{
  "data": {
    "a": {
      "slug": "lfm3-audio-2b",
      "name": "LFM3-Audio-2B",
      "vendor": "Liquid AI",
      "vendorUrl": "https://www.liquid.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "LFM3-Audio-2B is Liquid AI's open-weight 2B audio model for on-device use, taking text or speech and answering in text, speech or both, with transcription, translation, tool calling and a 32K-token context, released on 27 October 2026.",
      "url": "https://www.anchorterminal.com/tools/lfm3-audio-2b",
      "markdownUrl": "https://www.anchorterminal.com/tools/lfm3-audio-2b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/lfm3-audio-2b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/lfm3-audio-2b.json",
      "dns": "lfm3-audio-2b.grade.anchorterminal.com",
      "repo": "https://huggingface.co/LiquidAI/LFM3-Audio-2B",
      "license": "LFM Open License v1.0 (free commercial use below $10M annual revenue)",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "LiquidAI/LFM3-Audio-2B"
        },
        {
          "registry": "huggingface",
          "name": "LiquidAI/LFM3-Audio-2B-GGUF"
        },
        {
          "registry": "huggingface",
          "name": "LiquidAI/LFM3-Audio-2B-MLX-4bit"
        },
        {
          "registry": "ollama",
          "name": "lfm3-audio"
        }
      ],
      "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 0.17 GB projector file and 4.16 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-audio-2b",
      "llmsTxt": "https://docs.liquid.ai/llms.txt",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "audio",
        "speech",
        "speech-to-speech",
        "transcription",
        "small",
        "edge",
        "tool-calling",
        "custom-licence",
        "safetensors",
        "gguf",
        "mlx",
        "onnx",
        "self-hosted"
      ],
      "lastRelease": "2026-11-03",
      "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": 56,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 85,
          "reach": 80,
          "risk": 40,
          "upkeep": 52
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-10"
        },
        "negative": 0,
        "verdict": "A 2B model that listens and speaks on an 8 GB machine, transcribes and translates 19 languages and turns spoken requests into tool calls. Only llama.cpp and Ollama run it in a release. The context is 32K, voice chat is English only, Liquid hasn't answered the repository's questions, and there are no safety results or data sources.",
        "bestFor": "Voice agents on a laptop or phone that need speech in, speech out and tool calls from one small model",
        "strengths": [
          "Speech in and speech out on an 8 GB machine, streamed",
          "Transcribes, speaks and translates 19 languages and locales",
          "Tool calls from spoken requests, in a documented format",
          "Reasoning can be turned on per text reply",
          "Liquid's own API serves it"
        ],
        "weaknesses": [
          "Only llama.cpp and Ollama run it in a release, though the card lists vLLM and SGLang",
          "A 32K-token context",
          "Voice chat is English only",
          "Neither question on the repository has a reply from Liquid",
          "Loading in Transformers runs code from the repository",
          "No safety results, security contact or training data description"
        ],
        "agentNotes": [
          "Start it with `llama-server -hf LiquidAI/LFM3-Audio-2B-GGUF:Q4_K_M --jinja` or `ollama run lfm3-audio`",
          "llama.cpp ships examples/realtime_client.py for live speech with this model",
          "Prefill the assistant turn with an open think tag to let it reason before a text reply",
          "The context is 32K",
          "Voice chat is English only, while transcription, synthesis and translation cover 19 languages and locales",
          "Liquid doesn't recommend it for agentic coding or complex multi-step 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": 85,
          "reach": 80,
          "risk": 40,
          "upkeep": 52
        },
        "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-Audio-2B-GGUF:Q4_K_M --jinja\n# or\nollama run lfm3-audio",
        "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-audio-2b"
      },
      "sameCompany": [
        "liquid-d1",
        "lfm2-5-2-6b",
        "lfm3-1-7b",
        "lfm3-omni-2b",
        "lfm3-vl-2b"
      ],
      "weights": {
        "trainedBy": "Liquid AI",
        "builtOn": "The LFM3 backbone (Liquid's own; the metadata names no base model)",
        "params": "2.00B",
        "context": 32000,
        "goodFor": "Small on-device audio model for voice chat, transcription, speech synthesis and translation in 19 languages and locales, questions about recordings and tool calls from spoken requests. Voice chat is English only.",
        "runtimes": [
          "llama.cpp",
          "Ollama"
        ],
        "builds": [
          {
            "name": "LFM3-Audio-2B",
            "url": "https://huggingface.co/LiquidAI/LFM3-Audio-2B",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 4.16,
            "memoryGB": 8,
            "format": "safetensors",
            "runsOn": [
              "Transformers"
            ],
            "note": "Loads in Transformers with the repository's own code. Liquid also publishes an ONNX repository. It has no FP8 or NVFP4 build of this model"
          },
          {
            "name": "LFM3-Audio-2B-GGUF Q8_0",
            "url": "https://huggingface.co/LiquidAI/LFM3-Audio-2B-GGUF",
            "who": "publisher",
            "precision": "q8_0",
            "sizeGB": 2.15,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "LFM3-Audio-2B-Q8_0.gguf (1.85 GB) plus mmproj-LFM3-Audio-2B-Q8_0.gguf (0.30 GB)"
          },
          {
            "name": "LFM3-Audio-2B-GGUF Q4_K_M",
            "url": "https://huggingface.co/LiquidAI/LFM3-Audio-2B-GGUF",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 1.27,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "LFM3-Audio-2B-Q4_K_M.gguf (1.10 GB) plus Liquid's projector file mmproj-LFM3-Audio-2B-Q4_K_M.gguf (0.17 GB), from the Hub's file list. The repository also holds Q4_0, Q8_0, F16 and BF16 files"
          },
          {
            "name": "LFM3-Audio-2B-MLX-4bit",
            "url": "https://huggingface.co/LiquidAI/LFM3-Audio-2B-MLX-4bit",
            "who": "publisher",
            "precision": "4bit",
            "sizeGB": 1.73,
            "memoryGB": 8,
            "format": "mlx",
            "runsOn": [
              "MLX-VLM"
            ],
            "note": "Liquid also publishes 8-bit and BF16 MLX builds"
          },
          {
            "name": "ollama lfm3-audio",
            "url": "https://ollama.com/library/lfm3-audio",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 1.7,
            "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-Audio-2B-GGUF:Q4_K_M --jinja",
        "runBuild": "LFM3-Audio-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-VL-2B",
            "url": "https://huggingface.co/LiquidAI/LFM3-VL-2B",
            "params": "1.92B",
            "listing": "https://www.anchorterminal.com/tools/lfm3-vl-2b"
          },
          {
            "name": "LFM3-Omni-2B",
            "url": "https://huggingface.co/LiquidAI/LFM3-Omni-2B",
            "params": "2.17B",
            "listing": "https://www.anchorterminal.com/tools/lfm3-omni-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-Audio-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-audio-2b.json"
    },
    "answer": "LFM3-Audio-2B scores 61 (C) on agent readiness against MiniMax-M2.5's 59 (C), and leads in every scored category.",
    "b": {
      "slug": "minimax-m2-5",
      "name": "MiniMax-M2.5",
      "vendor": "MiniMax",
      "vendorUrl": "https://www.minimax.io",
      "kind": "weights",
      "category": "open-weights",
      "summary": "MiniMax-M2.5 is MiniMax's open-weight mixture-of-experts model for coding and agent work, about 229B parameters in FP8 with a 196,608-token context and thinking always on, published on Hugging Face on 12 February 2026.",
      "url": "https://www.anchorterminal.com/tools/minimax-m2-5",
      "markdownUrl": "https://www.anchorterminal.com/tools/minimax-m2-5.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/minimax-m2-5.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/minimax-m2-5.json",
      "dns": "minimax-m2-5.grade.anchorterminal.com",
      "repo": "https://huggingface.co/MiniMaxAI/MiniMax-M2.5",
      "license": "MiniMax Model License (model) and modified MIT (code)",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "MiniMaxAI/MiniMax-M2.5"
        }
      ],
      "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 use, commercial use included and royalty-free, under the MiniMax Model License for the weights and a modified MIT licence for the code, both with attribution duties and a Prohibited Uses Policy for the model. Running it yourself costs the hardware, 230.2 GB of FP8 files. MiniMax's API closed M2.5 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-M2.5",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "reasoning",
        "tool-calling",
        "coding",
        "custom-licence",
        "safetensors",
        "fp8",
        "self-hosted"
      ],
      "lastRelease": "2026-02-12",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 59,
        "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": 76,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "capability": 73,
          "interface": 75,
          "reach": 65,
          "risk": 30,
          "upkeep": 37
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-11-04"
        },
        "negative": 0,
        "verdict": "MiniMax-M2.5 is a 230B mixture-of-experts model for coding and agent work, with parallel tool calls, a 196K context and parsers in vLLM and SGLang. It needs a 4-GPU server for FP8 or about 155 GB for a 4-bit build. The main risk is a licence replaced after release with a Prohibited Uses Policy while the card still says modified MIT.",
        "bestFor": "Teams with a 4 to 8 GPU server who want an open agent and coding model with parallel tool calls and can live with its use policy",
        "strengths": [
          "Runs in released vLLM, SGLang, llama.cpp, Transformers and LM Studio, and in Ollama 0.40.14 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 196,608 tokens of context",
          "Safetensors only, in the publisher's organisation with a SHA-256 for every shard",
          "No gate or account, and served by two inference providers on Hugging Face"
        ],
        "weaknesses": [
          "230 GB in FP8, and about 155 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",
          "The card says modified MIT and links to a deleted file, while the licence in the repository adds a Prohibited Uses Policy and termination for breach",
          "No safety evaluation, agent guidance or security contact",
          "No MiniMax reply in discussions since April 2026, and the card leaves out limitations, architecture, parameters and a cut-off"
        ],
        "agentNotes": [
          "Start it with vLLM on 4 GPUs of 96 GB, SAFETENSORS_FAST_GPU=1 vllm serve MiniMaxAI/MiniMax-M2.5 --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",
          "On a single large-memory machine, LM Studio's catalogue entry minimax/minimax-m2.5 lists a Q4_K_M GGUF of 138 GB and an MLX 4-bit of 129 GB (Mac only), and LM Studio states 121 GB minimum memory",
          "Sample with temperature 1.0, top_p 0.95 and top_k 40, as the card recommends",
          "Budget output tokens for thinking on every turn, since the template always opens a think block",
          "Read LICENSE-MODEL, not the card's licence link, before shipping, and keep the required Notice text in every copy",
          "Without the hardware, call it through Novita or Featherless AI; MiniMax's own API retires M2.5 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": 59
          }
        ],
        "editorialScores": {
          "capability": 73,
          "interface": 75,
          "reach": 65,
          "risk": 30,
          "upkeep": 37
        },
        "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-M2.5 --trust-remote-code \\\n  --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser minimax_m2 \\\n  --reasoning-parser minimax_m2_append_think\n# or, with llama.cpp and LM Studio's GGUF:\nllama-server -hf lmstudio-community/MiniMax-M2.5-GGUF:Q4_K_M --jinja",
        "http": "curl http://localhost:8000/v1/chat/completions \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\"model\": \"MiniMaxAI/MiniMax-M2.5\", \"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-m2-5"
      },
      "sameCompany": [
        "minimax-api",
        "minimax-video",
        "minimax-stt",
        "minimax-speech",
        "minimax-voice-cloning",
        "minimax-code",
        "minimax-m2-7",
        "minimax-m3-1-flash"
      ],
      "weights": {
        "trainedBy": "MiniMax",
        "params": "228.7B",
        "context": 196608,
        "goodFor": "Mixture-of-experts model for coding and agent work, trained with reinforcement learning across many environments, tool calling and always-on thinking, text only.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "LM Studio",
          "Transformers"
        ],
        "builds": [
          {
            "name": "lmstudio-community MiniMax-M2.5-GGUF (Q4_K_M)",
            "url": "https://huggingface.co/lmstudio-community/MiniMax-M2.5-GGUF",
            "who": "lmstudio",
            "precision": "q4_k_m",
            "sizeGB": 138.34,
            "memoryGB": 192,
            "format": "gguf",
            "runsOn": [
              "LM Studio",
              "llama.cpp"
            ],
            "note": "From lmstudio-community, LM Studio's organisation on the Hub. 138.34 GB of Q4_K_M weights, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it yet"
          },
          {
            "name": "lmstudio-community MiniMax-M2.5-GGUF (Q6_K)",
            "url": "https://huggingface.co/lmstudio-community/MiniMax-M2.5-GGUF",
            "who": "lmstudio",
            "precision": "q6_k",
            "sizeGB": 187.76,
            "memoryGB": 256,
            "format": "gguf",
            "runsOn": [
              "LM Studio",
              "llama.cpp"
            ],
            "note": "From lmstudio-community, LM Studio's organisation on the Hub. 187.76 GB of Q6_K weights, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it yet"
          },
          {
            "name": "MiniMax-M2.5",
            "url": "https://huggingface.co/MiniMaxAI/MiniMax-M2.5",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 230.1,
            "memoryGB": 512,
            "format": "safetensors",
            "note": "Native FP8; the only build MiniMax publishes"
          },
          {
            "name": "lmstudio-community MiniMax-M2.5-GGUF (Q8_0)",
            "url": "https://huggingface.co/lmstudio-community/MiniMax-M2.5-GGUF",
            "who": "lmstudio",
            "precision": "q8_0",
            "sizeGB": 243.14,
            "memoryGB": 512,
            "format": "gguf",
            "runsOn": [
              "LM Studio",
              "llama.cpp"
            ],
            "note": "From lmstudio-community, LM Studio's organisation on the Hub. 243.14 GB of Q8_0 weights, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it yet"
          }
        ],
        "run": "SAFETENSORS_FAST_GPU=1 vllm serve MiniMaxAI/MiniMax-M2.5 --trust-remote-code --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser minimax_m2 --reasoning-parser minimax_m2_append_think",
        "runBuild": "MiniMax-M2.5",
        "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-M2.7",
            "url": "https://huggingface.co/MiniMaxAI/MiniMax-M2.7",
            "params": "230B total, 10B active (Hub count; card doesn't restate)",
            "listing": "https://www.anchorterminal.com/tools/minimax-m2-7"
          }
        ],
        "olderCount": 7,
        "unrestrictedCopies": 5
      },
      "area": "models",
      "provenance": {
        "legalEntity": "MiniMax AI (named in the licences; 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 MiniMax Model License and the MIT code licence name MiniMax AI. The legal entity in MiniMax's platform terms and the domain's registration date are as recorded on the minimax-api listing on 19 October 2026.",
          "Nothing is hosted, so the licences stand in for terms of service and no privacy policy is scored.",
          "No changelog. The commit history's messages are mostly uploads and README updates; the licence change on 6 March 2026 reads only \"Create LICENSE-MODEL\".",
          "www.minimax.io/.well-known/security.txt redirects to the home page on 21 October 2026, so there is no security.txt.",
          "MiniMax's site (www.minimax.io/models/text) links to huggingface.co/MiniMaxAI/MiniMax-M2.5, so the repository is the publisher's own."
        ],
        "score": 60
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/minimax-m2-5.json",
      "live": {
        "slug": "minimax-m2-5",
        "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": 15,
        "category": "reach",
        "leads": "lfm3-audio-2b",
        "name": "Reach",
        "reasons": {
          "a": "Memory 30, Liquid's Q4_K_M GGUF is 1.10 GB with its 0.17 GB projector file, and 1.27 GB × 1.2 = 1.5 GB, which fits 8 GB, well inside the 16 GB line. Runtimes 10, Released llama.cpp has the lfm3 architecture with the LFM3 audio projector and speech output (src/models/lfm3.cpp and tools/mtmd at b12242, with a realtime client example that starts this model), and Ollama's library has an lfm3-audio entry. 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-audio-2b among its models, so the publisher hosts it. 30 + 10 + 15 + 10 + 10 + 5 = 80.",
          "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 MLX 4-bit at 128.7 GB, about 154 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 v0.33.0, SGLang v0.5.23, llama.cpp and Transformers, an LM Studio catalogue entry, and Ollama 0.40.14, whose pinned llama.cpp b11963 has the minimax-m2 architecture, though Ollama's library lists no build. Builds earn 10 for one publisher precision, the FP8 release. Terms earn 10 for a custom licence that allows free commercial use under a use policy. Download earns 10, with no gate or account. Hosted earns 5, since Novita and Featherless AI serve it through Hugging Face, while MiniMax's own API is closed to new users and retires it on 18 November 2026. 5+25+10+10+10+5 = 65."
        },
        "scores": {
          "a": 80,
          "b": 65
        }
      },
      {
        "by": 15,
        "category": "upkeep",
        "leads": "lfm3-audio-2b",
        "name": "Documentation and upkeep",
        "reasons": {
          "a": "Model card 17, Intended use (local agents, tool calling, structured outputs, fine-tuning), limitations (not recommended for agentic coding or complex multi-step reasoning, voice chat in English only), an architecture section, parameters (2B), context (32K) and 19 speech languages and locales. Training data 5, the card has no training section. Last change 20, the last commit, on 3 November 2026, changed the card's navigation icons, seven days before this check, and the one before it, on 31 October, edited the card. Responsiveness 0, Neither of the 2 discussions that aren't pull requests (27 and 31 October 2026) has a reply from a LiquidAI member. Version policy 5, LFM3 came out in new repositories and the LFM2.5 models stay in theirs. File health 5, the card gives a 32K context while config.json gives the language model 128,000 positions and Liquid's API lists 128,000 for the hosted model, so the card and the files don't agree on context. 17 + 5 + 20 + 0 + 5 + 5 = 52.",
          "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 5 for a general description with no cut-off. The last change earns 20, a card edit on 26 October 2026 (commit 2515a3ea, a link renamed from MiniMax Agent to MiniMax Code), 9 days before this check; the files, template and code are unchanged since February. Responsiveness earns 5, with a MiniMax reply in 2 of the 40 most recent discussions and none since April. Version policy earns 5, since earlier and later models sit in their own repositories without dated notes. File health earns nothing, because the card's licence metadata still contradicts the licence in the files and an FP8 output report still waits without a reply. 2+5+20+5+5+0 = 37."
        },
        "scores": {
          "a": 52,
          "b": 37
        }
      },
      {
        "by": 10,
        "category": "risk",
        "leads": "lfm3-audio-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 Policy goes beyond the law, MiniMax can end the licence for any breach and replaced its terms three weeks after release, and that change on 6 March 2026 came with no dated note while the card still links to a deleted file. The security programme earns nothing, with no security.txt, disclosure policy, SECURITY.md or advisories, and only a general contact address on the card."
        },
        "scores": {
          "a": 40,
          "b": 30
        }
      },
      {
        "by": 10,
        "category": "interface",
        "leads": "lfm3-audio-2b",
        "name": "Agent interface",
        "reasons": {
          "a": "Chat template 20, chat_template.jinja in the repository (9,328 characters), with roles, audio, tools, deferred tools and tool results. 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 names turning spoken requests into tool calls as one of its four uses. Structured output 5, the card says the model is designed for structured outputs without saying how to ask for JSON. Liquid's API guide documents `response_format` with lfm3-1.7b as its example. Reasoning control 10, the card documents reasoning per request for text replies: the caller prefills the assistant turn with an open think tag to let the model reason, or a closed one for a direct answer, with a use_thinking example. Context 5, 32K tokens of input and output per the card. 20 + 25 + 20 + 5 + 10 + 5 = 85.",
          "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 196,608 tokens with no position scaling."
        },
        "scores": {
          "a": 85,
          "b": 75
        }
      }
    ],
    "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 Model License (model) and modified MIT (code)",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "yes",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-11-03",
        "b": "2026-02-12",
        "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-Audio-2B scores 61 (C) on agent readiness against MiniMax-M2.5's 59 (C), and leads in every scored category.",
        "question": "Which is better for AI agents, LFM3-Audio-2B or MiniMax-M2.5?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 80 against 65",
          "Risk, 40 against 30",
          "Agent interface, 85 against 75",
          "Documentation and upkeep, 52 against 37"
        ],
        "also": null,
        "goodFor": "Voice agents on a laptop or phone that need speech in, speech out and tool calls from one small model",
        "slug": "lfm3-audio-2b",
        "watchFor": "Only llama.cpp and Ollama run it in a release, though the card lists vLLM and SGLang"
      },
      {
        "aheadOn": null,
        "also": null,
        "goodFor": "Teams with a 4 to 8 GPU server who want an open agent and coding model with parallel tool calls and can live with its use policy",
        "slug": "minimax-m2-5",
        "watchFor": "230 GB in FP8, and about 155 GB for the smallest 4-bit library build, more than a 128 GB machine"
      }
    ],
    "history": {
      "lfm3-audio-2b": null,
      "minimax-m2-5": [
        {
          "causes": [
            "product"
          ],
          "date": "2026-11-04",
          "delta": 1.5,
          "from": 57.5,
          "fromGrade": "C",
          "to": 59,
          "toGrade": "C"
        }
      ]
    },
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
      {
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        "title": "Agents-A1-4B vs LFM3-Audio-2B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-lfm3-audio-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-minimax-m2-5.json",
        "title": "Agents-A1-4B vs MiniMax-M2.5",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-minimax-m2-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-lfm3-audio-2b.json",
        "title": "Apertus v1.5 8B vs LFM3-Audio-2B",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-lfm3-audio-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minimax-m2-5.json",
        "title": "Apertus v1.5 8B vs MiniMax-M2.5",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minimax-m2-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-lfm3-audio-2b.json",
        "title": "Apodex-1.1-mini vs LFM3-Audio-2B",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-lfm3-audio-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minimax-m2-5.json",
        "title": "Apodex-1.1-mini vs MiniMax-M2.5",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minimax-m2-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-lfm3-audio-2b.json",
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        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-lfm3-audio-2b"
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        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-minimax-m2-5.json",
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        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-minimax-m2-5"
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      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-lfm3-audio-2b.json",
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        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-lfm3-audio-2b"
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      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-minimax-m2-5.json",
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        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-minimax-m2-5"
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      {
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        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-lfm3-audio-2b"
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        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-minimax-m2-5"
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      {
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        "url": "https://www.anchorterminal.com/compare/cola2-vs-lfm3-audio-2b"
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        "url": "https://www.anchorterminal.com/compare/cola2-vs-minimax-m2-5"
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        "title": "Cosmos-Reason2-2B vs LFM3-Audio-2B",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-lfm3-audio-2b"
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      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-minimax-m2-5.json",
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        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-minimax-m2-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-lfm3-audio-2b.json",
        "title": "Darwin-36B-Opus vs LFM3-Audio-2B",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-lfm3-audio-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-minimax-m2-5.json",
        "title": "Darwin-36B-Opus vs MiniMax-M2.5",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-minimax-m2-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-lfm3-audio-2b.json",
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        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-lfm3-audio-2b"
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      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-minimax-m2-5.json",
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        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-minimax-m2-5"
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      {
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        "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-vl-8b-instruct"
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        "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwopus3-8-27b-flash-v2"
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        "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-recursive-flint.json",
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        "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-recursive-flint"
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        "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-ripple-65b-preview.json",
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        "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-ripple-65b-preview"
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        "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-step-3-7-flash"
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        "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-swift-1-5-qwen3-8-27b.json",
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        "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-swift-1-5-qwen3-8-27b"
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        "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-swift-1-5-qwen3-8-flash-next.json",
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        "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-swift-1-5-qwen3-8-flash-next"
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        "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-thinkingcap-qwen3-8-27b.json",
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        "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-thinkingcap-qwen3-8-27b"
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      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-unlimited-ocr-2.json",
        "title": "MiniMax-M2.5 vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-unlimited-ocr-2"
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        "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-wevisone-vl-2b.json",
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        "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-wevisone-vl-4b.json",
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        "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-wevisone-vl-4b"
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        "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-zaya1-8b.json",
        "title": "MiniMax-M2.5 vs ZAYA1-8B",
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    ],
    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 15,
        "edge": "lfm3-audio-2b",
        "key": "reach",
        "lfm3-audio-2b": 80,
        "minimax-m2-5": 65,
        "name": "Reach",
        "weight": 25
      },
      {
        "by": 10,
        "edge": "lfm3-audio-2b",
        "key": "risk",
        "lfm3-audio-2b": 40,
        "minimax-m2-5": 30,
        "name": "Risk",
        "weight": 20
      },
      {
        "by": 10,
        "edge": "lfm3-audio-2b",
        "key": "interface",
        "lfm3-audio-2b": 85,
        "minimax-m2-5": 75,
        "name": "Agent interface",
        "weight": 10
      },
      {
        "by": 15,
        "edge": "lfm3-audio-2b",
        "key": "upkeep",
        "lfm3-audio-2b": 52,
        "minimax-m2-5": 37,
        "name": "Documentation and upkeep",
        "weight": 10
      }
    ],
    "summary": "LFM3-Audio-2B scores 61 (C) on agent readiness against MiniMax-M2.5's 59 (C), and leads in every scored category. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "lfm3-audio-2b": "A 2B model that listens and speaks on an 8 GB machine, transcribes and translates 19 languages and turns spoken requests into tool calls. Only llama.cpp and Ollama run it in a release. The context is 32K, voice chat is English only, Liquid hasn't answered the repository's questions, and there are no safety results or data sources.",
      "minimax-m2-5": "MiniMax-M2.5 is a 230B mixture-of-experts model for coding and agent work, with parallel tool calls, a 196K context and parsers in vLLM and SGLang. It needs a 4-GPU server for FP8 or about 155 GB for a 4-bit build. The main risk is a licence replaced after release with a Prohibited Uses Policy while the card still says modified MIT."
    }
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  "links": {
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  "markdown": "LFM3-Audio-2B scores 61 (C) on agent readiness against MiniMax-M2.5's 59 (C), and leads in every scored category. Both do open-weight models you run yourself.\n\n- LFM3-Audio-2B: grade C, 61/100, rank #70 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/lfm3-audio-2b.md · JSON https://www.anchorterminal.com/api/v1/tools/lfm3-audio-2b.json\n- MiniMax-M2.5: grade C, 59/100, rank #99 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/minimax-m2-5.md · JSON https://www.anchorterminal.com/api/v1/tools/minimax-m2-5.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### LFM3-Audio-2B (C)\n\nGood for: Voice agents on a laptop or phone that need speech in, speech out and tool calls from one small model\n\nAhead on:\n- Reach, 80 against 65\n- Risk, 40 against 30\n- Agent interface, 85 against 75\n- Documentation and upkeep, 52 against 37\n\nWatch for: Only llama.cpp and Ollama run it in a release, though the card lists vLLM and SGLang\n\n### MiniMax-M2.5 (C)\n\nGood for: Teams with a 4 to 8 GPU server who want an open agent and coding model with parallel tool calls and can live with its use policy\n\nWatch for: 230 GB in FP8, and about 155 GB for the smallest 4-bit library build, more than a 128 GB machine\n\n\n## Why the edge\n\n### Reach: LFM3-Audio-2B +15\n\n- LFM3-Audio-2B (80): Memory 30, Liquid's Q4_K_M GGUF is 1.10 GB with its 0.17 GB projector file, and 1.27 GB × 1.2 = 1.5 GB, which fits 8 GB, well inside the 16 GB line. Runtimes 10, Released llama.cpp has the lfm3 architecture with the LFM3 audio projector and speech output (src/models/lfm3.cpp and tools/mtmd at b12242, with a realtime client example that starts this model), and Ollama's library has an lfm3-audio entry. 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-audio-2b among its models, so the publisher hosts it. 30 + 10 + 15 + 10 + 10 + 5 = 80.\n- MiniMax-M2.5 (65): 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 MLX 4-bit at 128.7 GB, about 154 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 v0.33.0, SGLang v0.5.23, llama.cpp and Transformers, an LM Studio catalogue entry, and Ollama 0.40.14, whose pinned llama.cpp b11963 has the minimax-m2 architecture, though Ollama's library lists no build. Builds earn 10 for one publisher precision, the FP8 release. Terms earn 10 for a custom licence that allows free commercial use under a use policy. Download earns 10, with no gate or account. Hosted earns 5, since Novita and Featherless AI serve it through Hugging Face, while MiniMax's own API is closed to new users and retires it on 18 November 2026. 5+25+10+10+10+5 = 65.\n\n### Documentation and upkeep: LFM3-Audio-2B +15\n\n- LFM3-Audio-2B (52): Model card 17, Intended use (local agents, tool calling, structured outputs, fine-tuning), limitations (not recommended for agentic coding or complex multi-step reasoning, voice chat in English only), an architecture section, parameters (2B), context (32K) and 19 speech languages and locales. Training data 5, the card has no training section. Last change 20, the last commit, on 3 November 2026, changed the card's navigation icons, seven days before this check, and the one before it, on 31 October, edited the card. Responsiveness 0, Neither of the 2 discussions that aren't pull requests (27 and 31 October 2026) has a reply from a LiquidAI member. Version policy 5, LFM3 came out in new repositories and the LFM2.5 models stay in theirs. File health 5, the card gives a 32K context while config.json gives the language model 128,000 positions and Liquid's API lists 128,000 for the hosted model, so the card and the files don't agree on context. 17 + 5 + 20 + 0 + 5 + 5 = 52.\n- MiniMax-M2.5 (37): 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 5 for a general description with no cut-off. The last change earns 20, a card edit on 26 October 2026 (commit 2515a3ea, a link renamed from MiniMax Agent to MiniMax Code), 9 days before this check; the files, template and code are unchanged since February. Responsiveness earns 5, with a MiniMax reply in 2 of the 40 most recent discussions and none since April. Version policy earns 5, since earlier and later models sit in their own repositories without dated notes. File health earns nothing, because the card's licence metadata still contradicts the licence in the files and an FP8 output report still waits without a reply. 2+5+20+5+5+0 = 37.\n\n### Risk: LFM3-Audio-2B +10\n\n- LFM3-Audio-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-M2.5 (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 Policy goes beyond the law, MiniMax can end the licence for any breach and replaced its terms three weeks after release, and that change on 6 March 2026 came with no dated note while the card still links to a deleted file. The security programme earns nothing, with no security.txt, disclosure policy, SECURITY.md or advisories, and only a general contact address on the card.\n\n### Agent interface: LFM3-Audio-2B +10\n\n- LFM3-Audio-2B (85): Chat template 20, chat_template.jinja in the repository (9,328 characters), with roles, audio, tools, deferred tools and tool results. 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 names turning spoken requests into tool calls as one of its four uses. Structured output 5, the card says the model is designed for structured outputs without saying how to ask for JSON. Liquid's API guide documents `response_format` with lfm3-1.7b as its example. Reasoning control 10, the card documents reasoning per request for text replies: the caller prefills the assistant turn with an open think tag to let the model reason, or a closed one for a direct answer, with a use_thinking example. Context 5, 32K tokens of input and output per the card. 20 + 25 + 20 + 5 + 10 + 5 = 85.\n- MiniMax-M2.5 (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 196,608 tokens with no position scaling.\n\n## Score by category\n\n| Category | Weight | LFM3-Audio-2B | MiniMax-M2.5 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 80 | 65 | LFM3-Audio-2B +15 |\n| Risk | 20% (20 this run) | 40 | 30 | LFM3-Audio-2B +10 |\n| Agent interface | 10% (10 this run) | 85 | 75 | LFM3-Audio-2B +10 |\n| Documentation and upkeep | 10% (10 this run) | 52 | 37 | LFM3-Audio-2B +15 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **59 · C** | |\n\n## Facts side by side\n\n| Fact | LFM3-Audio-2B | MiniMax-M2.5 |\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 Model License (model) and modified MIT (code) |\n| Read-only variant documented | no | no |\n| llms.txt | yes | no |\n| Last release | 2026-11-03 | 2026-02-12 |\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-Audio-2B\n\nNo score change since it was listed.\n\n### MiniMax-M2.5\n\n- +1.5 on 4 November 2026: product\n\n## Verdicts\n\n**LFM3-Audio-2B.** A 2B model that listens and speaks on an 8 GB machine, transcribes and translates 19 languages and turns spoken requests into tool calls. Only llama.cpp and Ollama run it in a release. The context is 32K, voice chat is English only, Liquid hasn't answered the repository's questions, and there are no safety results or data sources.\n\n**MiniMax-M2.5.** MiniMax-M2.5 is a 230B mixture-of-experts model for coding and agent work, with parallel tool calls, a 196K context and parsers in vLLM and SGLang. It needs a 4-GPU server for FP8 or about 155 GB for a 4-bit build. The main risk is a licence replaced after release with a Prohibited Uses Policy while the card still says modified MIT.\n\n## Before you call either\n\n### LFM3-Audio-2B\n\n1. Start it with `llama-server -hf LiquidAI/LFM3-Audio-2B-GGUF:Q4_K_M --jinja` or `ollama run lfm3-audio`\n2. llama.cpp ships examples/realtime_client.py for live speech with this model\n3. Prefill the assistant turn with an open think tag to let it reason before a text reply\n4. The context is 32K\n5. Voice chat is English only, while transcription, synthesis and translation cover 19 languages and locales\n6. Liquid doesn't recommend it for agentic coding or complex multi-step reasoning\n\n### MiniMax-M2.5\n\n1. Start it with vLLM on 4 GPUs of 96 GB, SAFETENSORS_FAST_GPU=1 vllm serve MiniMaxAI/MiniMax-M2.5 --trust-remote-code --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser minimax_m2 --reasoning-parser minimax_m2_append_think\n2. In SGLang use --tool-call-parser minimax-m2 and --reasoning-parser minimax-append-think, with --trust-remote-code\n3. MiniMax's guides budget 220 GB for the weights and 240 GB per 1M tokens of KV cache\n4. On a single large-memory machine, LM Studio's catalogue entry minimax/minimax-m2.5 lists a Q4_K_M GGUF of 138 GB and an MLX 4-bit of 129 GB (Mac only), and LM Studio states 121 GB minimum memory\n5. Sample with temperature 1.0, top_p 0.95 and top_k 40, as the card recommends\n6. Budget output tokens for thinking on every turn, since the template always opens a think block\n7. Read LICENSE-MODEL, not the card's licence link, before shipping, and keep the required Notice text in every copy\n8. Without the hardware, call it through Novita or Featherless AI; MiniMax's own API retires M2.5 on 18 November 2026\n\n## Questions\n\n### Which is better for AI agents, LFM3-Audio-2B or MiniMax-M2.5?\n\nLFM3-Audio-2B scores 61 (C) on agent readiness against MiniMax-M2.5's 59 (C), and leads in every scored category.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-minimax-m2-5.json, and with the fewest tokens: https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-minimax-m2-5.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"lfm3-audio-2b\", \"b\": \"minimax-m2-5\"}`. From a terminal: `anchor compare lfm3-audio-2b minimax-m2-5`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/lfm3-audio-2b.json and https://www.anchorterminal.com/api/v1/tools/minimax-m2-5.json\n\n## Other comparisons with LFM3-Audio-2B or MiniMax-M2.5\n\n- [Agents-A1-4B vs LFM3-Audio-2B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-lfm3-audio-2b.md)\n- [Agents-A1-4B vs MiniMax-M2.5](https://www.anchorterminal.com/compare/agents-a1-4b-vs-minimax-m2-5.md)\n- [Apertus v1.5 8B vs LFM3-Audio-2B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-lfm3-audio-2b.md)\n- [Apertus v1.5 8B vs MiniMax-M2.5](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minimax-m2-5.md)\n- [Apodex-1.1-mini vs LFM3-Audio-2B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-lfm3-audio-2b.md)\n- [Apodex-1.1-mini vs MiniMax-M2.5](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minimax-m2-5.md)\n- [Arete-27B vs 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