{
  "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 Super 120B A12B scores 66.7 (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-super-120b-a12b",
      "name": "Nemotron 3 Super 120B A12B",
      "vendor": "NVIDIA",
      "vendorUrl": "https://developer.nvidia.com/nemotron",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Nemotron 3 Super is NVIDIA's open-weight hybrid Mamba-2 and mixture-of-experts reasoning model for agents, 120B parameters with 12B active, published on Hugging Face in BF16, FP8 and NVFP4 under the NVIDIA Nemotron Open Model License.",
      "url": "https://www.anchorterminal.com/tools/nemotron-3-super-120b-a12b",
      "markdownUrl": "https://www.anchorterminal.com/tools/nemotron-3-super-120b-a12b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/nemotron-3-super-120b-a12b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/nemotron-3-super-120b-a12b.json",
      "dns": "nemotron-3-super-120b-a12b.grade.anchorterminal.com",
      "repo": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16",
      "license": "NVIDIA Nemotron Open Model License",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16"
        },
        {
          "registry": "huggingface",
          "name": "nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-FP8"
        },
        {
          "registry": "huggingface",
          "name": "nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4"
        },
        {
          "registry": "ollama",
          "name": "nemotron-3-super"
        }
      ],
      "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, 247.2 GB of BF16 files (8× H100-80GB minimum per the card), 80.3 GB for NVIDIA's NVFP4 build or 87 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-Super-120B-A12B-BF16",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "mamba",
        "hybrid",
        "reasoning",
        "tool-calling",
        "long-context",
        "safetensors",
        "fp8",
        "nvfp4",
        "self-hosted"
      ],
      "lastRelease": "2026-03-11",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 66.7,
        "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": 11,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "capability": 54,
          "interface": 90,
          "reach": 75,
          "risk": 65,
          "upkeep": 70
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-11-01"
        },
        "negative": 0,
        "verdict": "A long-context agent cousin with trained parallel tool calls, a reasoning switch and a full account of its training data, under a free commercial licence. It needs about 100 GB of GPU memory, a 128 GB unified machine or Blackwell hardware for NVIDIA's NVFP4. The main risks are serving commands that run repository code and no published safety results.",
        "bestFor": "Agents on a 128 GB unified machine or Blackwell GPUs that want a long-context, tool-calling reasoning model with documented training data",
        "strengths": [
          "Runs in released Transformers, vLLM, SGLang, llama.cpp and Ollama, with NVIDIA's own FP8 and NVFP4 builds",
          "Training data listed by dataset with token counts and cut-offs, much of it published",
          "Tool calls documented with a named parser, several per turn, and thinking off, low effort or budgeted per request",
          "Free commercial licence with no threshold or use policy, no gate, and NVIDIA's own API serves it",
          "Context of 262,144 tokens by default and up to 1M per the card"
        ],
        "weaknesses": [
          "The card's serving commands pass --trust-remote-code",
          "No safety evaluation results published",
          "No NVIDIA reply in any of the repository's nine discussions",
          "Needs about 96 to 104 GB even at 4-bit, and NVIDIA's NVFP4 build needs Blackwell",
          "Structured output claimed but not documented"
        ],
        "agentNotes": [
          "On the reference machine start it with `ollama run nemotron-3-super`, the 87 GB q4_K_M build, the only one that fits (about 104 GB with the margin)",
          "Raise the Strix Halo GPU memory limit first with `amd-ttm --set 100`, since the default of about 62.8 GiB is far below what it needs",
          "With vLLM serve it with `--reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder` (SGLang uses `--reasoning-parser nemotron_3`)",
          "Use `temperature=1.0` and `top_p=0.95` for every task, as the card says",
          "Thinking is on by default. Pass `enable_thinking: false` or `low_effort: true` in `chat_template_kwargs` for short tasks",
          "For coding agents pass `force_nonempty_content: true` in `chat_template_kwargs`, so a cut-off reasoning trace still returns content",
          "Raise context past 262,144 only with the card's flags (`VLLM_ALLOW_LONG_MAX_MODEL_LEN=1`, `--max-model-len 1048576`)"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 2.8,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "high",
            "grade": "B",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 66.7
          }
        ],
        "editorialScores": {
          "capability": 54,
          "interface": 90,
          "reach": 75,
          "risk": 65,
          "upkeep": 70
        },
        "provenanceScore": 76,
        "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-super\n# or, with the card's vLLM command (abridged):\npip install vllm==0.18.1\nvllm serve nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16 --served-model-name nvidia/nemotron-3-super \\\n  --tensor-parallel-size 8 --max-model-len 262144 --kv-cache-dtype fp8 --mamba-ssm-cache-dtype float32 \\\n  --trust-remote-code --reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder\n# or, with llama.cpp and LM Studio's GGUF:\nllama-server -hf lmstudio-community/NVIDIA-Nemotron-3-Super-120B-A12B-GGUF:Q4_K_M --jinja",
        "http": "curl http://localhost:8000/v1/chat/completions \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\n    \"model\": \"nvidia/nemotron-3-super\",\n    \"messages\": [{\"role\": \"user\", \"content\": \"What is the capital of Japan?\"}],\n    \"temperature\": 1.0, \"top_p\": 0.95,\n    \"chat_template_kwargs\": {\"enable_thinking\": false}\n  }'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/nemotron-3-super-120b-a12b"
      },
      "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-30b-a3b",
        "nemotron-3-nano-4b",
        "nemotron-3-nano-omni-30b-a3b-reasoning",
        "nemotron-3-ultra-550b-a55b",
        "nemotron-3-5-lightning-30b-a3b"
      ],
      "weights": {
        "trainedBy": "NVIDIA",
        "builtOn": "nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-Base-BF16",
        "params": "120B",
        "active": "12B",
        "context": 1048576,
        "goodFor": "Hybrid Mamba and mixture-of-experts reasoning model for agents, text only, with tool calling, a low-effort mode and a reasoning budget.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "unsloth NVIDIA-Nemotron-3-Super-120B-A12B-GGUF (UD-Q4_K_S)",
            "url": "https://huggingface.co/unsloth/NVIDIA-Nemotron-3-Super-120B-A12B-GGUF",
            "who": "unsloth",
            "precision": "q4_k_s",
            "sizeGB": 79.02,
            "memoryGB": 96,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "From Unsloth, a third party's conversion and not NVIDIA's. 79.02 GB of UD-Q4_K_S weights, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it"
          },
          {
            "name": "NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4",
            "who": "publisher",
            "precision": "nvfp4",
            "sizeGB": 80.3,
            "memoryGB": 128,
            "format": "safetensors",
            "note": "Pre-trained in NVFP4; runs on one B200 or DGX Spark per its card. NVFP4 needs NVIDIA Blackwell"
          },
          {
            "name": "ollama nemotron-3-super:latest",
            "url": "https://ollama.com/library/nemotron-3-super:latest",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 87,
            "memoryGB": 128,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "Also the smallest Ollama build. Size as Ollama's tags page shows it, rounded to whole GB above 10 GB; the Linux build is the llama.cpp variant"
          },
          {
            "name": "lmstudio-community NVIDIA-Nemotron-3-Super-120B-A12B-GGUF (Q4_K_M)",
            "url": "https://huggingface.co/lmstudio-community/NVIDIA-Nemotron-3-Super-120B-A12B-GGUF",
            "who": "lmstudio",
            "precision": "q4_k_m",
            "sizeGB": 86.05,
            "memoryGB": 128,
            "format": "gguf",
            "runsOn": [
              "LM Studio",
              "llama.cpp"
            ],
            "note": "From lmstudio-community, LM Studio's organisation on the Hub. 86.05 GB of Q4_K_M weights, from the Hub's model information on 10 November 2026. LM Studio's own catalogue page for this model wasn't checked. Recorded after the last grading, so Reach doesn't count it yet"
          },
          {
            "name": "NVIDIA-Nemotron-3-Super-120B-A12B-FP8",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-FP8",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 128.4,
            "memoryGB": 192,
            "format": "safetensors"
          },
          {
            "name": "lmstudio-community NVIDIA-Nemotron-3-Super-120B-A12B-GGUF (Q6_K)",
            "url": "https://huggingface.co/lmstudio-community/NVIDIA-Nemotron-3-Super-120B-A12B-GGUF",
            "who": "lmstudio",
            "precision": "q6_k",
            "sizeGB": 112.92,
            "memoryGB": 192,
            "format": "gguf",
            "runsOn": [
              "LM Studio",
              "llama.cpp"
            ],
            "note": "From lmstudio-community, LM Studio's organisation on the Hub. 112.92 GB of Q6_K weights, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it yet"
          },
          {
            "name": "lmstudio-community NVIDIA-Nemotron-3-Super-120B-A12B-GGUF (Q8_0)",
            "url": "https://huggingface.co/lmstudio-community/NVIDIA-Nemotron-3-Super-120B-A12B-GGUF",
            "who": "lmstudio",
            "precision": "q8_0",
            "sizeGB": 128.47,
            "memoryGB": 192,
            "format": "gguf",
            "runsOn": [
              "LM Studio",
              "llama.cpp"
            ],
            "note": "From lmstudio-community, LM Studio's organisation on the Hub. 128.47 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-Super-120B-A12B-BF16",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 247.2,
            "memoryGB": 512,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)"
            ]
          }
        ],
        "run": "vllm serve nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4 --served-model-name nvidia/nemotron-3-super --max-model-len 262144 --kv-cache-dtype fp8 --mamba-ssm-cache-dtype float32 --trust-remote-code --reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder",
        "runBuild": "NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4",
        "checked": "2026-10-21",
        "minMemoryGB": 96
      },
      "family": {
        "lab": "NVIDIA (Nemotron)",
        "family": "Nemotron 3",
        "url": "https://www.anchorterminal.com/open-weights/#nvidia",
        "others": [
          {
            "name": "Nemotron 3 Nano 30B-A3B",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16",
            "params": "30B total, 3B active (31.6B per the safetensors index)",
            "listing": "https://www.anchorterminal.com/tools/nemotron-3-nano-30b-a3b"
          },
          {
            "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 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": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16/commits/main",
        "securityTxt": "unknown",
        "checked": "2026-10-21",
        "notes": [
          "The card names NVIDIA Corporation as model developer, and the licence's notice reads Licensed by NVIDIA Corporation. The Hugging Face organisation nvidia is verified and linked from developer.nvidia.com. RDAP gives 20 April 1993 for nvidia.com.",
          "Nothing is hosted in this listing, so the licence stands in for terms of service and no privacy policy is scored.",
          "The changelog is the repository's commit history. Since 10 March 2026 only the card has changed, with some messages that say what (Update BF16 configs, Update KV Cache/Mamba dtype, Update for MTPv2) and several that read Update README.md.",
          "www.nvidia.com/.well-known/security.txt answered 403 to our reader on 21 October 2026, so it is counted as unchecked rather than absent. The card links NVIDIA's vulnerability submission page."
        ],
        "score": 76
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/nemotron-3-super-120b-a12b.json",
      "live": {
        "slug": "nemotron-3-super-120b-a12b",
        "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-Super-120B-A12B-BF16/commits/main",
            "kind": "changelog",
            "status": 200,
            "checkedAt": "2026-11-10T16:23:20.620593124Z",
            "changedAt": "2026-11-10T16:23:20.620593124Z",
            "fingerprint": "b8fce0f674d8"
          }
        ],
        "updatedAt": "2026-11-10T16:23:20.620593124Z"
      }
    },
    "edges": [
      {
        "by": 22,
        "category": "upkeep",
        "leads": "nemotron-3-super-120b-a12b",
        "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, all seven items with limitations in the linked subcard. Training data earns 20, datasets listed with sources and token counts and the corpora published. Last change earns 20, counting NVIDIA's own inference code for the model, as on Nemotron 3 Ultra and Nano. The repositories last changed with a card edit on 25 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 0, with no NVIDIA reply in any of the nine discussions, re-counted on 1 November. Version policy earns 5 as a first release. File health earns 5, since a generation report waits without a reply and one card link is dead. 20+20+20+0+5+5 = 70."
        },
        "scores": {
          "a": 48,
          "b": 70
        }
      },
      {
        "by": 20,
        "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 10, because the smallest 4-bit build, NVIDIA's 80.3 GB NVFP4, needs about 96 GB with the fifth added, which fits the reference machine but not 64 GB, and Ollama's 87 GB build lands in the same band. Runtimes earn the full 25 from released Transformers, vLLM, SGLang, llama.cpp and Ollama. Builds earn 15 for NVIDIA's own FP8 and NVFP4. Terms earn 10, since the Nemotron licence is custom even though it's free for commercial use with no threshold or use policy. Download earns 10, with no gate or account. Hosted earns 5, since NVIDIA's own API serves it."
        },
        "scores": {
          "a": 95,
          "b": 75
        }
      },
      {
        "by": 20,
        "category": "interface",
        "leads": "nemotron-3-super-120b-a12b",
        "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, a Jinja file in the repository. The tool-calling format earns 25, since NVIDIA documents the format, names the qwen3_coder parser for three runtimes and shows a tool call in its cookbook. Trained tool calling earns 20, with tool-use reinforcement learning and several calls per turn in the template. Structured output earns 5, claimed from training with no instructions. Reasoning control earns 10, thinking off, low effort or a budget per request with the trace returned as reasoning_content. Context earns 10 for 262,144 tokens in the config and up to 1M per the card."
        },
        "scores": {
          "a": 70,
          "b": 90
        }
      },
      {
        "by": 10,
        "category": "risk",
        "leads": "nemotron-3-super-120b-a12b",
        "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 10 off because the documented serving commands pass trust-remote-code and the repository ships model and parser code. Provenance earns 15, files in NVIDIA's linked organisation with a hash for every file. Safety evaluation earns 0, since NVIDIA says it evaluated content safety but publishes no results. Agent guidance earns 10, a subcard that names prompt injection and says what safeguards to add, without an instruction hierarchy or concrete mitigations. Licence risk keeps all 15. The security programme earns 10 for the vulnerability submission route on the card and NVIDIA's public bulletins."
        },
        "scores": {
          "a": 55,
          "b": 65
        }
      }
    ],
    "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",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-10-30",
        "b": "2026-03-11",
        "name": "Last release"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Terms last updated"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Privacy policy last updated"
      },
      {
        "a": "none",
        "b": "2.8/5 (4)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Nemotron 3 Super 120B A12B scores 66.7 (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 Super 120B A12B?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 95 against 75"
        ],
        "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, 65 against 55",
          "Agent interface, 90 against 70",
          "Documentation and upkeep, 70 against 48"
        ],
        "also": null,
        "goodFor": "Agents on a 128 GB unified machine or Blackwell GPUs that want a long-context, tool-calling reasoning model with documented training data",
        "slug": "nemotron-3-super-120b-a12b",
        "watchFor": "The card's serving commands pass --trust-remote-code"
      }
    ],
    "history": {
      "minicpm-v-4-7-1b": null,
      "nemotron-3-super-120b-a12b": [
        {
          "causes": [
            "correction"
          ],
          "date": "2026-11-01",
          "delta": 0.5,
          "from": 66.2,
          "fromGrade": "B",
          "to": 66.7,
          "toGrade": "B"
        }
      ]
    },
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
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        "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-ornith-1-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-otel-2-0-llm-31b-it.json",
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        "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-otel-2-0-llm-31b-it"
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        "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-recursive-flint"
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        "title": "Nemotron 3 Super 120B A12B vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-ripple-65b-preview"
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      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-sensenova-loop-9b-preview.json",
        "title": "Nemotron 3 Super 120B A12B vs SenseNova-Loop-9B-Preview",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-sensenova-loop-9b-preview"
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      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-smollm3-3b.json",
        "title": "Nemotron 3 Super 120B A12B vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-smollm3-3b"
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        "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-spark-x2-6-4b"
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        "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-step-3-7-flash.json",
        "title": "Nemotron 3 Super 120B A12B vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-step-3-7-flash"
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        "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Nemotron 3 Super 120B A12B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-swift-1-5-qwen3-8-27b"
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        "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Nemotron 3 Super 120B A12B vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-swift-1-5-qwen3-8-flash-next"
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        "title": "Nemotron 3 Super 120B A12B vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-thinkingcap-qwen3-8-27b"
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      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-unlimited-ocr-2.json",
        "title": "Nemotron 3 Super 120B A12B vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-wevisone-vl-2b.json",
        "title": "Nemotron 3 Super 120B A12B vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-wevisone-vl-4b.json",
        "title": "Nemotron 3 Super 120B A12B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-zaya1-8b.json",
        "title": "Nemotron 3 Super 120B A12B vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-zaya1-8b"
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    ],
    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 20,
        "edge": "minicpm-v-4-7-1b",
        "key": "reach",
        "minicpm-v-4-7-1b": 95,
        "name": "Reach",
        "nemotron-3-super-120b-a12b": 75,
        "weight": 25
      },
      {
        "by": 10,
        "edge": "nemotron-3-super-120b-a12b",
        "key": "risk",
        "minicpm-v-4-7-1b": 55,
        "name": "Risk",
        "nemotron-3-super-120b-a12b": 65,
        "weight": 20
      },
      {
        "by": 20,
        "edge": "nemotron-3-super-120b-a12b",
        "key": "interface",
        "minicpm-v-4-7-1b": 70,
        "name": "Agent interface",
        "nemotron-3-super-120b-a12b": 90,
        "weight": 10
      },
      {
        "by": 22,
        "edge": "nemotron-3-super-120b-a12b",
        "key": "upkeep",
        "minicpm-v-4-7-1b": 48,
        "name": "Documentation and upkeep",
        "nemotron-3-super-120b-a12b": 70,
        "weight": 10
      }
    ],
    "summary": "Nemotron 3 Super 120B A12B scores 66.7 (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-super-120b-a12b": "A long-context agent cousin with trained parallel tool calls, a reasoning switch and a full account of its training data, under a free commercial licence. It needs about 100 GB of GPU memory, a 128 GB unified machine or Blackwell hardware for NVIDIA's NVFP4. The main risks are serving commands that run repository code and no published safety results."
    }
  },
  "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-super-120b-a12b.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-nemotron-3-super-120b-a12b.min.md"
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  "markdown": "Nemotron 3 Super 120B A12B scores 66.7 (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 Super 120B A12B: grade B, 66.7/100, rank #15 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-super-120b-a12b.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-super-120b-a12b.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 75\n\nWatch for: No limitations section, safety results, agent-use guidance or security contact\n\n### Nemotron 3 Super 120B A12B (B)\n\nGood for: Agents on a 128 GB unified machine or Blackwell GPUs that want a long-context, tool-calling reasoning model with documented training data\n\nAhead on:\n- Risk, 65 against 55\n- Agent interface, 90 against 70\n- Documentation and upkeep, 70 against 48\n\nWatch for: The card's serving commands pass --trust-remote-code\n\n\n## Why the edge\n\n### Documentation and upkeep: Nemotron 3 Super 120B A12B +22\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 Super 120B A12B (70): The model card earns 20, all seven items with limitations in the linked subcard. Training data earns 20, datasets listed with sources and token counts and the corpora published. Last change earns 20, counting NVIDIA's own inference code for the model, as on Nemotron 3 Ultra and Nano. The repositories last changed with a card edit on 25 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 0, with no NVIDIA reply in any of the nine discussions, re-counted on 1 November. Version policy earns 5 as a first release. File health earns 5, since a generation report waits without a reply and one card link is dead. 20+20+20+0+5+5 = 70.\n\n### Reach: MiniCPM-V 4.7 1B +20\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 Super 120B A12B (75): Memory earns 10, because the smallest 4-bit build, NVIDIA's 80.3 GB NVFP4, needs about 96 GB with the fifth added, which fits the reference machine but not 64 GB, and Ollama's 87 GB build lands in the same band. Runtimes earn the full 25 from released Transformers, vLLM, SGLang, llama.cpp and Ollama. Builds earn 15 for NVIDIA's own FP8 and NVFP4. Terms earn 10, since the Nemotron licence is custom even though it's free for commercial use with no threshold or use policy. Download earns 10, with no gate or account. Hosted earns 5, since NVIDIA's own API serves it.\n\n### Agent interface: Nemotron 3 Super 120B A12B +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 Super 120B A12B (90): The chat template earns 20, a Jinja file in the repository. The tool-calling format earns 25, since NVIDIA documents the format, names the qwen3_coder parser for three runtimes and shows a tool call in its cookbook. Trained tool calling earns 20, with tool-use reinforcement learning and several calls per turn in the template. Structured output earns 5, claimed from training with no instructions. Reasoning control earns 10, thinking off, low effort or a budget per request with the trace returned as reasoning_content. Context earns 10 for 262,144 tokens in the config and up to 1M per the card.\n\n### Risk: Nemotron 3 Super 120B A12B +10\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 Super 120B A12B (65): File safety earns 15, safetensors only but with 10 off because the documented serving commands pass trust-remote-code and the repository ships model and parser code. Provenance earns 15, files in NVIDIA's linked organisation with a hash for every file. Safety evaluation earns 0, since NVIDIA says it evaluated content safety but publishes no results. Agent guidance earns 10, a subcard that names prompt injection and says what safeguards to add, without an instruction hierarchy or concrete mitigations. Licence risk keeps all 15. The security programme earns 10 for the vulnerability submission route on the card and NVIDIA's public bulletins.\n\n## Score by category\n\n| Category | Weight | MiniCPM-V 4.7 1B | Nemotron 3 Super 120B A12B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 95 | 75 | MiniCPM-V 4.7 1B +20 |\n| Risk | 20% (20 this run) | 55 | 65 | Nemotron 3 Super 120B A12B +10 |\n| Agent interface | 10% (10 this run) | 70 | 90 | Nemotron 3 Super 120B A12B +20 |\n| Documentation and upkeep | 10% (10 this run) | 48 | 70 | Nemotron 3 Super 120B A12B +22 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **66.7 · B** | |\n\n## Facts side by side\n\n| Fact | MiniCPM-V 4.7 1B | Nemotron 3 Super 120B A12B |\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 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-30 | 2026-03-11 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | none | 2.8/5 (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 Super 120B A12B\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 Super 120B A12B.** A long-context agent cousin with trained parallel tool calls, a reasoning switch and a full account of its training data, under a free commercial licence. It needs about 100 GB of GPU memory, a 128 GB unified machine or Blackwell hardware for NVIDIA's NVFP4. The main risks are serving commands that run repository code and no published safety results.\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 Super 120B A12B\n\n1. On the reference machine start it with `ollama run nemotron-3-super`, the 87 GB q4_K_M build, the only one that fits (about 104 GB with the margin)\n2. Raise the Strix Halo GPU memory limit first with `amd-ttm --set 100`, since the default of about 62.8 GiB is far below what it needs\n3. With vLLM serve it with `--reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder` (SGLang uses `--reasoning-parser nemotron_3`)\n4. Use `temperature=1.0` and `top_p=0.95` for every task, as the card says\n5. Thinking is on by default. Pass `enable_thinking: false` or `low_effort: true` in `chat_template_kwargs` for short tasks\n6. For coding agents pass `force_nonempty_content: true` in `chat_template_kwargs`, so a cut-off reasoning trace still returns content\n7. Raise context past 262,144 only with the card's flags (`VLLM_ALLOW_LONG_MAX_MODEL_LEN=1`, `--max-model-len 1048576`)\n\n## Questions\n\n### Which is better for AI agents, MiniCPM-V 4.7 1B or Nemotron 3 Super 120B A12B?\n\nNemotron 3 Super 120B A12B scores 66.7 (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-super-120b-a12b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-nemotron-3-super-120b-a12b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"minicpm-v-4-7-1b\", \"b\": \"nemotron-3-super-120b-a12b\"}`. 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