{
  "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. Liquid now lists it as deprecated, replaced by LFM3-1.7B.",
      "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",
      "dns": "lfm2-5-2-6b.grade.anchorterminal.com",
      "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": "2026-11-03",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 50.3,
        "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": 1518,
        "categoryRank": 108,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "capability": 13,
          "interface": 75,
          "reach": 90,
          "risk": 50,
          "upkeep": 57
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-11-04"
        },
        "negative": 0,
        "verdict": "A small, fast model for tool calls, extraction and long-context steps on a laptop or phone, which Liquid now lists as deprecated in favour of LFM3-1.7B. Liquid's own 4-bit build is 1.6 GB and five common runtimes run it. It always thinks, 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": [
          "Liquid lists it as deprecated, replaced by LFM3-1.7B; deprecated models stay downloadable but get no further updates",
          "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"
        ],
        "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": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 50.3
          }
        ],
        "editorialScores": {
          "capability": 13,
          "interface": 75,
          "reach": 90,
          "risk": 50,
          "upkeep": 57
        },
        "provenanceScore": 62,
        "checked": {
          "categories": {
            "capability": "2026-11-04",
            "interface": "2026-11-04",
            "reach": "2026-11-04",
            "risk": "2026-11-04",
            "upkeep": "2026-11-04"
          },
          "last": "2026-11-04",
          "lastMoved": "2026-11-04",
          "oldest": "2026-11-04",
          "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"
      },
      "supersededBy": [
        "lfm3-1-7b"
      ],
      "sameCompany": [
        "liquid-d1",
        "lfm3-1-7b",
        "lfm3-audio-2b",
        "lfm3-omni-2b",
        "lfm3-vl-2b"
      ],
      "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, 1,593,894,912 bytes by its LFS pointer. The repository also holds Q4_K_M (1,674,455,040 bytes), Q5_K_M, Q6_K, Q8_0, 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, per the 21 October 2026 check"
          }
        ],
        "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": "2026-11-04",
        "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": "2026-11-04",
        "notes": [
          "The licence names Liquid AI, Inc. as licensor. The domain registration date is carried from the 21 October check, which took it from a registry data service this run doesn't use.",
          "Nothing is hosted for this listing, so the licence stands in for terms of service and no privacy policy is scored. Liquid's site terms (30 September 2026) cover its hosted services.",
          "No changelog. The commits are README edits, a config fix and a generation-config fix; Liquid's deprecations page lists this model as deprecated, replaced by LFM3-1.7B, without a date.",
          "/.well-known/security.txt answered 404 on www.liquid.ai on 4 November 2026.",
          "Liquid's site and docs link to huggingface.co/LiquidAI, so the repositories are the publisher's own."
        ],
        "score": 62
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/lfm2-5-2-6b.json",
      "live": {
        "slug": "lfm2-5-2-6b",
        "securityTxt": {
          "url": "https://liquid.ai/.well-known/security.txt",
          "state": "none",
          "checkedAt": "2026-11-10T16:08:13.778560021Z"
        },
        "llmsTxt": {
          "url": "https://docs.liquid.ai/llms.txt",
          "ok": true,
          "status": 200,
          "checkedAt": "2026-11-10T15:43:30.139732436Z"
        },
        "domain": {
          "domain": "liquid.ai",
          "registered": "2017-12-16",
          "source": "https://rdap.identitydigital.services/rdap/domain/liquid.ai",
          "checkedAt": "2026-11-08T14:48:59.460510541Z"
        },
        "updatedAt": "2026-11-10T16:08:13.778560021Z"
      }
    },
    "answer": "LongCat-2.5 and LFM2.5-2.6B score within a point of each other on agent readiness, 50.5 (D) and 50.3 (D). LFM2.5-2.6B leads on reach and documentation and upkeep.",
    "b": {
      "slug": "longcat-2-5",
      "name": "LongCat-2.5",
      "vendor": "Meituan (LongCat)",
      "vendorUrl": "https://longcat.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "LongCat-2.5 is Meituan's open-weight flagship, a multimodal mixture-of-experts model of 1.6T parameters with about 48B active per token, built for long agentic coding work. MIT licence. The weights are 3,562 GB, and the card documents deployment on NPU hardware only.",
      "url": "https://www.anchorterminal.com/tools/longcat-2-5",
      "markdownUrl": "https://www.anchorterminal.com/tools/longcat-2-5.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/longcat-2-5.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/longcat-2-5.json",
      "dns": "longcat-2-5.grade.anchorterminal.com",
      "repo": "https://huggingface.co/meituan-longcat/LongCat-2.5",
      "license": "MIT",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "meituan-longcat/LongCat-2.5"
        },
        {
          "registry": "huggingface",
          "name": "meituan-longcat/LongCat-2.5-W4A4"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Neither repository is gated (checked 2026-11-10).",
      "pricing": "free",
      "pricingNotes": "Free to download and use under the MIT licence. Running it yourself is a data-centre job: 3,562 GB in BF16, or 1,336 GB for Meituan's 4-bit build, which the card says is for INT4 computation on NPU accelerators (checked 2026-11-10).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-11-10).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-11-10"
      },
      "docsUrl": "https://huggingface.co/meituan-longcat/LongCat-2.5",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "multimodal",
        "reasoning",
        "tool-calling",
        "agentic-coding",
        "mit",
        "safetensors",
        "int4",
        "npu",
        "self-hosted"
      ],
      "lastRelease": "2026-11-10",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 50.5,
        "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": 1518,
        "categoryRank": 107,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 75,
          "reach": 35,
          "risk": 60,
          "upkeep": 46
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-10"
        },
        "negative": 0,
        "verdict": "Meituan's flagship is MIT-licensed and documented for tool calling and switchable reasoning, with strong numbers of its own on agentic coding. Almost nobody can run it: 3,562 GB of weights, a 1,336 GB 4-bit build for NPU accelerators, and no released runtime with its architecture. A model to watch until a runtime or a provider picks it up.",
        "bestFor": "Operators with NPU clusters, and anyone tracking what the open flagships can do",
        "strengths": [
          "MIT licence, ungated",
          "Tool calling documented with a worked example",
          "Reasoning can be switched per request and comes back as reasoning_content",
          "Meituan publishes its own 4-bit build",
          "Updated on the day we checked"
        ],
        "weaknesses": [
          "3,562 GB in BF16 and 1,336 GB at 4 bits",
          "No released runtime has its architecture",
          "The only documented deployment is on NPU hardware through Meituan's fork",
          "config.json names model code that isn't in the repository",
          "No stated context length, languages, safety results or data sources",
          "No inference provider serves it"
        ],
        "agentNotes": [
          "You can't run this on a workstation or a single GPU server. Plan for a provider, and none serves it yet",
          "If an operator serves it, call it as an OpenAI-compatible chat API with tools in the function format",
          "Pass `chat_template_kwargs` with `enable_thinking` true or false",
          "With thinking on, keep earlier reasoning in the history (`save_history_reasoning_content` true), as the card recommends",
          "The card's results use temperature 1.0 and top_p 0.95",
          "Its benchmark table is Meituan's own. The unmarked scores are in-house"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 0,
        "avgRating": 0,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "D",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 50.5
          }
        ],
        "editorialScores": {
          "interface": 75,
          "reach": 35,
          "risk": 60,
          "upkeep": 46
        },
        "provenanceScore": 49,
        "checked": {
          "categories": {
            "interface": "2026-11-10",
            "reach": "2026-11-10",
            "risk": "2026-11-10",
            "upkeep": "2026-11-10"
          },
          "last": "2026-11-10",
          "oldest": "2026-11-10",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "# No released runtime loads it. Meituan's NPU instructions are in its SGLang fork:\n# github.com/meituan-longcat/SGLang-FluentLLM, branch longcat-2.5-npu, npu_test/ReadMe.md",
        "http": "# The card's example calls an OpenAI-compatible chat completions API with tools, and passes\n# extra_body={\"chat_template_kwargs\": {\"enable_thinking\": true, \"save_history_reasoning_content\": true}}"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/longcat-2-5"
      },
      "weights": {
        "trainedBy": "Meituan (LongCat team)",
        "builtOn": "LongCat-2.0 and LongCat-Next, per the card",
        "params": "1.6T",
        "active": "48B",
        "context": 524288,
        "goodFor": "Long agentic coding and research runs with images, for an operator with NPU clusters. Nobody else can run it yet.",
        "runtimes": [],
        "builds": [
          {
            "name": "LongCat-2.5-W4A4",
            "url": "https://huggingface.co/meituan-longcat/LongCat-2.5-W4A4",
            "who": "publisher",
            "precision": "int4",
            "sizeGB": 1336,
            "memoryGB": 2048,
            "format": "safetensors",
            "note": "Meituan's 4-bit build of the experts' weights and activations, for INT4 computation on NPU accelerators"
          },
          {
            "name": "LongCat-2.5 (BF16 safetensors)",
            "url": "https://huggingface.co/meituan-longcat/LongCat-2.5",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 3562,
            "memoryGB": 8192,
            "format": "safetensors",
            "note": "210 files"
          }
        ],
        "run": "See github.com/meituan-longcat/SGLang-FluentLLM, branch longcat-2.5-npu",
        "runBuild": "LongCat-2.5-W4A4",
        "checked": "2026-11-10",
        "minMemoryGB": 2048
      },
      "family": {
        "lab": "Meituan (LongCat)",
        "family": "LongCat-2",
        "url": "https://www.anchorterminal.com/open-weights/#meituan-longcat",
        "others": [
          {
            "name": "LongCat-2.5-W4A4",
            "url": "https://huggingface.co/meituan-longcat/LongCat-2.5-W4A4"
          },
          {
            "name": "LongCat-2.0",
            "url": "https://huggingface.co/meituan-longcat/LongCat-2.0"
          }
        ]
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Meituan, named in the card's licence section. The legal entity wasn't looked up",
        "domain": "longcat.ai",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "https://huggingface.co/meituan-longcat/LongCat-2.5/blob/main/LICENSE",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/meituan-longcat/LongCat-2.5/commits/main",
        "securityTxt": "unknown",
        "checked": "2026-11-10",
        "notes": [
          "longcat.ai has no robots.txt (the request returns a not-found message) and its pages are JavaScript applications that gave no text on 10 November 2026, so the site's own statements weren't read.",
          "The domain's registration date wasn't looked up.",
          "Nothing is hosted under this listing, so the MIT licence stands in for terms of service and no privacy policy is scored.",
          "The changelog is the commit history, 50 commits or more since 4 November 2026."
        ],
        "score": 49
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/longcat-2-5.json"
    },
    "edges": [
      {
        "by": 55,
        "category": "reach",
        "leads": "lfm2-5-2-6b",
        "name": "Reach",
        "reasons": {
          "a": "Memory earns the full 30, since Liquid's own 4-bit GGUF is 1.59 GB and fits 8 GB with room to spare. Runtimes earn 25, with the lfm2 architecture in released llama.cpp, vLLM, SGLang and Transformers, and in Ollama through the llama.cpp version it pins. Builds earn 15 for Liquid's own GGUF, MLX and ONNX builds at many precisions. Terms earn 10, a custom licence that is free for commercial use under $10M of annual revenue. Download earns 10, with no gate. Hosted earns nothing, as Hugging Face maps no inference provider and Liquid's docs list no hosted API for it.",
          "b": "Memory 0, the smallest build is Meituan's W4A4 repository, 1,336 GB, and 1,336 GB × 1.2 = 1,603 GB, far above 256 GB. The BF16 files are 3,562 GB. Runtimes 0, none of the six runs it in a released version: vLLM 0.33.0, SGLang 0.5.23, Transformers 5.22.2 and llama.cpp b12242 have no LongcatCausalLM or pro_v code, and the model code config.json names isn't in the repository. Builds 10, one quantised build from Meituan, the W4A4 repository (4-bit experts, 1,336 GB). Terms 15, MIT in the metadata, the LICENSE file and the card, with nothing added. Download 10, the Hub API reports gated false for both repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it. 0 + 0 + 10 + 15 + 10 + 0 = 35."
        },
        "scores": {
          "a": 90,
          "b": 35
        }
      },
      {
        "by": 11,
        "category": "upkeep",
        "leads": "lfm2-5-2-6b",
        "name": "Documentation and upkeep",
        "reasons": {
          "a": "The model card earns 17, with everything but a knowledge cut-off. Training data earns 5 for a general description without a cut-off. The last change earns 20, a card commit on 3 November 2026 naming LFM3-1.7B as the new version. Responsiveness earns 5, with Liquid replies in 2 of 16 discussions. Version policy earns 5, as Liquid now lists the model as deprecated with a replacement but no date. File health earns 5, since the files agree but a report about the card waits without a reply.",
          "b": "Model card 11, Intended use, limitations (the usage considerations), the architecture and the parameter counts are on the card. Training data 5, A general description of the training stages and where the reinforcement learning tasks came from, with no sources, sizes or cut-off. Last change 20, the last commit, on 10 November 2026, updated the card, on the day of this check. Responsiveness 0, Neither of the 2 discussions that aren't pull requests (9 and 10 November 2026) has a reply from a meituan-longcat member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, LongCat-2.0 stays in its own repositories and 2.5 came out in new ones, with no dated note on the older ones that we read. File health 5, the template is in both places and agrees, but config.json's auto_map names configuration_longcat_oe and modeling_longcat_oe, which aren't in the repository, and the tokenizer config's 131,072-token length is a quarter of the config's 524,288 positions. 11 + 5 + 20 + 0 + 5 + 5 = 46."
        },
        "scores": {
          "a": 57,
          "b": 46
        }
      },
      {
        "by": 10,
        "category": "risk",
        "leads": "longcat-2-5",
        "name": "Risk",
        "reasons": {
          "a": "File safety earns 25, safetensors and GGUF only, and nothing from the repository runs to load it or build prompts. Provenance earns 15, files in the LiquidAI organisation Liquid links to, with a checksum for every file. Safety evaluation and agent guidance earn nothing, since Liquid publishes neither. Licence risk earns 10, losing 5 for the revenue threshold, with no use policy and no change since release. The security programme earns nothing, since no security.txt, disclosure policy or security contact was found.",
          "b": "File safety 25, safetensors only, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, the files sit in the meituan-longcat organisation, the name of Meituan's GitHub organisation that the card links for deployment, and the card's licence names Meituan (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions under Usage Considerations: not evaluated for every use, assess accuracy, safety and fairness before sensitive use, and follow the law. Licence risk 15, MIT with no use policy, no threshold and no right to change the terms. Security programme 0, no way to report a flaw on the card beyond a general contact, and Meituan's own security pages weren't looked up, so this scores as absent and is an open question. 25 + 15 + 0 + 5 + 15 + 0 = 60."
        },
        "scores": {
          "a": 50,
          "b": 60
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Liquid AI",
        "b": "Meituan (LongCat)",
        "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": "MIT",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "yes",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-11-03",
        "b": "2026-11-10",
        "name": "Last release"
      },
      {
        "a": "no document linked",
        "b": "not read yet",
        "name": "Terms last updated"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Privacy policy last updated"
      },
      {
        "a": "2.5/5 (4)",
        "b": "none",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "LongCat-2.5 and LFM2.5-2.6B score within a point of each other on agent readiness, 50.5 (D) and 50.3 (D). LFM2.5-2.6B leads on reach and documentation and upkeep.",
        "question": "Which is better for AI agents, LFM2.5-2.6B or LongCat-2.5?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 90 against 35",
          "Documentation and upkeep, 57 against 46"
        ],
        "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": "Liquid lists it as deprecated, replaced by LFM3-1.7B; deprecated models stay downloadable but get no further updates"
      },
      {
        "aheadOn": [
          "Risk, 60 against 50"
        ],
        "also": null,
        "goodFor": "Operators with NPU clusters, and anyone tracking what the open flagships can do",
        "slug": "longcat-2-5",
        "watchFor": "3,562 GB in BF16 and 1,336 GB at 4 bits"
      }
    ],
    "history": {
      "lfm2-5-2-6b": [
        {
          "causes": [
            "correction"
          ],
          "date": "2026-11-04",
          "delta": -1.2,
          "from": 51.5,
          "fromGrade": "D",
          "to": 50.3,
          "toGrade": "D"
        }
      ],
      "longcat-2-5": null
    },
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-lfm2-5-2-6b.json",
        "title": "Agents-A1-4B vs LFM2.5-2.6B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-lfm2-5-2-6b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-longcat-2-5.json",
        "title": "Agents-A1-4B vs LongCat-2.5",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-longcat-2-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-lfm2-5-2-6b.json",
        "title": "Apertus v1.5 8B vs LFM2.5-2.6B",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-lfm2-5-2-6b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-longcat-2-5.json",
        "title": "Apertus v1.5 8B vs LongCat-2.5",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-longcat-2-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-lfm2-5-2-6b.json",
        "title": "Apodex-1.1-mini vs LFM2.5-2.6B",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-lfm2-5-2-6b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-longcat-2-5.json",
        "title": "Apodex-1.1-mini vs LongCat-2.5",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-longcat-2-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-lfm2-5-2-6b.json",
        "title": "Arete-27B vs LFM2.5-2.6B",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-lfm2-5-2-6b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-longcat-2-5.json",
        "title": "Arete-27B vs LongCat-2.5",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-longcat-2-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-lfm2-5-2-6b.json",
        "title": "BigBang-v1 vs LFM2.5-2.6B",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-lfm2-5-2-6b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-longcat-2-5.json",
        "title": "BigBang-v1 vs LongCat-2.5",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-longcat-2-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-lfm2-5-2-6b.json",
        "title": "Carbon-X vs LFM2.5-2.6B",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-lfm2-5-2-6b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-longcat-2-5.json",
        "title": "Carbon-X vs LongCat-2.5",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-longcat-2-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-lfm2-5-2-6b.json",
        "title": "Cola2 vs LFM2.5-2.6B",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-lfm2-5-2-6b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-longcat-2-5.json",
        "title": "Cola2 vs LongCat-2.5",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-longcat-2-5"
      },
      {
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      }
    ],
    "summary": "LongCat-2.5 and LFM2.5-2.6B score within a point of each other on agent readiness, 50.5 (D) and 50.3 (D). LFM2.5-2.6B leads on reach and documentation and upkeep. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "lfm2-5-2-6b": "A small, fast model for tool calls, extraction and long-context steps on a laptop or phone, which Liquid now lists as deprecated in favour of LFM3-1.7B. Liquid's own 4-bit build is 1.6 GB and five common runtimes run it. It always thinks, and organisations over $10M of revenue need another licence. There are no safety results or security contact.",
      "longcat-2-5": "Meituan's flagship is MIT-licensed and documented for tool calling and switchable reasoning, with strong numbers of its own on agentic coding. Almost nobody can run it: 3,562 GB of weights, a 1,336 GB 4-bit build for NPU accelerators, and no released runtime with its architecture. A model to watch until a runtime or a provider picks it up."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-longcat-2-5",
    "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-longcat-2-5.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-longcat-2-5.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-longcat-2-5.min.md"
  },
  "markdown": "LongCat-2.5 and LFM2.5-2.6B score within a point of each other on agent readiness, 50.5 (D) and 50.3 (D). LFM2.5-2.6B leads on reach and documentation and upkeep. Both do open-weight models you run yourself.\n\n- LFM2.5-2.6B: grade D, 50.3/100, rank #146 of 160 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- LongCat-2.5: grade D, 50.5/100, rank #145 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/longcat-2-5.md · JSON https://www.anchorterminal.com/api/v1/tools/longcat-2-5.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### 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, 90 against 35\n- Documentation and upkeep, 57 against 46\n\nWatch for: Liquid lists it as deprecated, replaced by LFM3-1.7B; deprecated models stay downloadable but get no further updates\n\n### LongCat-2.5 (D)\n\nGood for: Operators with NPU clusters, and anyone tracking what the open flagships can do\n\nAhead on:\n- Risk, 60 against 50\n\nWatch for: 3,562 GB in BF16 and 1,336 GB at 4 bits\n\n\n## Why the edge\n\n### Reach: LFM2.5-2.6B +55\n\n- LFM2.5-2.6B (90): Memory earns the full 30, since Liquid's own 4-bit GGUF is 1.59 GB and fits 8 GB with room to spare. Runtimes earn 25, with the lfm2 architecture in released llama.cpp, vLLM, SGLang and Transformers, and in Ollama through the llama.cpp version it pins. Builds earn 15 for Liquid's own GGUF, MLX and ONNX builds at many precisions. Terms earn 10, a custom licence that is free for commercial use under $10M of annual revenue. Download earns 10, with no gate. Hosted earns nothing, as Hugging Face maps no inference provider and Liquid's docs list no hosted API for it.\n- LongCat-2.5 (35): Memory 0, the smallest build is Meituan's W4A4 repository, 1,336 GB, and 1,336 GB × 1.2 = 1,603 GB, far above 256 GB. The BF16 files are 3,562 GB. Runtimes 0, none of the six runs it in a released version: vLLM 0.33.0, SGLang 0.5.23, Transformers 5.22.2 and llama.cpp b12242 have no LongcatCausalLM or pro_v code, and the model code config.json names isn't in the repository. Builds 10, one quantised build from Meituan, the W4A4 repository (4-bit experts, 1,336 GB). Terms 15, MIT in the metadata, the LICENSE file and the card, with nothing added. Download 10, the Hub API reports gated false for both repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it. 0 + 0 + 10 + 15 + 10 + 0 = 35.\n\n### Documentation and upkeep: LFM2.5-2.6B +11\n\n- LFM2.5-2.6B (57): The model card earns 17, with everything but a knowledge cut-off. Training data earns 5 for a general description without a cut-off. The last change earns 20, a card commit on 3 November 2026 naming LFM3-1.7B as the new version. Responsiveness earns 5, with Liquid replies in 2 of 16 discussions. Version policy earns 5, as Liquid now lists the model as deprecated with a replacement but no date. File health earns 5, since the files agree but a report about the card waits without a reply.\n- LongCat-2.5 (46): Model card 11, Intended use, limitations (the usage considerations), the architecture and the parameter counts are on the card. Training data 5, A general description of the training stages and where the reinforcement learning tasks came from, with no sources, sizes or cut-off. Last change 20, the last commit, on 10 November 2026, updated the card, on the day of this check. Responsiveness 0, Neither of the 2 discussions that aren't pull requests (9 and 10 November 2026) has a reply from a meituan-longcat member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, LongCat-2.0 stays in its own repositories and 2.5 came out in new ones, with no dated note on the older ones that we read. File health 5, the template is in both places and agrees, but config.json's auto_map names configuration_longcat_oe and modeling_longcat_oe, which aren't in the repository, and the tokenizer config's 131,072-token length is a quarter of the config's 524,288 positions. 11 + 5 + 20 + 0 + 5 + 5 = 46.\n\n### Risk: LongCat-2.5 +10\n\n- LFM2.5-2.6B (50): File safety earns 25, safetensors and GGUF only, and nothing from the repository runs to load it or build prompts. Provenance earns 15, files in the LiquidAI organisation Liquid links to, with a checksum for every file. Safety evaluation and agent guidance earn nothing, since Liquid publishes neither. Licence risk earns 10, losing 5 for the revenue threshold, with no use policy and no change since release. The security programme earns nothing, since no security.txt, disclosure policy or security contact was found.\n- LongCat-2.5 (60): File safety 25, safetensors only, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, the files sit in the meituan-longcat organisation, the name of Meituan's GitHub organisation that the card links for deployment, and the card's licence names Meituan (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions under Usage Considerations: not evaluated for every use, assess accuracy, safety and fairness before sensitive use, and follow the law. Licence risk 15, MIT with no use policy, no threshold and no right to change the terms. Security programme 0, no way to report a flaw on the card beyond a general contact, and Meituan's own security pages weren't looked up, so this scores as absent and is an open question. 25 + 15 + 0 + 5 + 15 + 0 = 60.\n\n## Score by category\n\n| Category | Weight | LFM2.5-2.6B | LongCat-2.5 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 90 | 35 | LFM2.5-2.6B +55 |\n| Risk | 20% (20 this run) | 50 | 60 | LongCat-2.5 +10 |\n| Agent interface | 10% (10 this run) | 75 | 75 | even |\n| Documentation and upkeep | 10% (10 this run) | 57 | 46 | LFM2.5-2.6B +11 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **50.3 · D** | **50.5 · D** | |\n\n## Facts side by side\n\n| Fact | LFM2.5-2.6B | LongCat-2.5 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Liquid AI | Meituan (LongCat) |\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) | MIT |\n| Read-only variant documented | no | no |\n| llms.txt | yes | no |\n| Last release | 2026-11-03 | 2026-11-10 |\n| Terms last updated | no document linked | not read yet |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | 2.5/5 (4) | none |\n\n## How the scores have moved\n\n### LFM2.5-2.6B\n\n- -1.2 on 4 November 2026: correction\n\n### LongCat-2.5\n\nNo score change since it was listed.\n\n## Verdicts\n\n**LFM2.5-2.6B.** A small, fast model for tool calls, extraction and long-context steps on a laptop or phone, which Liquid now lists as deprecated in favour of LFM3-1.7B. Liquid's own 4-bit build is 1.6 GB and five common runtimes run it. It always thinks, and organisations over $10M of revenue need another licence. There are no safety results or security contact.\n\n**LongCat-2.5.** Meituan's flagship is MIT-licensed and documented for tool calling and switchable reasoning, with strong numbers of its own on agentic coding. Almost nobody can run it: 3,562 GB of weights, a 1,336 GB 4-bit build for NPU accelerators, and no released runtime with its architecture. A model to watch until a runtime or a provider picks it up.\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### LongCat-2.5\n\n1. You can't run this on a workstation or a single GPU server. Plan for a provider, and none serves it yet\n2. If an operator serves it, call it as an OpenAI-compatible chat API with tools in the function format\n3. Pass `chat_template_kwargs` with `enable_thinking` true or false\n4. With thinking on, keep earlier reasoning in the history (`save_history_reasoning_content` true), as the card recommends\n5. The card's results use temperature 1.0 and top_p 0.95\n6. Its benchmark table is Meituan's own. The unmarked scores are in-house\n\n## Questions\n\n### Which is better for AI agents, LFM2.5-2.6B or LongCat-2.5?\n\nLongCat-2.5 and LFM2.5-2.6B score within a point of each other on agent readiness, 50.5 (D) and 50.3 (D). LFM2.5-2.6B leads on reach and documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-longcat-2-5.json, and with the fewest tokens: https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-longcat-2-5.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"lfm2-5-2-6b\", \"b\": \"longcat-2-5\"}`. From a terminal: `anchor compare lfm2-5-2-6b longcat-2-5`\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/longcat-2-5.json\n\n## Other comparisons with LFM2.5-2.6B or LongCat-2.5\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 LongCat-2.5](https://www.anchorterminal.com/compare/agents-a1-4b-vs-longcat-2-5.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 LongCat-2.5](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-longcat-2-5.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 LongCat-2.5](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-longcat-2-5.md)\n- [Arete-27B vs 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