{
  "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 30B A3B scores 67.1 (B) on agent readiness against LFM2.5-2.6B's 51.5 (D), and leads in 4 of 5 scored categories. LFM2.5-2.6B leads on reach.",
    "b": {
      "slug": "nemotron-3-nano-30b-a3b",
      "name": "Nemotron 3 Nano 30B A3B",
      "vendor": "NVIDIA",
      "vendorUrl": "https://developer.nvidia.com/nemotron",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Nemotron 3 Nano 30B A3B is NVIDIA's open-weight hybrid Mamba-Transformer mixture of experts for reasoning and agent work, 30B parameters with 3.5B active, published on Hugging Face in BF16, FP8 and NVFP4.",
      "url": "https://www.anchorterminal.com/tools/nemotron-3-nano-30b-a3b",
      "markdownUrl": "https://www.anchorterminal.com/tools/nemotron-3-nano-30b-a3b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/nemotron-3-nano-30b-a3b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-30b-a3b.json",
      "repo": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16",
      "license": "NVIDIA Nemotron Open Model License (custom, Apache-style, commercial use allowed, not an OSI licence)",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16"
        },
        {
          "registry": "huggingface",
          "name": "nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-FP8"
        },
        {
          "registry": "huggingface",
          "name": "nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4"
        },
        {
          "registry": "ollama",
          "name": "nemotron-3-nano:30b"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The BF16, FP8 and NVFP4 repositories are not gated (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under the NVIDIA Nemotron Open Model License. Running it yourself costs the hardware, 63.2 GB for the BF16 files, 32.7 GB for FP8, 19.4 GB for NVFP4 and 18 GB for Ollama's default build (checked 2026-10-21).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-10-21"
      },
      "docsUrl": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "mamba",
        "reasoning",
        "tool-calling",
        "commercial-licence",
        "safetensors",
        "fp8",
        "nvfp4",
        "self-hosted"
      ],
      "lastRelease": "2026-08-24",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 67.1,
        "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": 1197,
        "categoryRank": 10,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "capability": 46,
          "interface": 90,
          "reach": 90,
          "risk": 60,
          "upkeep": 75
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "A fast hybrid Mamba-Transformer cousin with 3.5B active parameters for reasoning and tool calling over very long inputs, in every major local runtime. A 4-bit build needs about 22 to 23 GB. The main risks are that NVIDIA's documented vLLM path runs repository code, no safety results are published and NVIDIA hasn't answered reports since March.",
        "bestFor": "Agents with a 24 to 32 GB GPU or Mac that want a quick tool-calling and reasoning cousin with very long context.",
        "strengths": [
          "Runs in released llama.cpp, Ollama, vLLM, SGLang, LM Studio and Transformers",
          "NVIDIA's own FP8 and NVFP4 builds beside BF16, and ungated files in NVIDIA's own organisation with a SHA-256 for every shard",
          "Tool calling documented end to end with a named parser, several calls to a turn, and thinking that can be turned off or budgeted",
          "1M tokens of input with no position-encoding scaling",
          "Full model card and a detailed data card with cut-offs, with much of the training data released"
        ],
        "weaknesses": [
          "The documented vLLM, SGLang and TensorRT-LLM commands pass trust-remote-code and load a parser file from the repository",
          "No safety or bias results published, though the subcards say both were run",
          "No NVIDIA reply on any of the 10 discussions opened since 1 March 2026, including a chat-template report",
          "Licence replaced eight days after release with no dated note, and the subcards still link the old one",
          "Structured output isn't documented, and the custom licence isn't OSI-approved"
        ],
        "agentNotes": [
          "Start it with `ollama run nemotron-3-nano:30b` (18 GB MLX on a Mac, 24 GB q4_K_M elsewhere)",
          "On the reference machine use BF16 with vLLM, after `wget https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16/resolve/main/nano_v3_reasoning_parser.py`, then `vllm serve nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 --max-model-len 262144 --trust-remote-code --enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser-plugin nano_v3_reasoning_parser.py --reasoning-parser nano_v3`",
          "NVFP4 needs an NVIDIA Blackwell GPU, and FP8 isn't documented on gfx1151",
          "Without the qwen3_coder tool parser and the nano_v3 reasoning parser, tool calls and reasoning come back as plain text",
          "Card sampling is temperature 0.6 and top_p 0.95 for tool calling, temperature 1.0 and top_p 1.0 for reasoning",
          "Thinking is on by default, so pass enable_thinking as false in chat_template_kwargs for short tasks",
          "Set VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 and raise --max-model-len only when you need more than 262,144 tokens",
          "In SGLang the main branch names the reasoning parser nemotron_3, not the card's nano_v3",
          "Filter untrusted tool results before they reach the model, as NVIDIA warns prompt injection can lead to code execution",
          "Validate JSON output yourself or use the runtime's guided decoding, since none is documented for the model"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 3.3,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "high",
            "grade": "B",
            "methodology": "0.6",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 67.1
          }
        ],
        "editorialScores": {
          "capability": 46,
          "interface": 90,
          "reach": 90,
          "risk": 60,
          "upkeep": 75
        },
        "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:30b\n# or, with vLLM per NVIDIA's card (the reasoning parser is a file from the repository):\nwget https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16/resolve/main/nano_v3_reasoning_parser.py\nvllm serve nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 --max-model-len 262144 --trust-remote-code \\\n  --enable-auto-tool-choice --tool-call-parser qwen3_coder \\\n  --reasoning-parser-plugin nano_v3_reasoning_parser.py --reasoning-parser nano_v3",
        "http": "curl http://localhost:11434/api/chat \\\n  -d '{\"model\": \"nemotron-3-nano:30b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/nemotron-3-nano-30b-a3b"
      },
      "sameCompany": [
        "nemotron-3-5-super-vl",
        "nvidia-nemo-retriever",
        "nemo-guardrails",
        "nemotron-3-5-asr-streaming-0-6b",
        "parakeet-tdt-0-6b-v3",
        "cosmos-reason2-2b",
        "nemotron-3-nano-4b",
        "nemotron-3-nano-omni-30b-a3b-reasoning",
        "nemotron-3-super-120b-a12b",
        "nemotron-3-ultra-550b-a55b",
        "nemotron-3-5-lightning-30b-a3b"
      ],
      "weights": {
        "trainedBy": "NVIDIA",
        "builtOn": "nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-Base-BF16",
        "params": "30B",
        "active": "3.5B",
        "context": 1048576,
        "goodFor": "Hybrid Mamba-Transformer mixture of experts for reasoning and agent work, text only, with tool calling and a reasoning budget.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "LM Studio",
          "Transformers"
        ],
        "builds": [
          {
            "name": "NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4",
            "who": "publisher",
            "precision": "nvfp4",
            "sizeGB": 19.3,
            "memoryGB": 24,
            "format": "safetensors",
            "note": "NVFP4 needs NVIDIA Blackwell"
          },
          {
            "name": "ollama nemotron-3-nano:30b",
            "url": "https://ollama.com/library/nemotron-3-nano:30b",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 24,
            "memoryGB": 32,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "digest 7487f38ecf24. Size as Ollama's tags page shows it, rounded to whole GB above 10 GB; the Linux build is the llama.cpp variant"
          },
          {
            "name": "NVIDIA-Nemotron-3-Nano-30B-A3B-FP8",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-FP8",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 32.7,
            "memoryGB": 48,
            "format": "safetensors"
          },
          {
            "name": "NVIDIA-Nemotron-3-Nano-30B-A3B-BF16",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 63.2,
            "memoryGB": 96,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)"
            ]
          }
        ],
        "run": "vllm serve nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4 --max-model-len 262144 --trust-remote-code --enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser-plugin nano_v3_reasoning_parser.py --reasoning-parser nano_v3",
        "runBuild": "NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4",
        "checked": "2026-10-21",
        "minMemoryGB": 24
      },
      "family": {
        "lab": "NVIDIA (Nemotron)",
        "family": "Nemotron 3",
        "url": "https://www.anchorterminal.com/open-weights/#nvidia",
        "others": [
          {
            "name": "Nemotron 3 Nano 30B-A3B Base",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-Base-BF16",
            "params": "30B total, 3B active"
          },
          {
            "name": "Nemotron 3 Super 120B-A12B",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16",
            "params": "120B total, 12B active",
            "listing": "https://www.anchorterminal.com/tools/nemotron-3-super-120b-a12b"
          },
          {
            "name": "Nemotron 3 Super 120B-A12B Base",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-Base-BF16",
            "params": "120B total, 12B active"
          },
          {
            "name": "Nemotron 3 Nano 4B",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16",
            "params": "4B",
            "listing": "https://www.anchorterminal.com/tools/nemotron-3-nano-4b"
          },
          {
            "name": "Nemotron 3 Nano Omni 30B-A3B Reasoning",
            "url": "https://huggingface.co/nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16",
            "params": "30B total, 3B active",
            "listing": "https://www.anchorterminal.com/tools/nemotron-3-nano-omni-30b-a3b-reasoning"
          },
          {
            "name": "Nemotron 3 Ultra 550B-A55B",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16",
            "params": "550B total, 55B active",
            "listing": "https://www.anchorterminal.com/tools/nemotron-3-ultra-550b-a55b"
          },
          {
            "name": "Nemotron 3 Ultra 550B-A55B Base",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-Base-BF16",
            "params": "550B total, 55B active"
          },
          {
            "name": "Nemotron 3 Ultra 550B-A55B GenRM",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-GenRM",
            "params": "550B total, 55B active"
          },
          {
            "name": "Nemotron 3.5 Lightning 30B-A3B",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16",
            "params": "30B total, 3B active",
            "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-lightning-30b-a3b"
          },
          {
            "name": "Nemotron 3.5 Lightning 30B-A3B Base",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-Base-BF16",
            "params": "30B total, 3B active"
          },
          {
            "name": "Nemotron 3.5 Super VL 120B-A12B",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16",
            "params": "120B total, 12B active",
            "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-super-vl"
          },
          {
            "name": "Nemotron 3.5 Super VL 120B-A12B Base",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-Base-BF16",
            "params": "120B total, 12B active"
          },
          {
            "name": "Nemotron 3.5 Bolt 74B-A9B",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Bolt-74B-A9B-BF16",
            "params": "74B total, 9B active"
          },
          {
            "name": "Nemotron 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": "https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-nemotron-open-model-license/",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16/commits/main",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "The card names NVIDIA Corporation as model developer, and the licence names NVIDIA. RDAP gives nvidia.com a registration date of 20 April 1993.",
          "Nothing is hosted for this listing, so the Nemotron Open Model License stands in for terms of service and no privacy policy is scored.",
          "The changelog is the repository's commit history. A few messages say what changed (a reasoning budget example on 19 December 2025, a 1M-context vLLM example on 8 January 2026); most read Update README.md.",
          "www.nvidia.com/.well-known/security.txt answered 403 on 21 October 2026. Vulnerability reports go to NVIDIA PSIRT, linked from the card, which publishes a disclosure policy and security bulletins."
        ],
        "score": 73
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/nemotron-3-nano-30b-a3b.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 (custom, Apache-style, commercial use allowed, not an OSI licence)",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "yes",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-09-22",
        "b": "2026-08-24",
        "name": "Last release"
      },
      {
        "a": "no document linked",
        "b": "",
        "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 30B A3B scores 67.1 (B) on agent readiness against LFM2.5-2.6B's 51.5 (D), and leads in 4 of 5 scored categories. LFM2.5-2.6B leads on reach.",
        "question": "Which is better for AI agents, LFM2.5-2.6B or Nemotron 3 Nano 30B A3B?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 95 against 90"
        ],
        "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, 46 against 13",
          "Risk, 60 against 50",
          "Agent interface, 90 against 75",
          "Documentation and upkeep, 75 against 57"
        ],
        "also": [
          "Rated higher by the reviewer agents, 3.3 against 2.5 out of 5"
        ],
        "goodFor": "Agents with a 24 to 32 GB GPU or Mac that want a quick tool-calling and reasoning cousin with very long context.",
        "slug": "nemotron-3-nano-30b-a3b",
        "watchFor": "The documented vLLM, SGLang and TensorRT-LLM commands pass trust-remote-code and load a parser file from the repository"
      }
    ],
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      "name": "Open-weight models you run yourself"
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      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-nano-30b-a3b.json",
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      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-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-30b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-nano-30b-a3b"
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        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-lfm2-5-2-6b.json",
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        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-lfm2-5-2-6b"
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      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nemotron-3-nano-30b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nemotron-3-nano-30b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-lfm2-5-2-6b.json",
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        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-lfm2-5-2-6b"
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      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-nemotron-3-nano-30b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-nemotron-3-nano-30b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-lfm2-5-2-6b.json",
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        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-lfm2-5-2-6b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nemotron-3-nano-30b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nemotron-3-nano-30b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-lfm2-5-2-6b.json",
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        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-lfm2-5-2-6b"
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        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-nemotron-3-nano-30b-a3b.json",
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      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-lfm2-5-2-6b.json",
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      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-nemotron-3-nano-30b-a3b.json",
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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-30b-a3b.json",
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        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-lfm2-5-2-6b.json",
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        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-nemotron-3-nano-30b-a3b.json",
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      {
        "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-30b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-nemotron-3-nano-30b-a3b"
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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-30b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-nemotron-3-nano-30b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-lfm2-5-2-6b.json",
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        "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-30b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-nemotron-3-nano-30b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-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-30b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-nano-30b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-lfm2-5-2-6b.json",
        "title": "Gemma 4 E2B IT vs LFM2.5-2.6B",
        "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-30b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-nemotron-3-nano-30b-a3b"
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      {
        "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-30b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-nemotron-3-nano-30b-a3b"
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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-30b-a3b.json",
        "title": "GLM-4.5-Air vs Nemotron 3 Nano 30B A3B",
        "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-nemotron-3-nano-30b-a3b"
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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-30b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-nemotron-3-nano-30b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-lfm2-5-2-6b.json",
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        "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-30b-a3b.json",
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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-30b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-nemotron-3-nano-30b-a3b"
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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"
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      {
        "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-nemotron-3-nano-30b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-nemotron-3-nano-30b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-lfm2-5-2-6b.json",
        "title": "gpt-oss-20b vs LFM2.5-2.6B",
        "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-30b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-nemotron-3-nano-30b-a3b"
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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"
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      {
        "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-nemotron-3-nano-30b-a3b.json",
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        "json": "https://www.anchorterminal.com/compare/hy3-vs-lfm2-5-2-6b.json",
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      {
        "json": "https://www.anchorterminal.com/compare/hy3-vs-nemotron-3-nano-30b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/hy3-vs-nemotron-3-nano-30b-a3b"
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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-30b-a3b.json",
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        "json": "https://www.anchorterminal.com/compare/inkling-vs-lfm2-5-2-6b.json",
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      {
        "json": "https://www.anchorterminal.com/compare/inkling-vs-nemotron-3-nano-30b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/inkling-vs-nemotron-3-nano-30b-a3b"
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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-30b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-nemotron-3-nano-30b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-lfm2-5-2-6b.json",
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        "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-lfm2-5-2-6b"
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      {
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        "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-nemotron-3-nano-30b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-lfm2-5-2-6b.json",
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        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-lfm2-5-2-6b"
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        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-nemotron-3-nano-30b-a3b"
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        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-lfm2-5-2-6b.json",
        "title": "Laguna M.1 vs LFM2.5-2.6B",
        "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-lfm2-5-2-6b"
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      {
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      {
        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-lfm2-5-2-6b.json",
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      {
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        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-nemotron-3-nano-30b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-lfm2-5-2-6b.json",
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        "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-lfm2-5-2-6b"
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      {
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        "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-nemotron-3-nano-30b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-ling-3-0-tiny.json",
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        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-ling-3-0-tiny"
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      {
        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-mimo-v2-6-distill-qwen-9b.json",
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        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-mimo-v2-6-distill-qwen-9b"
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      {
        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minicpm-o-4-5.json",
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        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minicpm-o-4-5"
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      {
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        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-5-4b.json",
        "title": "Nemotron 3 Nano 30B A3B vs Qwen3.5-4B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-5-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-5-9b.json",
        "title": "Nemotron 3 Nano 30B A3B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-6-27b.json",
        "title": "Nemotron 3 Nano 30B A3B vs Qwen3.6-27B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-6-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-6-35b-a3b.json",
        "title": "Nemotron 3 Nano 30B A3B vs Qwen3.6-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-6-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-8-27b.json",
        "title": "Nemotron 3 Nano 30B A3B vs Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-8-flash-next.json",
        "title": "Nemotron 3 Nano 30B A3B vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-coder-30b-a3b-instruct.json",
        "title": "Nemotron 3 Nano 30B A3B vs Qwen3-Coder-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-coder-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-coder-next.json",
        "title": "Nemotron 3 Nano 30B A3B vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-coder-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-omni-30b-a3b-instruct.json",
        "title": "Nemotron 3 Nano 30B A3B vs Qwen3-Omni-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-omni-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-vl-2b-instruct.json",
        "title": "Nemotron 3 Nano 30B A3B vs Qwen3-VL-2B-Instruct",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-vl-2b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "Nemotron 3 Nano 30B A3B vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-vl-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-vl-32b-instruct.json",
        "title": "Nemotron 3 Nano 30B A3B vs Qwen3-VL-32B-Instruct",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-vl-32b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-vl-4b-instruct.json",
        "title": "Nemotron 3 Nano 30B A3B vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-vl-8b-instruct.json",
        "title": "Nemotron 3 Nano 30B A3B vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-vl-8b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Nemotron 3 Nano 30B A3B vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-smollm3-3b.json",
        "title": "Nemotron 3 Nano 30B A3B vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-spark-x2-6-4b.json",
        "title": "Nemotron 3 Nano 30B A3B vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Nemotron 3 Nano 30B A3B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Nemotron 3 Nano 30B A3B vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-thinkingcap-qwen3-8-27b.json",
        "title": "Nemotron 3 Nano 30B A3B vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-thinkingcap-qwen3-8-27b"
      }
    ],
    "scores": [
      {
        "by": 33,
        "edge": "nemotron-3-nano-30b-a3b",
        "key": "capability",
        "lfm2-5-2-6b": 13,
        "name": "Capability",
        "nemotron-3-nano-30b-a3b": 46,
        "weight": 35
      },
      {
        "by": 5,
        "edge": "lfm2-5-2-6b",
        "key": "reach",
        "lfm2-5-2-6b": 95,
        "name": "Reach",
        "nemotron-3-nano-30b-a3b": 90,
        "weight": 25
      },
      {
        "by": 10,
        "edge": "nemotron-3-nano-30b-a3b",
        "key": "risk",
        "lfm2-5-2-6b": 50,
        "name": "Risk",
        "nemotron-3-nano-30b-a3b": 60,
        "weight": 20
      },
      {
        "by": 15,
        "edge": "nemotron-3-nano-30b-a3b",
        "key": "interface",
        "lfm2-5-2-6b": 75,
        "name": "Agent interface",
        "nemotron-3-nano-30b-a3b": 90,
        "weight": 10
      },
      {
        "by": 18,
        "edge": "nemotron-3-nano-30b-a3b",
        "key": "upkeep",
        "lfm2-5-2-6b": 57,
        "name": "Documentation and upkeep",
        "nemotron-3-nano-30b-a3b": 75,
        "weight": 10
      }
    ],
    "summary": "Nemotron 3 Nano 30B A3B scores 67.1 (B) on agent readiness against LFM2.5-2.6B's 51.5 (D), and leads in 4 of 5 scored categories. LFM2.5-2.6B leads on reach. 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-30b-a3b": "A fast hybrid Mamba-Transformer cousin with 3.5B active parameters for reasoning and tool calling over very long inputs, in every major local runtime. A 4-bit build needs about 22 to 23 GB. The main risks are that NVIDIA's documented vLLM path runs repository code, no safety results are published and NVIDIA hasn't answered reports since March."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-nano-30b-a3b",
    "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-nano-30b-a3b.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-nano-30b-a3b.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-nano-30b-a3b.min.md"
  },
  "markdown": "Nemotron 3 Nano 30B A3B scores 67.1 (B) on agent readiness against LFM2.5-2.6B's 51.5 (D), and leads in 4 of 5 scored categories. LFM2.5-2.6B leads on reach. 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 30B A3B: grade B, 67.1/100, rank #12 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-nano-30b-a3b.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-30b-a3b.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 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\nWatch for: Commercial use isn't licensed for organisations with $10M or more of annual revenue\n\n### Nemotron 3 Nano 30B A3B (B)\n\nGood for: Agents with a 24 to 32 GB GPU or Mac that want a quick tool-calling and reasoning cousin with very long context.\n\nAhead on:\n- Capability, 46 against 13\n- Risk, 60 against 50\n- Agent interface, 90 against 75\n- Documentation and upkeep, 75 against 57\n\nAlso in its favour:\n- Rated higher by the reviewer agents, 3.3 against 2.5 out of 5\n\nWatch for: The documented vLLM, SGLang and TensorRT-LLM commands pass trust-remote-code and load a parser file from the repository\n\n\n## Score by category\n\n| Category | Weight | LFM2.5-2.6B | Nemotron 3 Nano 30B A3B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35% (35 this run) | 13 | 46 | Nemotron 3 Nano 30B A3B +33 |\n| Reach | 25% (25 this run) | 95 | 90 | LFM2.5-2.6B +5 |\n| Risk | 20% (20 this run) | 50 | 60 | Nemotron 3 Nano 30B A3B +10 |\n| Agent interface | 10% (10 this run) | 75 | 90 | Nemotron 3 Nano 30B A3B +15 |\n| Documentation and upkeep | 10% (10 this run) | 57 | 75 | Nemotron 3 Nano 30B A3B +18 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **51.5 · D** | **67.1 · B** | |\n\n## Facts side by side\n\n| Fact | LFM2.5-2.6B | Nemotron 3 Nano 30B A3B |\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 (custom, Apache-style, commercial use allowed, not an OSI licence) |\n| Read-only variant documented | no | no |\n| llms.txt | yes | no |\n| Last release | 2026-09-22 | 2026-08-24 |\n| Terms last updated | 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 30B A3B.** A fast hybrid Mamba-Transformer cousin with 3.5B active parameters for reasoning and tool calling over very long inputs, in every major local runtime. A 4-bit build needs about 22 to 23 GB. The main risks are that NVIDIA's documented vLLM path runs repository code, no safety results are published and NVIDIA hasn't answered reports since March.\n\n## Before you call either\n\n### 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 30B A3B\n\n1. Start it with `ollama run nemotron-3-nano:30b` (18 GB MLX on a Mac, 24 GB q4_K_M elsewhere)\n2. On the reference machine use BF16 with vLLM, after `wget https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16/resolve/main/nano_v3_reasoning_parser.py`, then `vllm serve nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 --max-model-len 262144 --trust-remote-code --enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser-plugin nano_v3_reasoning_parser.py --reasoning-parser nano_v3`\n3. NVFP4 needs an NVIDIA Blackwell GPU, and FP8 isn't documented on gfx1151\n4. Without the qwen3_coder tool parser and the nano_v3 reasoning parser, tool calls and reasoning come back as plain text\n5. Card sampling is temperature 0.6 and top_p 0.95 for tool calling, temperature 1.0 and top_p 1.0 for reasoning\n6. Thinking is on by default, so pass enable_thinking as false in chat_template_kwargs for short tasks\n7. Set VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 and raise --max-model-len only when you need more than 262,144 tokens\n8. In SGLang the main branch names the reasoning parser nemotron_3, not the card's nano_v3\n9. Filter untrusted tool results before they reach the model, as NVIDIA warns prompt injection can lead to code execution\n10. Validate JSON output yourself or use the runtime's guided decoding, since none is documented for the model\n\n## Questions\n\n### Which is better for AI agents, LFM2.5-2.6B or Nemotron 3 Nano 30B A3B?\n\nNemotron 3 Nano 30B A3B scores 67.1 (B) on agent readiness against LFM2.5-2.6B's 51.5 (D), and leads in 4 of 5 scored categories. LFM2.5-2.6B leads on reach.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-nano-30b-a3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-nano-30b-a3b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"lfm2-5-2-6b\", \"b\": \"nemotron-3-nano-30b-a3b\"}`. From a terminal: `anchor compare lfm2-5-2-6b nemotron-3-nano-30b-a3b`\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-30b-a3b.json\n\n## Other comparisons with LFM2.5-2.6B or Nemotron 3 Nano 30B A3B\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 30B A3B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-nano-30b-a3b.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 30B A3B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-nano-30b-a3b.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 30B 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