{
  "data": {
    "a": {
      "slug": "minicpm-v-4-7-1b",
      "name": "MiniCPM-V 4.7 1B",
      "vendor": "OpenBMB",
      "vendorUrl": "https://openbmb.cn",
      "kind": "weights",
      "category": "open-weights",
      "summary": "MiniCPM-V 4.7 1B is OpenBMB's small open-weight vision-language model, 1.55B parameters, that reads images and video on a phone or laptop, calls tools, and ships in BF16, 4-bit and GGUF builds under Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/minicpm-v-4-7-1b",
      "markdownUrl": "https://www.anchorterminal.com/tools/minicpm-v-4-7-1b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/minicpm-v-4-7-1b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/minicpm-v-4-7-1b.json",
      "dns": "minicpm-v-4-7-1b.grade.anchorterminal.com",
      "repo": "https://huggingface.co/openbmb/MiniCPM-V-4.7-1B",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "openbmb/MiniCPM-V-4.7-1B"
        },
        {
          "registry": "huggingface",
          "name": "openbmb/MiniCPM-V-4.7-1B-gguf"
        },
        {
          "registry": "huggingface",
          "name": "openbmb/MiniCPM-V-4.7-1B-AWQ"
        },
        {
          "registry": "huggingface",
          "name": "openbmb/MiniCPM-V-4.7-1B-GPTQ"
        },
        {
          "registry": "huggingface",
          "name": "openbmb/MiniCPM-V-4.7-1B-int4"
        },
        {
          "registry": "ollama",
          "name": "openbmb/minicpm-v4.7:1b"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The main repository and OpenBMB's GGUF, AWQ, GPTQ and int4 repositories are not gated (checked 2026-11-10).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0, named in the card's metadata and carried as a LICENSE file in OpenBMB's GitHub repository. Running it yourself costs the hardware, 3.1 GB for the BF16 file or about 1.5 GB for the IQ4_XS GGUF with its projector (checked 2026-11-10).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-11-10).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-11-10"
      },
      "docsUrl": "https://huggingface.co/openbmb/MiniCPM-V-4.7-1B",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "multimodal",
        "vision",
        "video",
        "on-device",
        "small",
        "tool-calling",
        "apache-2.0",
        "safetensors",
        "gguf",
        "awq",
        "gptq",
        "self-hosted"
      ],
      "lastRelease": "2026-10-30",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 61,
        "grade": "C",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1518,
        "categoryRank": 60,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 70,
          "reach": 95,
          "risk": 55,
          "upkeep": 48
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-10"
        },
        "negative": 0,
        "verdict": "A vision-language cousin small enough for a phone, under plain Apache 2.0, that every runtime we count can load. The projector is bigger than the language model, so plan for about 2 GB. The card says nothing about limits or training data and doesn't show how a tool call comes back.",
        "bestFor": "Agents that need a cheap local model to read screenshots, photos and short video on a phone, a laptop or beside a bigger model",
        "strengths": [
          "Runs in about 2 GB from OpenBMB's own GGUF with its projector",
          "Runs in llama.cpp, Ollama, vLLM, SGLang and Transformers from their released versions",
          "Plain Apache 2.0, ungated, safetensors and GGUF only, no remote code",
          "Images, several images and video in one small model, with thinking off by default",
          "OpenBMB publishes 4-bit builds, a GGUF repository and a speculative-decoding head itself"
        ],
        "weaknesses": [
          "No limitations section, safety results, agent-use guidance or security contact",
          "Training data isn't described, and the card gives no context length, languages or knowledge cut-off",
          "The card shows a tool-calling request but not the reply, and names no parser",
          "Nobody has asked a question on the repository yet, so there's no record of OpenBMB answering for this size",
          "No inference provider is mapped on Hugging Face"
        ],
        "agentNotes": [
          "Start it with `llama-server -hf openbmb/MiniCPM-V-4.7-1B-gguf:Q4_K_M --jinja`, or `ollama run openbmb/minicpm-v4.7:1b`",
          "For image input in llama.cpp add `mmproj-model-f16.gguf`, or the smaller 4x projector",
          "The GGUF files at the repository root have thinking off. Use the files in `thinking/` when you want the trace",
          "Use temperature 0.7, top_p 0.95 and top_k 100 in instruct mode, as the card recommends",
          "Check tool calls in your runtime first, since the card names no parser",
          "Validate JSON output yourself, since structured output isn't documented"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 0,
        "avgRating": 0,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "C",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 61
          }
        ],
        "editorialScores": {
          "interface": 70,
          "reach": 95,
          "risk": 55,
          "upkeep": 48
        },
        "provenanceScore": 58,
        "checked": {
          "categories": {
            "interface": "2026-11-10",
            "reach": "2026-11-10",
            "risk": "2026-11-10",
            "upkeep": "2026-11-10"
          },
          "last": "2026-11-10",
          "oldest": "2026-11-10",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "ollama run openbmb/minicpm-v4.7:1b\n# or, with llama.cpp and OpenBMB's own GGUF:\nllama-server -hf openbmb/MiniCPM-V-4.7-1B-gguf:Q4_K_M --jinja\n# or, with Transformers' server, as the card gives it:\npip install \"transformers[serving]\u003e=5.18.0\"\ntransformers serve openbmb/MiniCPM-V-4.7-1B --port 8000 --host 0.0.0.0",
        "http": "curl http://localhost:11434/api/chat \\\n  -d '{\"model\": \"openbmb/minicpm-v4.7:1b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/minicpm-v-4-7-1b"
      },
      "sameCompany": [
        "minicpm-v-4-7-35b-a3b",
        "minicpm-o-4-5",
        "minicpm5-2b"
      ],
      "weights": {
        "trainedBy": "OpenBMB (ModelBest and Tsinghua NLP Lab)",
        "builtOn": "Qwen3.5 and Qwen3.6 as the language backbone and SigLIP2 as the vision module, per the card's acknowledgements. Not declared in the metadata. The text config is qwen3_5_text with 24 layers",
        "params": "1.55B",
        "context": 262144,
        "goodFor": "Small vision-language model for images, several images and video on a phone or a laptop, with tool calling and thinking off by default.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "MiniCPM-V-4.7-1B",
            "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-1B",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 3.11,
            "memoryGB": 8,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)",
              "Transformers"
            ]
          },
          {
            "name": "MiniCPM-V-4.7-1B-AWQ",
            "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-1B-AWQ",
            "who": "publisher",
            "precision": "w4a16",
            "sizeGB": 2.37,
            "memoryGB": 8,
            "format": "safetensors",
            "runsOn": [
              "vLLM"
            ],
            "note": "AWQ, 4-bit weights with 16-bit activations"
          },
          {
            "name": "MiniCPM-V-4.7-1B-GPTQ",
            "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-1B-GPTQ",
            "who": "publisher",
            "precision": "w4a16",
            "sizeGB": 1.87,
            "memoryGB": 8,
            "format": "safetensors",
            "runsOn": [
              "vLLM"
            ],
            "note": "GPTQ, 4-bit weights with 16-bit activations"
          },
          {
            "name": "MiniCPM-V-4.7-1B-int4",
            "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-1B-int4",
            "who": "publisher",
            "precision": "int4",
            "sizeGB": 1.57,
            "memoryGB": 8,
            "format": "safetensors",
            "runsOn": [
              "vLLM"
            ]
          },
          {
            "name": "MiniCPM-V-4_7-1B-F16",
            "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-1B-gguf/blob/main/MiniCPM-V-4_7-1B-F16.gguf",
            "who": "publisher",
            "precision": "f16",
            "sizeGB": 3.13,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ],
            "note": "2.02 GB of weights plus the 1.11 GB projector"
          },
          {
            "name": "MiniCPM-V-4_7-1B-Q8_0",
            "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-1B-gguf/blob/main/MiniCPM-V-4_7-1B-Q8_0.gguf",
            "who": "publisher",
            "precision": "q8_0",
            "sizeGB": 2.19,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ],
            "note": "1.08 GB of weights plus the 1.11 GB projector"
          },
          {
            "name": "MiniCPM-V-4_7-1B-Q6_K",
            "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-1B-gguf/blob/main/MiniCPM-V-4_7-1B-Q6_K.gguf",
            "who": "publisher",
            "precision": "q6_k",
            "sizeGB": 1.95,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ],
            "note": "0.84 GB of weights plus the 1.11 GB projector"
          },
          {
            "name": "MiniCPM-V-4_7-1B-Q4_K_M",
            "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-1B-gguf/blob/main/MiniCPM-V-4_7-1B-Q4_K_M.gguf",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 1.78,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ],
            "note": "0.67 GB of weights plus mmproj-model-f16.gguf (1.11 GB), or 1.57 GB with the 4x projector (0.90 GB). The same file with thinking on is in the thinking/ folder"
          },
          {
            "name": "MiniCPM-V-4_7-1B-IQ4_XS",
            "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-1B-gguf/blob/main/MiniCPM-V-4_7-1B-IQ4_XS.gguf",
            "who": "publisher",
            "precision": "iq4_xs",
            "sizeGB": 1.54,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ],
            "note": "0.64 GB of weights plus the 0.90 GB 4x projector, the smallest at 4 bits. The IQ3 files are below the 4-bit line and are not listed"
          },
          {
            "name": "ollama openbmb/minicpm-v4.7:1b",
            "url": "https://ollama.com/openbmb/minicpm-v4.7",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 1.8,
            "memoryGB": 8,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "OpenBMB's own namespace on Ollama, not Ollama's library. 1.8 GB as the tags page shows it, with q8_0 (2.2 GB) and fp16 (3.1 GB) tags beside it"
          }
        ],
        "run": "llama-server -hf openbmb/MiniCPM-V-4.7-1B-gguf:Q4_K_M --jinja",
        "runBuild": "MiniCPM-V-4_7-1B-Q4_K_M",
        "checked": "2026-11-10",
        "minMemoryGB": 8
      },
      "family": {
        "lab": "OpenBMB (MiniCPM)",
        "family": "MiniCPM-V 4",
        "url": "https://www.anchorterminal.com/open-weights/#openbmb",
        "others": [
          {
            "name": "MiniCPM-V 4.7 35B-A3B",
            "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B",
            "params": "35.2B total, 3B active (MoE; 35,212,875,824 per the safetensors count)",
            "listing": "https://www.anchorterminal.com/tools/minicpm-v-4-7-35b-a3b"
          }
        ],
        "olderCount": 22
      },
      "area": "models",
      "provenance": {
        "legalEntity": "OpenBMB (named in the GitHub LICENSE's copyright line)",
        "domain": "openbmb.cn",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://github.com/OpenBMB/MiniCPM-V",
        "securityTxt": "none",
        "checked": "2026-11-10",
        "notes": [
          "No legal entity is named on the card. OpenBMB is the open-source organisation of ModelBest and Tsinghua University's NLP lab, as the MiniCPM5-2B listing records.",
          "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.",
          "The changelog is the GitHub README's dated news list (the release on 12 October 2026, DSpark heads on 15 October) and the repositories' commit messages, which say what changed.",
          "openbmb.cn/.well-known/security.txt returns 404 and github.com/OpenBMB/MiniCPM-V has no SECURITY.md.",
          "unchecked: the domain's registration date."
        ],
        "score": 58
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/minicpm-v-4-7-1b.json"
    },
    "answer": "Nemotron 3 Nano 30B A3B scores 67.6 (B) on agent readiness against MiniCPM-V 4.7 1B's 61 (C), and leads in 3 of 4 scored categories. MiniCPM-V 4.7 1B leads on reach.",
    "b": {
      "slug": "nemotron-3-nano-30b-a3b",
      "name": "Nemotron 3 Nano 30B A3B",
      "vendor": "NVIDIA",
      "vendorUrl": "https://developer.nvidia.com/nemotron",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Nemotron 3 Nano 30B A3B is NVIDIA's open-weight hybrid Mamba-Transformer mixture of experts for reasoning and agent work, 30B parameters with 3.5B active, published on Hugging Face in BF16, FP8 and NVFP4.",
      "url": "https://www.anchorterminal.com/tools/nemotron-3-nano-30b-a3b",
      "markdownUrl": "https://www.anchorterminal.com/tools/nemotron-3-nano-30b-a3b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/nemotron-3-nano-30b-a3b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-30b-a3b.json",
      "dns": "nemotron-3-nano-30b-a3b.grade.anchorterminal.com",
      "repo": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16",
      "license": "NVIDIA Nemotron Open Model License (custom, Apache-style, commercial use allowed, not an OSI licence)",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16"
        },
        {
          "registry": "huggingface",
          "name": "nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-FP8"
        },
        {
          "registry": "huggingface",
          "name": "nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4"
        },
        {
          "registry": "ollama",
          "name": "nemotron-3-nano:30b"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The BF16, FP8 and NVFP4 repositories are not gated (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under the NVIDIA Nemotron Open Model License. Running it yourself costs the hardware, 63.2 GB for the BF16 files, 32.7 GB for FP8, 19.4 GB for NVFP4 and 18 GB for Ollama's default build (checked 2026-10-21).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-11-01"
      },
      "docsUrl": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "mamba",
        "reasoning",
        "tool-calling",
        "commercial-licence",
        "safetensors",
        "fp8",
        "nvfp4",
        "self-hosted"
      ],
      "lastRelease": "2026-08-24",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 67.6,
        "grade": "B",
        "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": 9,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "capability": 46,
          "interface": 90,
          "reach": 90,
          "risk": 60,
          "upkeep": 80
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-11-01"
        },
        "negative": 0,
        "verdict": "A fast hybrid Mamba-Transformer cousin with 3.5B active parameters for reasoning and tool calling over very long inputs, in every major local runtime. A 4-bit build needs about 22 to 23 GB. The main risks are that NVIDIA's documented vLLM path runs repository code, no safety results are published and NVIDIA hasn't answered reports since March.",
        "bestFor": "Agents with a 24 to 32 GB GPU or Mac that want a quick tool-calling and reasoning cousin with very long context.",
        "strengths": [
          "Runs in released llama.cpp, Ollama, vLLM, SGLang, LM Studio and Transformers",
          "NVIDIA's own FP8 and NVFP4 builds beside BF16, and ungated files in NVIDIA's own organisation with a SHA-256 for every shard",
          "Tool calling documented end to end with a named parser, several calls to a turn, and thinking that can be turned off or budgeted",
          "1M tokens of input with no position-encoding scaling",
          "Full model card and a detailed data card with cut-offs, with much of the training data released"
        ],
        "weaknesses": [
          "The documented vLLM, SGLang and TensorRT-LLM commands pass trust-remote-code and load a parser file from the repository",
          "No safety or bias results published, though the subcards say both were run",
          "No NVIDIA reply on any of the nine questions others opened since 1 March 2026, including a chat-template report",
          "Licence replaced eight days after release with no dated note, and the subcards still link the old one",
          "Structured output isn't documented, and the custom licence isn't OSI-approved"
        ],
        "agentNotes": [
          "Start it with `ollama run nemotron-3-nano:30b` (18 GB MLX on a Mac, 24 GB q4_K_M elsewhere)",
          "On the reference machine use BF16 with vLLM, after `wget https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16/resolve/main/nano_v3_reasoning_parser.py`, then `vllm serve nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 --max-model-len 262144 --trust-remote-code --enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser-plugin nano_v3_reasoning_parser.py --reasoning-parser nano_v3`",
          "NVFP4 needs an NVIDIA Blackwell GPU, and FP8 isn't documented on gfx1151",
          "Without the qwen3_coder tool parser and the nano_v3 reasoning parser, tool calls and reasoning come back as plain text",
          "Card sampling is temperature 0.6 and top_p 0.95 for tool calling, temperature 1.0 and top_p 1.0 for reasoning",
          "Thinking is on by default, so pass enable_thinking as false in chat_template_kwargs for short tasks",
          "Set VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 and raise --max-model-len only when you need more than 262,144 tokens",
          "In SGLang the main branch names the reasoning parser nemotron_3, not the card's nano_v3",
          "Filter untrusted tool results before they reach the model, as NVIDIA warns prompt injection can lead to code execution",
          "Validate JSON output yourself or use the runtime's guided decoding, since none is documented for the model"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 3.3,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "high",
            "grade": "B",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 67.6
          }
        ],
        "editorialScores": {
          "capability": 46,
          "interface": 90,
          "reach": 90,
          "risk": 60,
          "upkeep": 80
        },
        "provenanceScore": 75,
        "checked": {
          "categories": {
            "capability": "2026-11-01",
            "interface": "2026-11-01",
            "reach": "2026-11-01",
            "risk": "2026-11-01",
            "upkeep": "2026-11-01"
          },
          "last": "2026-11-01",
          "lastMoved": "2026-11-01",
          "oldest": "2026-11-01",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "ollama run nemotron-3-nano:30b\n# or, with vLLM per NVIDIA's card (the reasoning parser is a file from the repository):\nwget https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16/resolve/main/nano_v3_reasoning_parser.py\nvllm serve nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 --max-model-len 262144 --trust-remote-code \\\n  --enable-auto-tool-choice --tool-call-parser qwen3_coder \\\n  --reasoning-parser-plugin nano_v3_reasoning_parser.py --reasoning-parser nano_v3\n# or, with llama.cpp and llama.cpp's own GGUF:\nllama-server -hf ggml-org/NVIDIA-Nemotron-3-Nano-30B-A3B-GGUF:Q4_K_M --jinja",
        "http": "curl http://localhost:11434/api/chat \\\n  -d '{\"model\": \"nemotron-3-nano:30b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/nemotron-3-nano-30b-a3b"
      },
      "sameCompany": [
        "nemotron-3-5-super-vl",
        "nvidia-nemo-retriever",
        "nemo-guardrails",
        "nemotron-3-5-asr-streaming-0-6b",
        "nemotron-3-5-transcribe-30b-a4b",
        "parakeet-tdt-0-6b-v3",
        "parakeet-unified-en-0-6b",
        "nemotron-3-5-parse",
        "cosmos-reason2-2b",
        "nemotron-3-5-bolt-e4b-4gb",
        "nemotron-3-nano-4b",
        "nemotron-3-nano-omni-30b-a3b-reasoning",
        "nemotron-3-super-120b-a12b",
        "nemotron-3-ultra-550b-a55b",
        "nemotron-3-5-lightning-30b-a3b"
      ],
      "weights": {
        "trainedBy": "NVIDIA",
        "builtOn": "nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-Base-BF16",
        "params": "30B",
        "active": "3.5B",
        "context": 1048576,
        "goodFor": "Hybrid Mamba-Transformer mixture of experts for reasoning and agent work, text only, with tool calling and a reasoning budget.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "LM Studio",
          "Transformers"
        ],
        "builds": [
          {
            "name": "NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4",
            "who": "publisher",
            "precision": "nvfp4",
            "sizeGB": 19.3,
            "memoryGB": 24,
            "format": "safetensors",
            "note": "NVFP4 needs NVIDIA Blackwell"
          },
          {
            "name": "ollama nemotron-3-nano:30b",
            "url": "https://ollama.com/library/nemotron-3-nano:30b",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 24,
            "memoryGB": 32,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "digest 7487f38ecf24. Size as Ollama's tags page shows it, rounded to whole GB above 10 GB; the Linux build is the llama.cpp variant"
          },
          {
            "name": "ggml-org NVIDIA-Nemotron-3-Nano-30B-A3B-GGUF (Q4_K_M)",
            "url": "https://huggingface.co/ggml-org/NVIDIA-Nemotron-3-Nano-30B-A3B-GGUF",
            "who": "llama.cpp",
            "precision": "q4_k_m",
            "sizeGB": 22.42,
            "memoryGB": 32,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "From ggml-org, the llama.cpp project's organisation. 22.42 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": "NVIDIA-Nemotron-3-Nano-30B-A3B-FP8",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-FP8",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 32.7,
            "memoryGB": 48,
            "format": "safetensors"
          },
          {
            "name": "ggml-org NVIDIA-Nemotron-3-Nano-30B-A3B-GGUF (Q8_0)",
            "url": "https://huggingface.co/ggml-org/NVIDIA-Nemotron-3-Nano-30B-A3B-GGUF",
            "who": "llama.cpp",
            "precision": "q8_0",
            "sizeGB": 33.59,
            "memoryGB": 48,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "From ggml-org, the llama.cpp project's organisation. 33.59 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": "NVIDIA-Nemotron-3-Nano-30B-A3B-BF16",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 63.2,
            "memoryGB": 96,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)"
            ]
          }
        ],
        "run": "vllm serve nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4 --max-model-len 262144 --trust-remote-code --enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser-plugin nano_v3_reasoning_parser.py --reasoning-parser nano_v3",
        "runBuild": "NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4",
        "checked": "2026-10-21",
        "minMemoryGB": 24
      },
      "family": {
        "lab": "NVIDIA (Nemotron)",
        "family": "Nemotron 3",
        "url": "https://www.anchorterminal.com/open-weights/#nvidia",
        "others": [
          {
            "name": "Nemotron 3 Nano 30B-A3B Base",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-Base-BF16",
            "params": "30B total, 3B active"
          },
          {
            "name": "Nemotron 3 Super 120B-A12B",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16",
            "params": "120B total, 12B active",
            "listing": "https://www.anchorterminal.com/tools/nemotron-3-super-120b-a12b"
          },
          {
            "name": "Nemotron 3 Super 120B-A12B Base",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-Base-BF16",
            "params": "120B total, 12B active"
          },
          {
            "name": "Nemotron 3 Nano 4B",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16",
            "params": "4B",
            "listing": "https://www.anchorterminal.com/tools/nemotron-3-nano-4b"
          },
          {
            "name": "Nemotron 3 Nano Omni 30B-A3B Reasoning",
            "url": "https://huggingface.co/nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16",
            "params": "30B total, 3B active",
            "listing": "https://www.anchorterminal.com/tools/nemotron-3-nano-omni-30b-a3b-reasoning"
          },
          {
            "name": "Nemotron 3 Ultra 550B-A55B",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16",
            "params": "550B total, 55B active",
            "listing": "https://www.anchorterminal.com/tools/nemotron-3-ultra-550b-a55b"
          },
          {
            "name": "Nemotron 3 Ultra 550B-A55B Base",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-Base-BF16",
            "params": "550B total, 55B active"
          },
          {
            "name": "Nemotron 3 Ultra 550B-A55B GenRM",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-GenRM",
            "params": "550B total, 55B active"
          },
          {
            "name": "Nemotron 3.5 Lightning 30B-A3B",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16",
            "params": "30B total, 3B active",
            "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-lightning-30b-a3b"
          },
          {
            "name": "Nemotron 3.5 Lightning 30B-A3B Base",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-Base-BF16",
            "params": "30B total, 3B active"
          },
          {
            "name": "Nemotron 3.5 Super VL 120B-A12B",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16",
            "params": "120B total, 12B active",
            "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-super-vl"
          },
          {
            "name": "Nemotron 3.5 Super VL 120B-A12B Base",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-Base-BF16",
            "params": "120B total, 12B active"
          },
          {
            "name": "Nemotron 3.5 Bolt 74B-A9B",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Bolt-74B-A9B-BF16",
            "params": "74B total, 9B active"
          },
          {
            "name": "Nemotron 3.5 Bolt E4B 4GB",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-4GB",
            "params": "8B total, 4B effective",
            "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-bolt-e4b-4gb"
          },
          {
            "name": "Nemotron Labs 3 Elastic 30B-A3B",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-Labs-3-Elastic-30B-A3B-BF16",
            "params": "30B total, 3B active"
          },
          {
            "name": "Nemotron Labs 3 Puzzle 75B-A9B",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-Labs-3-Puzzle-75B-A9B-BF16",
            "params": "75B total, 9B active"
          },
          {
            "name": "Nemotron 3 Labs Ultra Math SFT",
            "url": "https://huggingface.co/nvidia/Nemotron-3-Labs-Ultra-Math-SFT",
            "params": "550B total, 55B active"
          },
          {
            "name": "Nemotron 3 Labs Ultra Math RL",
            "url": "https://huggingface.co/nvidia/Nemotron-3-Labs-Ultra-Math-RL",
            "params": "550B total, 55B active"
          },
          {
            "name": "Nemotron Labs 3 Competitive Coding 550B-A55B",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-Labs-3-Competitive-Coding-550B-A55B-NVFP4",
            "params": "550B total, 55B active"
          },
          {
            "name": "nemotron-3.5-asr-streaming-0.6b",
            "url": "https://huggingface.co/nvidia/nemotron-3.5-asr-streaming-0.6b",
            "params": "0.6B (638M per the safetensors count)",
            "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-asr-streaming-0-6b"
          },
          {
            "name": "Nemotron 3.5 Parse",
            "url": "https://huggingface.co/nvidia/Nemotron-3.5-Parse",
            "params": "0.631B (630,800,496 trainable parameters per the card)",
            "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-parse"
          },
          {
            "name": "Nemotron 3.5 Transcribe 30B-A4B",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Transcribe-30B-A4B",
            "params": "30B total, about 4.3B active (33,697,781,029 by the Hub's safetensors count)",
            "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-transcribe-30b-a4b"
          }
        ],
        "olderCount": 47,
        "unrestrictedCopies": 5
      },
      "area": "models",
      "provenance": {
        "legalEntity": "NVIDIA Corporation",
        "domain": "nvidia.com",
        "domainRegistered": "1993-04-20",
        "endpointOnVendorDomain": null,
        "terms": "https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-nemotron-open-model-license/",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16/commits/main",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "The card names NVIDIA Corporation as model developer, and the licence names NVIDIA. RDAP gives nvidia.com a registration date of 20 April 1993.",
          "Nothing is hosted for this listing, so the Nemotron Open Model License stands in for terms of service and no privacy policy is scored.",
          "The changelog is the repository's commit history. A few messages say what changed (a reasoning budget example on 19 December 2025, a 1M-context vLLM example on 8 January 2026); most read Update README.md.",
          "www.nvidia.com/.well-known/security.txt answered 403 on 21 October 2026. Vulnerability reports go to NVIDIA PSIRT, linked from the card, which publishes a disclosure policy and security bulletins."
        ],
        "score": 75
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/nemotron-3-nano-30b-a3b.json",
      "live": {
        "slug": "nemotron-3-nano-30b-a3b",
        "securityTxt": {
          "url": "https://nvidia.com/.well-known/security.txt",
          "state": "unknown",
          "checkedAt": "2026-11-10T16:08:00.271598775Z"
        },
        "domain": {
          "domain": "nvidia.com",
          "registered": "1993-04-20",
          "source": "https://rdap.verisign.com/com/v1/domain/nvidia.com",
          "checkedAt": "2026-11-08T15:03:11.895084282Z"
        },
        "pages": [
          {
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16/commits/main",
            "kind": "changelog",
            "status": 200,
            "checkedAt": "2026-11-10T16:23:18.236938919Z",
            "changedAt": "2026-11-10T16:23:18.236938919Z",
            "fingerprint": "d16f84ee772a"
          },
          {
            "url": "https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-nemotron-open-model-license/",
            "kind": "terms",
            "status": 304,
            "checkedAt": "2026-11-10T16:43:25.039992347Z",
            "changedAt": "2026-10-28T04:52:55.000219692Z",
            "fingerprint": "093132ed6fed"
          }
        ],
        "updatedAt": "2026-11-10T16:43:25.039992347Z"
      }
    },
    "edges": [
      {
        "by": 32,
        "category": "upkeep",
        "leads": "nemotron-3-nano-30b-a3b",
        "name": "Documentation and upkeep",
        "reasons": {
          "a": "Model card 8, Intended use (image, multi-image and video understanding on device), architecture (early visual-token compression inside the vision encoder) and parameters (1B) are there. Training data 0, no description of the training data on the card. Last change 20, the last commit, on 30 October 2026, updated the card and removed an unused asset folder, eleven days before this check. Responsiveness 0, the repository has no discussions that aren't pull requests (two pull requests in all), so nobody has asked anything yet and the line reads as none answered, as settled in calibration. Version policy 10, MiniCPM-V 4.5 and 4.6 stay in their own repositories and the GitHub README dates each release. File health 10, Config, template and card agree on the architecture and the thinking switch, and no report waits without a reply, since there are no discussions. 8 + 0 + 20 + 0 + 10 + 10 = 48.",
          "b": "The model card earns 20, with all seven items on the card and its subcards. Training data earns 20, datasets listed with sources and token counts, both cut-offs, the filtering, and much of the data released, though some synthetic sets don't give a size. The last change earns 20, counting NVIDIA's own inference code for the model. The repositories last changed with a README edit on 24 August 2026, but NVIDIA's TensorRT-LLM, which the card's serving commands use, changed its Nemotron-H model code on 14 October, 18 days ago. Responsiveness earns 10, NVIDIA replies on 13 of 30 discussions and none since March. Version policy earns 5 for a first release with no dated notes. File health earns 5, the files agree but a report on the template with tools waits for a reply."
        },
        "scores": {
          "a": 48,
          "b": 80
        }
      },
      {
        "by": 20,
        "category": "interface",
        "leads": "nemotron-3-nano-30b-a3b",
        "name": "Agent interface",
        "reasons": {
          "a": "Chat template 20, chat_template.jinja in the repository, 7,249 characters. Tool-calling format 10, the card shows tools declared in the OpenAI tools array in a request to Transformers' server, but not how a call comes back, and names no parser for vLLM or SGLang. Trained tool calling 20, the template loops over tool_calls, so it renders several calls in one assistant turn, and the card gives a tool-calling request as a supported use. Structured output 0, Neither JSON nor JSON Schema output is documented or claimed for the model on the card. Reasoning control 10, the card passes enable_thinking per request in its examples, the template has been off by default since 19 October 2026, and the GGUF repository keeps thinking builds in their own folder. Context 10, 262,144 tokens, max_position_embeddings in the text config. 20 + 10 + 20 + 0 + 10 + 10 = 70.",
          "b": "The chat template earns 20 as a Jinja file in the repository. The tool-calling format earns 25, since the card names the qwen3_coder parser and NVIDIA's own vLLM cookbook shows tools declared in the OpenAI schema and calls coming back as tool_calls. Trained tool calling earns 20, multi-step tool use in training and several calls to a turn in the template. Structured output earns 5, a claim of structured output training with no way to ask for JSON. Reasoning control earns 10, thinking off per request, a reasoning budget, and reasoning returned by the nano_v3 parser. Context earns 10 for 1M tokens with no scaling."
        },
        "scores": {
          "a": 70,
          "b": 90
        }
      },
      {
        "by": 5,
        "category": "reach",
        "leads": "minicpm-v-4-7-1b",
        "name": "Reach",
        "reasons": {
          "a": "Memory 30, OpenBMB's own IQ4_XS GGUF is 0.64 GB, 1.54 GB with the 0.90 GB vision projector, and 1.54 GB × 1.2 = 1.9 GB, which fits 16 GB with room to spare. Runtimes 25, MiniCPMV4_7ForConditionalGeneration in vLLM's registry at v0.33.0, the minicpmv4_7 model type in SGLang's minicpmv.py at v0.5.23, the minicpmv4_7 model in Transformers at v5.22.2, a MiniCPM-V 4.7 guide and conversion code in llama.cpp at b12242, and an openbmb/minicpm-v4.7:1b tag in OpenBMB's namespace on Ollama, read as the 35B-A3B listing's is. Builds 15, OpenBMB publishes AWQ, GPTQ and int4 safetensors builds and a GGUF repository from IQ3_XS to F16, so quantised builds at two precisions or more. Terms 15, Apache 2.0 in the card's metadata and as the LICENSE of OpenBMB's MiniCPM-V repository, an OSI licence. Download 10, gated is false on the main repository and on the GGUF, AWQ, GPTQ and int4 repositories, so the files download with no account or terms. Hosted 0, Hugging Face lists no inference provider for the model (an empty provider mapping on 10 November 2026), and no provider's own list was read. 30 + 25 + 15 + 15 + 10 + 0 = 95.",
          "b": "Memory earns 25 because the smallest 4-bit build is Ollama's NVFP4 MLX build at 18 GB, 21.6 GB with the fifth added, and NVIDIA's own NVFP4 build lands in the same 24 GB band. Runtimes earn the full 25, with released llama.cpp, Ollama, vLLM, SGLang, LM Studio and Transformers all running it. Builds earn 15 for NVIDIA's own FP8 and NVFP4 builds. Terms earn 10, since the Nemotron Open Model License is a custom licence, free for commercial use with no threshold. Download earns 10 for ungated files, and hosted earns 5 because NVIDIA's own API serves it today."
        },
        "scores": {
          "a": 95,
          "b": 90
        }
      },
      {
        "by": 5,
        "category": "risk",
        "leads": "nemotron-3-nano-30b-a3b",
        "name": "Risk",
        "reasons": {
          "a": "File safety 25, safetensors in the main repository and the AWQ, GPTQ and int4 builds, GGUF in the GGUF build, with no .bin, .pt, .pth or .py files in the main repository. Provenance 15, files in the openbmb organisation that OpenBMB's GitHub README links to (10), with a SHA-256 for both large files in the main repository (5). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations or disclaimer section, and no prompt-injection guidance, instruction hierarchy or statement of trained refusals. Licence risk 15, Apache 2.0 has no use policy, field-of-use limit, threshold or change clause, nothing is added on the card, and the LICENSE in OpenBMB's GitHub repository resolved on 10 November 2026. Security programme 0, no SECURITY.md in OpenBMB/MiniCPM-V and no security.txt on openbmb.cn (both 404 on 10 November 2026), and no advisories found. 25 + 15 + 0 + 0 + 15 + 0 = 55.",
          "b": "File safety earns 15, safetensors only but with the documented serving path passing trust-remote-code and loading a reasoning parser file from the repository. Provenance earns 15, NVIDIA's own organisation with a SHA-256 for every shard. Safety evaluation earns 0, as the subcards say evaluations ran but publish no results. Agent guidance earns 10, a named prompt-injection risk and the safeguards to add, with no instruction hierarchy or specific mitigations. Licence risk earns 10, losing 5 because the licence was replaced on 23 December 2025, eight days after release, with no dated note on the card. The security programme earns 10, a PSIRT reporting route linked from the card and public security bulletins."
        },
        "scores": {
          "a": 55,
          "b": 60
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "OpenBMB",
        "b": "NVIDIA",
        "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": "NVIDIA Nemotron Open Model License (custom, Apache-style, commercial use allowed, not an OSI licence)",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-10-30",
        "b": "2026-08-24",
        "name": "Last release"
      },
      {
        "a": "no document linked",
        "b": "2025-12-15",
        "name": "Terms last updated"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Privacy policy last updated"
      },
      {
        "a": "not read yet",
        "b": "not found in the text",
        "name": "Customer content may train models"
      },
      {
        "a": "not read yet",
        "b": "not found in the text",
        "name": "Terms restrict automated access"
      },
      {
        "a": "not read yet",
        "b": "not found in the text",
        "name": "Terms restrict benchmarking"
      },
      {
        "a": "not read yet",
        "b": "not found in the text",
        "name": "Terms or service can change without notice"
      },
      {
        "a": "not read yet",
        "b": "not found in the text",
        "name": "Arbitration or class-action waiver"
      },
      {
        "a": "none",
        "b": "3.3/5 (4)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Nemotron 3 Nano 30B A3B scores 67.6 (B) on agent readiness against MiniCPM-V 4.7 1B's 61 (C), and leads in 3 of 4 scored categories. MiniCPM-V 4.7 1B leads on reach.",
        "question": "Which is better for AI agents, MiniCPM-V 4.7 1B or Nemotron 3 Nano 30B A3B?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 95 against 90"
        ],
        "also": null,
        "goodFor": "Agents that need a cheap local model to read screenshots, photos and short video on a phone, a laptop or beside a bigger model",
        "slug": "minicpm-v-4-7-1b",
        "watchFor": "No limitations section, safety results, agent-use guidance or security contact"
      },
      {
        "aheadOn": [
          "Risk, 60 against 55",
          "Agent interface, 90 against 70",
          "Documentation and upkeep, 80 against 48"
        ],
        "also": null,
        "goodFor": "Agents with a 24 to 32 GB GPU or Mac that want a quick tool-calling and reasoning cousin with very long context.",
        "slug": "nemotron-3-nano-30b-a3b",
        "watchFor": "The documented vLLM, SGLang and TensorRT-LLM commands pass trust-remote-code and load a parser file from the repository"
      }
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      "minicpm-v-4-7-1b": null,
      "nemotron-3-nano-30b-a3b": [
        {
          "causes": [
            "correction"
          ],
          "date": "2026-11-01",
          "delta": 0.5,
          "from": 67.1,
          "fromGrade": "B",
          "to": 67.6,
          "toGrade": "B"
        }
      ]
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      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
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      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-nano-30b-a3b.json",
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        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minicpm-v-4-7-1b.json",
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        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minicpm-v-4-7-1b"
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      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-nano-30b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minicpm-v-4-7-1b"
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        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nemotron-3-nano-30b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-nemotron-3-nano-30b-a3b"
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        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-minicpm-v-4-7-1b"
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        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-nemotron-3-nano-30b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-nemotron-3-nano-30b-a3b"
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        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-minicpm-v-4-7-1b.json",
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        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-minicpm-v-4-7-1b"
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        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-nemotron-3-nano-30b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-nemotron-3-nano-30b-a3b"
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        "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-minicpm-v-4-7-1b.json",
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        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-minicpm-v-4-7-1b"
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        "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-nano-30b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-nano-30b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-minicpm-v-4-7-1b.json",
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        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-nemotron-3-nano-30b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-nemotron-3-nano-30b-a3b"
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        "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-minicpm-v-4-7-1b.json",
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        "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-minicpm-v-4-7-1b"
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        "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-nemotron-3-nano-30b-a3b.json",
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        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-minicpm-v-4-7-1b.json",
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        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-minicpm-v-4-7-1b"
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        "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-minicpm-v-4-7-1b"
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        "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-nemotron-3-nano-30b-a3b"
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        "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-minicpm-v-4-7-1b"
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        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-nemotron-3-nano-30b-a3b"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-minicpm-v-4-7-1b.json",
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        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-ornith-1-5-397b"
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        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen-agentworld-35b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-5-397b-a17b.json",
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      {
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        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-5-9b.json",
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        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-6-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-8-27b"
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        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-vl-8b-instruct"
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        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-recursive-flint"
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        "title": "Nemotron 3 Nano 30B A3B vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-ripple-65b-preview"
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      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-sensenova-loop-9b-preview.json",
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        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-sensenova-loop-9b-preview"
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        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-smollm3-3b"
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        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-swift-1-5-qwen3-8-27b"
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        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-swift-1-5-qwen3-8-flash-next.json",
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        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-thinkingcap-qwen3-8-27b.json",
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        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-thinkingcap-qwen3-8-27b"
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      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-wevisone-vl-2b.json",
        "title": "Nemotron 3 Nano 30B A3B vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-wevisone-vl-2b"
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        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-wevisone-vl-4b.json",
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        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-zaya1-8b.json",
        "title": "Nemotron 3 Nano 30B A3B vs ZAYA1-8B",
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    ],
    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 5,
        "edge": "minicpm-v-4-7-1b",
        "key": "reach",
        "minicpm-v-4-7-1b": 95,
        "name": "Reach",
        "nemotron-3-nano-30b-a3b": 90,
        "weight": 25
      },
      {
        "by": 5,
        "edge": "nemotron-3-nano-30b-a3b",
        "key": "risk",
        "minicpm-v-4-7-1b": 55,
        "name": "Risk",
        "nemotron-3-nano-30b-a3b": 60,
        "weight": 20
      },
      {
        "by": 20,
        "edge": "nemotron-3-nano-30b-a3b",
        "key": "interface",
        "minicpm-v-4-7-1b": 70,
        "name": "Agent interface",
        "nemotron-3-nano-30b-a3b": 90,
        "weight": 10
      },
      {
        "by": 32,
        "edge": "nemotron-3-nano-30b-a3b",
        "key": "upkeep",
        "minicpm-v-4-7-1b": 48,
        "name": "Documentation and upkeep",
        "nemotron-3-nano-30b-a3b": 80,
        "weight": 10
      }
    ],
    "summary": "Nemotron 3 Nano 30B A3B scores 67.6 (B) on agent readiness against MiniCPM-V 4.7 1B's 61 (C), and leads in 3 of 4 scored categories. MiniCPM-V 4.7 1B leads on reach. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "minicpm-v-4-7-1b": "A vision-language cousin small enough for a phone, under plain Apache 2.0, that every runtime we count can load. The projector is bigger than the language model, so plan for about 2 GB. The card says nothing about limits or training data and doesn't show how a tool call comes back.",
      "nemotron-3-nano-30b-a3b": "A fast hybrid Mamba-Transformer cousin with 3.5B active parameters for reasoning and tool calling over very long inputs, in every major local runtime. A 4-bit build needs about 22 to 23 GB. The main risks are that NVIDIA's documented vLLM path runs repository code, no safety results are published and NVIDIA hasn't answered reports since March."
    }
  },
  "kind": "anchor.page",
  "links": {
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    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-nemotron-3-nano-30b-a3b.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-nemotron-3-nano-30b-a3b.min.md"
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  "markdown": "Nemotron 3 Nano 30B A3B scores 67.6 (B) on agent readiness against MiniCPM-V 4.7 1B's 61 (C), and leads in 3 of 4 scored categories. MiniCPM-V 4.7 1B leads on reach. Both do open-weight models you run yourself.\n\n- MiniCPM-V 4.7 1B: grade C, 61/100, rank #76 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/minicpm-v-4-7-1b.md · JSON https://www.anchorterminal.com/api/v1/tools/minicpm-v-4-7-1b.json\n- Nemotron 3 Nano 30B A3B: grade B, 67.6/100, rank #12 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-nano-30b-a3b.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-30b-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### MiniCPM-V 4.7 1B (C)\n\nGood for: Agents that need a cheap local model to read screenshots, photos and short video on a phone, a laptop or beside a bigger model\n\nAhead on:\n- Reach, 95 against 90\n\nWatch for: No limitations section, safety results, agent-use guidance or security contact\n\n### Nemotron 3 Nano 30B A3B (B)\n\nGood for: Agents with a 24 to 32 GB GPU or Mac that want a quick tool-calling and reasoning cousin with very long context.\n\nAhead on:\n- Risk, 60 against 55\n- Agent interface, 90 against 70\n- Documentation and upkeep, 80 against 48\n\nWatch for: The documented vLLM, SGLang and TensorRT-LLM commands pass trust-remote-code and load a parser file from the repository\n\n\n## Why the edge\n\n### Documentation and upkeep: Nemotron 3 Nano 30B A3B +32\n\n- MiniCPM-V 4.7 1B (48): Model card 8, Intended use (image, multi-image and video understanding on device), architecture (early visual-token compression inside the vision encoder) and parameters (1B) are there. Training data 0, no description of the training data on the card. Last change 20, the last commit, on 30 October 2026, updated the card and removed an unused asset folder, eleven days before this check. Responsiveness 0, the repository has no discussions that aren't pull requests (two pull requests in all), so nobody has asked anything yet and the line reads as none answered, as settled in calibration. Version policy 10, MiniCPM-V 4.5 and 4.6 stay in their own repositories and the GitHub README dates each release. File health 10, Config, template and card agree on the architecture and the thinking switch, and no report waits without a reply, since there are no discussions. 8 + 0 + 20 + 0 + 10 + 10 = 48.\n- Nemotron 3 Nano 30B A3B (80): The model card earns 20, with all seven items on the card and its subcards. Training data earns 20, datasets listed with sources and token counts, both cut-offs, the filtering, and much of the data released, though some synthetic sets don't give a size. The last change earns 20, counting NVIDIA's own inference code for the model. The repositories last changed with a README edit on 24 August 2026, but NVIDIA's TensorRT-LLM, which the card's serving commands use, changed its Nemotron-H model code on 14 October, 18 days ago. Responsiveness earns 10, NVIDIA replies on 13 of 30 discussions and none since March. Version policy earns 5 for a first release with no dated notes. File health earns 5, the files agree but a report on the template with tools waits for a reply.\n\n### Agent interface: Nemotron 3 Nano 30B A3B +20\n\n- MiniCPM-V 4.7 1B (70): Chat template 20, chat_template.jinja in the repository, 7,249 characters. Tool-calling format 10, the card shows tools declared in the OpenAI tools array in a request to Transformers' server, but not how a call comes back, and names no parser for vLLM or SGLang. Trained tool calling 20, the template loops over tool_calls, so it renders several calls in one assistant turn, and the card gives a tool-calling request as a supported use. Structured output 0, Neither JSON nor JSON Schema output is documented or claimed for the model on the card. Reasoning control 10, the card passes enable_thinking per request in its examples, the template has been off by default since 19 October 2026, and the GGUF repository keeps thinking builds in their own folder. Context 10, 262,144 tokens, max_position_embeddings in the text config. 20 + 10 + 20 + 0 + 10 + 10 = 70.\n- Nemotron 3 Nano 30B A3B (90): The chat template earns 20 as a Jinja file in the repository. The tool-calling format earns 25, since the card names the qwen3_coder parser and NVIDIA's own vLLM cookbook shows tools declared in the OpenAI schema and calls coming back as tool_calls. Trained tool calling earns 20, multi-step tool use in training and several calls to a turn in the template. Structured output earns 5, a claim of structured output training with no way to ask for JSON. Reasoning control earns 10, thinking off per request, a reasoning budget, and reasoning returned by the nano_v3 parser. Context earns 10 for 1M tokens with no scaling.\n\n### Reach: MiniCPM-V 4.7 1B +5\n\n- MiniCPM-V 4.7 1B (95): Memory 30, OpenBMB's own IQ4_XS GGUF is 0.64 GB, 1.54 GB with the 0.90 GB vision projector, and 1.54 GB × 1.2 = 1.9 GB, which fits 16 GB with room to spare. Runtimes 25, MiniCPMV4_7ForConditionalGeneration in vLLM's registry at v0.33.0, the minicpmv4_7 model type in SGLang's minicpmv.py at v0.5.23, the minicpmv4_7 model in Transformers at v5.22.2, a MiniCPM-V 4.7 guide and conversion code in llama.cpp at b12242, and an openbmb/minicpm-v4.7:1b tag in OpenBMB's namespace on Ollama, read as the 35B-A3B listing's is. Builds 15, OpenBMB publishes AWQ, GPTQ and int4 safetensors builds and a GGUF repository from IQ3_XS to F16, so quantised builds at two precisions or more. Terms 15, Apache 2.0 in the card's metadata and as the LICENSE of OpenBMB's MiniCPM-V repository, an OSI licence. Download 10, gated is false on the main repository and on the GGUF, AWQ, GPTQ and int4 repositories, so the files download with no account or terms. Hosted 0, Hugging Face lists no inference provider for the model (an empty provider mapping on 10 November 2026), and no provider's own list was read. 30 + 25 + 15 + 15 + 10 + 0 = 95.\n- Nemotron 3 Nano 30B A3B (90): Memory earns 25 because the smallest 4-bit build is Ollama's NVFP4 MLX build at 18 GB, 21.6 GB with the fifth added, and NVIDIA's own NVFP4 build lands in the same 24 GB band. Runtimes earn the full 25, with released llama.cpp, Ollama, vLLM, SGLang, LM Studio and Transformers all running it. Builds earn 15 for NVIDIA's own FP8 and NVFP4 builds. Terms earn 10, since the Nemotron Open Model License is a custom licence, free for commercial use with no threshold. Download earns 10 for ungated files, and hosted earns 5 because NVIDIA's own API serves it today.\n\n### Risk: Nemotron 3 Nano 30B A3B +5\n\n- MiniCPM-V 4.7 1B (55): File safety 25, safetensors in the main repository and the AWQ, GPTQ and int4 builds, GGUF in the GGUF build, with no .bin, .pt, .pth or .py files in the main repository. Provenance 15, files in the openbmb organisation that OpenBMB's GitHub README links to (10), with a SHA-256 for both large files in the main repository (5). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations or disclaimer section, and no prompt-injection guidance, instruction hierarchy or statement of trained refusals. Licence risk 15, Apache 2.0 has no use policy, field-of-use limit, threshold or change clause, nothing is added on the card, and the LICENSE in OpenBMB's GitHub repository resolved on 10 November 2026. Security programme 0, no SECURITY.md in OpenBMB/MiniCPM-V and no security.txt on openbmb.cn (both 404 on 10 November 2026), and no advisories found. 25 + 15 + 0 + 0 + 15 + 0 = 55.\n- Nemotron 3 Nano 30B A3B (60): File safety earns 15, safetensors only but with the documented serving path passing trust-remote-code and loading a reasoning parser file from the repository. Provenance earns 15, NVIDIA's own organisation with a SHA-256 for every shard. Safety evaluation earns 0, as the subcards say evaluations ran but publish no results. Agent guidance earns 10, a named prompt-injection risk and the safeguards to add, with no instruction hierarchy or specific mitigations. Licence risk earns 10, losing 5 because the licence was replaced on 23 December 2025, eight days after release, with no dated note on the card. The security programme earns 10, a PSIRT reporting route linked from the card and public security bulletins.\n\n## Score by category\n\n| Category | Weight | MiniCPM-V 4.7 1B | Nemotron 3 Nano 30B A3B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 95 | 90 | MiniCPM-V 4.7 1B +5 |\n| Risk | 20% (20 this run) | 55 | 60 | Nemotron 3 Nano 30B A3B +5 |\n| Agent interface | 10% (10 this run) | 70 | 90 | Nemotron 3 Nano 30B A3B +20 |\n| Documentation and upkeep | 10% (10 this run) | 48 | 80 | Nemotron 3 Nano 30B A3B +32 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **67.6 · B** | |\n\n## Facts side by side\n\n| Fact | MiniCPM-V 4.7 1B | Nemotron 3 Nano 30B A3B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | OpenBMB | NVIDIA |\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 | NVIDIA Nemotron Open Model License (custom, Apache-style, commercial use allowed, not an OSI licence) |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-30 | 2026-08-24 |\n| Terms last updated | no document linked | 2025-12-15 |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models | not read yet | not found in the text |\n| Terms restrict automated access | not read yet | not found in the text |\n| Terms restrict benchmarking | not read yet | not found in the text |\n| Terms or service can change without notice | not read yet | not found in the text |\n| Arbitration or class-action waiver | not read yet | not found in the text |\n| Agent reviews | none | 3.3/5 (4) |\n\n## How the scores have moved\n\n### MiniCPM-V 4.7 1B\n\nNo score change since it was listed.\n\n### Nemotron 3 Nano 30B A3B\n\n- +0.5 on 1 November 2026: correction\n\n## Verdicts\n\n**MiniCPM-V 4.7 1B.** A vision-language cousin small enough for a phone, under plain Apache 2.0, that every runtime we count can load. The projector is bigger than the language model, so plan for about 2 GB. The card says nothing about limits or training data and doesn't show how a tool call comes back.\n\n**Nemotron 3 Nano 30B A3B.** A fast hybrid Mamba-Transformer cousin with 3.5B active parameters for reasoning and tool calling over very long inputs, in every major local runtime. A 4-bit build needs about 22 to 23 GB. The main risks are that NVIDIA's documented vLLM path runs repository code, no safety results are published and NVIDIA hasn't answered reports since March.\n\n## Before you call either\n\n### MiniCPM-V 4.7 1B\n\n1. Start it with `llama-server -hf openbmb/MiniCPM-V-4.7-1B-gguf:Q4_K_M --jinja`, or `ollama run openbmb/minicpm-v4.7:1b`\n2. For image input in llama.cpp add `mmproj-model-f16.gguf`, or the smaller 4x projector\n3. The GGUF files at the repository root have thinking off. Use the files in `thinking/` when you want the trace\n4. Use temperature 0.7, top_p 0.95 and top_k 100 in instruct mode, as the card recommends\n5. Check tool calls in your runtime first, since the card names no parser\n6. Validate JSON output yourself, since structured output isn't documented\n\n### Nemotron 3 Nano 30B A3B\n\n1. Start it with `ollama run nemotron-3-nano:30b` (18 GB MLX on a Mac, 24 GB q4_K_M elsewhere)\n2. On the reference machine use BF16 with vLLM, after `wget https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16/resolve/main/nano_v3_reasoning_parser.py`, then `vllm serve nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 --max-model-len 262144 --trust-remote-code --enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser-plugin nano_v3_reasoning_parser.py --reasoning-parser nano_v3`\n3. NVFP4 needs an NVIDIA Blackwell GPU, and FP8 isn't documented on gfx1151\n4. Without the qwen3_coder tool parser and the nano_v3 reasoning parser, tool calls and reasoning come back as plain text\n5. Card sampling is temperature 0.6 and top_p 0.95 for tool calling, temperature 1.0 and top_p 1.0 for reasoning\n6. Thinking is on by default, so pass enable_thinking as false in chat_template_kwargs for short tasks\n7. Set VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 and raise --max-model-len only when you need more than 262,144 tokens\n8. In SGLang the main branch names the reasoning parser nemotron_3, not the card's nano_v3\n9. Filter untrusted tool results before they reach the model, as NVIDIA warns prompt injection can lead to code execution\n10. Validate JSON output yourself or use the runtime's guided decoding, since none is documented for the model\n\n## Questions\n\n### Which is better for AI agents, MiniCPM-V 4.7 1B or Nemotron 3 Nano 30B A3B?\n\nNemotron 3 Nano 30B A3B scores 67.6 (B) on agent readiness against MiniCPM-V 4.7 1B's 61 (C), and leads in 3 of 4 scored categories. MiniCPM-V 4.7 1B leads on reach.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-nemotron-3-nano-30b-a3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-nemotron-3-nano-30b-a3b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"minicpm-v-4-7-1b\", \"b\": \"nemotron-3-nano-30b-a3b\"}`. 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