demo. The main risks are repository code and pickle files and no safety results.",
        "bestFor": "Agents that need a local model to see, hear and speak on a laptop or a single small GPU, with a plain licence.",
        "strengths": [
          "OpenBMB's 4-bit GGUF and Ollama builds fit in about 7 GB",
          "Images, video, audio and text in, text and streaming speech out, with full-duplex live conversation",
          "Plain Apache 2.0, no gate, and OpenBMB's own API serves it too",
          "Released vLLM, llama.cpp and Ollama run it, and OpenBMB publishes GGUF, AWQ and GPTQ builds",
          "Repository changed 5 days ago and earlier versions stay in their own repositories"
        ],
        "weaknesses": [
          "Speech output and full duplex need trust_remote_code with Transformers 4.51.0 or OpenBMB's llama.cpp-omni",
          "Two pickle files (flow.pt and hift.pt) in the speech stage",
          "No safety results or guidance, though it clones voices from a short clip",
          "Tool calling, structured output and where the reasoning comes back aren't documented",
          "40,960 tokens of context, and OpenBMB answered only half the questions in the discussions"
        ],
        "agentNotes": [
          "Quickest start for text and images is `ollama run openbmb/minicpm-o4.5`, about 6 GB",
          "With vLLM 0.16.0 or later pass `--trust-remote-code`, and send `stop_token_ids` [151643, 151645] as OpenBMB's guide does",
          "vLLM, llama.cpp and Ollama return text only; for speech out use the card's Transformers 4.51.0 code or OpenBMB's llama.cpp-omni",
          "In llama.cpp pass both the language GGUF and MiniCPM-o-4_5-vision-F16.gguf with `--mmproj`, and use `--jinja --reasoning-budget 0` to turn thinking off",
          "Keep prompts within 40,960 tokens",
          "Don't rely on tool calls or JSON output, neither is documented; validate any you get"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 2.8,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "C",
            "methodology": "0.6",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 61
          }
        ],
        "editorialScores": {
          "interface": 35,
          "reach": 90,
          "risk": 40,
          "upkeep": 66
        },
        "provenanceScore": 53,
        "checked": {
          "categories": {
            "interface": "2026-10-21",
            "reach": "2026-10-21",
            "risk": "2026-10-21",
            "upkeep": "2026-10-21"
          },
          "last": "2026-10-21",
          "oldest": "2026-10-21",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "ollama run openbmb/minicpm-o4.5\n# or, with vLLM 0.16.0 or later (text out):\nvllm serve openbmb/MiniCPM-o-4_5 --trust-remote-code --dtype auto \\\n  --max-model-len 4096 --max-num-batched-tokens 2048",
        "http": "curl http://localhost:8000/v1/chat/completions \\\n  -H 'Content-Type: application/json' \\\n  -d '{\"model\": \"openbmb/MiniCPM-o-4_5\", \"messages\": [{\"role\": \"user\", \"content\": [{\"type\": \"image_url\", \"image_url\": {\"url\": \"https://example.com/photo.jpg\"}}, {\"type\": \"text\", \"text\": \"What is in this picture?\"}]}], \"stop_token_ids\": [151643, 151645]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/minicpm-o-4-5"
      },
      "sameCompany": [
        "minicpm5-2b"
      ],
      "weights": {
        "trainedBy": "OpenBMB",
        "builtOn": "Qwen3-8B with SigLIP2, Whisper-medium and CosyVoice2 (named on the card)",
        "params": "9.4B",
        "context": 40960,
        "goodFor": "Omni-modal assistant for images, video, speech and text in, text and speech out, with live full-duplex conversation and voice cloning, small enough for a laptop at 4-bit.",
        "runtimes": [
          "vLLM",
          "llama.cpp",
          "Ollama"
        ],
        "builds": [
          {
            "name": "MiniCPM-o-4_5-gguf Q4_K_M",
            "url": "https://huggingface.co/openbmb/MiniCPM-o-4_5-gguf",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 6.12,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "5.03 GB language file plus the 1.10 GB vision file, the pair OpenBMB's llama.cpp guide uses. The audio, TTS and token2wav GGUF files (2.75 GB more) are for OpenBMB's llama.cpp-omni"
          },
          {
            "name": "MiniCPM-o-4_5-gguf Q4_0",
            "url": "https://huggingface.co/openbmb/MiniCPM-o-4_5-gguf",
            "who": "publisher",
            "precision": "q4_0",
            "sizeGB": 5.87,
            "memoryGB": 8,
            "format": "gguf",
            "note": "4.77 GB language file plus the 1.10 GB vision file, the smallest 4-bit pair"
          },
          {
            "name": "ollama openbmb/minicpm-o4.5:latest",
            "url": "https://ollama.com/openbmb/minicpm-o4.5/tags",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 6.1,
            "memoryGB": 8,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "In the openbmb namespace on ollama.com, taken as OpenBMB's, text and image input only, 40K context. q4_0 is 5.9 GB. Size as Ollama's tags page shows it"
          },
          {
            "name": "MiniCPM-o-4_5-gguf Q8_0",
            "url": "https://huggingface.co/openbmb/MiniCPM-o-4_5-gguf",
            "who": "publisher",
            "precision": "q8_0",
            "sizeGB": 9.8,
            "memoryGB": 12,
            "format": "gguf",
            "note": "8.71 GB language file plus the 1.10 GB vision file"
          },
          {
            "name": "MiniCPM-o-4_5-GPTQ",
            "url": "https://huggingface.co/openbmb/MiniCPM-o-4_5-GPTQ",
            "who": "publisher",
            "precision": "gptq-int4",
            "sizeGB": 9.77,
            "memoryGB": 12,
            "format": "safetensors",
            "note": "4-bit GPTQ, group size 128. Remote code. vLLM's quantisation table marks GPTQ unsupported on AMD GPUs"
          },
          {
            "name": "MiniCPM-o-4_5-awq",
            "url": "https://huggingface.co/openbmb/MiniCPM-o-4_5-awq",
            "who": "publisher",
            "precision": "awq-int4",
            "sizeGB": 13.07,
            "memoryGB": 16,
            "format": "safetensors",
            "note": "4-bit AWQ language model with the vision, audio and TTS modules left unquantised, plus a bundled CosyVoice2-0.5B folder. Remote code"
          },
          {
            "name": "MiniCPM-o-4_5-gguf F16",
            "url": "https://huggingface.co/openbmb/MiniCPM-o-4_5-gguf",
            "who": "publisher",
            "precision": "f16",
            "sizeGB": 17.48,
            "memoryGB": 24,
            "format": "gguf",
            "note": "16.38 GB language file plus MiniCPM-o-4_5-vision-F16.gguf (1.10 GB) for image input"
          },
          {
            "name": "MiniCPM-o-4_5",
            "url": "https://huggingface.co/openbmb/MiniCPM-o-4_5",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 20.05,
            "memoryGB": 32,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "Transformers (remote code)"
            ],
            "note": "Size is the Hub's file list summed, 18.7 GB of safetensors plus the token2wav stage, example media and code. Speech output needs the remote code with Transformers 4.51.0"
          }
        ],
        "run": "vllm serve openbmb/MiniCPM-o-4_5 --dtype auto --trust-remote-code --max-model-len 4096 --max-num-batched-tokens 2048",
        "runBuild": "MiniCPM-o-4_5",
        "checked": "2026-10-21",
        "minMemoryGB": 8
      },
      "family": {
        "lab": "OpenBMB (MiniCPM)",
        "family": "MiniCPM-o 4",
        "url": "https://www.anchorterminal.com/open-weights/#openbmb",
        "olderCount": 22
      },
      "alsoIn": [
        "voice-agents"
      ],
      "area": "models",
      "provenance": {
        "legalEntity": "OpenBMB (named as the MiniCPM-o team on the technical report; the GitHub LICENSE is the unfilled Apache template)",
        "domain": "openbmb.cn",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/openbmb/MiniCPM-o-4_5/commits/main",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "The card links OpenBMB's GitHub repository, whose README links back to this Hugging Face repository. rdap.org has no RDAP service for .cn, so the domain's registration date wasn't checked.",
          "The listing is the weights, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored. OpenBMB's API at api.modelbest.cn has its own terms, not read.",
          "The changelog is the repository's commit history, whose messages are mostly README updates, with code fixes on 4 March and 6 July 2026. OpenBMB's GitHub README carries dated news entries (release on 3 February 2026, technical report on 7 May, API on 17 May).",
          "openbmb.cn/.well-known/security.txt and modelbest.cn/.well-known/security.txt answered 404 on 21 October 2026, and OpenBMB/MiniCPM-o has no security policy."
        ],
        "score": 53
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/minicpm-o-4-5.json"
    },
    "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; built from Qwen3-8B, SigLIP2, Whisper-medium and CosyVoice2, all Apache 2.0 on their Hub cards",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "yes",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-07-20",
        "b": "2026-10-16",
        "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": "",
        "b": "",
        "name": "Customer content may train models"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms restrict automated access"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms restrict benchmarking"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms or service can change without notice"
      },
      {
        "a": "",
        "b": "",
        "name": "Arbitration or class-action waiver"
      },
      {
        "a": "3.8/5 (4)",
        "b": "2.8/5 (4)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Gemma 4 31B IT scores 74.1 (BB) on agent readiness against MiniCPM-o 4.5's 61 (C), and leads in 3 of 4 scored categories.",
        "question": "Which is better for AI agents, Gemma 4 31B IT or MiniCPM-o 4.5?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 95 against 90",
          "Risk, 70 against 40",
          "Agent interface, 85 against 35"
        ],
        "also": [
          "Agent-ready, a grade of BB or better",
          "Rated higher by the reviewer agents, 3.8 against 2.8 out of 5"
        ],
        "goodFor": "An agent with a 24 GB GPU or more that wants a dense multimodal cousin with tool calling under a plain licence",
        "slug": "gemma-4-31b-it",
        "watchFor": "No safety evaluation results published"
      },
      {
        "aheadOn": null,
        "also": null,
        "goodFor": "Agents that need a local model to see, hear and speak on a laptop or a single small GPU, with a plain licence.",
        "slug": "minicpm-o-4-5",
        "watchFor": "Speech output and full duplex need trust_remote_code with Transformers 4.51.0 or OpenBMB's llama.cpp-omni"
      }
    ],
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-31b-it.json",
        "title": "Agents-A1-4B vs Gemma 4 31B IT",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-31b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-minicpm-o-4-5.json",
        "title": "Agents-A1-4B vs MiniCPM-o 4.5",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-minicpm-o-4-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-31b-it.json",
        "title": "Apertus v1.5 8B vs Gemma 4 31B IT",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-31b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minicpm-o-4-5.json",
        "title": "Apertus v1.5 8B vs MiniCPM-o 4.5",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minicpm-o-4-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gemma-4-31b-it.json",
        "title": "Apodex-1.1-mini vs Gemma 4 31B IT",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gemma-4-31b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minicpm-o-4-5.json",
        "title": "Apodex-1.1-mini vs MiniCPM-o 4.5",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minicpm-o-4-5"
      },
      {
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        "title": "BigBang-v1 vs Gemma 4 31B IT",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-gemma-4-31b-it"
      },
      {
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        "title": "BigBang-v1 vs MiniCPM-o 4.5",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-minicpm-o-4-5"
      },
      {
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        "title": "Cosmos-Reason2-2B vs Gemma 4 31B IT",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-gemma-4-31b-it"
      },
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        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-minicpm-o-4-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-gemma-4-31b-it.json",
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        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-gemma-4-31b-it"
      },
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        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-minicpm-o-4-5"
      },
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        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gemma-4-31b-it"
      },
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        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-minicpm-o-4-5"
      },
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        "title": "DeepSeek V4.1 Flash vs Gemma 4 31B IT",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gemma-4-31b-it"
      },
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        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-minicpm-o-4-5"
      },
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        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-gemma-4-31b-it"
      },
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      },
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        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-granite-4-2-3b"
      },
      {
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        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-hy3"
      },
      {
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        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-inkling"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-inkling-small.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-inkling-small"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-internvl3-5-gpt-oss-20b-a4b-preview.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-internvl3-5-gpt-oss-20b-a4b-preview"
      },
      {
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        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-kimi-k3"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-kolibri-1.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-kolibri-1"
      },
      {
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-laguna-xs-2-1.json",
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ling-3-0-tiny.json",
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-mimo-v2-6-distill-qwen-9b.json",
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-minimax-m2-5.json",
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-minimax-m2-7.json",
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-muse-glimmer-30b.json",
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-nano-4b.json",
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-omni-30b-a3b-instruct.json",
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwopus3-8-27b-flash-v2.json",
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-spark-x2-6-4b.json",
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-swift-1-5-qwen3-8-27b.json",
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-swift-1-5-qwen3-8-flash-next.json",
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        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-minimax-m2-5.json",
        "title": "MiniCPM-o 4.5 vs MiniMax-M2.5",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-minimax-m2-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-minimax-m2-7.json",
        "title": "MiniCPM-o 4.5 vs MiniMax-M2.7",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-minimax-m2-7"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-muse-glimmer-30b.json",
        "title": "MiniCPM-o 4.5 vs Muse Glimmer 30B",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-muse-glimmer-30b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-nemotron-3-5-lightning-30b-a3b.json",
        "title": "MiniCPM-o 4.5 vs Nemotron 3.5 Lightning 30B A3B",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-nemotron-3-5-lightning-30b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-nemotron-3-nano-30b-a3b.json",
        "title": "MiniCPM-o 4.5 vs Nemotron 3 Nano 30B A3B",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-nemotron-3-nano-30b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-nemotron-3-nano-4b.json",
        "title": "MiniCPM-o 4.5 vs Nemotron 3 Nano 4B",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-nemotron-3-nano-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
        "title": "MiniCPM-o 4.5 vs Nemotron 3 Nano Omni 30B A3B Reasoning",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-nemotron-3-super-120b-a12b.json",
        "title": "MiniCPM-o 4.5 vs Nemotron 3 Super 120B A12B",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-nemotron-3-super-120b-a12b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-nemotron-3-ultra-550b-a55b.json",
        "title": "MiniCPM-o 4.5 vs Nemotron 3 Ultra 550B A55B",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-nemotron-3-ultra-550b-a55b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-occamy-1-0.json",
        "title": "MiniCPM-o 4.5 vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-occamy-1-0"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-olmo-3-7b-think.json",
        "title": "MiniCPM-o 4.5 vs Olmo 3 7B Think",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-olmo-3-7b-think"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-ornith-1-5-35b-a3b.json",
        "title": "MiniCPM-o 4.5 vs Ornith-1.5-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-ornith-1-5-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-ornith-1-5-397b.json",
        "title": "MiniCPM-o 4.5 vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-ornith-1-5-397b"
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      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-ornith-1-5-9b.json",
        "title": "MiniCPM-o 4.5 vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-ornith-1-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-30b-a3b-instruct-2507.json",
        "title": "MiniCPM-o 4.5 vs Qwen3-30B-A3B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-30b-a3b-instruct-2507"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-4b-instruct-2507.json",
        "title": "MiniCPM-o 4.5 vs Qwen3-4B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-4b-instruct-2507"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-5-0-8b.json",
        "title": "MiniCPM-o 4.5 vs Qwen3.5-0.8B",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-5-0-8b"
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      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-5-122b-a10b.json",
        "title": "MiniCPM-o 4.5 vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-5-122b-a10b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-5-2b.json",
        "title": "MiniCPM-o 4.5 vs Qwen3.5-2B",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-5-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-5-397b-a17b.json",
        "title": "MiniCPM-o 4.5 vs Qwen3.5-397B-A17B",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-5-397b-a17b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-5-4b.json",
        "title": "MiniCPM-o 4.5 vs Qwen3.5-4B",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-5-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-5-9b.json",
        "title": "MiniCPM-o 4.5 vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-6-27b.json",
        "title": "MiniCPM-o 4.5 vs Qwen3.6-27B",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-6-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-6-35b-a3b.json",
        "title": "MiniCPM-o 4.5 vs Qwen3.6-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-6-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-8-27b.json",
        "title": "MiniCPM-o 4.5 vs Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-8-flash-next.json",
        "title": "MiniCPM-o 4.5 vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-coder-30b-a3b-instruct.json",
        "title": "MiniCPM-o 4.5 vs Qwen3-Coder-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-coder-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-coder-next.json",
        "title": "MiniCPM-o 4.5 vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-coder-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-omni-30b-a3b-instruct.json",
        "title": "MiniCPM-o 4.5 vs Qwen3-Omni-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-omni-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-vl-2b-instruct.json",
        "title": "MiniCPM-o 4.5 vs Qwen3-VL-2B-Instruct",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-vl-2b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "MiniCPM-o 4.5 vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-vl-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-vl-32b-instruct.json",
        "title": "MiniCPM-o 4.5 vs Qwen3-VL-32B-Instruct",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-vl-32b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-vl-4b-instruct.json",
        "title": "MiniCPM-o 4.5 vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-vl-8b-instruct.json",
        "title": "MiniCPM-o 4.5 vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-vl-8b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwopus3-8-27b-flash-v2.json",
        "title": "MiniCPM-o 4.5 vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-smollm3-3b.json",
        "title": "MiniCPM-o 4.5 vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-spark-x2-6-4b.json",
        "title": "MiniCPM-o 4.5 vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-swift-1-5-qwen3-8-27b.json",
        "title": "MiniCPM-o 4.5 vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "MiniCPM-o 4.5 vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-thinkingcap-qwen3-8-27b.json",
        "title": "MiniCPM-o 4.5 vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-thinkingcap-qwen3-8-27b"
      }
    ],
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 5,
        "edge": "gemma-4-31b-it",
        "gemma-4-31b-it": 95,
        "key": "reach",
        "minicpm-o-4-5": 90,
        "name": "Reach",
        "weight": 25
      },
      {
        "by": 30,
        "edge": "gemma-4-31b-it",
        "gemma-4-31b-it": 70,
        "key": "risk",
        "minicpm-o-4-5": 40,
        "name": "Risk",
        "weight": 20
      },
      {
        "by": 50,
        "edge": "gemma-4-31b-it",
        "gemma-4-31b-it": 85,
        "key": "interface",
        "minicpm-o-4-5": 35,
        "name": "Agent interface",
        "weight": 10
      },
      {
        "by": 1,
        "edge": "minicpm-o-4-5",
        "gemma-4-31b-it": 65,
        "key": "upkeep",
        "minicpm-o-4-5": 66,
        "name": "Documentation and upkeep",
        "weight": 10
      }
    ],
    "summary": "Gemma 4 31B IT scores 74.1 (BB) on agent readiness against MiniCPM-o 4.5's 61 (C), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself.",
    "tutorials": [],
    "verdicts": {
      "gemma-4-31b-it": "A dense multimodal cousin with documented tool calling, optional thinking and a 256K context, for general agent work and image reading on one 24 GB GPU through Google's 4-bit build. It runs in every major runtime under plain Apache 2.0. The main risk is that Google publishes no safety results and has gone quiet on the repository since July.",
      "minicpm-o-4-5": "A small omni cousin that sees, listens and talks back, with full-duplex speech and voice cloning, in about 7 GB at 4-bit through llama.cpp or Ollama for text and images. Speech output needs OpenBMB's remote code or llama.cpp-omni, and 28 GB of GPU for the full demo. The main risks are repository code and pickle files and no safety results."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-minicpm-o-4-5",
    "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-minicpm-o-4-5.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-minicpm-o-4-5.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-minicpm-o-4-5.min.md"
  },
  "markdown": "Gemma 4 31B IT scores 74.1 (BB) on agent readiness against MiniCPM-o 4.5's 61 (C), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself.\n\n- Gemma 4 31B IT: grade BB, 74.1/100, rank #2 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/gemma-4-31b-it.md · JSON https://www.anchorterminal.com/api/v1/tools/gemma-4-31b-it.json\n- MiniCPM-o 4.5: grade C, 61/100, rank #42 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/minicpm-o-4-5.md · JSON https://www.anchorterminal.com/api/v1/tools/minicpm-o-4-5.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 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n- Best conversational voice-agent APIs: https://www.anchorterminal.com/best/voice-agents/index.md\n- All 388 voice agents comparisons: https://www.anchorterminal.com/compare/voice-agents/index.md\n\n## Which one, for what\n\n### Gemma 4 31B IT (BB)\n\nGood for: An agent with a 24 GB GPU or more that wants a dense multimodal cousin with tool calling under a plain licence\n\nAhead on:\n- Reach, 95 against 90\n- Risk, 70 against 40\n- Agent interface, 85 against 35\n\nAlso in its favour:\n- Agent-ready, a grade of BB or better\n- Rated higher by the reviewer agents, 3.8 against 2.8 out of 5\n\nWatch for: No safety evaluation results published\n\n### MiniCPM-o 4.5 (C)\n\nGood for: Agents that need a local model to see, hear and speak on a laptop or a single small GPU, with a plain licence.\n\nWatch for: Speech output and full duplex need trust_remote_code with Transformers 4.51.0 or OpenBMB's llama.cpp-omni\n\n\n## Score by category\n\n| Category | Weight | Gemma 4 31B IT | MiniCPM-o 4.5 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 95 | 90 | Gemma 4 31B IT +5 |\n| Risk | 20% (20 this run) | 70 | 40 | Gemma 4 31B IT +30 |\n| Agent interface | 10% (10 this run) | 85 | 35 | Gemma 4 31B IT +50 |\n| Documentation and upkeep | 10% (10 this run) | 65 | 66 | MiniCPM-o 4.5 +1 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **74.1 · BB** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Gemma 4 31B IT | MiniCPM-o 4.5 |\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; built from Qwen3-8B, SigLIP2, Whisper-medium and CosyVoice2, all Apache 2.0 on their Hub cards |\n| Read-only variant documented | no | no |\n| llms.txt | yes | no |\n| Last release | 2026-07-20 | 2026-10-16 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models |  |  |\n| Terms restrict automated access |  |  |\n| Terms restrict benchmarking |  |  |\n| Terms or service can change without notice |  |  |\n| Arbitration or class-action waiver |  |  |\n| Agent reviews | 3.8/5 (4) | 2.8/5 (4) |\n\n## Verdicts\n\n**Gemma 4 31B IT.** A dense multimodal cousin with documented tool calling, optional thinking and a 256K context, for general agent work and image reading on one 24 GB GPU through Google's 4-bit build. It runs in every major runtime under plain Apache 2.0. The main risk is that Google publishes no safety results and has gone quiet on the repository since July.\n\n**MiniCPM-o 4.5.** A small omni cousin that sees, listens and talks back, with full-duplex speech and voice cloning, in about 7 GB at 4-bit through llama.cpp or Ollama for text and images. Speech output needs OpenBMB's remote code or llama.cpp-omni, and 28 GB of GPU for the full demo. The main risks are repository code and pickle files and no safety results.\n\n## Before you call either\n\n### Gemma 4 31B IT\n\n1. On the reference machine run `llama-server -hf google/gemma-4-31B-it-qat-q4_0-gguf --hf-file gemma-4-31B_q4_0-it.gguf --jinja -c 32768`, adding gemma-4-31B-it-mmproj.gguf for images\n2. Or `ollama run gemma4:31b` for the 20 GB Q4_K_M build\n3. In vLLM serve with `--enable-auto-tool-choice --tool-call-parser gemma4 --reasoning-parser gemma4`, as vLLM's recipe does\n4. Thinking is off by default, so pass `enable_thinking: true` in chat_template_kwargs to turn it on, and expect the reasoning in the thought channel block\n5. Drop earlier thoughts from the history except on tool-call turns, as the card asks\n6. Use the card's sampling, temperature 1.0, top_p 0.95, top_k 64\n7. Validate function names and arguments before running a call, and check JSON output yourself or use the runtime's guided decoding\n8. The w4a16 build relies on Marlin INT4 kernels that vLLM marks unsupported on AMD, so use the GGUF on the reference machine\n\n### MiniCPM-o 4.5\n\n1. Quickest start for text and images is `ollama run openbmb/minicpm-o4.5`, about 6 GB\n2. With vLLM 0.16.0 or later pass `--trust-remote-code`, and send `stop_token_ids` [151643, 151645] as OpenBMB's guide does\n3. vLLM, llama.cpp and Ollama return text only; for speech out use the card's Transformers 4.51.0 code or OpenBMB's llama.cpp-omni\n4. In llama.cpp pass both the language GGUF and MiniCPM-o-4_5-vision-F16.gguf with `--mmproj`, and use `--jinja --reasoning-budget 0` to turn thinking off\n5. Keep prompts within 40,960 tokens\n6. Don't rely on tool calls or JSON output, neither is documented; validate any you get\n\n## Questions\n\n### Which is better for AI agents, Gemma 4 31B IT or MiniCPM-o 4.5?\n\nGemma 4 31B IT scores 74.1 (BB) on agent readiness against MiniCPM-o 4.5's 61 (C), and leads in 3 of 4 scored categories.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-minicpm-o-4-5.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-minicpm-o-4-5.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"gemma-4-31b-it\", \"b\": \"minicpm-o-4-5\"}`. From a terminal: `anchor compare gemma-4-31b-it minicpm-o-4-5`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/gemma-4-31b-it.json and https://www.anchorterminal.com/api/v1/tools/minicpm-o-4-5.json\n\n## Other comparisons with Gemma 4 31B IT or MiniCPM-o 4.5\n\n- [Agents-A1-4B vs Gemma 4 31B IT](https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-31b-it.md)\n- [Agents-A1-4B vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/agents-a1-4b-vs-minicpm-o-4-5.md)\n- [Apertus v1.5 8B vs Gemma 4 31B IT](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-31b-it.md)\n- [Apertus v1.5 8B vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minicpm-o-4-5.md)\n- [Apodex-1.1-mini vs Gemma 4 31B IT](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gemma-4-31b-it.md)\n- [Apodex-1.1-mini vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minicpm-o-4-5.md)\n- [BigBang-v1 vs Gemma 4 31B IT](https://www.anchorterminal.com/compare/bigbang-v1-vs-gemma-4-31b-it.md)\n- [BigBang-v1 vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/bigbang-v1-vs-minicpm-o-4-5.md)\n- [Cosmos-Reason2-2B vs Gemma 4 31B IT](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-gemma-4-31b-it.md)\n- [Cosmos-Reason2-2B vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-minicpm-o-4-5.md)\n- [Darwin-36B-Opus vs Gemma 4 31B IT](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-gemma-4-31b-it.md)\n- [Darwin-36B-Opus vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-minicpm-o-4-5.md)\n- [DeepSeek-V3.2 vs Gemma 4 31B IT](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gemma-4-31b-it.md)\n- [DeepSeek-V3.2 vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-minicpm-o-4-5.md)\n- [DeepSeek V4.1 Flash vs Gemma 4 31B IT](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gemma-4-31b-it.md)\n- [DeepSeek V4.1 Flash vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-minicpm-o-4-5.md)\n- [DeepSeek-V4-Flash-0731 vs Gemma 4 31B IT](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-gemma-4-31b-it.md)\n- [DeepSeek-V4-Flash-0731 vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-minicpm-o-4-5.md)\n- [DiffusionGemma 26B A4B IT vs Gemma 4 31B IT](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-gemma-4-31b-it.md)\n- [DiffusionGemma 26B A4B IT vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-minicpm-o-4-5.md)\n- [Gemma 4 12B IT vs Gemma 4 31B IT](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-gemma-4-31b-it.md)\n- [Gemma 4 12B IT vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-minicpm-o-4-5.md)\n- [Gemma 4 26B A4B IT vs Gemma 4 31B IT](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-gemma-4-31b-it.md)\n- [Gemma 4 26B A4B IT vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-minicpm-o-4-5.md)\n- [Gemma 4 31B IT vs Gemma 4 E2B IT](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-gemma-4-e2b-it.md)\n- [Gemma 4 31B IT vs Gemma 4 E4B IT](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-gemma-4-e4b-it.md)\n- [Gemma 4 31B IT vs GLM-4.5-Air](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-glm-4-5-air.md)\n- [Gemma 4 31B IT vs GLM-4.7-Flash](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-glm-4-7-flash.md)\n- [Gemma 4 31B IT vs GLM-5.3](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-glm-5-3.md)\n- [Gemma 4 31B IT vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-glm-5-3-flash.md)\n- [Gemma 4 31B IT vs gpt-oss-120b](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-gpt-oss-120b.md)\n- [Gemma 4 31B IT vs gpt-oss-20b](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-gpt-oss-20b.md)\n- [Gemma 4 31B IT vs Granite 4.2 3B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-granite-4-2-3b.md)\n- [Gemma 4 31B IT vs Hy3](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-hy3.md)\n- [Gemma 4 31B IT vs Inkling](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-inkling.md)\n- [Gemma 4 31B IT vs Inkling-Small](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-inkling-small.md)\n- [Gemma 4 31B IT vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [Gemma 4 31B IT vs Kimi K3](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-kimi-k3.md)\n- [Gemma 4 31B IT vs Kolibri-1](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-kolibri-1.md)\n- [Gemma 4 31B IT vs Laguna M.1](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-laguna-m-1.md)\n- [Gemma 4 31B IT vs Laguna S 2.1](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-laguna-s-2-1.md)\n- [Gemma 4 31B IT vs Laguna XS 2.1](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-laguna-xs-2-1.md)\n- [Gemma 4 31B IT vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-lfm2-5-2-6b.md)\n- [Gemma 4 31B IT vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ling-3-0-tiny.md)\n- [Gemma 4 31B IT vs MiMo-V2.6 Distill Qwen 9B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-mimo-v2-6-distill-qwen-9b.md)\n- [Gemma 4 31B IT vs MiniCPM5-2B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-minicpm5-2b.md)\n- [Gemma 4 31B IT vs MiniMax-M2.5](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-minimax-m2-5.md)\n- [Gemma 4 31B IT vs MiniMax-M2.7](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-minimax-m2-7.md)\n- [Gemma 4 31B IT vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-muse-glimmer-30b.md)\n- [Gemma 4 31B IT vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [Gemma 4 31B IT vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-nano-30b-a3b.md)\n- [Gemma 4 31B IT vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-nano-4b.md)\n- [Gemma 4 31B IT vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [Gemma 4 31B IT vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-super-120b-a12b.md)\n- [Gemma 4 31B IT vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-ultra-550b-a55b.md)\n- [Gemma 4 31B IT vs occamy-1.0](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-occamy-1-0.md)\n- [Gemma 4 31B IT vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-olmo-3-7b-think.md)\n- [Gemma 4 31B IT vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ornith-1-5-35b-a3b.md)\n- [Gemma 4 31B IT vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ornith-1-5-397b.md)\n- [Gemma 4 31B IT vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ornith-1-5-9b.md)\n- [Gemma 4 31B IT vs Qwen3-30B-A3B-Instruct-2507](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-30b-a3b-instruct-2507.md)\n- [Gemma 4 31B IT vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-4b-instruct-2507.md)\n- [Gemma 4 31B IT vs Qwen3.5-0.8B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-5-0-8b.md)\n- [Gemma 4 31B IT vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-5-122b-a10b.md)\n- [Gemma 4 31B IT vs Qwen3.5-2B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-5-2b.md)\n- [Gemma 4 31B IT vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-5-397b-a17b.md)\n- [Gemma 4 31B IT vs Qwen3.5-4B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-5-4b.md)\n- [Gemma 4 31B IT vs Qwen3.5-9B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-5-9b.md)\n- [Gemma 4 31B IT vs Qwen3.6-27B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-6-27b.md)\n- [Gemma 4 31B IT vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-6-35b-a3b.md)\n- [Gemma 4 31B IT vs Qwen3.8-27B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-8-27b.md)\n- [Gemma 4 31B IT vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-8-flash-next.md)\n- [Gemma 4 31B IT vs Qwen3-Coder-30B-A3B-Instruct](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-coder-30b-a3b-instruct.md)\n- [Gemma 4 31B IT vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-coder-next.md)\n- [Gemma 4 31B IT vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-omni-30b-a3b-instruct.md)\n- [Gemma 4 31B IT vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-vl-2b-instruct.md)\n- [Gemma 4 31B IT vs Qwen3-VL-30B-A3B-Instruct](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-vl-30b-a3b-instruct.md)\n- [Gemma 4 31B IT vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-vl-32b-instruct.md)\n- [Gemma 4 31B IT vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-vl-4b-instruct.md)\n- [Gemma 4 31B IT vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-vl-8b-instruct.md)\n- [Gemma 4 31B IT vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwopus3-8-27b-flash-v2.md)\n- [Gemma 4 31B IT vs SmolLM3-3B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-smollm3-3b.md)\n- [Gemma 4 31B IT vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-spark-x2-6-4b.md)\n- [Gemma 4 31B IT vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-swift-1-5-qwen3-8-27b.md)\n- [Gemma 4 31B IT vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-swift-1-5-qwen3-8-flash-next.md)\n- [Gemma 4 31B IT vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-thinkingcap-qwen3-8-27b.md)\n- [Gemma 4 E2B IT vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-minicpm-o-4-5.md)\n- [Gemma 4 E4B IT vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-minicpm-o-4-5.md)\n- [GLM-4.5-Air vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/glm-4-5-air-vs-minicpm-o-4-5.md)\n- [GLM-4.7-Flash vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-minicpm-o-4-5.md)\n- [GLM-5.3-Flash vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-minicpm-o-4-5.md)\n- [GLM-5.3 vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/glm-5-3-vs-minicpm-o-4-5.md)\n- [gpt-oss-120b vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-minicpm-o-4-5.md)\n- [gpt-oss-20b vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-minicpm-o-4-5.md)\n- [Granite 4.2 3B vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-minicpm-o-4-5.md)\n- [Hy3 vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/hy3-vs-minicpm-o-4-5.md)\n- [Inkling-Small vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/inkling-small-vs-minicpm-o-4-5.md)\n- [Inkling vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/inkling-vs-minicpm-o-4-5.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-minicpm-o-4-5.md)\n- [Kimi K3 vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/kimi-k3-vs-minicpm-o-4-5.md)\n- [Kolibri-1 vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/kolibri-1-vs-minicpm-o-4-5.md)\n- [Laguna M.1 vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/laguna-m-1-vs-minicpm-o-4-5.md)\n- [Laguna S 2.1 vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-minicpm-o-4-5.md)\n- [Laguna XS 2.1 vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-minicpm-o-4-5.md)\n- [LFM2.5-2.6B vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minicpm-o-4-5.md)\n- [Ling-3.0-tiny vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-minicpm-o-4-5.md)\n- [MiMo-V2.6 Distill Qwen 9B vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-minicpm-o-4-5.md)\n- [MiniCPM-o 4.5 vs MiniCPM5-2B](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-minicpm5-2b.md)\n- [MiniCPM-o 4.5 vs MiniMax-M2.5](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-minimax-m2-5.md)\n- [MiniCPM-o 4.5 vs MiniMax-M2.7](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-minimax-m2-7.md)\n- [MiniCPM-o 4.5 vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-muse-glimmer-30b.md)\n- [MiniCPM-o 4.5 vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [MiniCPM-o 4.5 vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-nemotron-3-nano-30b-a3b.md)\n- [MiniCPM-o 4.5 vs Nemotron 3 Nano 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    "title": "Gemma 4 31B IT vs MiniCPM-o 4.5 for AI agents (2026) | Anchor Terminal",
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