{
  "data": {
    "a": {
      "slug": "lfm3-1-7b",
      "name": "LFM3-1.7B",
      "vendor": "Liquid AI",
      "vendorUrl": "https://www.liquid.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "LFM3-1.7B is Liquid AI's open-weight 1.73B text model for on-device agents, with tool calling, deferred tools, switchable thinking and a 256K-token context, released on 27 October 2026 with Liquid's own GGUF, FP8, NVFP4, MLX and ONNX builds.",
      "url": "https://www.anchorterminal.com/tools/lfm3-1-7b",
      "markdownUrl": "https://www.anchorterminal.com/tools/lfm3-1-7b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/lfm3-1-7b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/lfm3-1-7b.json",
      "dns": "lfm3-1-7b.grade.anchorterminal.com",
      "repo": "https://huggingface.co/LiquidAI/LFM3-1.7B",
      "license": "LFM Open License v1.0 (free commercial use below $10M annual revenue)",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "LiquidAI/LFM3-1.7B"
        },
        {
          "registry": "huggingface",
          "name": "LiquidAI/LFM3-1.7B-GGUF"
        },
        {
          "registry": "huggingface",
          "name": "LiquidAI/LFM3-1.7B-MLX-4bit"
        },
        {
          "registry": "ollama",
          "name": "lfm3"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository and Liquid's GGUF, MLX and ONNX repositories are not gated (checked 2026-11-10).",
      "pricing": "free",
      "pricingNotes": "Free to download and use under the LFM Open License v1.0, commercial use included for organisations with under $10M in annual revenue; above that, commercial use isn't licensed. Running it yourself costs little hardware, 1.10 GB for Liquid's Q4_K_M GGUF and 3.46 GB in BF16. Liquid's API also serves it, at prices shown in its console (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://docs.liquid.ai/lfm/models/lfm3-1.7b",
      "llmsTxt": "https://docs.liquid.ai/llms.txt",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "dense",
        "small",
        "edge",
        "reasoning",
        "tool-calling",
        "long-context",
        "custom-licence",
        "safetensors",
        "gguf",
        "mlx",
        "onnx",
        "self-hosted"
      ],
      "lastRelease": "2026-11-09",
      "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": 55,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 100,
          "reach": 85,
          "risk": 40,
          "upkeep": 67
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-10"
        },
        "negative": 0,
        "verdict": "A 1.73B model that fits an 8 GB machine with room to spare, with a documented tool-call format, deferred tools, a thinking switch and a 256K context. It runs today in llama.cpp, Ollama and LM Studio only, loads in Transformers through repository code, and has no safety results, security contact or data sources.",
        "bestFor": "Agents on a laptop, phone or small server that need tool calling and a long context from a model under 2 GB at 4 bits",
        "strengths": [
          "Fits an 8 GB machine from Liquid's own GGUF, at 1.10 GB for Q4_K_M",
          "The card and docs document the tool-call format, deferred tools and tool search",
          "A 256K-token context, trained in mid-training per the card",
          "Thinking can be turned off per request",
          "Liquid's own API serves it, and its guide documents JSON Schema output"
        ],
        "weaknesses": [
          "vLLM and SGLang have no released support, though the card lists both",
          "Loading in Transformers runs code from the repository",
          "No safety results and no security contact",
          "Liquid says turning thinking off can be inconsistent",
          "The licence stops free commercial use at $10M in annual revenue",
          "Training data is described without sources or a cut-off"
        ],
        "agentNotes": [
          "Start it with `llama-server -hf LiquidAI/LFM3-1.7B-GGUF:Q4_K_M --jinja` or `ollama run lfm3`",
          "Use temperature 0.1, top_k 50 and repetition penalty 1.1 for chat, tools and agents, per the card",
          "Pass `enable_thinking=False` for direct answers, and expect it to reason anyway on hard tasks",
          "For a large tool catalogue mark tools `defer_loading` and give it a search tool",
          "Don't plan on vLLM or SGLang until a release carries LFM3",
          "Liquid doesn't recommend it for agentic coding or complex multi-step reasoning"
        ],
        "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": 100,
          "reach": 85,
          "risk": 40,
          "upkeep": 67
        },
        "provenanceScore": 73,
        "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": "llama-server -hf LiquidAI/LFM3-1.7B-GGUF:Q4_K_M --jinja\n# or\nollama run lfm3",
        "http": "curl http://localhost:8080/v1/chat/completions \\\n  -H 'Content-Type: application/json' \\\n  -d '{\"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/lfm3-1-7b"
      },
      "sameCompany": [
        "liquid-d1",
        "lfm2-5-2-6b",
        "lfm3-audio-2b",
        "lfm3-omni-2b",
        "lfm3-vl-2b"
      ],
      "weights": {
        "trainedBy": "Liquid AI",
        "builtOn": "LiquidAI/LFM3-1.7B-Base",
        "params": "1.73B",
        "context": 262144,
        "goodFor": "Small on-device agent model for tool calling, tool search over large catalogues, structured output and long-context work on a laptop or phone, not for agentic coding or complex multi-step reasoning.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "LM Studio"
        ],
        "builds": [
          {
            "name": "LFM3-1.7B",
            "url": "https://huggingface.co/LiquidAI/LFM3-1.7B",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 3.46,
            "memoryGB": 8,
            "format": "safetensors",
            "runsOn": [
              "Transformers"
            ],
            "note": "Loads in Transformers with the repository's own code. Liquid also publishes FP8 (2.21 GB) and NVFP4 (1.83 GB) safetensors builds, which the card pairs with SGLang, and an ONNX repository"
          },
          {
            "name": "LFM3-1.7B-GGUF Q8_0",
            "url": "https://huggingface.co/LiquidAI/LFM3-1.7B-GGUF",
            "who": "publisher",
            "precision": "q8_0",
            "sizeGB": 1.85,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "llama.cpp",
              "LM Studio"
            ]
          },
          {
            "name": "LFM3-1.7B-GGUF Q4_K_M",
            "url": "https://huggingface.co/LiquidAI/LFM3-1.7B-GGUF",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 1.1,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "llama.cpp",
              "LM Studio"
            ],
            "note": "LFM3-1.7B-Q4_K_M.gguf, from the Hub's file list. The repository also holds a quantisation-aware LFM3-1.7B-QAD-Q4_0.gguf (1.05 GB), Q8_0, F16 and BF16 files"
          },
          {
            "name": "LFM3-1.7B-MLX-4bit",
            "url": "https://huggingface.co/LiquidAI/LFM3-1.7B-MLX-4bit",
            "who": "publisher",
            "precision": "4bit",
            "sizeGB": 0.97,
            "memoryGB": 8,
            "format": "mlx",
            "runsOn": [
              "MLX"
            ],
            "note": "Liquid also publishes 5, 6 and 8-bit and BF16 MLX builds"
          },
          {
            "name": "ollama lfm3",
            "url": "https://ollama.com/library/lfm3",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 1.1,
            "memoryGB": 8,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "Ollama's library build, size as its tags page shows it on 10 November 2026. The library also has q8_0, bf16, mxfp8 and nvfp4 tags"
          }
        ],
        "run": "llama-server -hf LiquidAI/LFM3-1.7B-GGUF:Q4_K_M --jinja",
        "runBuild": "LFM3-1.7B-GGUF Q4_K_M",
        "checked": "2026-11-10",
        "minMemoryGB": 8
      },
      "family": {
        "lab": "Liquid AI",
        "family": "LFM3",
        "url": "https://www.anchorterminal.com/open-weights/#liquid",
        "others": [
          {
            "name": "LFM3-1.7B-Base",
            "url": "https://huggingface.co/LiquidAI/LFM3-1.7B-Base",
            "params": "1.73B"
          },
          {
            "name": "LFM3-VL-2B",
            "url": "https://huggingface.co/LiquidAI/LFM3-VL-2B",
            "params": "1.92B",
            "listing": "https://www.anchorterminal.com/tools/lfm3-vl-2b"
          },
          {
            "name": "LFM3-Omni-2B",
            "url": "https://huggingface.co/LiquidAI/LFM3-Omni-2B",
            "params": "2.17B",
            "listing": "https://www.anchorterminal.com/tools/lfm3-omni-2b"
          },
          {
            "name": "LFM3-Audio-2B",
            "url": "https://huggingface.co/LiquidAI/LFM3-Audio-2B",
            "params": "2.00B",
            "listing": "https://www.anchorterminal.com/tools/lfm3-audio-2b"
          }
        ]
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Liquid AI, Inc. (the licensor named in the LFM Open License)",
        "domain": "liquid.ai",
        "domainRegistered": "2017-12-16",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/LiquidAI/LFM3-1.7B/commits/main",
        "securityTxt": "none",
        "checked": "2026-11-10",
        "notes": [
          "The licence names Liquid AI, Inc. as licensor. The domain's registration date is as recorded on the liquid-d1 listing on 8 October 2026",
          "www.liquid.ai/.well-known/security.txt answered 404 on 10 November 2026"
        ],
        "score": 73
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/lfm3-1-7b.json"
    },
    "answer": "Nemotron 3 Super 120B A12B scores 66.7 (B) on agent readiness against LFM3-1.7B's 61 (C), and leads in 2 of 4 scored categories. LFM3-1.7B leads on reach and agent interface.",
    "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": 25,
        "category": "risk",
        "leads": "nemotron-3-super-120b-a12b",
        "name": "Risk",
        "reasons": {
          "a": "File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, files in the LiquidAI organisation, which Liquid's docs page for the model and its blog post link to (10). Safety evaluation 0, no safety results on the card or in Liquid's blog post for the LFM3 family, neither of which mentions safety testing. Agent guidance 0, Nothing on prompt injection, an instruction hierarchy, what refusals cover or safeguards to add. Licence risk 10, 15 less 5 for the $10M annual revenue threshold on commercial use. Security programme 0, no way to report a flaw found. 15 + 15 + 0 + 0 + 10 + 0 = 40.",
          "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": 40,
          "b": 65
        }
      },
      {
        "by": 10,
        "category": "reach",
        "leads": "lfm3-1-7b",
        "name": "Reach",
        "reasons": {
          "a": "Memory 30, Liquid's Q4_K_M GGUF is 1.10 GB, and 1.10 GB × 1.2 = 1.3 GB, which fits 8 GB, well inside the 16 GB line. Runtimes 15, Released llama.cpp has the lfm3 architecture (src/models/lfm3.cpp at b12242), Ollama's library has an lfm3 entry, and LM Studio's catalogue runs the architecture (its liquid/lfm3-vl-2b entry; lmstudio-community publishes a GGUF of this model). Builds 15, Liquid's GGUF repository holds Q4_0, Q4_K_M, Q8_0, F16 and BF16 files, and Liquid publishes 4-bit and 8-bit MLX builds and an ONNX repository, all in the LiquidAI organisation. Terms 10, the LFM Open License v1.0, a custom licence that lets anyone use it commercially for free below a threshold of $10M annual revenue. Download 10, the Hub API reports gated false for the model and its GGUF, MLX and ONNX repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, Liquid's own API lists lfm3-1.7b among its models, so the publisher hosts it. 30 + 15 + 15 + 10 + 10 + 5 = 85.",
          "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": 85,
          "b": 75
        }
      },
      {
        "by": 10,
        "category": "interface",
        "leads": "lfm3-1-7b",
        "name": "Agent interface",
        "reasons": {
          "a": "Chat template 20, chat_template.jinja in the repository (12,129 characters), with roles, tools, deferred tools, tool results and the thinking switch. Tool-calling format 25, the card and Liquid's tool-use page document tools passed through `tools=` in apply_chat_template, calls returned as XML and results sent back as tool messages. Trained tool calling 20, the template loops over every entry in an assistant turn's tool_calls, so several calls fit in one turn, and the card says the model was trained on tool calls and agentic traces and with reinforcement learning inside agent harnesses. Structured output 15, Liquid's API guide documents `response_format` with a JSON Schema, with lfm3-1.7b as the model in its examples, and says the schema is checked before generation starts. Reasoning control 10, the card documents `enable_thinking=False` in the chat template to turn reasoning off per request, with reasoning on by default inside think tags, and `preserve_thinking` for earlier turns. Context 10, 256K tokens per the card, which says the mid-training stage extended the context window to 256K, and max_position_embeddings 262144 in config.json. 20 + 25 + 20 + 15 + 10 + 10 = 100.",
          "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": 100,
          "b": 90
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Liquid AI",
        "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": "LFM Open License v1.0 (free commercial use below $10M annual revenue)",
        "b": "NVIDIA Nemotron Open Model License",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "yes",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-11-09",
        "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 LFM3-1.7B's 61 (C), and leads in 2 of 4 scored categories. LFM3-1.7B leads on reach and agent interface.",
        "question": "Which is better for AI agents, LFM3-1.7B or Nemotron 3 Super 120B A12B?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 85 against 75",
          "Agent interface, 100 against 90"
        ],
        "also": null,
        "goodFor": "Agents on a laptop, phone or small server that need tool calling and a long context from a model under 2 GB at 4 bits",
        "slug": "lfm3-1-7b",
        "watchFor": "vLLM and SGLang have no released support, though the card lists both"
      },
      {
        "aheadOn": [
          "Risk, 65 against 40"
        ],
        "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": {
      "lfm3-1-7b": 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"
    },
    "others": [
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-lfm3-1-7b.json",
        "title": "Agents-A1-4B vs LFM3-1.7B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-lfm3-1-7b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-super-120b-a12b.json",
        "title": "Agents-A1-4B vs Nemotron 3 Super 120B A12B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-super-120b-a12b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-lfm3-1-7b.json",
        "title": "Apertus v1.5 8B vs LFM3-1.7B",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-lfm3-1-7b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-super-120b-a12b.json",
        "title": "Apertus v1.5 8B vs Nemotron 3 Super 120B A12B",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-super-120b-a12b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-lfm3-1-7b.json",
        "title": "Apodex-1.1-mini vs LFM3-1.7B",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-lfm3-1-7b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nemotron-3-super-120b-a12b.json",
        "title": "Apodex-1.1-mini vs Nemotron 3 Super 120B A12B",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nemotron-3-super-120b-a12b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-lfm3-1-7b.json",
        "title": "Arete-27B vs LFM3-1.7B",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-lfm3-1-7b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-nemotron-3-super-120b-a12b.json",
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        "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Nemotron 3 Super 120B A12B vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-recursive-flint.json",
        "title": "Nemotron 3 Super 120B A12B vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-ripple-65b-preview.json",
        "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"
      },
      {
        "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"
      },
      {
        "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"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-spark-x2-6-4b.json",
        "title": "Nemotron 3 Super 120B A12B vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-spark-x2-6-4b"
      },
      {
        "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"
      },
      {
        "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"
      },
      {
        "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"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-thinkingcap-qwen3-8-27b.json",
        "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"
      },
      {
        "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"
      }
    ],
    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 10,
        "edge": "lfm3-1-7b",
        "key": "reach",
        "lfm3-1-7b": 85,
        "name": "Reach",
        "nemotron-3-super-120b-a12b": 75,
        "weight": 25
      },
      {
        "by": 25,
        "edge": "nemotron-3-super-120b-a12b",
        "key": "risk",
        "lfm3-1-7b": 40,
        "name": "Risk",
        "nemotron-3-super-120b-a12b": 65,
        "weight": 20
      },
      {
        "by": 10,
        "edge": "lfm3-1-7b",
        "key": "interface",
        "lfm3-1-7b": 100,
        "name": "Agent interface",
        "nemotron-3-super-120b-a12b": 90,
        "weight": 10
      },
      {
        "by": 3,
        "edge": "nemotron-3-super-120b-a12b",
        "key": "upkeep",
        "lfm3-1-7b": 67,
        "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 LFM3-1.7B's 61 (C), and leads in 2 of 4 scored categories. LFM3-1.7B leads on reach and agent interface. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "lfm3-1-7b": "A 1.73B model that fits an 8 GB machine with room to spare, with a documented tool-call format, deferred tools, a thinking switch and a 256K context. It runs today in llama.cpp, Ollama and LM Studio only, loads in Transformers through repository code, and has no safety results, security contact or data sources.",
      "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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    "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-nemotron-3-super-120b-a12b.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-nemotron-3-super-120b-a12b.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/lfm3-1-7b-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 LFM3-1.7B's 61 (C), and leads in 2 of 4 scored categories. LFM3-1.7B leads on reach and agent interface. Both do open-weight models you run yourself.\n\n- LFM3-1.7B: grade C, 61/100, rank #69 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/lfm3-1-7b.md · JSON https://www.anchorterminal.com/api/v1/tools/lfm3-1-7b.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### LFM3-1.7B (C)\n\nGood for: Agents on a laptop, phone or small server that need tool calling and a long context from a model under 2 GB at 4 bits\n\nAhead on:\n- Reach, 85 against 75\n- Agent interface, 100 against 90\n\nWatch for: vLLM and SGLang have no released support, though the card lists both\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 40\n\nWatch for: The card's serving commands pass --trust-remote-code\n\n\n## Why the edge\n\n### Risk: Nemotron 3 Super 120B A12B +25\n\n- LFM3-1.7B (40): File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, files in the LiquidAI organisation, which Liquid's docs page for the model and its blog post link to (10). Safety evaluation 0, no safety results on the card or in Liquid's blog post for the LFM3 family, neither of which mentions safety testing. Agent guidance 0, Nothing on prompt injection, an instruction hierarchy, what refusals cover or safeguards to add. Licence risk 10, 15 less 5 for the $10M annual revenue threshold on commercial use. Security programme 0, no way to report a flaw found. 15 + 15 + 0 + 0 + 10 + 0 = 40.\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### Reach: LFM3-1.7B +10\n\n- LFM3-1.7B (85): Memory 30, Liquid's Q4_K_M GGUF is 1.10 GB, and 1.10 GB × 1.2 = 1.3 GB, which fits 8 GB, well inside the 16 GB line. Runtimes 15, Released llama.cpp has the lfm3 architecture (src/models/lfm3.cpp at b12242), Ollama's library has an lfm3 entry, and LM Studio's catalogue runs the architecture (its liquid/lfm3-vl-2b entry; lmstudio-community publishes a GGUF of this model). Builds 15, Liquid's GGUF repository holds Q4_0, Q4_K_M, Q8_0, F16 and BF16 files, and Liquid publishes 4-bit and 8-bit MLX builds and an ONNX repository, all in the LiquidAI organisation. Terms 10, the LFM Open License v1.0, a custom licence that lets anyone use it commercially for free below a threshold of $10M annual revenue. Download 10, the Hub API reports gated false for the model and its GGUF, MLX and ONNX repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, Liquid's own API lists lfm3-1.7b among its models, so the publisher hosts it. 30 + 15 + 15 + 10 + 10 + 5 = 85.\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: LFM3-1.7B +10\n\n- LFM3-1.7B (100): Chat template 20, chat_template.jinja in the repository (12,129 characters), with roles, tools, deferred tools, tool results and the thinking switch. Tool-calling format 25, the card and Liquid's tool-use page document tools passed through `tools=` in apply_chat_template, calls returned as XML and results sent back as tool messages. Trained tool calling 20, the template loops over every entry in an assistant turn's tool_calls, so several calls fit in one turn, and the card says the model was trained on tool calls and agentic traces and with reinforcement learning inside agent harnesses. Structured output 15, Liquid's API guide documents `response_format` with a JSON Schema, with lfm3-1.7b as the model in its examples, and says the schema is checked before generation starts. Reasoning control 10, the card documents `enable_thinking=False` in the chat template to turn reasoning off per request, with reasoning on by default inside think tags, and `preserve_thinking` for earlier turns. Context 10, 256K tokens per the card, which says the mid-training stage extended the context window to 256K, and max_position_embeddings 262144 in config.json. 20 + 25 + 20 + 15 + 10 + 10 = 100.\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## Score by category\n\n| Category | Weight | LFM3-1.7B | Nemotron 3 Super 120B A12B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 85 | 75 | LFM3-1.7B +10 |\n| Risk | 20% (20 this run) | 40 | 65 | Nemotron 3 Super 120B A12B +25 |\n| Agent interface | 10% (10 this run) | 100 | 90 | LFM3-1.7B +10 |\n| Documentation and upkeep | 10% (10 this run) | 67 | 70 | Nemotron 3 Super 120B A12B +3 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **66.7 · B** | |\n\n## Facts side by side\n\n| Fact | LFM3-1.7B | Nemotron 3 Super 120B A12B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Liquid AI | 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 | LFM Open License v1.0 (free commercial use below $10M annual revenue) | NVIDIA Nemotron Open Model License |\n| Read-only variant documented | no | no |\n| llms.txt | yes | no |\n| Last release | 2026-11-09 | 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### LFM3-1.7B\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**LFM3-1.7B.** A 1.73B model that fits an 8 GB machine with room to spare, with a documented tool-call format, deferred tools, a thinking switch and a 256K context. It runs today in llama.cpp, Ollama and LM Studio only, loads in Transformers through repository code, and has no safety results, security contact or data sources.\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### LFM3-1.7B\n\n1. Start it with `llama-server -hf LiquidAI/LFM3-1.7B-GGUF:Q4_K_M --jinja` or `ollama run lfm3`\n2. Use temperature 0.1, top_k 50 and repetition penalty 1.1 for chat, tools and agents, per the card\n3. Pass `enable_thinking=False` for direct answers, and expect it to reason anyway on hard tasks\n4. For a large tool catalogue mark tools `defer_loading` and give it a search tool\n5. Don't plan on vLLM or SGLang until a release carries LFM3\n6. Liquid doesn't recommend it for agentic coding or complex multi-step reasoning\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, LFM3-1.7B or Nemotron 3 Super 120B A12B?\n\nNemotron 3 Super 120B A12B scores 66.7 (B) on agent readiness against LFM3-1.7B's 61 (C), and leads in 2 of 4 scored categories. LFM3-1.7B leads on reach and agent interface.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/lfm3-1-7b-vs-nemotron-3-super-120b-a12b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/lfm3-1-7b-vs-nemotron-3-super-120b-a12b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"lfm3-1-7b\", \"b\": \"nemotron-3-super-120b-a12b\"}`. From a terminal: `anchor compare lfm3-1-7b nemotron-3-super-120b-a12b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/lfm3-1-7b.json and https://www.anchorterminal.com/api/v1/tools/nemotron-3-super-120b-a12b.json\n\n## Other comparisons with LFM3-1.7B or Nemotron 3 Super 120B A12B\n\n- [Agents-A1-4B vs LFM3-1.7B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-lfm3-1-7b.md)\n- [Agents-A1-4B vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-super-120b-a12b.md)\n- [Apertus v1.5 8B vs LFM3-1.7B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-lfm3-1-7b.md)\n- [Apertus v1.5 8B vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-super-120b-a12b.md)\n- [Apodex-1.1-mini vs LFM3-1.7B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-lfm3-1-7b.md)\n- [Apodex-1.1-mini vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nemotron-3-super-120b-a12b.md)\n- [Arete-27B vs LFM3-1.7B](https://www.anchorterminal.com/compare/arete-27b-vs-lfm3-1-7b.md)\n- [Arete-27B vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/arete-27b-vs-nemotron-3-super-120b-a12b.md)\n- [BigBang-v1 vs LFM3-1.7B](https://www.anchorterminal.com/compare/bigbang-v1-vs-lfm3-1-7b.md)\n- [BigBang-v1 vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/bigbang-v1-vs-nemotron-3-super-120b-a12b.md)\n- [Carbon-X vs LFM3-1.7B](https://www.anchorterminal.com/compare/carbon-x-vs-lfm3-1-7b.md)\n- [Carbon-X vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/carbon-x-vs-nemotron-3-super-120b-a12b.md)\n- [Cola2 vs LFM3-1.7B](https://www.anchorterminal.com/compare/cola2-vs-lfm3-1-7b.md)\n- [Cola2 vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/cola2-vs-nemotron-3-super-120b-a12b.md)\n- [Cosmos-Reason2-2B vs 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