{
  "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": "LFM3-VL-2B scores 61 (C) on agent readiness against LFM2.5-2.6B's 50.3 (D), and leads in 2 of 4 scored categories. LFM2.5-2.6B leads on reach and risk.",
    "b": {
      "slug": "lfm3-vl-2b",
      "name": "LFM3-VL-2B",
      "vendor": "Liquid AI",
      "vendorUrl": "https://www.liquid.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "LFM3-VL-2B is Liquid AI's open-weight 1.92B vision-language model for on-device use, taking text, images and video with thinking, tool calling and a 128K-token context, released on 27 October 2026 with Liquid's own GGUF, FP8, NVFP4, MLX and ONNX builds.",
      "url": "https://www.anchorterminal.com/tools/lfm3-vl-2b",
      "markdownUrl": "https://www.anchorterminal.com/tools/lfm3-vl-2b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/lfm3-vl-2b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/lfm3-vl-2b.json",
      "dns": "lfm3-vl-2b.grade.anchorterminal.com",
      "repo": "https://huggingface.co/LiquidAI/LFM3-VL-2B",
      "license": "LFM Open License v1.0 (free commercial use below $10M annual revenue)",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "LiquidAI/LFM3-VL-2B"
        },
        {
          "registry": "huggingface",
          "name": "LiquidAI/LFM3-VL-2B-GGUF"
        },
        {
          "registry": "huggingface",
          "name": "LiquidAI/LFM3-VL-2B-MLX-4bit"
        },
        {
          "registry": "ollama",
          "name": "lfm3-vl"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository and Liquid's GGUF, MLX and ONNX repositories are not gated (checked 2026-11-10).",
      "pricing": "free",
      "pricingNotes": "Free to download and use under the LFM Open License v1.0, commercial use included for organisations with under $10M in annual revenue; above that, commercial use isn't licensed. Running it yourself costs little hardware, 1.10 GB for Liquid's Q4_K_M GGUF with its 0.20 GB projector file and 3.84 GB in BF16. Liquid's API also serves it, at prices shown in its console (checked 2026-11-10).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-11-10).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-11-10"
      },
      "docsUrl": "https://docs.liquid.ai/lfm/models/lfm3-vl-2b",
      "llmsTxt": "https://docs.liquid.ai/llms.txt",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "vision",
        "video",
        "small",
        "edge",
        "reasoning",
        "tool-calling",
        "custom-licence",
        "safetensors",
        "gguf",
        "mlx",
        "onnx",
        "self-hosted"
      ],
      "lastRelease": "2026-11-09",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 61,
        "grade": "C",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1518,
        "categoryRank": 58,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 90,
          "reach": 85,
          "risk": 40,
          "upkeep": 77
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-10"
        },
        "negative": 0,
        "verdict": "A 1.92B vision model that reads images and video on an 8 GB machine, with a documented tool-call format and thinking. It runs today in llama.cpp, Ollama and LM Studio, while vLLM and SGLang support is still unreleased. Transformers needs repository code, and there are no safety results, security contact or data sources.",
        "bestFor": "Agents on a laptop, phone or robot that need to read screens, documents, images and video from a model under 2 GB at 4 bits",
        "strengths": [
          "Reads images and video on an 8 GB machine from Liquid's own GGUF",
          "Tool calling, deferred tools and thinking in a 1.92B vision model",
          "A 128K-token context",
          "Liquid answered most questions on the repository",
          "Liquid's own API serves it"
        ],
        "weaknesses": [
          "vLLM and SGLang support is unreleased, which the card says itself",
          "Loading in Transformers runs code from the repository",
          "No safety results and no security contact",
          "How to ask for JSON isn't documented for this model",
          "The licence stops free commercial use at $10M in annual revenue",
          "Not recommended for coding, multi-turn conversations or complex reasoning"
        ],
        "agentNotes": [
          "Start it with `llama-server -hf LiquidAI/LFM3-VL-2B-GGUF:Q4_K_M --jinja` or `ollama run lfm3-vl`, with llama.cpp b11960 or later",
          "Use temperature 0.1, top_k 50 and repetition penalty 1.1, per the card",
          "Video is sampled at 2 frames a second up to 768 frames, and each frame costs context. Video input in llama.cpp needs ffmpeg",
          "Don't plan on vLLM or SGLang until a release carries LFM3-VL",
          "Validate JSON output yourself",
          "Liquid doesn't recommend it for coding, multi-turn conversations or complex reasoning"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 0,
        "avgRating": 0,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "C",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 61
          }
        ],
        "editorialScores": {
          "interface": 90,
          "reach": 85,
          "risk": 40,
          "upkeep": 77
        },
        "provenanceScore": 73,
        "checked": {
          "categories": {
            "interface": "2026-11-10",
            "reach": "2026-11-10",
            "risk": "2026-11-10",
            "upkeep": "2026-11-10"
          },
          "last": "2026-11-10",
          "oldest": "2026-11-10",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "llama-server -hf LiquidAI/LFM3-VL-2B-GGUF:Q4_K_M --jinja\n# or\nollama run lfm3-vl",
        "http": "curl http://localhost:8080/v1/chat/completions \\\n  -H 'Content-Type: application/json' \\\n  -d '{\"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/lfm3-vl-2b"
      },
      "sameCompany": [
        "liquid-d1",
        "lfm2-5-2-6b",
        "lfm3-1-7b",
        "lfm3-audio-2b",
        "lfm3-omni-2b"
      ],
      "weights": {
        "trainedBy": "Liquid AI",
        "builtOn": "LiquidAI/LFM3-1.7B-Base",
        "params": "1.92B",
        "context": 128000,
        "goodFor": "Small on-device vision model for questions about video, documents, charts and app screens, finding objects and spatial reasoning, not for coding, multi-turn conversations or complex reasoning.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "LM Studio"
        ],
        "builds": [
          {
            "name": "LFM3-VL-2B",
            "url": "https://huggingface.co/LiquidAI/LFM3-VL-2B",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 3.84,
            "memoryGB": 8,
            "format": "safetensors",
            "runsOn": [
              "Transformers"
            ],
            "note": "Loads in Transformers with the repository's own code. Liquid also publishes FP8 (2.59 GB) and NVFP4 (2.21 GB) safetensors builds and an ONNX repository"
          },
          {
            "name": "LFM3-VL-2B-GGUF Q8_0",
            "url": "https://huggingface.co/LiquidAI/LFM3-VL-2B-GGUF",
            "who": "publisher",
            "precision": "q8_0",
            "sizeGB": 2.05,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "llama.cpp",
              "LM Studio"
            ],
            "note": "LFM3-VL-2B-Q8_0.gguf (1.85 GB) plus mmproj-LFM3-VL-2B-Q8_0.gguf (0.20 GB)"
          },
          {
            "name": "LFM3-VL-2B-GGUF Q4_K_M",
            "url": "https://huggingface.co/LiquidAI/LFM3-VL-2B-GGUF",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 1.3,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "llama.cpp",
              "LM Studio"
            ],
            "note": "LFM3-VL-2B-Q4_K_M.gguf (1.10 GB) plus Liquid's projector file mmproj-LFM3-VL-2B-Q8_0.gguf (0.20 GB), from the Hub's file list. The repository also holds Q4_0, Q8_0, F16 and BF16 files"
          },
          {
            "name": "LFM3-VL-2B-MLX-4bit",
            "url": "https://huggingface.co/LiquidAI/LFM3-VL-2B-MLX-4bit",
            "who": "publisher",
            "precision": "4bit",
            "sizeGB": 1.59,
            "memoryGB": 8,
            "format": "mlx",
            "runsOn": [
              "MLX-VLM"
            ],
            "note": "Liquid also publishes 5, 6 and 8-bit and BF16 MLX builds"
          },
          {
            "name": "ollama lfm3-vl",
            "url": "https://ollama.com/library/lfm3-vl",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 1.5,
            "memoryGB": 8,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "Ollama's library build, size as its tags page shows it on 10 November 2026. The library also has q8_0, bf16, mxfp8 and nvfp4 tags"
          }
        ],
        "run": "llama-server -hf LiquidAI/LFM3-VL-2B-GGUF:Q4_K_M --jinja",
        "runBuild": "LFM3-VL-2B-GGUF Q4_K_M",
        "checked": "2026-11-10",
        "minMemoryGB": 8
      },
      "family": {
        "lab": "Liquid AI",
        "family": "LFM3",
        "url": "https://www.anchorterminal.com/open-weights/#liquid",
        "others": [
          {
            "name": "LFM3-1.7B-Base",
            "url": "https://huggingface.co/LiquidAI/LFM3-1.7B-Base",
            "params": "1.73B"
          },
          {
            "name": "LFM3-1.7B",
            "url": "https://huggingface.co/LiquidAI/LFM3-1.7B",
            "params": "1.73B",
            "listing": "https://www.anchorterminal.com/tools/lfm3-1-7b"
          },
          {
            "name": "LFM3-Omni-2B",
            "url": "https://huggingface.co/LiquidAI/LFM3-Omni-2B",
            "params": "2.17B",
            "listing": "https://www.anchorterminal.com/tools/lfm3-omni-2b"
          },
          {
            "name": "LFM3-Audio-2B",
            "url": "https://huggingface.co/LiquidAI/LFM3-Audio-2B",
            "params": "2.00B",
            "listing": "https://www.anchorterminal.com/tools/lfm3-audio-2b"
          }
        ]
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Liquid AI, Inc. (the licensor named in the LFM Open License)",
        "domain": "liquid.ai",
        "domainRegistered": "2017-12-16",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/LiquidAI/LFM3-VL-2B/commits/main",
        "securityTxt": "none",
        "checked": "2026-11-10",
        "notes": [
          "The licence names Liquid AI, Inc. as licensor. The domain's registration date is as recorded on the liquid-d1 listing on 8 October 2026",
          "www.liquid.ai/.well-known/security.txt answered 404 on 10 November 2026"
        ],
        "score": 73
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/lfm3-vl-2b.json"
    },
    "edges": [
      {
        "by": 20,
        "category": "upkeep",
        "leads": "lfm3-vl-2b",
        "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 17, Intended use (questions about video, documents, charts and app screens, finding objects, spatial reasoning), a limitation (not recommended for coding, multi-turn conversations or complex reasoning), architecture (language model, 14-layer vision encoder and projector), parameters (1.92B), context (128K) and 16 languages. Training data 5, A general description: the base's 22T tokens of pre-training with the kinds of data added since LFM2, then vision mid-training on image and video data, supervised fine-tuning, vision reinforcement learning and distillation. Last change 20, the last commit, on 9 November 2026, added the OpenVINO checkpoint to the card's inference table, a day before this check. Responsiveness 20, Of the 5 discussions that aren't pull requests (27 to 31 October 2026), 3 have a reply from a LiquidAI member, which is most. Version policy 5, LFM3 came out in new repositories and the LFM2.5 models stay in theirs. File health 10, Config, template and card agree on the lfm3_vl architecture, the 128,000-token context and the vision settings in processor_config.json. 17 + 5 + 20 + 20 + 5 + 10 = 77."
        },
        "scores": {
          "a": 57,
          "b": 77
        }
      },
      {
        "by": 15,
        "category": "interface",
        "leads": "lfm3-vl-2b",
        "name": "Agent interface",
        "reasons": {
          "a": "The chat template earns 20 for chat_template.jinja in the repository. The tool-calling format earns 25, as Liquid's card and tool-use page say how tools go in and how calls come back, and its vLLM and SGLang pages name the lfm2 parser. Trained tool calling earns 20, with several calls in one Python list and agentic RL inside agent harnesses. Structured output earns nothing, as JSON output isn't documented for the model. Reasoning control earns nothing, since the model always thinks. Context earns 10 for 131,072 tokens trained.",
          "b": "Chat template 20, chat_template.jinja in the repository (12,129 characters), with roles, images and video, tools, deferred tools, tool results and the thinking switch. Tool-calling format 25, the card and Liquid's tool-use page document tools passed through `tools=` in apply_chat_template, calls returned as XML and results sent back as tool messages. Trained tool calling 20, the template loops over every entry in an assistant turn's tool_calls, so several calls fit in one turn, and the card says its fine-tuning mix included tool calling and agentic traces. Structured output 5, the card's vLLM and SGLang commands set a structured-output backend and its fine-tuning table has a JSON extraction notebook, but nothing says how to ask this model for JSON. Liquid's API guide documents `response_format` with lfm3-1.7b as its example. Reasoning control 10, Liquid's chat-template page documents `enable_thinking=False` in apply_chat_template for LFM3, with reasoning on by default inside think tags, and the card documents `preserve_thinking` and reports a no-thinking configuration in its benchmarks. Context 10, 128K tokens of input and output per the card, and max_position_embeddings 128000 for the language model in config.json. 20 + 25 + 20 + 5 + 10 + 10 = 90."
        },
        "scores": {
          "a": 75,
          "b": 90
        }
      },
      {
        "by": 10,
        "category": "risk",
        "leads": "lfm2-5-2-6b",
        "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 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, files in the LiquidAI organisation, which Liquid's docs page for the model and its blog post link to (10). Safety evaluation 0, no safety results on the card or in Liquid's blog post for the LFM3 family, neither of which mentions safety testing. Agent guidance 0, Nothing on prompt injection, an instruction hierarchy, what refusals cover or safeguards to add. Licence risk 10, 15 less 5 for the $10M annual revenue threshold on commercial use. Security programme 0, no way to report a flaw found. 15 + 15 + 0 + 0 + 10 + 0 = 40."
        },
        "scores": {
          "a": 50,
          "b": 40
        }
      },
      {
        "by": 5,
        "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 30, Liquid's Q4_K_M GGUF is 1.10 GB with its 0.20 GB projector file, and 1.30 GB × 1.2 = 1.6 GB, which fits 8 GB, well inside the 16 GB line. Runtimes 15, Released llama.cpp has the lfm3 architecture and the LFM3-VL projector (src/models/lfm3.cpp and tools/mtmd at b12242), Ollama's library has an lfm3-vl entry, and LM Studio's catalogue lists liquid/lfm3-vl-2b. Builds 15, Liquid's GGUF repository holds Q4_0, Q4_K_M, Q8_0, F16 and BF16 files, and Liquid publishes 4-bit and 8-bit MLX builds and an ONNX repository, all in the LiquidAI organisation. Terms 10, the LFM Open License v1.0, a custom licence that lets anyone use it commercially for free below a threshold of $10M annual revenue. Download 10, the Hub API reports gated false for the model and its GGUF, MLX and ONNX repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, Liquid's own API lists lfm3-vl-2b among its models, so the publisher hosts it. 30 + 15 + 15 + 10 + 10 + 5 = 85."
        },
        "scores": {
          "a": 90,
          "b": 85
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Liquid AI",
        "b": "Liquid AI",
        "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": "LFM Open License v1.0 (free commercial use below $10M annual revenue)",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "yes",
        "b": "yes",
        "name": "llms.txt"
      },
      {
        "a": "2026-11-03",
        "b": "2026-11-09",
        "name": "Last release"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Terms last updated"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Privacy policy last updated"
      },
      {
        "a": "2.5/5 (4)",
        "b": "none",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "LFM3-VL-2B scores 61 (C) on agent readiness against LFM2.5-2.6B's 50.3 (D), and leads in 2 of 4 scored categories. LFM2.5-2.6B leads on reach and risk.",
        "question": "Which is better for AI agents, LFM2.5-2.6B or LFM3-VL-2B?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 90 against 85",
          "Risk, 50 against 40"
        ],
        "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": [
          "Agent interface, 90 against 75",
          "Documentation and upkeep, 77 against 57"
        ],
        "also": null,
        "goodFor": "Agents on a laptop, phone or robot that need to read screens, documents, images and video from a model under 2 GB at 4 bits",
        "slug": "lfm3-vl-2b",
        "watchFor": "vLLM and SGLang support is unreleased, which the card says itself"
      }
    ],
    "history": {
      "lfm2-5-2-6b": [
        {
          "causes": [
            "correction"
          ],
          "date": "2026-11-04",
          "delta": -1.2,
          "from": 51.5,
          "fromGrade": "D",
          "to": 50.3,
          "toGrade": "D"
        }
      ],
      "lfm3-vl-2b": 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-lfm3-vl-2b.json",
        "title": "Agents-A1-4B vs LFM3-VL-2B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-lfm3-vl-2b"
      },
      {
        "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-lfm3-vl-2b.json",
        "title": "Apertus v1.5 8B vs LFM3-VL-2B",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-lfm3-vl-2b"
      },
      {
        "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-lfm3-vl-2b.json",
        "title": "Apodex-1.1-mini vs LFM3-VL-2B",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-lfm3-vl-2b"
      },
      {
        "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-lfm3-vl-2b.json",
        "title": "Arete-27B vs LFM3-VL-2B",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-lfm3-vl-2b"
      },
      {
        "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-lfm3-vl-2b.json",
        "title": "BigBang-v1 vs LFM3-VL-2B",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-lfm3-vl-2b"
      },
      {
        "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-lfm3-vl-2b.json",
        "title": "Carbon-X vs LFM3-VL-2B",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-lfm3-vl-2b"
      },
      {
        "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-lfm3-vl-2b.json",
        "title": "Cola2 vs LFM3-VL-2B",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-lfm3-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-lfm2-5-2-6b.json",
        "title": "Cosmos-Reason2-2B vs LFM2.5-2.6B",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-lfm2-5-2-6b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-lfm3-vl-2b.json",
        "title": "Cosmos-Reason2-2B vs LFM3-VL-2B",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-lfm3-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-lfm2-5-2-6b.json",
        "title": "Darwin-36B-Opus vs LFM2.5-2.6B",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-lfm2-5-2-6b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-lfm3-vl-2b.json",
        "title": "Darwin-36B-Opus vs LFM3-VL-2B",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-lfm3-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-lfm2-5-2-6b.json",
        "title": "DeepSeek-V3.2 vs LFM2.5-2.6B",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-lfm2-5-2-6b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-lfm3-vl-2b.json",
        "title": "DeepSeek-V3.2 vs LFM3-VL-2B",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-lfm3-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-lfm2-5-2-6b.json",
        "title": "DeepSeek V4.1 Flash vs LFM2.5-2.6B",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-lfm2-5-2-6b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-lfm3-vl-2b.json",
        "title": "DeepSeek V4.1 Flash vs LFM3-VL-2B",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-lfm3-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-lfm2-5-2-6b.json",
        "title": "DeepSeek-V4-Flash-0731 vs LFM2.5-2.6B",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-lfm2-5-2-6b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-lfm3-vl-2b.json",
        "title": "DeepSeek-V4-Flash-0731 vs LFM3-VL-2B",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-lfm3-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-lfm2-5-2-6b.json",
        "title": "Devstral Small 2 vs LFM2.5-2.6B",
        "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-lfm2-5-2-6b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-lfm3-vl-2b.json",
        "title": "Devstral Small 2 vs LFM3-VL-2B",
        "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-lfm3-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-lfm2-5-2-6b.json",
        "title": "DiffusionGemma 26B A4B IT vs LFM2.5-2.6B",
        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-lfm2-5-2-6b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-lfm3-vl-2b.json",
        "title": "DiffusionGemma 26B A4B IT vs LFM3-VL-2B",
        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-lfm3-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-lfm2-5-2-6b.json",
        "title": "EXAONE 4.5 33B vs LFM2.5-2.6B",
        "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-lfm2-5-2-6b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-lfm3-vl-2b.json",
        "title": "EXAONE 4.5 33B vs LFM3-VL-2B",
        "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-lfm3-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-lfm2-5-2-6b.json",
        "title": "Gemma 4 12B IT vs LFM2.5-2.6B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-lfm2-5-2-6b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-lfm3-vl-2b.json",
        "title": "Gemma 4 12B IT vs LFM3-VL-2B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-lfm3-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-lfm2-5-2-6b.json",
        "title": "Gemma 4 26B A4B IT vs LFM2.5-2.6B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-lfm2-5-2-6b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-lfm3-vl-2b.json",
        "title": "Gemma 4 26B A4B IT vs LFM3-VL-2B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-lfm3-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-lfm2-5-2-6b.json",
        "title": "Gemma 4 31B IT vs LFM2.5-2.6B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-lfm2-5-2-6b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-lfm3-vl-2b.json",
        "title": "Gemma 4 31B IT vs LFM3-VL-2B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-lfm3-vl-2b"
      },
      {
        "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"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-lfm3-vl-2b.json",
        "title": "Gemma 4 E2B IT vs LFM3-VL-2B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-lfm3-vl-2b"
      },
      {
        "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"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-lfm3-vl-2b.json",
        "title": "Gemma 4 E4B IT vs LFM3-VL-2B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-lfm3-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-lfm2-5-2-6b.json",
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        "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-lfm2-5-2-6b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-lfm3-vl-2b.json",
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        "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-lfm3-vl-2b"
      },
      {
        "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"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-lfm3-vl-2b.json",
        "title": "GLM-4.7-Flash vs LFM3-VL-2B",
        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-lfm3-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-lfm2-5-2-6b.json",
        "title": "GLM-5.3-Flash vs LFM2.5-2.6B",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-lfm2-5-2-6b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-lfm3-vl-2b.json",
        "title": "GLM-5.3-Flash vs LFM3-VL-2B",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-lfm3-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-lfm2-5-2-6b.json",
        "title": "GLM-5.3 vs LFM2.5-2.6B",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-lfm2-5-2-6b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-lfm3-vl-2b.json",
        "title": "GLM-5.3 vs LFM3-VL-2B",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-lfm3-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-lfm2-5-2-6b.json",
        "title": "gpt-oss-120b vs LFM2.5-2.6B",
        "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-lfm2-5-2-6b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-lfm3-vl-2b.json",
        "title": "gpt-oss-120b vs LFM3-VL-2B",
        "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-lfm3-vl-2b"
      },
      {
        "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"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-lfm3-vl-2b.json",
        "title": "gpt-oss-20b vs LFM3-VL-2B",
        "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-lfm3-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-lfm2-5-2-6b.json",
        "title": "Granite 4.2 30B vs LFM2.5-2.6B",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-lfm2-5-2-6b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-lfm3-vl-2b.json",
        "title": "Granite 4.2 30B vs LFM3-VL-2B",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-lfm3-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-lfm2-5-2-6b.json",
        "title": "Granite 4.2 3B vs LFM2.5-2.6B",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-lfm2-5-2-6b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-lfm3-vl-2b.json",
        "title": "Granite 4.2 3B vs LFM3-VL-2B",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-lfm3-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-lfm2-5-2-6b.json",
        "title": "Granite 4.2 8B vs LFM2.5-2.6B",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-lfm2-5-2-6b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-lfm3-vl-2b.json",
        "title": "Granite 4.2 8B vs LFM3-VL-2B",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-lfm3-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-lfm2-5-2-6b.json",
        "title": "Hy-Memory-30B-A3B vs LFM2.5-2.6B",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-lfm2-5-2-6b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-lfm3-vl-2b.json",
        "title": "Hy-Memory-30B-A3B vs LFM3-VL-2B",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-lfm3-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy3-vs-lfm2-5-2-6b.json",
        "title": "Hy3 vs LFM2.5-2.6B",
        "url": "https://www.anchorterminal.com/compare/hy3-vs-lfm2-5-2-6b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy3-vs-lfm3-vl-2b.json",
        "title": "Hy3 vs LFM3-VL-2B",
        "url": "https://www.anchorterminal.com/compare/hy3-vs-lfm3-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/inkling-small-vs-lfm2-5-2-6b.json",
        "title": "Inkling-Small vs LFM2.5-2.6B",
        "url": "https://www.anchorterminal.com/compare/inkling-small-vs-lfm2-5-2-6b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/inkling-small-vs-lfm3-vl-2b.json",
        "title": "Inkling-Small vs LFM3-VL-2B",
        "url": "https://www.anchorterminal.com/compare/inkling-small-vs-lfm3-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/inkling-vs-lfm2-5-2-6b.json",
        "title": "Inkling vs LFM2.5-2.6B",
        "url": "https://www.anchorterminal.com/compare/inkling-vs-lfm2-5-2-6b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/inkling-vs-lfm3-vl-2b.json",
        "title": "Inkling vs LFM3-VL-2B",
        "url": "https://www.anchorterminal.com/compare/inkling-vs-lfm3-vl-2b"
      },
      {
        "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"
      },
      {
        "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-lfm3-vl-2b.json",
        "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs LFM3-VL-2B",
        "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-lfm3-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-lfm2-5-2-6b.json",
        "title": "Kimi K3.1 Pro vs LFM2.5-2.6B",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-lfm2-5-2-6b"
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        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "LFM3-VL-2B vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-vl-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-vl-32b-instruct.json",
        "title": "LFM3-VL-2B vs Qwen3-VL-32B-Instruct",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-vl-32b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-vl-4b-instruct.json",
        "title": "LFM3-VL-2B vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-vl-8b-instruct.json",
        "title": "LFM3-VL-2B vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-vl-8b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwopus3-8-27b-flash-v2.json",
        "title": "LFM3-VL-2B vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-recursive-flint.json",
        "title": "LFM3-VL-2B vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-ripple-65b-preview.json",
        "title": "LFM3-VL-2B vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-sensenova-loop-9b-preview.json",
        "title": "LFM3-VL-2B vs SenseNova-Loop-9B-Preview",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-sensenova-loop-9b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-smollm3-3b.json",
        "title": "LFM3-VL-2B vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-spark-x2-6-4b.json",
        "title": "LFM3-VL-2B vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-step-3-7-flash.json",
        "title": "LFM3-VL-2B vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-step-3-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "LFM3-VL-2B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "LFM3-VL-2B vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-thinkingcap-qwen3-8-27b.json",
        "title": "LFM3-VL-2B vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-thinkingcap-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-unlimited-ocr-2.json",
        "title": "LFM3-VL-2B vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-wevisone-vl-2b.json",
        "title": "LFM3-VL-2B vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-wevisone-vl-4b.json",
        "title": "LFM3-VL-2B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-zaya1-8b.json",
        "title": "LFM3-VL-2B vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-zaya1-8b"
      }
    ],
    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 5,
        "edge": "lfm2-5-2-6b",
        "key": "reach",
        "lfm2-5-2-6b": 90,
        "lfm3-vl-2b": 85,
        "name": "Reach",
        "weight": 25
      },
      {
        "by": 10,
        "edge": "lfm2-5-2-6b",
        "key": "risk",
        "lfm2-5-2-6b": 50,
        "lfm3-vl-2b": 40,
        "name": "Risk",
        "weight": 20
      },
      {
        "by": 15,
        "edge": "lfm3-vl-2b",
        "key": "interface",
        "lfm2-5-2-6b": 75,
        "lfm3-vl-2b": 90,
        "name": "Agent interface",
        "weight": 10
      },
      {
        "by": 20,
        "edge": "lfm3-vl-2b",
        "key": "upkeep",
        "lfm2-5-2-6b": 57,
        "lfm3-vl-2b": 77,
        "name": "Documentation and upkeep",
        "weight": 10
      }
    ],
    "summary": "LFM3-VL-2B scores 61 (C) on agent readiness against LFM2.5-2.6B's 50.3 (D), and leads in 2 of 4 scored categories. LFM2.5-2.6B leads on reach and risk. 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.",
      "lfm3-vl-2b": "A 1.92B vision model that reads images and video on an 8 GB machine, with a documented tool-call format and thinking. It runs today in llama.cpp, Ollama and LM Studio, while vLLM and SGLang support is still unreleased. Transformers needs repository code, and there are no safety results, security contact or data sources."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-lfm3-vl-2b",
    "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-lfm3-vl-2b.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-lfm3-vl-2b.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-lfm3-vl-2b.min.md"
  },
  "markdown": "LFM3-VL-2B scores 61 (C) on agent readiness against LFM2.5-2.6B's 50.3 (D), and leads in 2 of 4 scored categories. LFM2.5-2.6B leads on reach and risk. 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- LFM3-VL-2B: grade C, 61/100, rank #72 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/lfm3-vl-2b.md · JSON https://www.anchorterminal.com/api/v1/tools/lfm3-vl-2b.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 85\n- Risk, 50 against 40\n\nWatch for: Liquid lists it as deprecated, replaced by LFM3-1.7B; deprecated models stay downloadable but get no further updates\n\n### LFM3-VL-2B (C)\n\nGood for: Agents on a laptop, phone or robot that need to read screens, documents, images and video from a model under 2 GB at 4 bits\n\nAhead on:\n- Agent interface, 90 against 75\n- Documentation and upkeep, 77 against 57\n\nWatch for: vLLM and SGLang support is unreleased, which the card says itself\n\n\n## Why the edge\n\n### Documentation and upkeep: LFM3-VL-2B +20\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- LFM3-VL-2B (77): Model card 17, Intended use (questions about video, documents, charts and app screens, finding objects, spatial reasoning), a limitation (not recommended for coding, multi-turn conversations or complex reasoning), architecture (language model, 14-layer vision encoder and projector), parameters (1.92B), context (128K) and 16 languages. Training data 5, A general description: the base's 22T tokens of pre-training with the kinds of data added since LFM2, then vision mid-training on image and video data, supervised fine-tuning, vision reinforcement learning and distillation. Last change 20, the last commit, on 9 November 2026, added the OpenVINO checkpoint to the card's inference table, a day before this check. Responsiveness 20, Of the 5 discussions that aren't pull requests (27 to 31 October 2026), 3 have a reply from a LiquidAI member, which is most. Version policy 5, LFM3 came out in new repositories and the LFM2.5 models stay in theirs. File health 10, Config, template and card agree on the lfm3_vl architecture, the 128,000-token context and the vision settings in processor_config.json. 17 + 5 + 20 + 20 + 5 + 10 = 77.\n\n### Agent interface: LFM3-VL-2B +15\n\n- LFM2.5-2.6B (75): The chat template earns 20 for chat_template.jinja in the repository. The tool-calling format earns 25, as Liquid's card and tool-use page say how tools go in and how calls come back, and its vLLM and SGLang pages name the lfm2 parser. Trained tool calling earns 20, with several calls in one Python list and agentic RL inside agent harnesses. Structured output earns nothing, as JSON output isn't documented for the model. Reasoning control earns nothing, since the model always thinks. Context earns 10 for 131,072 tokens trained.\n- LFM3-VL-2B (90): Chat template 20, chat_template.jinja in the repository (12,129 characters), with roles, images and video, tools, deferred tools, tool results and the thinking switch. Tool-calling format 25, the card and Liquid's tool-use page document tools passed through `tools=` in apply_chat_template, calls returned as XML and results sent back as tool messages. Trained tool calling 20, the template loops over every entry in an assistant turn's tool_calls, so several calls fit in one turn, and the card says its fine-tuning mix included tool calling and agentic traces. Structured output 5, the card's vLLM and SGLang commands set a structured-output backend and its fine-tuning table has a JSON extraction notebook, but nothing says how to ask this model for JSON. Liquid's API guide documents `response_format` with lfm3-1.7b as its example. Reasoning control 10, Liquid's chat-template page documents `enable_thinking=False` in apply_chat_template for LFM3, with reasoning on by default inside think tags, and the card documents `preserve_thinking` and reports a no-thinking configuration in its benchmarks. Context 10, 128K tokens of input and output per the card, and max_position_embeddings 128000 for the language model in config.json. 20 + 25 + 20 + 5 + 10 + 10 = 90.\n\n### Risk: LFM2.5-2.6B +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- LFM3-VL-2B (40): File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, files in the LiquidAI organisation, which Liquid's docs page for the model and its blog post link to (10). Safety evaluation 0, no safety results on the card or in Liquid's blog post for the LFM3 family, neither of which mentions safety testing. Agent guidance 0, Nothing on prompt injection, an instruction hierarchy, what refusals cover or safeguards to add. Licence risk 10, 15 less 5 for the $10M annual revenue threshold on commercial use. Security programme 0, no way to report a flaw found. 15 + 15 + 0 + 0 + 10 + 0 = 40.\n\n### Reach: LFM2.5-2.6B +5\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- LFM3-VL-2B (85): Memory 30, Liquid's Q4_K_M GGUF is 1.10 GB with its 0.20 GB projector file, and 1.30 GB × 1.2 = 1.6 GB, which fits 8 GB, well inside the 16 GB line. Runtimes 15, Released llama.cpp has the lfm3 architecture and the LFM3-VL projector (src/models/lfm3.cpp and tools/mtmd at b12242), Ollama's library has an lfm3-vl entry, and LM Studio's catalogue lists liquid/lfm3-vl-2b. Builds 15, Liquid's GGUF repository holds Q4_0, Q4_K_M, Q8_0, F16 and BF16 files, and Liquid publishes 4-bit and 8-bit MLX builds and an ONNX repository, all in the LiquidAI organisation. Terms 10, the LFM Open License v1.0, a custom licence that lets anyone use it commercially for free below a threshold of $10M annual revenue. Download 10, the Hub API reports gated false for the model and its GGUF, MLX and ONNX repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, Liquid's own API lists lfm3-vl-2b among its models, so the publisher hosts it. 30 + 15 + 15 + 10 + 10 + 5 = 85.\n\n## Score by category\n\n| Category | Weight | LFM2.5-2.6B | LFM3-VL-2B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 90 | 85 | LFM2.5-2.6B +5 |\n| Risk | 20% (20 this run) | 50 | 40 | LFM2.5-2.6B +10 |\n| Agent interface | 10% (10 this run) | 75 | 90 | LFM3-VL-2B +15 |\n| Documentation and upkeep | 10% (10 this run) | 57 | 77 | LFM3-VL-2B +20 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **50.3 · D** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | LFM2.5-2.6B | LFM3-VL-2B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Liquid AI | Liquid AI |\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) | LFM Open License v1.0 (free commercial use below $10M annual revenue) |\n| Read-only variant documented | no | no |\n| llms.txt | yes | yes |\n| Last release | 2026-11-03 | 2026-11-09 |\n| Terms last updated | no document linked | no document linked |\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### LFM3-VL-2B\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**LFM3-VL-2B.** A 1.92B vision model that reads images and video on an 8 GB machine, with a documented tool-call format and thinking. It runs today in llama.cpp, Ollama and LM Studio, while vLLM and SGLang support is still unreleased. Transformers needs repository code, and there are no safety results, security contact or data sources.\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### LFM3-VL-2B\n\n1. Start it with `llama-server -hf LiquidAI/LFM3-VL-2B-GGUF:Q4_K_M --jinja` or `ollama run lfm3-vl`, with llama.cpp b11960 or later\n2. Use temperature 0.1, top_k 50 and repetition penalty 1.1, per the card\n3. Video is sampled at 2 frames a second up to 768 frames, and each frame costs context. Video input in llama.cpp needs ffmpeg\n4. Don't plan on vLLM or SGLang until a release carries LFM3-VL\n5. Validate JSON output yourself\n6. Liquid doesn't recommend it for coding, multi-turn conversations or complex reasoning\n\n## Questions\n\n### Which is better for AI agents, LFM2.5-2.6B or LFM3-VL-2B?\n\nLFM3-VL-2B scores 61 (C) on agent readiness against LFM2.5-2.6B's 50.3 (D), and leads in 2 of 4 scored categories. LFM2.5-2.6B leads on reach and risk.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-lfm3-vl-2b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-lfm3-vl-2b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"lfm2-5-2-6b\", \"b\": \"lfm3-vl-2b\"}`. From a terminal: `anchor compare lfm2-5-2-6b lfm3-vl-2b`\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/lfm3-vl-2b.json\n\n## Other comparisons with LFM2.5-2.6B or LFM3-VL-2B\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 LFM3-VL-2B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-lfm3-vl-2b.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 LFM3-VL-2B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-lfm3-vl-2b.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 LFM3-VL-2B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-lfm3-vl-2b.md)\n- [Arete-27B vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/arete-27b-vs-lfm2-5-2-6b.md)\n- [Arete-27B vs LFM3-VL-2B](https://www.anchorterminal.com/compare/arete-27b-vs-lfm3-vl-2b.md)\n- [BigBang-v1 vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/bigbang-v1-vs-lfm2-5-2-6b.md)\n- [BigBang-v1 vs LFM3-VL-2B](https://www.anchorterminal.com/compare/bigbang-v1-vs-lfm3-vl-2b.md)\n- [Carbon-X vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/carbon-x-vs-lfm2-5-2-6b.md)\n- [Carbon-X vs LFM3-VL-2B](https://www.anchorterminal.com/compare/carbon-x-vs-lfm3-vl-2b.md)\n- [Cola2 vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/cola2-vs-lfm2-5-2-6b.md)\n- [Cola2 vs LFM3-VL-2B](https://www.anchorterminal.com/compare/cola2-vs-lfm3-vl-2b.md)\n- [Cosmos-Reason2-2B vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-lfm2-5-2-6b.md)\n- [Cosmos-Reason2-2B vs LFM3-VL-2B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-lfm3-vl-2b.md)\n- [Darwin-36B-Opus vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-lfm2-5-2-6b.md)\n- [Darwin-36B-Opus vs LFM3-VL-2B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-lfm3-vl-2b.md)\n- [DeepSeek-V3.2 vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-lfm2-5-2-6b.md)\n- [DeepSeek-V3.2 vs LFM3-VL-2B](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-lfm3-vl-2b.md)\n- [DeepSeek V4.1 Flash vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-lfm2-5-2-6b.md)\n- [DeepSeek V4.1 Flash vs LFM3-VL-2B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-lfm3-vl-2b.md)\n- [DeepSeek-V4-Flash-0731 vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-lfm2-5-2-6b.md)\n- [DeepSeek-V4-Flash-0731 vs LFM3-VL-2B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-lfm3-vl-2b.md)\n- [Devstral Small 2 vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/devstral-small-2-vs-lfm2-5-2-6b.md)\n- [Devstral Small 2 vs LFM3-VL-2B](https://www.anchorterminal.com/compare/devstral-small-2-vs-lfm3-vl-2b.md)\n- [DiffusionGemma 26B A4B IT vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-lfm2-5-2-6b.md)\n- [DiffusionGemma 26B A4B IT vs LFM3-VL-2B](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-lfm3-vl-2b.md)\n- [EXAONE 4.5 33B vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-lfm2-5-2-6b.md)\n- [EXAONE 4.5 33B vs LFM3-VL-2B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-lfm3-vl-2b.md)\n- [Gemma 4 12B IT vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-lfm2-5-2-6b.md)\n- [Gemma 4 12B IT vs LFM3-VL-2B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-lfm3-vl-2b.md)\n- [Gemma 4 26B A4B IT vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-lfm2-5-2-6b.md)\n- [Gemma 4 26B A4B IT vs LFM3-VL-2B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-lfm3-vl-2b.md)\n- [Gemma 4 31B IT vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-lfm2-5-2-6b.md)\n- [Gemma 4 31B IT vs LFM3-VL-2B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-lfm3-vl-2b.md)\n- [Gemma 4 E2B IT vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-lfm2-5-2-6b.md)\n- [Gemma 4 E2B IT vs LFM3-VL-2B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-lfm3-vl-2b.md)\n- [Gemma 4 E4B IT vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-lfm2-5-2-6b.md)\n- [Gemma 4 E4B IT vs LFM3-VL-2B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-lfm3-vl-2b.md)\n- [GLM-4.5-Air vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-lfm2-5-2-6b.md)\n- [GLM-4.5-Air vs LFM3-VL-2B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-lfm3-vl-2b.md)\n- [GLM-4.7-Flash vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-lfm2-5-2-6b.md)\n- [GLM-4.7-Flash vs LFM3-VL-2B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-lfm3-vl-2b.md)\n- [GLM-5.3-Flash vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-lfm2-5-2-6b.md)\n- [GLM-5.3-Flash vs LFM3-VL-2B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-lfm3-vl-2b.md)\n- [GLM-5.3 vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/glm-5-3-vs-lfm2-5-2-6b.md)\n- [GLM-5.3 vs LFM3-VL-2B](https://www.anchorterminal.com/compare/glm-5-3-vs-lfm3-vl-2b.md)\n- [gpt-oss-120b vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-lfm2-5-2-6b.md)\n- [gpt-oss-120b vs LFM3-VL-2B](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-lfm3-vl-2b.md)\n- [gpt-oss-20b vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-lfm2-5-2-6b.md)\n- [gpt-oss-20b vs LFM3-VL-2B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-lfm3-vl-2b.md)\n- [Granite 4.2 30B vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/granite-4-2-30b-vs-lfm2-5-2-6b.md)\n- [Granite 4.2 30B vs LFM3-VL-2B](https://www.anchorterminal.com/compare/granite-4-2-30b-vs-lfm3-vl-2b.md)\n- [Granite 4.2 3B vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-lfm2-5-2-6b.md)\n- [Granite 4.2 3B vs LFM3-VL-2B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-lfm3-vl-2b.md)\n- [Granite 4.2 8B vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-lfm2-5-2-6b.md)\n- [Granite 4.2 8B vs LFM3-VL-2B](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-lfm3-vl-2b.md)\n- [Hy-Memory-30B-A3B vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-lfm2-5-2-6b.md)\n- [Hy-Memory-30B-A3B vs LFM3-VL-2B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-lfm3-vl-2b.md)\n- [Hy3 vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/hy3-vs-lfm2-5-2-6b.md)\n- [Hy3 vs LFM3-VL-2B](https://www.anchorterminal.com/compare/hy3-vs-lfm3-vl-2b.md)\n- [Inkling-Small vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/inkling-small-vs-lfm2-5-2-6b.md)\n- [Inkling-Small vs LFM3-VL-2B](https://www.anchorterminal.com/compare/inkling-small-vs-lfm3-vl-2b.md)\n- [Inkling vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/inkling-vs-lfm2-5-2-6b.md)\n- [Inkling vs LFM3-VL-2B](https://www.anchorterminal.com/compare/inkling-vs-lfm3-vl-2b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-lfm2-5-2-6b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs LFM3-VL-2B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-lfm3-vl-2b.md)\n- [Kimi K3.1 Pro vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-lfm2-5-2-6b.md)\n- [Kimi K3.1 Pro vs LFM3-VL-2B](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-lfm3-vl-2b.md)\n- [Kimi K3 vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/kimi-k3-vs-lfm2-5-2-6b.md)\n- [Kimi K3 vs LFM3-VL-2B](https://www.anchorterminal.com/compare/kimi-k3-vs-lfm3-vl-2b.md)\n- [Kolibri-1 vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/kolibri-1-vs-lfm2-5-2-6b.md)\n- [Kolibri-1 vs LFM3-VL-2B](https://www.anchorterminal.com/compare/kolibri-1-vs-lfm3-vl-2b.md)\n- [Kronos vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/kronos-vs-lfm2-5-2-6b.md)\n- [Kronos vs LFM3-VL-2B](https://www.anchorterminal.com/compare/kronos-vs-lfm3-vl-2b.md)\n- [Laguna M.1 vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/laguna-m-1-vs-lfm2-5-2-6b.md)\n- [Laguna M.1 vs LFM3-VL-2B](https://www.anchorterminal.com/compare/laguna-m-1-vs-lfm3-vl-2b.md)\n- [Laguna S 2.1 vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-lfm2-5-2-6b.md)\n- [Laguna S 2.1 vs LFM3-VL-2B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-lfm3-vl-2b.md)\n- [Laguna XS 2.1 vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-lfm2-5-2-6b.md)\n- [Laguna XS 2.1 vs LFM3-VL-2B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-lfm3-vl-2b.md)\n- [LBS-27B vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/lbs-27b-vs-lfm2-5-2-6b.md)\n- [LBS-27B vs LFM3-VL-2B](https://www.anchorterminal.com/compare/lbs-27b-vs-lfm3-vl-2b.md)\n- [LFM2.5-2.6B vs LFM3-1.7B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-lfm3-1-7b.md)\n- [LFM2.5-2.6B vs LFM3-Audio-2B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-lfm3-audio-2b.md)\n- [LFM2.5-2.6B vs LFM3-Omni-2B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-lfm3-omni-2b.md)\n- [LFM2.5-2.6B vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-ling-3-0-tiny.md)\n- [LFM2.5-2.6B vs Ling-3.1-flash-1019](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-ling-3-1-flash-1019.md)\n- [LFM2.5-2.6B vs LongCat-2.5](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-longcat-2-5.md)\n- [LFM2.5-2.6B vs MiMo-V2.6 Distill Qwen 9B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-mimo-v2-6-distill-qwen-9b.md)\n- [LFM2.5-2.6B vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-mimo-v2-6-flash-rl.md)\n- [LFM2.5-2.6B vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minicpm-o-4-5.md)\n- [LFM2.5-2.6B vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minicpm-v-4-7-1b.md)\n- [LFM2.5-2.6B vs MiniCPM-V 4.7 35B-A3B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minicpm-v-4-7-35b-a3b.md)\n- [LFM2.5-2.6B vs MiniCPM5-2B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minicpm5-2b.md)\n- [LFM2.5-2.6B vs MiniMax-M2.5](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minimax-m2-5.md)\n- [LFM2.5-2.6B vs MiniMax-M2.7](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minimax-m2-7.md)\n- [LFM2.5-2.6B vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minimax-m3-1-flash.md)\n- [LFM2.5-2.6B vs Mistral Large 4](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-mistral-large-4.md)\n- [LFM2.5-2.6B vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-muse-glimmer-30b.md)\n- [LFM2.5-2.6B vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nanbeige-4-2-3b.md)\n- [LFM2.5-2.6B vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nanbeige-4-3-33b.md)\n- [LFM2.5-2.6B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [LFM2.5-2.6B vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [LFM2.5-2.6B vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-nano-30b-a3b.md)\n- [LFM2.5-2.6B vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-nano-4b.md)\n- [LFM2.5-2.6B vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [LFM2.5-2.6B vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-super-120b-a12b.md)\n- [LFM2.5-2.6B vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-ultra-550b-a55b.md)\n- [LFM2.5-2.6B vs North Mini Code 1.0](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-north-mini-code-1-0.md)\n- [LFM2.5-2.6B vs occamy-1.0](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-occamy-1-0.md)\n- [LFM2.5-2.6B vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-olmo-3-7b-think.md)\n- [LFM2.5-2.6B vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-ornith-1-5-122b.md)\n- [LFM2.5-2.6B vs 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