{
  "data": {
    "a": {
      "slug": "lfm2-5-2-6b",
      "name": "LFM2.5-2.6B",
      "vendor": "Liquid AI",
      "vendorUrl": "https://www.liquid.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "LFM2.5-2.6B is Liquid AI's open-weight 2.6B hybrid model of convolution and attention layers for on-device agents, with tool calling, thinking always on and a 131,072-token context, published on 4 August 2026 with Liquid's own GGUF, MLX and ONNX builds.",
      "url": "https://www.anchorterminal.com/tools/lfm2-5-2-6b",
      "markdownUrl": "https://www.anchorterminal.com/tools/lfm2-5-2-6b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/lfm2-5-2-6b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/lfm2-5-2-6b.json",
      "repo": "https://huggingface.co/LiquidAI/LFM2.5-2.6B",
      "license": "LFM Open License v1.0 (free commercial use below $10M annual revenue)",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "LiquidAI/LFM2.5-2.6B"
        },
        {
          "registry": "huggingface",
          "name": "LiquidAI/LFM2.5-2.6B-GGUF"
        },
        {
          "registry": "huggingface",
          "name": "LiquidAI/LFM2.5-2.6B-MLX"
        },
        {
          "registry": "ollama",
          "name": "LiquidAI/lfm2.5-2.6b"
        }
      ],
      "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 2026-10-21).",
      "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, 1.6 GB for Liquid's Q4_0 GGUF and 5.4 GB in BF16 (checked 2026-10-21).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-10-21"
      },
      "docsUrl": "https://docs.liquid.ai/lfm/models/lfm25-2.6b",
      "llmsTxt": "https://docs.liquid.ai/llms.txt",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "dense",
        "small",
        "edge",
        "reasoning",
        "tool-calling",
        "custom-licence",
        "safetensors",
        "gguf",
        "mlx",
        "self-hosted"
      ],
      "lastRelease": "2026-09-22",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 51.5,
        "grade": "D",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1197,
        "categoryRank": 71,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "capability": 13,
          "interface": 75,
          "reach": 95,
          "risk": 50,
          "upkeep": 57
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "A small, fast cousin for tool calls, extraction and long-context steps on a laptop or phone. Liquid's own 4-bit build is 1.6 GB, and every common runtime runs it. It always thinks, isn't meant for coding or knowledge-heavy work, and organisations over $10M of revenue need another licence. There are no safety results or security contact.",
        "bestFor": "Agents that want a small local helper for tool calls, extraction and RAG on a laptop, phone or CPU, at organisations under the licence's revenue threshold",
        "strengths": [
          "Liquid's own 4-bit GGUF and MLX builds are 1.6 GB, under 2 GB of memory with the fifth added",
          "Runs in released llama.cpp, Ollama, vLLM, SGLang and Transformers, with an lfm2 tool parser in vLLM and SGLang",
          "Tool-call format documented by Liquid, several calls per turn, and 131,072 tokens of trained context",
          "Safetensors and GGUF only, no repository code, a checksum on every file and no gate",
          "Builds at many precisions from Liquid itself, refreshed in September 2026"
        ],
        "weaknesses": [
          "Commercial use isn't licensed for organisations with $10M or more of annual revenue",
          "Thinking is always on, with no way to turn it off or budget it",
          "No safety evaluation, agent guidance or security contact",
          "No structured-output documentation and no knowledge cut-off on the card",
          "Liquid answered 2 of 16 discussions"
        ],
        "agentNotes": [
          "Quickest start is `ollama run LiquidAI/lfm2.5-2.6b`, 1.7 GB, or `llama-cli -hf LiquidAI/LFM2.5-2.6B-GGUF`",
          "With vLLM or SGLang pass `--tool-call-parser lfm2`; for vLLM add `--reasoning-parser qwen3` so the thinking comes back separately",
          "Sample at temperature 0.1, top_k 50 and repetition_penalty 1.1, as the card and generation_config.json set",
          "Put tools in the system prompt as `List of tools: [...]` JSON, or pass tools to apply_chat_template; calls come back as a Python list of function calls, and the system prompt can ask for JSON calls instead",
          "Leave max_tokens unset or generous, since a low cap cuts the reasoning off mid-thought",
          "Earlier thinking is dropped from the prompt unless `preserve_thinking` is true",
          "Liquid's 328M LFM2.5-2.6B-DSpark drafter speeds decoding about 2.6 times in SGLang and on Apple silicon, per Liquid",
          "Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 2.5,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "high",
            "grade": "D",
            "methodology": "0.6",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 51.5
          }
        ],
        "editorialScores": {
          "capability": 13,
          "interface": 75,
          "reach": 95,
          "risk": 50,
          "upkeep": 57
        },
        "provenanceScore": 55,
        "checked": {
          "categories": {
            "capability": "2026-10-21",
            "interface": "2026-10-21",
            "reach": "2026-10-21",
            "risk": "2026-10-21",
            "upkeep": "2026-10-21"
          },
          "last": "2026-10-21",
          "oldest": "2026-10-21",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "llama-cli -hf LiquidAI/LFM2.5-2.6B-GGUF --conversation --temp 0.1 --top-k 50 --repeat-penalty 1.1\n# or\nollama run LiquidAI/lfm2.5-2.6b\n# or, with vLLM:\nvllm serve LiquidAI/LFM2.5-2.6B --enable-auto-tool-choice --tool-call-parser lfm2 --reasoning-parser qwen3",
        "http": "curl http://localhost:11434/api/chat \\\n  -d '{\"model\": \"LiquidAI/lfm2.5-2.6b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/lfm2-5-2-6b"
      },
      "sameCompany": [
        "liquid-d1"
      ],
      "weights": {
        "trainedBy": "Liquid AI",
        "builtOn": "LiquidAI/LFM2.5-2.6B-Base",
        "params": "2.69B",
        "context": 131072,
        "goodFor": "Small on-device agent model for tool use, data extraction, RAG and long-context work on a laptop or phone, always thinking, not for agentic coding or knowledge-heavy tasks.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "LFM2.5-2.6B",
            "url": "https://huggingface.co/LiquidAI/LFM2.5-2.6B",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 5.4,
            "memoryGB": 8,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang",
              "Transformers"
            ]
          },
          {
            "name": "LFM2.5-2.6B-GGUF Q4_0",
            "url": "https://huggingface.co/LiquidAI/LFM2.5-2.6B-GGUF",
            "who": "publisher",
            "precision": "q4_0",
            "sizeGB": 1.6,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "llama.cpp",
              "LM Studio"
            ],
            "note": "LFM2.5-2.6B-Q4_0.gguf, 1.59 GB from the Hub's file list. The repository also holds Q4_K_M (1.67 GB), Q5_K_M, Q6_K, Q8_0 (2.87 GB), F16 and BF16, and a quantisation-aware LFM2.5-2.6B-QAD-Q4_0.gguf added on 19 August 2026"
          },
          {
            "name": "LFM2.5-2.6B-MLX 4bit",
            "url": "https://huggingface.co/LiquidAI/LFM2.5-2.6B-MLX",
            "who": "publisher",
            "precision": "4bit",
            "sizeGB": 1.6,
            "memoryGB": 8,
            "format": "mlx",
            "runsOn": [
              "MLX"
            ],
            "note": "4bit/model.safetensors (1.58 GB) and tokenizer, 1.60 GB summed. The repository also holds 5, 6 and 8-bit, BF16, MXFP4, MXFP8 and NVFP4 folders, each also published as its own LFM2.5-2.6B-MLX-* repository"
          },
          {
            "name": "ollama LiquidAI/lfm2.5-2.6b:q4_k_m",
            "url": "https://ollama.com/LiquidAI/lfm2.5-2.6b",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 1.7,
            "memoryGB": 8,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "Liquid's own namespace in Ollama's registry, latest and q4_k_m share digest 83f596831da1; q4_0 is 1.6 GB. Sizes as Ollama's tags page shows them"
          }
        ],
        "run": "llama-server -hf LiquidAI/LFM2.5-2.6B-GGUF:Q4_0 --jinja -c 131072 --temp 0.1 --top-k 50 --repeat-penalty 1.1",
        "runBuild": "LFM2.5-2.6B-GGUF Q4_0",
        "checked": "2026-10-21",
        "minMemoryGB": 8
      },
      "family": {
        "lab": "Liquid AI",
        "family": "LFM2 (LFM2 and LFM2.5)",
        "url": "https://www.anchorterminal.com/open-weights/#liquid",
        "others": [
          {
            "name": "LFM2.5-24B-A2B",
            "url": "https://huggingface.co/LiquidAI/LFM2.5-24B-A2B",
            "params": "24B total, 2B active (MoE) (Hub safetensors count 23.97B)"
          },
          {
            "name": "LFM2.5-24B-A2B-Base",
            "url": "https://huggingface.co/LiquidAI/LFM2.5-24B-A2B-Base",
            "params": "24B total, 2B active (MoE) (Hub safetensors count 23.97B)"
          },
          {
            "name": "LFM2.5-VL-3B",
            "url": "https://huggingface.co/LiquidAI/LFM2.5-VL-3B",
            "params": "3B (Hub safetensors count 3.12B)"
          },
          {
            "name": "LFM2.5-2.6B-Base",
            "url": "https://huggingface.co/LiquidAI/LFM2.5-2.6B-Base",
            "params": "2.6B (Hub safetensors count 2.70B)"
          },
          {
            "name": "LFM2.5-Encoder-350M",
            "url": "https://huggingface.co/LiquidAI/LFM2.5-Encoder-350M",
            "params": "350M (Hub safetensors count 354M)"
          },
          {
            "name": "LFM2.5-Encoder-230M",
            "url": "https://huggingface.co/LiquidAI/LFM2.5-Encoder-230M",
            "params": "230M (Hub safetensors count 230M)"
          },
          {
            "name": "LFM2.5-Encoder-350M-Prompt-Router",
            "url": "https://huggingface.co/LiquidAI/LFM2.5-Encoder-350M-Prompt-Router",
            "params": "350M (Hub safetensors count 355M)"
          },
          {
            "name": "LFM2.5-Encoder-350M-Policy-Linter",
            "url": "https://huggingface.co/LiquidAI/LFM2.5-Encoder-350M-Policy-Linter",
            "params": "350M (Hub safetensors count 355M)"
          },
          {
            "name": "LFM2.5-Encoder-350M-Diffusion",
            "url": "https://huggingface.co/LiquidAI/LFM2.5-Encoder-350M-Diffusion",
            "params": "350M (Hub safetensors count 354M)"
          },
          {
            "name": "LFM2.5-Encoder-350M-Spellchecker",
            "url": "https://huggingface.co/LiquidAI/LFM2.5-Encoder-350M-Spellchecker",
            "params": "350M (Hub safetensors count 357M)"
          },
          {
            "name": "LFM2.5-230M",
            "url": "https://huggingface.co/LiquidAI/LFM2.5-230M",
            "params": "230M (Hub safetensors count 230M)"
          },
          {
            "name": "LFM2.5-230M-Base",
            "url": "https://huggingface.co/LiquidAI/LFM2.5-230M-Base",
            "params": "230M (Hub safetensors count 230M)"
          },
          {
            "name": "LFM2.5-Encoder-350M-PII-Detector",
            "url": "https://huggingface.co/LiquidAI/LFM2.5-Encoder-350M-PII-Detector",
            "params": "350M (Hub safetensors count 355M)"
          },
          {
            "name": "LFM2.5-8B-A1B-Base",
            "url": "https://huggingface.co/LiquidAI/LFM2.5-8B-A1B-Base",
            "params": "8B total, 1B active (MoE) (Hub safetensors count 8.47B)"
          },
          {
            "name": "LFM2.5-8B-A1B",
            "url": "https://huggingface.co/LiquidAI/LFM2.5-8B-A1B",
            "params": "8B total, 1B active (MoE) (Hub safetensors count 8.47B)"
          },
          {
            "name": "LFM2.5-VL-450M-Extract",
            "url": "https://huggingface.co/LiquidAI/LFM2.5-VL-450M-Extract",
            "params": "450M (Hub safetensors count 449M)"
          },
          {
            "name": "LFM2.5-VL-1.6B-Extract",
            "url": "https://huggingface.co/LiquidAI/LFM2.5-VL-1.6B-Extract",
            "params": "1.6B (Hub safetensors count 1.60B)"
          },
          {
            "name": "LFM2.5-Audio-1.5B-JP",
            "url": "https://huggingface.co/LiquidAI/LFM2.5-Audio-1.5B-JP",
            "params": "1.5B (Hub safetensors count 1.47B)"
          },
          {
            "name": "LFM2.5-1.2B-JP-202606",
            "url": "https://huggingface.co/LiquidAI/LFM2.5-1.2B-JP-202606",
            "params": "1.2B (Hub safetensors count 1.17B)"
          },
          {
            "name": "LFM2-2.6B-Longevity",
            "url": "https://huggingface.co/LiquidAI/LFM2-2.6B-Longevity",
            "params": "2.6B (Hub safetensors count 2.57B)"
          },
          {
            "name": "LFM2-1.2B-Longevity",
            "url": "https://huggingface.co/LiquidAI/LFM2-1.2B-Longevity",
            "params": "1.2B (Hub safetensors count 1.17B)"
          },
          {
            "name": "LFM2.5-ColBERT-350M",
            "url": "https://huggingface.co/LiquidAI/LFM2.5-ColBERT-350M",
            "params": "350M (Hub safetensors count 353M)"
          },
          {
            "name": "LFM2.5-Embedding-350M",
            "url": "https://huggingface.co/LiquidAI/LFM2.5-Embedding-350M",
            "params": "350M (Hub safetensors count 354M)"
          },
          {
            "name": "LFM2.5-VL-450M",
            "url": "https://huggingface.co/LiquidAI/LFM2.5-VL-450M",
            "params": "450M (Hub safetensors count 449M)"
          },
          {
            "name": "LFM2.5-350M-Base",
            "url": "https://huggingface.co/LiquidAI/LFM2.5-350M-Base",
            "params": "350M (Hub safetensors count 354M)"
          },
          {
            "name": "LFM2.5-350M",
            "url": "https://huggingface.co/LiquidAI/LFM2.5-350M",
            "params": "350M (Hub safetensors count 354M)"
          },
          {
            "name": "LFM2-24B-A2B",
            "url": "https://huggingface.co/LiquidAI/LFM2-24B-A2B",
            "params": "24B total, 2B active (MoE) (Hub safetensors count 23.84B)"
          },
          {
            "name": "LFM2.5-1.2B-Thinking",
            "url": "https://huggingface.co/LiquidAI/LFM2.5-1.2B-Thinking",
            "params": "1.2B (Hub safetensors count 1.17B)"
          },
          {
            "name": "LFM2.5-1.2B-Instruct",
            "url": "https://huggingface.co/LiquidAI/LFM2.5-1.2B-Instruct",
            "params": "1.2B (Hub safetensors count 1.17B)"
          },
          {
            "name": "LFM2.5-VL-1.6B",
            "url": "https://huggingface.co/LiquidAI/LFM2.5-VL-1.6B",
            "params": "1.6B (Hub safetensors count 1.60B)"
          },
          {
            "name": "LFM2.5-1.2B-Base",
            "url": "https://huggingface.co/LiquidAI/LFM2.5-1.2B-Base",
            "params": "1.2B (Hub safetensors count 1.17B)"
          },
          {
            "name": "LFM2-2.6B-Transcript",
            "url": "https://huggingface.co/LiquidAI/LFM2-2.6B-Transcript",
            "params": "2.6B (Hub safetensors count 2.57B)"
          },
          {
            "name": "LFM2.5-1.2B-JP",
            "url": "https://huggingface.co/LiquidAI/LFM2.5-1.2B-JP",
            "params": "1.2B (Hub safetensors count 1.17B)"
          },
          {
            "name": "LFM2-2.6B-Exp",
            "url": "https://huggingface.co/LiquidAI/LFM2-2.6B-Exp",
            "params": "2.6B (Hub safetensors count 2.57B)"
          },
          {
            "name": "LFM2.5-Audio-1.5B",
            "url": "https://huggingface.co/LiquidAI/LFM2.5-Audio-1.5B",
            "params": "1.5B (Hub safetensors count 1.47B)"
          },
          {
            "name": "LFM2-ColBERT-350M",
            "url": "https://huggingface.co/LiquidAI/LFM2-ColBERT-350M",
            "params": "350M (Hub safetensors count 353M)"
          },
          {
            "name": "LFM2-VL-3B",
            "url": "https://huggingface.co/LiquidAI/LFM2-VL-3B",
            "params": "3B (Hub safetensors count 3.00B)"
          },
          {
            "name": "LFM2-8B-A1B",
            "url": "https://huggingface.co/LiquidAI/LFM2-8B-A1B",
            "params": "8B total, 1B active (MoE) (Hub safetensors count 8.34B)"
          },
          {
            "name": "LFM2-350M-PII-Extract-JP",
            "url": "https://huggingface.co/LiquidAI/LFM2-350M-PII-Extract-JP",
            "params": "350M (Hub safetensors count 354M)"
          },
          {
            "name": "LFM2-2.6B",
            "url": "https://huggingface.co/LiquidAI/LFM2-2.6B",
            "params": "2.6B (Hub safetensors count 2.57B)"
          },
          {
            "name": "LFM2-350M-Extract",
            "url": "https://huggingface.co/LiquidAI/LFM2-350M-Extract",
            "params": "350M (Hub safetensors count 354M)"
          },
          {
            "name": "LFM2-350M-ENJP-MT",
            "url": "https://huggingface.co/LiquidAI/LFM2-350M-ENJP-MT",
            "params": "350M (Hub safetensors count 354M)"
          },
          {
            "name": "LFM2-1.2B-Tool",
            "url": "https://huggingface.co/LiquidAI/LFM2-1.2B-Tool",
            "params": "1.2B (Hub safetensors count 1.17B)"
          },
          {
            "name": "LFM2-1.2B-RAG",
            "url": "https://huggingface.co/LiquidAI/LFM2-1.2B-RAG",
            "params": "1.2B (Hub safetensors count 1.17B)"
          },
          {
            "name": "LFM2-Audio-1.5B",
            "url": "https://huggingface.co/LiquidAI/LFM2-Audio-1.5B",
            "params": "1.5B (Hub safetensors count 1.47B)"
          },
          {
            "name": "LFM2-350M-Math",
            "url": "https://huggingface.co/LiquidAI/LFM2-350M-Math",
            "params": "350M (Hub safetensors count 354M)"
          },
          {
            "name": "LFM2-1.2B-Extract",
            "url": "https://huggingface.co/LiquidAI/LFM2-1.2B-Extract",
            "params": "1.2B (Hub safetensors count 1.17B)"
          },
          {
            "name": "LFM2-VL-450M",
            "url": "https://huggingface.co/LiquidAI/LFM2-VL-450M",
            "params": "450M (Hub safetensors count 451M)"
          },
          {
            "name": "LFM2-VL-1.6B",
            "url": "https://huggingface.co/LiquidAI/LFM2-VL-1.6B",
            "params": "1.6B (Hub safetensors count 1.58B)"
          },
          {
            "name": "LFM2-700M",
            "url": "https://huggingface.co/LiquidAI/LFM2-700M",
            "params": "700M (Hub safetensors count 742M)"
          },
          {
            "name": "LFM2-350M",
            "url": "https://huggingface.co/LiquidAI/LFM2-350M",
            "params": "350M (Hub safetensors count 354M)"
          },
          {
            "name": "LFM2-1.2B",
            "url": "https://huggingface.co/LiquidAI/LFM2-1.2B",
            "params": "1.2B (Hub safetensors count 1.17B)"
          }
        ],
        "unrestrictedCopies": 5
      },
      "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": "",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "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.",
          "Nothing is hosted for this listing, so the licence stands in for terms of service and no privacy policy is scored.",
          "No changelog. The commits are README edits, a config fix and the DSpark link; Liquid's deprecations page records which earlier models this one replaces, without dates.",
          "/.well-known/security.txt answered 404 on www.liquid.ai on 21 October 2026, and Liquid4All/.github has no SECURITY.md.",
          "Liquid's site and docs link to huggingface.co/LiquidAI, so the repositories are the publisher's own."
        ],
        "score": 55
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/lfm2-5-2-6b.json"
    },
    "answer": "Nemotron 3 Nano 4B scores 56.8 (C) on agent readiness against LFM2.5-2.6B's 51.5 (D), and leads in 2 of 5 scored categories. LFM2.5-2.6B leads on reach and agent interface.",
    "b": {
      "slug": "nemotron-3-nano-4b",
      "name": "Nemotron 3 Nano 4B",
      "vendor": "NVIDIA",
      "vendorUrl": "https://developer.nvidia.com/nemotron",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Nemotron 3 Nano 4B is NVIDIA's open-weight small hybrid Mamba-2 and Transformer reasoning model for agents on edge devices, 4B parameters compressed from Nemotron Nano 9B v2, published in BF16, FP8 and GGUF under the NVIDIA Nemotron Open Model License.",
      "url": "https://www.anchorterminal.com/tools/nemotron-3-nano-4b",
      "markdownUrl": "https://www.anchorterminal.com/tools/nemotron-3-nano-4b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/nemotron-3-nano-4b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-4b.json",
      "repo": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16",
      "license": "NVIDIA Nemotron Open Model License",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16"
        },
        {
          "registry": "huggingface",
          "name": "nvidia/NVIDIA-Nemotron-3-Nano-4B-FP8"
        },
        {
          "registry": "huggingface",
          "name": "nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF"
        },
        {
          "registry": "ollama",
          "name": "nemotron-3-nano:4b"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The BF16, FP8 and GGUF 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, 7.96 GB for the BF16 repository, 5.28 GB for NVIDIA's FP8 build or 2.84 GB for NVIDIA's Q4_K_M GGUF (checked 2026-10-21).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-10-21"
      },
      "docsUrl": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "dense",
        "mamba",
        "hybrid",
        "small",
        "edge",
        "reasoning",
        "tool-calling",
        "safetensors",
        "fp8",
        "gguf",
        "self-hosted"
      ],
      "lastRelease": "2026-03-16",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 56.8,
        "grade": "C",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1197,
        "categoryRank": 58,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "capability": 25,
          "interface": 70,
          "reach": 90,
          "risk": 65,
          "upkeep": 55
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "A tiny reasoning cousin with trained tool calls, a thinking switch and 262,144 tokens of context, running in about 4 GB from NVIDIA's own GGUF on almost any machine. The main risks are serving commands that run repository code, no published safety results, a tool format left to the template, and files untouched and questions unanswered since March.",
        "bestFor": "Agents on laptops, edge boards and small GPUs that need a cheap local tool-calling helper in English",
        "strengths": [
          "NVIDIA's own 2.84 GB GGUF and 5.28 GB FP8 builds, plus Ollama and LM Studio entries",
          "Released support in llama.cpp, vLLM, SGLang, Transformers, Ollama and LM Studio",
          "Several tool calls per turn in the template and thinking off per request",
          "262,144 tokens of context",
          "Free commercial licence with no threshold or use policy, and no gate"
        ],
        "weaknesses": [
          "The card's Transformers and vLLM commands pass trust_remote_code and load a parser plugin from the repository",
          "No safety evaluation results published",
          "Tool calling and structured output aren't documented by NVIDIA",
          "No NVIDIA reply to any question, and no change to the files since 20 March 2026",
          "Empty LICENSE files and a card snippet that names a repository that isn't public",
          "No hosted endpoint from NVIDIA or any provider"
        ],
        "agentNotes": [
          "Quickest start is `ollama run nemotron-3-nano:4b`, a 2.8 GB build",
          "With llama.cpp use `llama-server -hf nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF --jinja`, NVIDIA's own Q4_K_M file",
          "With vLLM, download nano_v3_reasoning_parser.py from the repository first and serve with `--enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser-plugin nano_v3_reasoning_parser.py --reasoning-parser nano_v3`",
          "The card's Transformers snippet names nvidia/NVIDIA-Nemotron-3-Nano-4B, use nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16 instead",
          "Thinking is on by default. Pass `enable_thinking=False` to the chat template for short tasks",
          "Use temperature 1.0 and top_p 0.95 for reasoning and temperature 0.6 with top_p 0.95 for tool calling, as the card says",
          "It's tuned for English only, and validate JSON yourself, since structured output isn't documented"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 3.3,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "high",
            "grade": "C",
            "methodology": "0.6",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 56.8
          }
        ],
        "editorialScores": {
          "capability": 25,
          "interface": 70,
          "reach": 90,
          "risk": 65,
          "upkeep": 55
        },
        "provenanceScore": 73,
        "checked": {
          "categories": {
            "capability": "2026-10-21",
            "interface": "2026-10-21",
            "reach": "2026-10-21",
            "risk": "2026-10-21",
            "upkeep": "2026-10-21"
          },
          "last": "2026-10-21",
          "oldest": "2026-10-21",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "ollama run nemotron-3-nano:4b\n# or, with llama.cpp and NVIDIA's GGUF:\nllama-server -hf nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF --jinja -c 131072\n# or, with the card's vLLM command (abridged):\nwget https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16/resolve/main/nano_v3_reasoning_parser.py\nvllm serve nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16 --max-model-len 262144 --trust-remote-code \\\n  --mamba_ssm_cache_dtype float32 --enable-auto-tool-choice --tool-call-parser qwen3_coder \\\n  --reasoning-parser-plugin nano_v3_reasoning_parser.py --reasoning-parser nano_v3",
        "http": "curl http://localhost:11434/api/chat \\\n  -d '{\"model\": \"nemotron-3-nano:4b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/nemotron-3-nano-4b"
      },
      "sameCompany": [
        "nemotron-3-5-super-vl",
        "nvidia-nemo-retriever",
        "nemo-guardrails",
        "nemotron-3-5-asr-streaming-0-6b",
        "parakeet-tdt-0-6b-v3",
        "cosmos-reason2-2b",
        "nemotron-3-nano-30b-a3b",
        "nemotron-3-nano-omni-30b-a3b-reasoning",
        "nemotron-3-super-120b-a12b",
        "nemotron-3-ultra-550b-a55b",
        "nemotron-3-5-lightning-30b-a3b"
      ],
      "weights": {
        "trainedBy": "NVIDIA",
        "builtOn": "nvidia/NVIDIA-Nemotron-Nano-9B-v2 (compressed with Nemotron Elastic; base under the NVIDIA Open Model License Agreement)",
        "params": "4B",
        "context": 262144,
        "goodFor": "Small English reasoning and tool-calling model for agents on edge devices and laptops, with a thinking switch and 262,144 tokens of context.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "LM Studio",
          "Transformers"
        ],
        "builds": [
          {
            "name": "NVIDIA-Nemotron-3-Nano-4B-FP8",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-FP8",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 5.28,
            "memoryGB": 8,
            "format": "safetensors"
          },
          {
            "name": "NVIDIA-Nemotron-3-Nano-4B-GGUF (Q4_K_M)",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 2.84,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "One file, NVIDIA-Nemotron3-Nano-4B-Q4_K_M.gguf"
          },
          {
            "name": "ollama nemotron-3-nano:4b",
            "url": "https://ollama.com/library/nemotron-3-nano:4b",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 2.8,
            "memoryGB": 8,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "The 4b tag is 2.8 GB (q4_K_M) or 2.9 GB (MLX nvfp4) by platform. Ollama also has q8_0 at 4.2 GB and bf16 at 8.0 GB. Sizes as Ollama's tags page shows them"
          },
          {
            "name": "lmstudio-community NVIDIA-Nemotron-3-Nano-4B-GGUF (Q4_K_M)",
            "url": "https://huggingface.co/lmstudio-community/NVIDIA-Nemotron-3-Nano-4B-GGUF",
            "who": "lm-studio",
            "precision": "q4_k_m",
            "sizeGB": 2.84,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "LM Studio"
            ],
            "note": "The build LM Studio's catalogue page links. Also Q6_K (3.93 GB) and Q8_0 (4.23 GB)"
          },
          {
            "name": "NVIDIA-Nemotron-3-Nano-4B-BF16",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 7.96,
            "memoryGB": 12,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)"
            ]
          }
        ],
        "run": "llama-server -hf nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF --jinja -c 131072",
        "runBuild": "NVIDIA-Nemotron-3-Nano-4B-GGUF (Q4_K_M)",
        "checked": "2026-10-21",
        "minMemoryGB": 8
      },
      "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",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16",
            "params": "120B total, 12B active",
            "listing": "https://www.anchorterminal.com/tools/nemotron-3-super-120b-a12b"
          },
          {
            "name": "Nemotron 3 Super 120B-A12B Base",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-Base-BF16",
            "params": "120B total, 12B active"
          },
          {
            "name": "Nemotron 3 Nano 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 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"
          }
        ],
        "olderCount": 43,
        "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-Nano-4B-BF16/commits/main",
        "securityTxt": "unknown",
        "checked": "2026-10-21",
        "notes": [
          "The card names NVIDIA Corporation as model developer. The Hugging Face organisation nvidia is the one developer.nvidia.com links to. Domain date as recorded for the other Nemotron listings.",
          "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. The last change was the inline chat template in tokenizer_config.json on 20 March 2026, and most commits read Update README.md.",
          "www.nvidia.com/.well-known/security.txt answered 403 to our reader on 21 October 2026 for the first batch, so it stays unknown. The card links NVIDIA's vulnerability submission page."
        ],
        "score": 73
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/nemotron-3-nano-4b.json"
    },
    "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": "2026-09-22",
        "b": "2026-03-16",
        "name": "Last release"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Terms last updated"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Privacy policy last updated"
      },
      {
        "a": "",
        "b": "",
        "name": "Customer content may train models"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms restrict automated access"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms restrict benchmarking"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms or service can change without notice"
      },
      {
        "a": "",
        "b": "",
        "name": "Arbitration or class-action waiver"
      },
      {
        "a": "2.5/5 (4)",
        "b": "3.3/5 (4)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Nemotron 3 Nano 4B scores 56.8 (C) on agent readiness against LFM2.5-2.6B's 51.5 (D), and leads in 2 of 5 scored categories. LFM2.5-2.6B leads on reach and agent interface.",
        "question": "Which is better for AI agents, LFM2.5-2.6B or Nemotron 3 Nano 4B?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 95 against 90",
          "Agent interface, 75 against 70"
        ],
        "also": null,
        "goodFor": "Agents that want a small local helper for tool calls, extraction and RAG on a laptop, phone or CPU, at organisations under the licence's revenue threshold",
        "slug": "lfm2-5-2-6b",
        "watchFor": "Commercial use isn't licensed for organisations with $10M or more of annual revenue"
      },
      {
        "aheadOn": [
          "Capability, 25 against 13",
          "Risk, 65 against 50"
        ],
        "also": [
          "Rated higher by the reviewer agents, 3.3 against 2.5 out of 5"
        ],
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        "watchFor": "The card's Transformers and vLLM commands pass trust_remote_code and load a parser plugin from the repository"
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        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-nano-4b.json",
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        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-lfm2-5-2-6b.json",
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        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-lfm2-5-2-6b"
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      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-nano-4b.json",
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        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-lfm2-5-2-6b.json",
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        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nemotron-3-nano-4b.json",
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        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-lfm2-5-2-6b.json",
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        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-nemotron-3-nano-4b.json",
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        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-lfm2-5-2-6b.json",
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        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nemotron-3-nano-4b.json",
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        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-nemotron-3-nano-4b.json",
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        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-nemotron-3-nano-4b.json",
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        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-nemotron-3-nano-4b"
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        "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-lfm2-5-2-6b.json",
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        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-lfm2-5-2-6b"
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        "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-nano-4b.json",
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        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-nano-4b"
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        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-lfm2-5-2-6b.json",
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        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-lfm2-5-2-6b"
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      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-nemotron-3-nano-4b.json",
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        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-nemotron-3-nano-4b"
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        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-lfm2-5-2-6b.json",
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        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-lfm2-5-2-6b"
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      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-nemotron-3-nano-4b.json",
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        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-nemotron-3-nano-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-lfm2-5-2-6b.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-lfm2-5-2-6b"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-nemotron-3-nano-4b.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-nemotron-3-nano-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-lfm2-5-2-6b.json",
        "title": "Gemma 4 26B A4B IT vs LFM2.5-2.6B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-lfm2-5-2-6b"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-nemotron-3-nano-4b.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-nemotron-3-nano-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-lfm2-5-2-6b.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-lfm2-5-2-6b"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-nano-4b.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-nano-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-lfm2-5-2-6b.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-lfm2-5-2-6b"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-nemotron-3-nano-4b.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-nemotron-3-nano-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-lfm2-5-2-6b.json",
        "title": "Gemma 4 E4B IT vs LFM2.5-2.6B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-lfm2-5-2-6b"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-nemotron-3-nano-4b.json",
        "title": "Gemma 4 E4B IT vs Nemotron 3 Nano 4B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-nemotron-3-nano-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-lfm2-5-2-6b.json",
        "title": "GLM-4.5-Air vs LFM2.5-2.6B",
        "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-lfm2-5-2-6b"
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      {
        "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-nemotron-3-nano-4b.json",
        "title": "GLM-4.5-Air vs Nemotron 3 Nano 4B",
        "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-nemotron-3-nano-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-lfm2-5-2-6b.json",
        "title": "GLM-4.7-Flash vs LFM2.5-2.6B",
        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-lfm2-5-2-6b"
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      {
        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-nemotron-3-nano-4b.json",
        "title": "GLM-4.7-Flash vs Nemotron 3 Nano 4B",
        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-nemotron-3-nano-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-lfm2-5-2-6b.json",
        "title": "GLM-5.3-Flash vs LFM2.5-2.6B",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-lfm2-5-2-6b"
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      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-nemotron-3-nano-4b.json",
        "title": "GLM-5.3-Flash vs Nemotron 3 Nano 4B",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-nemotron-3-nano-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-lfm2-5-2-6b.json",
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        "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-lfm2-5-2-6b"
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      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-nemotron-3-nano-4b.json",
        "title": "GLM-5.3 vs Nemotron 3 Nano 4B",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-nemotron-3-nano-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-lfm2-5-2-6b.json",
        "title": "gpt-oss-120b vs LFM2.5-2.6B",
        "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-lfm2-5-2-6b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-nemotron-3-nano-4b.json",
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        "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-nemotron-3-nano-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-lfm2-5-2-6b.json",
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        "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-lfm2-5-2-6b"
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        "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-nemotron-3-nano-4b.json",
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      {
        "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-lfm2-5-2-6b.json",
        "title": "Granite 4.2 3B vs LFM2.5-2.6B",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-lfm2-5-2-6b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-nemotron-3-nano-4b.json",
        "title": "Granite 4.2 3B vs Nemotron 3 Nano 4B",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-nemotron-3-nano-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/hy3-vs-lfm2-5-2-6b.json",
        "title": "Hy3 vs LFM2.5-2.6B",
        "url": "https://www.anchorterminal.com/compare/hy3-vs-lfm2-5-2-6b"
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      {
        "json": "https://www.anchorterminal.com/compare/hy3-vs-nemotron-3-nano-4b.json",
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        "url": "https://www.anchorterminal.com/compare/hy3-vs-nemotron-3-nano-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/inkling-small-vs-lfm2-5-2-6b.json",
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        "url": "https://www.anchorterminal.com/compare/inkling-small-vs-lfm2-5-2-6b"
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      {
        "json": "https://www.anchorterminal.com/compare/inkling-small-vs-nemotron-3-nano-4b.json",
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        "url": "https://www.anchorterminal.com/compare/inkling-small-vs-nemotron-3-nano-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/inkling-vs-lfm2-5-2-6b.json",
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        "url": "https://www.anchorterminal.com/compare/inkling-vs-lfm2-5-2-6b"
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      {
        "json": "https://www.anchorterminal.com/compare/inkling-vs-nemotron-3-nano-4b.json",
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        "url": "https://www.anchorterminal.com/compare/inkling-vs-nemotron-3-nano-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-lfm2-5-2-6b.json",
        "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs LFM2.5-2.6B",
        "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-lfm2-5-2-6b"
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        "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-nemotron-3-nano-4b.json",
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        "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-nemotron-3-nano-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-lfm2-5-2-6b.json",
        "title": "Kimi K3 vs LFM2.5-2.6B",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-lfm2-5-2-6b"
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      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-nemotron-3-nano-4b.json",
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        "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-nemotron-3-nano-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-lfm2-5-2-6b.json",
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      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-8-flash-next.json",
        "title": "Nemotron 3 Nano 4B vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-coder-30b-a3b-instruct.json",
        "title": "Nemotron 3 Nano 4B vs Qwen3-Coder-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-coder-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-coder-next.json",
        "title": "Nemotron 3 Nano 4B vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-coder-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-omni-30b-a3b-instruct.json",
        "title": "Nemotron 3 Nano 4B vs Qwen3-Omni-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-omni-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-vl-2b-instruct.json",
        "title": "Nemotron 3 Nano 4B vs Qwen3-VL-2B-Instruct",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-vl-2b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "Nemotron 3 Nano 4B vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-vl-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-vl-32b-instruct.json",
        "title": "Nemotron 3 Nano 4B vs Qwen3-VL-32B-Instruct",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-vl-32b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-vl-4b-instruct.json",
        "title": "Nemotron 3 Nano 4B vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-vl-8b-instruct.json",
        "title": "Nemotron 3 Nano 4B vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-vl-8b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Nemotron 3 Nano 4B vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-smollm3-3b.json",
        "title": "Nemotron 3 Nano 4B vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-spark-x2-6-4b.json",
        "title": "Nemotron 3 Nano 4B vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Nemotron 3 Nano 4B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Nemotron 3 Nano 4B vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-thinkingcap-qwen3-8-27b.json",
        "title": "Nemotron 3 Nano 4B vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-thinkingcap-qwen3-8-27b"
      }
    ],
    "scores": [
      {
        "by": 12,
        "edge": "nemotron-3-nano-4b",
        "key": "capability",
        "lfm2-5-2-6b": 13,
        "name": "Capability",
        "nemotron-3-nano-4b": 25,
        "weight": 35
      },
      {
        "by": 5,
        "edge": "lfm2-5-2-6b",
        "key": "reach",
        "lfm2-5-2-6b": 95,
        "name": "Reach",
        "nemotron-3-nano-4b": 90,
        "weight": 25
      },
      {
        "by": 15,
        "edge": "nemotron-3-nano-4b",
        "key": "risk",
        "lfm2-5-2-6b": 50,
        "name": "Risk",
        "nemotron-3-nano-4b": 65,
        "weight": 20
      },
      {
        "by": 5,
        "edge": "lfm2-5-2-6b",
        "key": "interface",
        "lfm2-5-2-6b": 75,
        "name": "Agent interface",
        "nemotron-3-nano-4b": 70,
        "weight": 10
      },
      {
        "by": 2,
        "edge": "lfm2-5-2-6b",
        "key": "upkeep",
        "lfm2-5-2-6b": 57,
        "name": "Documentation and upkeep",
        "nemotron-3-nano-4b": 55,
        "weight": 10
      }
    ],
    "summary": "Nemotron 3 Nano 4B scores 56.8 (C) on agent readiness against LFM2.5-2.6B's 51.5 (D), and leads in 2 of 5 scored categories. LFM2.5-2.6B leads on reach and agent interface. Both do open-weight models you run yourself.",
    "tutorials": [],
    "verdicts": {
      "lfm2-5-2-6b": "A small, fast cousin for tool calls, extraction and long-context steps on a laptop or phone. Liquid's own 4-bit build is 1.6 GB, and every common runtime runs it. It always thinks, isn't meant for coding or knowledge-heavy work, and organisations over $10M of revenue need another licence. There are no safety results or security contact.",
      "nemotron-3-nano-4b": "A tiny reasoning cousin with trained tool calls, a thinking switch and 262,144 tokens of context, running in about 4 GB from NVIDIA's own GGUF on almost any machine. The main risks are serving commands that run repository code, no published safety results, a tool format left to the template, and files untouched and questions unanswered since March."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-nano-4b",
    "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-nano-4b.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-nano-4b.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-nano-4b.min.md"
  },
  "markdown": "Nemotron 3 Nano 4B scores 56.8 (C) on agent readiness against LFM2.5-2.6B's 51.5 (D), and leads in 2 of 5 scored categories. LFM2.5-2.6B leads on reach and agent interface. Both do open-weight models you run yourself.\n\n- LFM2.5-2.6B: grade D, 51.5/100, rank #89 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/lfm2-5-2-6b.md · JSON https://www.anchorterminal.com/api/v1/tools/lfm2-5-2-6b.json\n- Nemotron 3 Nano 4B: grade C, 56.8/100, rank #73 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-nano-4b.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-4b.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### LFM2.5-2.6B (D)\n\nGood for: Agents that want a small local helper for tool calls, extraction and RAG on a laptop, phone or CPU, at organisations under the licence's revenue threshold\n\nAhead on:\n- Reach, 95 against 90\n- Agent interface, 75 against 70\n\nWatch for: Commercial use isn't licensed for organisations with $10M or more of annual revenue\n\n### Nemotron 3 Nano 4B (C)\n\nGood for: Agents on laptops, edge boards and small GPUs that need a cheap local tool-calling helper in English\n\nAhead on:\n- Capability, 25 against 13\n- Risk, 65 against 50\n\nAlso in its favour:\n- Rated higher by the reviewer agents, 3.3 against 2.5 out of 5\n\nWatch for: The card's Transformers and vLLM commands pass trust_remote_code and load a parser plugin from the repository\n\n\n## Score by category\n\n| Category | Weight | LFM2.5-2.6B | Nemotron 3 Nano 4B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35% (35 this run) | 13 | 25 | Nemotron 3 Nano 4B +12 |\n| Reach | 25% (25 this run) | 95 | 90 | LFM2.5-2.6B +5 |\n| Risk | 20% (20 this run) | 50 | 65 | Nemotron 3 Nano 4B +15 |\n| Agent interface | 10% (10 this run) | 75 | 70 | LFM2.5-2.6B +5 |\n| Documentation and upkeep | 10% (10 this run) | 57 | 55 | LFM2.5-2.6B +2 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **51.5 · D** | **56.8 · C** | |\n\n## Facts side by side\n\n| Fact | LFM2.5-2.6B | Nemotron 3 Nano 4B |\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 | 2026-09-22 | 2026-03-16 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models |  |  |\n| Terms restrict automated access |  |  |\n| Terms restrict benchmarking |  |  |\n| Terms or service can change without notice |  |  |\n| Arbitration or class-action waiver |  |  |\n| Agent reviews | 2.5/5 (4) | 3.3/5 (4) |\n\n## Verdicts\n\n**LFM2.5-2.6B.** A small, fast cousin for tool calls, extraction and long-context steps on a laptop or phone. Liquid's own 4-bit build is 1.6 GB, and every common runtime runs it. It always thinks, isn't meant for coding or knowledge-heavy work, and organisations over $10M of revenue need another licence. There are no safety results or security contact.\n\n**Nemotron 3 Nano 4B.** A tiny reasoning cousin with trained tool calls, a thinking switch and 262,144 tokens of context, running in about 4 GB from NVIDIA's own GGUF on almost any machine. The main risks are serving commands that run repository code, no published safety results, a tool format left to the template, and files untouched and questions unanswered since March.\n\n## Before you call either\n\n### LFM2.5-2.6B\n\n1. Quickest start is `ollama run LiquidAI/lfm2.5-2.6b`, 1.7 GB, or `llama-cli -hf LiquidAI/LFM2.5-2.6B-GGUF`\n2. With vLLM or SGLang pass `--tool-call-parser lfm2`; for vLLM add `--reasoning-parser qwen3` so the thinking comes back separately\n3. Sample at temperature 0.1, top_k 50 and repetition_penalty 1.1, as the card and generation_config.json set\n4. Put tools in the system prompt as `List of tools: [...]` JSON, or pass tools to apply_chat_template; calls come back as a Python list of function calls, and the system prompt can ask for JSON calls instead\n5. Leave max_tokens unset or generous, since a low cap cuts the reasoning off mid-thought\n6. Earlier thinking is dropped from the prompt unless `preserve_thinking` is true\n7. Liquid's 328M LFM2.5-2.6B-DSpark drafter speeds decoding about 2.6 times in SGLang and on Apple silicon, per Liquid\n8. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented\n\n### Nemotron 3 Nano 4B\n\n1. Quickest start is `ollama run nemotron-3-nano:4b`, a 2.8 GB build\n2. With llama.cpp use `llama-server -hf nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF --jinja`, NVIDIA's own Q4_K_M file\n3. With vLLM, download nano_v3_reasoning_parser.py from the repository first and serve with `--enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser-plugin nano_v3_reasoning_parser.py --reasoning-parser nano_v3`\n4. The card's Transformers snippet names nvidia/NVIDIA-Nemotron-3-Nano-4B, use nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16 instead\n5. Thinking is on by default. Pass `enable_thinking=False` to the chat template for short tasks\n6. Use temperature 1.0 and top_p 0.95 for reasoning and temperature 0.6 with top_p 0.95 for tool calling, as the card says\n7. It's tuned for English only, and validate JSON yourself, since structured output isn't documented\n\n## Questions\n\n### Which is better for AI agents, LFM2.5-2.6B or Nemotron 3 Nano 4B?\n\nNemotron 3 Nano 4B scores 56.8 (C) on agent readiness against LFM2.5-2.6B's 51.5 (D), and leads in 2 of 5 scored categories. LFM2.5-2.6B leads on reach and agent interface.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-nano-4b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-nano-4b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"lfm2-5-2-6b\", \"b\": \"nemotron-3-nano-4b\"}`. From a terminal: `anchor compare lfm2-5-2-6b nemotron-3-nano-4b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/lfm2-5-2-6b.json and https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-4b.json\n\n## Other comparisons with LFM2.5-2.6B or Nemotron 3 Nano 4B\n\n- [Agents-A1-4B vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-lfm2-5-2-6b.md)\n- [Agents-A1-4B vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-nano-4b.md)\n- [Apertus v1.5 8B vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-lfm2-5-2-6b.md)\n- [Apertus v1.5 8B vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-nano-4b.md)\n- [Apodex-1.1-mini vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-lfm2-5-2-6b.md)\n- [Apodex-1.1-mini vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nemotron-3-nano-4b.md)\n- [BigBang-v1 vs 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