{
  "data": {
    "a": {
      "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"
    },
    "answer": "LFM3-VL-2B scores 61 (C) on agent readiness against MiniMax-M2.7's 57.6 (C), and leads in every scored category.",
    "b": {
      "slug": "minimax-m2-7",
      "name": "MiniMax-M2.7",
      "vendor": "MiniMax",
      "vendorUrl": "https://www.minimax.io",
      "kind": "weights",
      "category": "open-weights",
      "summary": "MiniMax-M2.7 is MiniMax's open-weight mixture-of-experts model for coding, agent harnesses and office work, about 229B parameters in FP8 with thinking always on, published on Hugging Face on 9 April 2026 under a non-commercial licence.",
      "url": "https://www.anchorterminal.com/tools/minimax-m2-7",
      "markdownUrl": "https://www.anchorterminal.com/tools/minimax-m2-7.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/minimax-m2-7.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/minimax-m2-7.json",
      "dns": "minimax-m2-7.grade.anchorterminal.com",
      "repo": "https://huggingface.co/MiniMaxAI/MiniMax-M2.7",
      "license": "MiniMax M2.7 non-commercial licence (MIT-style terms, commercial use needs MiniMax's written authorisation)",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "MiniMaxAI/MiniMax-M2.7"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and to use for personal, research and non-profit purposes. Since 11 April 2026 the licence prohibits commercial use without MiniMax's prior written authorisation, and commercial users must display \"Built with MiniMax M2.7\". Running it yourself costs the hardware, 230.2 GB of FP8 files. MiniMax's API closed M2.7 to new users on 19 October 2026 and retires it on 18 November 2026 (checked 2026-10-21).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-10-21"
      },
      "docsUrl": "https://huggingface.co/MiniMaxAI/MiniMax-M2.7",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "reasoning",
        "tool-calling",
        "coding",
        "non-commercial",
        "safetensors",
        "fp8",
        "self-hosted"
      ],
      "lastRelease": "2026-04-20",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 57.6,
        "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": 80,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "capability": 71,
          "interface": 75,
          "reach": 60,
          "risk": 30,
          "upkeep": 42
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-11-04"
        },
        "negative": 0,
        "verdict": "A 230B mixture-of-experts model for coding and agent harnesses, with parallel tool calls, about 200K of context and parsers in vLLM and SGLang. It needs a 4 GPU server for FP8 or about 166 GB for a 4-bit library build, and it always thinks. The main risk is the licence, switched to non-commercial two days after release.",
        "bestFor": "Researchers and personal projects with a 4 to 8 GPU server who want an open agent and coding model with parallel tool calls",
        "strengths": [
          "Runs in released vLLM, SGLang, llama.cpp, Transformers and LM Studio, and in Ollama through its pinned llama.cpp",
          "Tool-call format documented by MiniMax with named vLLM and SGLang parsers and a parsing function",
          "Several tool calls in one block, and about 200K tokens of context",
          "Safetensors only, in the publisher's organisation with a SHA-256 for every shard",
          "No gate or account, and served by three inference providers on Hugging Face"
        ],
        "weaknesses": [
          "Commercial use needs MiniMax's written authorisation since a licence swap on 11 April 2026, made without a note",
          "230 GB in FP8, and about 166 GB for the smallest 4-bit library build, more than a 128 GB machine",
          "Thinking is always on, with no documented way to turn it off or budget it",
          "No safety evaluation, agent guidance or security contact, and every loading route runs repository code",
          "No MiniMax reply in discussions since June 2026, and the card leaves out limitations, architecture, parameters and a cut-off"
        ],
        "agentNotes": [
          "Check the use is personal, research or non-profit before starting it; anything commercial needs MiniMax's written authorisation, and commercial users must show Built with MiniMax M2.7",
          "Start it with vLLM on 4 GPUs of 96 GB, SAFETENSORS_FAST_GPU=1 vllm serve MiniMaxAI/MiniMax-M2.7 --trust-remote-code --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser minimax_m2 --reasoning-parser minimax_m2_append_think",
          "In SGLang use --tool-call-parser minimax-m2 and --reasoning-parser minimax-append-think, with --trust-remote-code",
          "MiniMax's guides budget 220 GB for the weights and 240 GB per 1M tokens of KV cache, with 196K the most per sequence",
          "On one large-memory machine, LM Studio's catalogue entry minimax/minimax-m2.7 lists a 138 GB Q4_K_M GGUF and states 138 GB minimum memory",
          "Sample with temperature 1.0, top_p 0.95 and top_k 40, as the card recommends, and budget output tokens for thinking on every turn",
          "Pin Transformers 4.57.1 as MiniMax's guide does; a report that it fails on Transformers v5 has no answer",
          "Without the hardware, call it through Novita, Featherless AI or DeepInfra; MiniMax's own API retires M2.7 on 18 November 2026"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 1.8,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "high",
            "grade": "C",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 57.6
          }
        ],
        "editorialScores": {
          "capability": 71,
          "interface": 75,
          "reach": 60,
          "risk": 30,
          "upkeep": 42
        },
        "provenanceScore": 60,
        "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": "# MiniMax's vLLM guide, 4 GPUs\nuv pip install vllm --torch-backend=auto\nSAFETENSORS_FAST_GPU=1 vllm serve MiniMaxAI/MiniMax-M2.7 --trust-remote-code \\\n  --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser minimax_m2 \\\n  --reasoning-parser minimax_m2_append_think",
        "http": "curl http://localhost:8000/v1/chat/completions \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\"model\": \"MiniMaxAI/MiniMax-M2.7\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}], \"temperature\": 1.0, \"top_p\": 0.95, \"top_k\": 40}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/minimax-m2-7"
      },
      "sameCompany": [
        "minimax-api",
        "minimax-video",
        "minimax-stt",
        "minimax-speech",
        "minimax-voice-cloning",
        "minimax-code",
        "minimax-m2-5",
        "minimax-m3-1-flash"
      ],
      "weights": {
        "trainedBy": "MiniMax",
        "params": "228.7B",
        "context": 204800,
        "goodFor": "Mixture-of-experts model for coding, agent harnesses and office documents, with parallel tool calls and always-on thinking, text only, for non-commercial use unless MiniMax authorises otherwise.",
        "runtimes": [
          "llama.cpp",
          "vLLM",
          "SGLang",
          "LM Studio",
          "Transformers",
          "Ollama"
        ],
        "builds": [
          {
            "name": "LM Studio minimax/minimax-m2.7 (Q4_K_M GGUF)",
            "url": "https://huggingface.co/lmstudio-community/MiniMax-M2.7-GGUF",
            "who": "lmstudio",
            "precision": "q4_k_m",
            "sizeGB": 138.3,
            "memoryGB": 192,
            "format": "gguf",
            "runsOn": [
              "LM Studio",
              "llama.cpp"
            ],
            "note": "MiniMax-M2.7-Q4_K_M split GGUF, 138.34 GB summed from the Hub's file list. The catalogue entry also lists Q6_K (187.8 GB) and Q8_0 (243.1 GB). LM Studio states 138 GB minimum memory"
          },
          {
            "name": "MiniMax-M2.7",
            "url": "https://huggingface.co/MiniMaxAI/MiniMax-M2.7",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 230.2,
            "memoryGB": 512,
            "format": "safetensors",
            "note": "Native FP8 E4M3 with 128 by 128 blocks; the only build MiniMax publishes. Size summed from the Hub's file list"
          }
        ],
        "run": "SAFETENSORS_FAST_GPU=1 vllm serve MiniMaxAI/MiniMax-M2.7 --trust-remote-code --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser minimax_m2 --reasoning-parser minimax_m2_append_think",
        "runBuild": "MiniMax-M2.7",
        "checked": "2026-11-04",
        "minMemoryGB": 192
      },
      "family": {
        "lab": "MiniMax",
        "family": "MiniMax-M2",
        "url": "https://www.anchorterminal.com/open-weights/#minimax",
        "others": [
          {
            "name": "MiniMax-M2",
            "url": "https://huggingface.co/MiniMaxAI/MiniMax-M2",
            "params": "230B total, 10B active"
          },
          {
            "name": "MiniMax-M2.1",
            "url": "https://huggingface.co/MiniMaxAI/MiniMax-M2.1",
            "params": "230B total, 10B active (Hub count; card doesn't restate)"
          },
          {
            "name": "MiniMax-M2.5",
            "url": "https://huggingface.co/MiniMaxAI/MiniMax-M2.5",
            "params": "230B total, 10B active (Hub count; card doesn't restate)",
            "listing": "https://www.anchorterminal.com/tools/minimax-m2-5"
          }
        ],
        "olderCount": 7,
        "unrestrictedCopies": 5
      },
      "area": "models",
      "provenance": {
        "legalEntity": "MiniMax (named in the licence's copyright line; MiniMax's platform terms name Nanonoble Pte. Ltd., Singapore)",
        "domain": "minimax.io",
        "domainRegistered": "2021-03-27",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "The current licence reads Copyright (c) 2026 MiniMax. The platform terms' legal entity and the domain's registration date are as recorded on the minimax-api listing.",
          "Nothing is hosted, so the licence stands in for terms of service and no privacy policy is scored.",
          "No changelog. Commit messages are uploads and README or LICENSE updates; the licence change on 11 April 2026 reads only \"Delete LICENSE-MODEL\" and \"Upload LICENSE with huggingface_hub\".",
          "www.minimax.io/.well-known/security.txt redirects to the home page on 21 October 2026, and MiniMax-AI/MiniMax-M2.7 has no SECURITY.md, so there is no security.txt.",
          "MiniMax's site (www.minimax.io/models/text) links to huggingface.co/MiniMaxAI, and the card links the GitHub repository, so the repository is the publisher's own."
        ],
        "score": 60
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/minimax-m2-7.json",
      "live": {
        "slug": "minimax-m2-7",
        "securityTxt": {
          "url": "https://minimax.io/.well-known/security.txt",
          "state": "none",
          "checkedAt": "2026-11-10T16:08:11.844974518Z"
        },
        "domain": {
          "domain": "minimax.io",
          "checkedAt": "2026-11-08T14:54:44.54388672Z"
        },
        "updatedAt": "2026-11-10T16:08:11.844974518Z"
      }
    },
    "edges": [
      {
        "by": 35,
        "category": "upkeep",
        "leads": "lfm3-vl-2b",
        "name": "Documentation and upkeep",
        "reasons": {
          "a": "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.",
          "b": "The model card earns 2, stating only intended use and leaving out limitations, architecture, parameters, context, natural languages and a cut-off. Training data earns nothing, since only the training process is described. The last change earns 20, a card commit on 26 October 2026 that renames a MiniMax Agent link to MiniMax Code, 9 days before this check; the files, template and code last changed on 20 April 2026. Responsiveness earns 10, with a MiniMax reply in 8 of 36 discussions, none since June. Version policy earns 5, since earlier and later models sit in their own repositories without dated notes. File health earns 5, because a report that it doesn't load in Transformers v5 waits without a reply."
        },
        "scores": {
          "a": 77,
          "b": 42
        }
      },
      {
        "by": 25,
        "category": "reach",
        "leads": "lfm3-vl-2b",
        "name": "Reach",
        "reasons": {
          "a": "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.",
          "b": "Memory earns 5. MiniMax only publishes its 230 GB FP8 release, and the smallest build of 4-bit or better from a runtime's own library is LM Studio's Q4_K_M GGUF at 138.3 GB, about 166 GB with the fifth added, so it needs more than our 107.4 GB machine and fits within 256 GB. Runtimes earn the full 25, with model code in released vLLM, SGLang, llama.cpp and Transformers, an LM Studio catalogue entry, and Ollama 0.40.14, whose pinned llama.cpp b11963 has the minimax-m2 architecture (capped at 25). Builds earn 10 for one publisher precision, the FP8 release. Terms earn 5, since the licence today allows only personal, research and non-profit use without MiniMax's written authorisation. Download earns 10, with no gate or account. Hosted earns 5, since Novita, Featherless AI and DeepInfra serve it through Hugging Face, while MiniMax's own API is closed to new users and retires it on 18 November 2026."
        },
        "scores": {
          "a": 85,
          "b": 60
        }
      },
      {
        "by": 15,
        "category": "interface",
        "leads": "lfm3-vl-2b",
        "name": "Agent interface",
        "reasons": {
          "a": "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.",
          "b": "The chat template earns 20 for chat_template.jinja in the repository. The tool-calling format earns 25, as MiniMax's own guide documents how tools go in and how calls come back, gives a parsing function, and its deployment guides name the vLLM and SGLang parsers. Trained tool calling earns 20, with several invoke blocks in one call. Structured output earns nothing, since JSON output isn't documented or claimed. Reasoning control earns nothing, because thinking is always on with no documented way to turn it off or budget it. Context earns 10 for 204,800 tokens in the config with no position scaling."
        },
        "scores": {
          "a": 90,
          "b": 75
        }
      },
      {
        "by": 10,
        "category": "risk",
        "leads": "lfm3-vl-2b",
        "name": "Risk",
        "reasons": {
          "a": "File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, files in the LiquidAI organisation, which Liquid's docs page for the model and its blog post link to (10). Safety evaluation 0, no safety results on the card or in Liquid's blog post for the LFM3 family, neither of which mentions safety testing. Agent guidance 0, Nothing on prompt injection, an instruction hierarchy, what refusals cover or safeguards to add. Licence risk 10, 15 less 5 for the $10M annual revenue threshold on commercial use. Security programme 0, no way to report a flaw found. 15 + 15 + 0 + 0 + 10 + 0 = 40.",
          "b": "File safety earns 15, safetensors only with no pickle files, less 10 because every documented loading route passes trust_remote_code for the repository's Python. Provenance earns the full 15, files in the organisation MiniMax links to with a SHA-256 for each shard. Safety evaluation and agent guidance earn nothing, as MiniMax publishes neither. Licence risk earns nothing (read 21 October 2026). The Prohibited Uses appendix goes beyond the law, MiniMax swapped a commercial licence for a non-commercial one two days after release, and neither that swap nor the amendment three days later carries a dated note. The security programme earns nothing, with no security.txt, disclosure policy, SECURITY.md or advisories."
        },
        "scores": {
          "a": 40,
          "b": 30
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Liquid AI",
        "b": "MiniMax",
        "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": "MiniMax M2.7 non-commercial licence (MIT-style terms, commercial use needs MiniMax's written authorisation)",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "yes",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-11-09",
        "b": "2026-04-20",
        "name": "Last release"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Terms last updated"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Privacy policy last updated"
      },
      {
        "a": "none",
        "b": "1.8/5 (4)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "LFM3-VL-2B scores 61 (C) on agent readiness against MiniMax-M2.7's 57.6 (C), and leads in every scored category.",
        "question": "Which is better for AI agents, LFM3-VL-2B or MiniMax-M2.7?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 85 against 60",
          "Risk, 40 against 30",
          "Agent interface, 90 against 75",
          "Documentation and upkeep, 77 against 42"
        ],
        "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"
      },
      {
        "aheadOn": null,
        "also": null,
        "goodFor": "Researchers and personal projects with a 4 to 8 GPU server who want an open agent and coding model with parallel tool calls",
        "slug": "minimax-m2-7",
        "watchFor": "Commercial use needs MiniMax's written authorisation since a licence swap on 11 April 2026, made without a note"
      }
    ],
    "history": {
      "lfm3-vl-2b": null,
      "minimax-m2-7": [
        {
          "causes": [
            "product"
          ],
          "date": "2026-11-04",
          "delta": 1.5,
          "from": 56.1,
          "fromGrade": "C",
          "to": 57.6,
          "toGrade": "C"
        }
      ]
    },
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
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      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-minimax-m2-7.json",
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        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-minimax-m2-7"
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      {
        "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"
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      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minimax-m2-7.json",
        "title": "Apertus v1.5 8B vs MiniMax-M2.7",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minimax-m2-7"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-lfm3-vl-2b.json",
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        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
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        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-nemotron-3-ultra-550b-a55b.json",
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        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-olmo-3-7b-think.json",
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        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-ornith-1-5-9b.json",
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        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-otel-2-0-llm-31b-it.json",
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        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-30b-a3b-instruct-2507.json",
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        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-5-9b.json",
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        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-vl-32b-instruct"
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        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-vl-4b-instruct"
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        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-vl-8b-instruct"
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        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwopus3-8-27b-flash-v2.json",
        "title": "MiniMax-M2.7 vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwopus3-8-27b-flash-v2"
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        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-recursive-flint.json",
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        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-recursive-flint"
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      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-ripple-65b-preview.json",
        "title": "MiniMax-M2.7 vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-ripple-65b-preview"
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      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-sensenova-loop-9b-preview.json",
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        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-sensenova-loop-9b-preview"
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        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-smollm3-3b.json",
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        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-swift-1-5-qwen3-8-27b"
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        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-swift-1-5-qwen3-8-flash-next.json",
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        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-thinkingcap-qwen3-8-27b"
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      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-unlimited-ocr-2.json",
        "title": "MiniMax-M2.7 vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-unlimited-ocr-2"
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      {
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        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-wevisone-vl-2b"
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      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-wevisone-vl-4b.json",
        "title": "MiniMax-M2.7 vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-zaya1-8b.json",
        "title": "MiniMax-M2.7 vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-zaya1-8b"
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    ],
    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 25,
        "edge": "lfm3-vl-2b",
        "key": "reach",
        "lfm3-vl-2b": 85,
        "minimax-m2-7": 60,
        "name": "Reach",
        "weight": 25
      },
      {
        "by": 10,
        "edge": "lfm3-vl-2b",
        "key": "risk",
        "lfm3-vl-2b": 40,
        "minimax-m2-7": 30,
        "name": "Risk",
        "weight": 20
      },
      {
        "by": 15,
        "edge": "lfm3-vl-2b",
        "key": "interface",
        "lfm3-vl-2b": 90,
        "minimax-m2-7": 75,
        "name": "Agent interface",
        "weight": 10
      },
      {
        "by": 35,
        "edge": "lfm3-vl-2b",
        "key": "upkeep",
        "lfm3-vl-2b": 77,
        "minimax-m2-7": 42,
        "name": "Documentation and upkeep",
        "weight": 10
      }
    ],
    "summary": "LFM3-VL-2B scores 61 (C) on agent readiness against MiniMax-M2.7's 57.6 (C), and leads in every scored category. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "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.",
      "minimax-m2-7": "A 230B mixture-of-experts model for coding and agent harnesses, with parallel tool calls, about 200K of context and parsers in vLLM and SGLang. It needs a 4 GPU server for FP8 or about 166 GB for a 4-bit library build, and it always thinks. The main risk is the licence, switched to non-commercial two days after release."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-minimax-m2-7",
    "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-minimax-m2-7.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-minimax-m2-7.md",
    "mcp": "https://www.anchorterminal.com/mcp",
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  "markdown": "LFM3-VL-2B scores 61 (C) on agent readiness against MiniMax-M2.7's 57.6 (C), and leads in every scored category. Both do open-weight models you run yourself.\n\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- MiniMax-M2.7: grade C, 57.6/100, rank #106 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/minimax-m2-7.md · JSON https://www.anchorterminal.com/api/v1/tools/minimax-m2-7.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### LFM3-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- Reach, 85 against 60\n- Risk, 40 against 30\n- Agent interface, 90 against 75\n- Documentation and upkeep, 77 against 42\n\nWatch for: vLLM and SGLang support is unreleased, which the card says itself\n\n### MiniMax-M2.7 (C)\n\nGood for: Researchers and personal projects with a 4 to 8 GPU server who want an open agent and coding model with parallel tool calls\n\nWatch for: Commercial use needs MiniMax's written authorisation since a licence swap on 11 April 2026, made without a note\n\n\n## Why the edge\n\n### Documentation and upkeep: LFM3-VL-2B +35\n\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- MiniMax-M2.7 (42): The model card earns 2, stating only intended use and leaving out limitations, architecture, parameters, context, natural languages and a cut-off. Training data earns nothing, since only the training process is described. The last change earns 20, a card commit on 26 October 2026 that renames a MiniMax Agent link to MiniMax Code, 9 days before this check; the files, template and code last changed on 20 April 2026. Responsiveness earns 10, with a MiniMax reply in 8 of 36 discussions, none since June. Version policy earns 5, since earlier and later models sit in their own repositories without dated notes. File health earns 5, because a report that it doesn't load in Transformers v5 waits without a reply.\n\n### Reach: LFM3-VL-2B +25\n\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- MiniMax-M2.7 (60): Memory earns 5. MiniMax only publishes its 230 GB FP8 release, and the smallest build of 4-bit or better from a runtime's own library is LM Studio's Q4_K_M GGUF at 138.3 GB, about 166 GB with the fifth added, so it needs more than our 107.4 GB machine and fits within 256 GB. Runtimes earn the full 25, with model code in released vLLM, SGLang, llama.cpp and Transformers, an LM Studio catalogue entry, and Ollama 0.40.14, whose pinned llama.cpp b11963 has the minimax-m2 architecture (capped at 25). Builds earn 10 for one publisher precision, the FP8 release. Terms earn 5, since the licence today allows only personal, research and non-profit use without MiniMax's written authorisation. Download earns 10, with no gate or account. Hosted earns 5, since Novita, Featherless AI and DeepInfra serve it through Hugging Face, while MiniMax's own API is closed to new users and retires it on 18 November 2026.\n\n### Agent interface: LFM3-VL-2B +15\n\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- MiniMax-M2.7 (75): The chat template earns 20 for chat_template.jinja in the repository. The tool-calling format earns 25, as MiniMax's own guide documents how tools go in and how calls come back, gives a parsing function, and its deployment guides name the vLLM and SGLang parsers. Trained tool calling earns 20, with several invoke blocks in one call. Structured output earns nothing, since JSON output isn't documented or claimed. Reasoning control earns nothing, because thinking is always on with no documented way to turn it off or budget it. Context earns 10 for 204,800 tokens in the config with no position scaling.\n\n### Risk: LFM3-VL-2B +10\n\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- MiniMax-M2.7 (30): File safety earns 15, safetensors only with no pickle files, less 10 because every documented loading route passes trust_remote_code for the repository's Python. Provenance earns the full 15, files in the organisation MiniMax links to with a SHA-256 for each shard. Safety evaluation and agent guidance earn nothing, as MiniMax publishes neither. Licence risk earns nothing (read 21 October 2026). The Prohibited Uses appendix goes beyond the law, MiniMax swapped a commercial licence for a non-commercial one two days after release, and neither that swap nor the amendment three days later carries a dated note. The security programme earns nothing, with no security.txt, disclosure policy, SECURITY.md or advisories.\n\n## Score by category\n\n| Category | Weight | LFM3-VL-2B | MiniMax-M2.7 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 85 | 60 | LFM3-VL-2B +25 |\n| Risk | 20% (20 this run) | 40 | 30 | LFM3-VL-2B +10 |\n| Agent interface | 10% (10 this run) | 90 | 75 | LFM3-VL-2B +15 |\n| Documentation and upkeep | 10% (10 this run) | 77 | 42 | LFM3-VL-2B +35 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **57.6 · C** | |\n\n## Facts side by side\n\n| Fact | LFM3-VL-2B | MiniMax-M2.7 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Liquid AI | MiniMax |\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) | MiniMax M2.7 non-commercial licence (MIT-style terms, commercial use needs MiniMax's written authorisation) |\n| Read-only variant documented | no | no |\n| llms.txt | yes | no |\n| Last release | 2026-11-09 | 2026-04-20 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | none | 1.8/5 (4) |\n\n## How the scores have moved\n\n### LFM3-VL-2B\n\nNo score change since it was listed.\n\n### MiniMax-M2.7\n\n- +1.5 on 4 November 2026: product\n\n## Verdicts\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**MiniMax-M2.7.** A 230B mixture-of-experts model for coding and agent harnesses, with parallel tool calls, about 200K of context and parsers in vLLM and SGLang. It needs a 4 GPU server for FP8 or about 166 GB for a 4-bit library build, and it always thinks. The main risk is the licence, switched to non-commercial two days after release.\n\n## Before you call either\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### MiniMax-M2.7\n\n1. Check the use is personal, research or non-profit before starting it; anything commercial needs MiniMax's written authorisation, and commercial users must show Built with MiniMax M2.7\n2. Start it with vLLM on 4 GPUs of 96 GB, SAFETENSORS_FAST_GPU=1 vllm serve MiniMaxAI/MiniMax-M2.7 --trust-remote-code --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser minimax_m2 --reasoning-parser minimax_m2_append_think\n3. In SGLang use --tool-call-parser minimax-m2 and --reasoning-parser minimax-append-think, with --trust-remote-code\n4. MiniMax's guides budget 220 GB for the weights and 240 GB per 1M tokens of KV cache, with 196K the most per sequence\n5. On one large-memory machine, LM Studio's catalogue entry minimax/minimax-m2.7 lists a 138 GB Q4_K_M GGUF and states 138 GB minimum memory\n6. Sample with temperature 1.0, top_p 0.95 and top_k 40, as the card recommends, and budget output tokens for thinking on every turn\n7. Pin Transformers 4.57.1 as MiniMax's guide does; a report that it fails on Transformers v5 has no answer\n8. Without the hardware, call it through Novita, Featherless AI or DeepInfra; MiniMax's own API retires M2.7 on 18 November 2026\n\n## Questions\n\n### Which is better for AI agents, LFM3-VL-2B or MiniMax-M2.7?\n\nLFM3-VL-2B scores 61 (C) on agent readiness against MiniMax-M2.7's 57.6 (C), and leads in every scored category.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-minimax-m2-7.json, and with the fewest tokens: https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-minimax-m2-7.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"lfm3-vl-2b\", \"b\": \"minimax-m2-7\"}`. From a terminal: `anchor compare lfm3-vl-2b minimax-m2-7`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/lfm3-vl-2b.json and https://www.anchorterminal.com/api/v1/tools/minimax-m2-7.json\n\n## Other comparisons with LFM3-VL-2B or MiniMax-M2.7\n\n- [Agents-A1-4B vs LFM3-VL-2B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-lfm3-vl-2b.md)\n- [Agents-A1-4B vs MiniMax-M2.7](https://www.anchorterminal.com/compare/agents-a1-4b-vs-minimax-m2-7.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- [Apertus v1.5 8B vs MiniMax-M2.7](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minimax-m2-7.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- [Apodex-1.1-mini vs MiniMax-M2.7](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minimax-m2-7.md)\n- [Arete-27B vs LFM3-VL-2B](https://www.anchorterminal.com/compare/arete-27b-vs-lfm3-vl-2b.md)\n- [Arete-27B vs MiniMax-M2.7](https://www.anchorterminal.com/compare/arete-27b-vs-minimax-m2-7.md)\n- [BigBang-v1 vs LFM3-VL-2B](https://www.anchorterminal.com/compare/bigbang-v1-vs-lfm3-vl-2b.md)\n- [BigBang-v1 vs MiniMax-M2.7](https://www.anchorterminal.com/compare/bigbang-v1-vs-minimax-m2-7.md)\n- [Carbon-X vs LFM3-VL-2B](https://www.anchorterminal.com/compare/carbon-x-vs-lfm3-vl-2b.md)\n- [Carbon-X vs MiniMax-M2.7](https://www.anchorterminal.com/compare/carbon-x-vs-minimax-m2-7.md)\n- [Cola2 vs LFM3-VL-2B](https://www.anchorterminal.com/compare/cola2-vs-lfm3-vl-2b.md)\n- [Cola2 vs MiniMax-M2.7](https://www.anchorterminal.com/compare/cola2-vs-minimax-m2-7.md)\n- [Cosmos-Reason2-2B vs LFM3-VL-2B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-lfm3-vl-2b.md)\n- [Cosmos-Reason2-2B vs MiniMax-M2.7](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-minimax-m2-7.md)\n- [Darwin-36B-Opus vs LFM3-VL-2B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-lfm3-vl-2b.md)\n- [Darwin-36B-Opus vs MiniMax-M2.7](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-minimax-m2-7.md)\n- [DeepSeek-V3.2 vs LFM3-VL-2B](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-lfm3-vl-2b.md)\n- [DeepSeek-V3.2 vs MiniMax-M2.7](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-minimax-m2-7.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 V4.1 Flash vs MiniMax-M2.7](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-minimax-m2-7.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- [DeepSeek-V4-Flash-0731 vs MiniMax-M2.7](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-minimax-m2-7.md)\n- [Devstral Small 2 vs LFM3-VL-2B](https://www.anchorterminal.com/compare/devstral-small-2-vs-lfm3-vl-2b.md)\n- [Devstral Small 2 vs MiniMax-M2.7](https://www.anchorterminal.com/compare/devstral-small-2-vs-minimax-m2-7.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- [DiffusionGemma 26B A4B IT vs MiniMax-M2.7](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-minimax-m2-7.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- [EXAONE 4.5 33B vs MiniMax-M2.7](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-minimax-m2-7.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 12B IT vs MiniMax-M2.7](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-minimax-m2-7.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 26B A4B IT vs MiniMax-M2.7](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-minimax-m2-7.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 31B IT vs MiniMax-M2.7](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-minimax-m2-7.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 E2B IT vs MiniMax-M2.7](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-minimax-m2-7.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- [Gemma 4 E4B IT vs MiniMax-M2.7](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-minimax-m2-7.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-4.5-Air vs MiniMax-M2.7](https://www.anchorterminal.com/compare/glm-4-5-air-vs-minimax-m2-7.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-4.7-Flash vs MiniMax-M2.7](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-minimax-m2-7.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-Flash vs MiniMax-M2.7](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-minimax-m2-7.md)\n- [GLM-5.3 vs LFM3-VL-2B](https://www.anchorterminal.com/compare/glm-5-3-vs-lfm3-vl-2b.md)\n- [GLM-5.3 vs MiniMax-M2.7](https://www.anchorterminal.com/compare/glm-5-3-vs-minimax-m2-7.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-120b vs MiniMax-M2.7](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-minimax-m2-7.md)\n- [gpt-oss-20b vs 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MiniMax-M2.7](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-minimax-m2-7.md)\n- [Hy3 vs LFM3-VL-2B](https://www.anchorterminal.com/compare/hy3-vs-lfm3-vl-2b.md)\n- [Hy3 vs MiniMax-M2.7](https://www.anchorterminal.com/compare/hy3-vs-minimax-m2-7.md)\n- [Inkling-Small vs LFM3-VL-2B](https://www.anchorterminal.com/compare/inkling-small-vs-lfm3-vl-2b.md)\n- [Inkling-Small vs MiniMax-M2.7](https://www.anchorterminal.com/compare/inkling-small-vs-minimax-m2-7.md)\n- [Inkling vs LFM3-VL-2B](https://www.anchorterminal.com/compare/inkling-vs-lfm3-vl-2b.md)\n- [Inkling vs MiniMax-M2.7](https://www.anchorterminal.com/compare/inkling-vs-minimax-m2-7.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- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs MiniMax-M2.7](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-minimax-m2-7.md)\n- [Kimi K3.1 Pro vs 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LFM3-VL-2B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-lfm3-vl-2b.md)\n- [Laguna S 2.1 vs MiniMax-M2.7](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-minimax-m2-7.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- [Laguna XS 2.1 vs MiniMax-M2.7](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-minimax-m2-7.md)\n- [LBS-27B vs LFM3-VL-2B](https://www.anchorterminal.com/compare/lbs-27b-vs-lfm3-vl-2b.md)\n- [LBS-27B vs MiniMax-M2.7](https://www.anchorterminal.com/compare/lbs-27b-vs-minimax-m2-7.md)\n- [LFM2.5-2.6B vs LFM3-VL-2B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-lfm3-vl-2b.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- [LFM3-1.7B vs LFM3-VL-2B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-lfm3-vl-2b.md)\n- [LFM3-1.7B vs MiniMax-M2.7](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-minimax-m2-7.md)\n- 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    "published": "2026-10-01",
    "section": "tools",
    "title": "LFM3-VL-2B vs MiniMax-M2.7 for AI agents in 2026: scores and prices",
    "toc": null,
    "updated": "2026-11-10",
    "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-minimax-m2-7"
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