{ "data": { "a": { "slug": "lfm3-omni-2b", "name": "LFM3-Omni-2B", "vendor": "Liquid AI", "vendorUrl": "https://www.liquid.ai", "kind": "weights", "category": "open-weights", "summary": "LFM3-Omni-2B is Liquid AI's open-weight 2.17B omni model for on-device use, taking text, images, video and audio and answering in text or speech, with tool calling and a 32K-token context, released on 27 October 2026 with GGUF and MLX builds.", "url": "https://www.anchorterminal.com/tools/lfm3-omni-2b", "markdownUrl": "https://www.anchorterminal.com/tools/lfm3-omni-2b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/lfm3-omni-2b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/lfm3-omni-2b.json", "dns": "lfm3-omni-2b.grade.anchorterminal.com", "repo": "https://huggingface.co/LiquidAI/LFM3-Omni-2B", "license": "LFM Open License v1.0 (free commercial use below $10M annual revenue)", "transports": [], "packages": [ { "registry": "huggingface", "name": "LiquidAI/LFM3-Omni-2B" }, { "registry": "huggingface", "name": "LiquidAI/LFM3-Omni-2B-GGUF" }, { "registry": "huggingface", "name": "LiquidAI/LFM3-Omni-2B-MLX-4bit" }, { "registry": "ollama", "name": "lfm3-omni" } ], "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 0.29 GB projector file and 4.49 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-omni-2b", "llmsTxt": "https://docs.liquid.ai/llms.txt", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "omni", "vision", "video", "audio", "speech", "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": 57, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 70, "reach": 80, "risk": 40, "upkeep": 49 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "One 2.17B model that sees, listens and talks on an 8 GB machine and turns spoken requests into tool calls. Only llama.cpp and Ollama run it in a release. The context is 32K, it doesn't reason, Liquid hasn't answered a question on the repository, and there are no safety results or data sources.", "bestFor": "Voice and camera agents on a laptop or phone that want one small model for hearing, seeing, speaking and calling tools", "strengths": [ "One model for text, images, video, audio and speech on an 8 GB machine", "Speech output and a realtime speech server in released llama.cpp", "Tool calls from spoken requests, in a documented format", "Updated a week before this check", "Liquid's own API serves it" ], "weaknesses": [ "Only llama.cpp and Ollama run it in a release", "A 32K-token context, which video and audio fill quickly", "It doesn't reason, and structured output isn't documented", "None of 12 questions on the repository has a reply from Liquid", "Loading in Transformers runs 14 Python files from the repository", "No safety results, security contact or training data description" ], "agentNotes": [ "Start it with `llama-server -hf LiquidAI/LFM3-Omni-2B-GGUF:Q4_K_M --jinja` or `ollama run lfm3-omni`", "Greedy decoding for text. For speech use audio_temperature 0.4 and audio_repetition_penalty 1.2, per the card", "The context is 32K, and llama.cpp samples video at 2 frames a second up to 384 frames", "Parse tool calls yourself from the XML it returns", "It doesn't reason, so don't pass enable_thinking", "Validate JSON output yourself" ], "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": 70, "reach": 80, "risk": 40, "upkeep": 49 }, "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-Omni-2B-GGUF:Q4_K_M --jinja\n# or\nollama run lfm3-omni", "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-omni-2b" }, "sameCompany": [ "liquid-d1", "lfm2-5-2-6b", "lfm3-1-7b", "lfm3-audio-2b", "lfm3-vl-2b" ], "weights": { "trainedBy": "Liquid AI", "builtOn": "The LFM3 backbone (Liquid's own; the metadata names no base model)", "params": "2.17B", "context": 32000, "goodFor": "One small on-device model in place of separate speech recognition, vision, language and speech models, for voice chat, questions about video with its soundtrack and tool calls from spoken requests. It doesn't reason.", "runtimes": [ "llama.cpp", "Ollama" ], "builds": [ { "name": "LFM3-Omni-2B", "url": "https://huggingface.co/LiquidAI/LFM3-Omni-2B", "who": "publisher", "precision": "bf16", "sizeGB": 4.49, "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-Omni-2B-GGUF Q8_0", "url": "https://huggingface.co/LiquidAI/LFM3-Omni-2B-GGUF", "who": "publisher", "precision": "q8_0", "sizeGB": 2.36, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "LFM3-Omni-2B-Q8_0.gguf (1.85 GB) plus mmproj-LFM3-Omni-2B-Q8_0.gguf (0.51 GB)" }, { "name": "LFM3-Omni-2B-GGUF Q4_K_M", "url": "https://huggingface.co/LiquidAI/LFM3-Omni-2B-GGUF", "who": "publisher", "precision": "q4_k_m", "sizeGB": 1.39, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "LFM3-Omni-2B-Q4_K_M.gguf (1.10 GB) plus Liquid's projector file mmproj-LFM3-Omni-2B-Q4_K_M.gguf (0.29 GB), from the Hub's file list. The repository also holds Q4_0, Q8_0, F16 and BF16 files" }, { "name": "LFM3-Omni-2B-MLX-4bit", "url": "https://huggingface.co/LiquidAI/LFM3-Omni-2B-MLX-4bit", "who": "publisher", "precision": "4bit", "sizeGB": 2.11, "memoryGB": 8, "format": "mlx", "runsOn": [ "MLX-VLM" ], "note": "Liquid also publishes 8-bit and BF16 MLX builds" }, { "name": "ollama lfm3-omni", "url": "https://ollama.com/library/lfm3-omni", "who": "ollama", "precision": "q4_k_m", "sizeGB": 2, "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-Omni-2B-GGUF:Q4_K_M --jinja", "runBuild": "LFM3-Omni-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-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-Omni-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-omni-2b.json" }, "answer": "LFM3-Omni-2B scores 61 (C) on agent readiness against MiniMax-M2.7's 57.6 (C), and leads in 3 of 4 scored categories. MiniMax-M2.7 leads on agent interface.", "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": 20, "category": "reach", "leads": "lfm3-omni-2b", "name": "Reach", "reasons": { "a": "Memory 30, Liquid's Q4_K_M GGUF is 1.10 GB with its 0.29 GB projector file, and 1.39 GB × 1.2 = 1.7 GB, which fits 8 GB, well inside the 16 GB line. Runtimes 10, Released llama.cpp has the lfm3 architecture with the LFM3 vision and audio projectors and speech output (src/models/lfm3.cpp and tools/mtmd at b12242, with a realtime client example), and Ollama's library has an lfm3-omni 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-omni-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 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": 80, "b": 60 } }, { "by": 10, "category": "risk", "leads": "lfm3-omni-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 } }, { "by": 7, "category": "upkeep", "leads": "lfm3-omni-2b", "name": "Documentation and upkeep", "reasons": { "a": "Model card 14, Intended use (one on-device model in place of separate speech recognition, vision, language and speech models), architecture, parameters (2.17B), context (32K) and the text and speech languages. 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 28 October, documented video frame rate and frame limits. Responsiveness 0, none of the 12 discussions that aren't pull requests (27 October to 9 November 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. 14 + 5 + 20 + 0 + 5 + 5 = 49.", "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": 49, "b": 42 } }, { "by": 5, "category": "interface", "leads": "minimax-m2-7", "name": "Agent interface", "reasons": { "a": "Chat template 20, chat_template.jinja in the repository (13,513 characters), with roles, images, video and 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 documents tool calling from spoken or written requests. Structured output 0, the card doesn't mention JSON or structured output for this model. Reasoning control 0, the card says this is a non-reasoning model and that `enable_thinking=True` is not supported, so there is no thinking to turn off or budget. Context 5, 32K tokens of input and output per the card. 20 + 25 + 20 + 0 + 0 + 5 = 70.", "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": 70, "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 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": "2026-11-03", "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-Omni-2B scores 61 (C) on agent readiness against MiniMax-M2.7's 57.6 (C), and leads in 3 of 4 scored categories. MiniMax-M2.7 leads on agent interface.", "question": "Which is better for AI agents, LFM3-Omni-2B or MiniMax-M2.7?" } ], "goodFor": [ { "aheadOn": [ "Reach, 80 against 60", "Risk, 40 against 30", "Documentation and upkeep, 49 against 42" ], "also": null, "goodFor": "Voice and camera agents on a laptop or phone that want one small model for hearing, seeing, speaking and calling tools", "slug": "lfm3-omni-2b", "watchFor": "Only llama.cpp and Ollama run it in a release" }, { "aheadOn": [ "Agent interface, 75 against 70" ], "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-omni-2b": null, "minimax-m2-7": [ { "causes": [ "product" ], "date": "2026-11-04", "delta": 1.5, "from": 56.1, "fromGrade": "C", "to": 57.6, 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"https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-5-2b.json", "title": "MiniMax-M2.7 vs Qwen3.5-2B", "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-5-2b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-5-397b-a17b.json", "title": "MiniMax-M2.7 vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-5-4b.json", "title": "MiniMax-M2.7 vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-5-9b.json", "title": "MiniMax-M2.7 vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-6-27b.json", "title": "MiniMax-M2.7 vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-6-35b-a3b.json", "title": "MiniMax-M2.7 vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-8-27b.json", "title": "MiniMax-M2.7 vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-8-flash-next.json", "title": "MiniMax-M2.7 vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-coder-30b-a3b-instruct.json", "title": "MiniMax-M2.7 vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-coder-next.json", "title": "MiniMax-M2.7 vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-omni-30b-a3b-instruct.json", "title": "MiniMax-M2.7 vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-vl-2b-instruct.json", "title": "MiniMax-M2.7 vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-vl-30b-a3b-instruct.json", "title": "MiniMax-M2.7 vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-vl-32b-instruct.json", "title": "MiniMax-M2.7 vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-vl-4b-instruct.json", "title": "MiniMax-M2.7 vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-vl-8b-instruct.json", "title": "MiniMax-M2.7 vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-vl-8b-instruct" }, { "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" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-recursive-flint.json", "title": "MiniMax-M2.7 vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-recursive-flint" }, { "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" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-sensenova-loop-9b-preview.json", "title": "MiniMax-M2.7 vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-smollm3-3b.json", "title": "MiniMax-M2.7 vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-spark-x2-6-4b.json", "title": "MiniMax-M2.7 vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-step-3-7-flash.json", "title": "MiniMax-M2.7 vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-swift-1-5-qwen3-8-27b.json", "title": "MiniMax-M2.7 vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-swift-1-5-qwen3-8-flash-next.json", "title": "MiniMax-M2.7 vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-thinkingcap-qwen3-8-27b.json", "title": "MiniMax-M2.7 vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-thinkingcap-qwen3-8-27b" }, { "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" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-wevisone-vl-2b.json", "title": "MiniMax-M2.7 vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-wevisone-vl-2b" }, { "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" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 20, "edge": "lfm3-omni-2b", "key": "reach", "lfm3-omni-2b": 80, "minimax-m2-7": 60, "name": "Reach", "weight": 25 }, { "by": 10, "edge": "lfm3-omni-2b", "key": "risk", "lfm3-omni-2b": 40, "minimax-m2-7": 30, "name": "Risk", "weight": 20 }, { "by": 5, "edge": "minimax-m2-7", "key": "interface", "lfm3-omni-2b": 70, "minimax-m2-7": 75, "name": "Agent interface", "weight": 10 }, { "by": 7, "edge": "lfm3-omni-2b", "key": "upkeep", "lfm3-omni-2b": 49, "minimax-m2-7": 42, "name": "Documentation and upkeep", "weight": 10 } ], "summary": "LFM3-Omni-2B scores 61 (C) on agent readiness against MiniMax-M2.7's 57.6 (C), and leads in 3 of 4 scored categories. MiniMax-M2.7 leads on agent interface. Both do open-weight models you run yourself.", "tasks": null, "tutorials": [], "verdicts": { "lfm3-omni-2b": "One 2.17B model that sees, listens and talks on an 8 GB machine and turns spoken requests into tool calls. Only llama.cpp and Ollama run it in a release. The context is 32K, it doesn't reason, Liquid hasn't answered a question on the repository, and there are no safety results 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-omni-2b-vs-minimax-m2-7", "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-minimax-m2-7.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-minimax-m2-7.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-minimax-m2-7.min.md" }, "markdown": "LFM3-Omni-2B scores 61 (C) on agent readiness against MiniMax-M2.7's 57.6 (C), and leads in 3 of 4 scored categories. MiniMax-M2.7 leads on agent interface. Both do open-weight models you run yourself.\n\n- LFM3-Omni-2B: grade C, 61/100, rank #71 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/lfm3-omni-2b.md · JSON https://www.anchorterminal.com/api/v1/tools/lfm3-omni-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-Omni-2B (C)\n\nGood for: Voice and camera agents on a laptop or phone that want one small model for hearing, seeing, speaking and calling tools\n\nAhead on:\n- Reach, 80 against 60\n- Risk, 40 against 30\n- Documentation and upkeep, 49 against 42\n\nWatch for: Only llama.cpp and Ollama run it in a release\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\nAhead on:\n- Agent interface, 75 against 70\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### Reach: LFM3-Omni-2B +20\n\n- LFM3-Omni-2B (80): Memory 30, Liquid's Q4_K_M GGUF is 1.10 GB with its 0.29 GB projector file, and 1.39 GB × 1.2 = 1.7 GB, which fits 8 GB, well inside the 16 GB line. Runtimes 10, Released llama.cpp has the lfm3 architecture with the LFM3 vision and audio projectors and speech output (src/models/lfm3.cpp and tools/mtmd at b12242, with a realtime client example), and Ollama's library has an lfm3-omni 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-omni-2b among its models, so the publisher hosts it. 30 + 10 + 15 + 10 + 10 + 5 = 80.\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### Risk: LFM3-Omni-2B +10\n\n- LFM3-Omni-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### Documentation and upkeep: LFM3-Omni-2B +7\n\n- LFM3-Omni-2B (49): Model card 14, Intended use (one on-device model in place of separate speech recognition, vision, language and speech models), architecture, parameters (2.17B), context (32K) and the text and speech languages. 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 28 October, documented video frame rate and frame limits. Responsiveness 0, none of the 12 discussions that aren't pull requests (27 October to 9 November 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. 14 + 5 + 20 + 0 + 5 + 5 = 49.\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### Agent interface: MiniMax-M2.7 +5\n\n- LFM3-Omni-2B (70): Chat template 20, chat_template.jinja in the repository (13,513 characters), with roles, images, video and 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 documents tool calling from spoken or written requests. Structured output 0, the card doesn't mention JSON or structured output for this model. Reasoning control 0, the card says this is a non-reasoning model and that `enable_thinking=True` is not supported, so there is no thinking to turn off or budget. Context 5, 32K tokens of input and output per the card. 20 + 25 + 20 + 0 + 0 + 5 = 70.\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## Score by category\n\n| Category | Weight | LFM3-Omni-2B | MiniMax-M2.7 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 80 | 60 | LFM3-Omni-2B +20 |\n| Risk | 20% (20 this run) | 40 | 30 | LFM3-Omni-2B +10 |\n| Agent interface | 10% (10 this run) | 70 | 75 | MiniMax-M2.7 +5 |\n| Documentation and upkeep | 10% (10 this run) | 49 | 42 | LFM3-Omni-2B +7 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **57.6 · C** | |\n\n## Facts side by side\n\n| Fact | LFM3-Omni-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 | 2026-11-03 | 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-Omni-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-Omni-2B.** One 2.17B model that sees, listens and talks on an 8 GB machine and turns spoken requests into tool calls. Only llama.cpp and Ollama run it in a release. The context is 32K, it doesn't reason, Liquid hasn't answered a question on the repository, and there are no safety results 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-Omni-2B\n\n1. Start it with `llama-server -hf LiquidAI/LFM3-Omni-2B-GGUF:Q4_K_M --jinja` or `ollama run lfm3-omni`\n2. Greedy decoding for text. For speech use audio_temperature 0.4 and audio_repetition_penalty 1.2, per the card\n3. The context is 32K, and llama.cpp samples video at 2 frames a second up to 384 frames\n4. Parse tool calls yourself from the XML it returns\n5. It doesn't reason, so don't pass enable_thinking\n6. Validate JSON output yourself\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-Omni-2B or MiniMax-M2.7?\n\nLFM3-Omni-2B scores 61 (C) on agent readiness against MiniMax-M2.7's 57.6 (C), and leads in 3 of 4 scored categories. MiniMax-M2.7 leads on agent interface.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-minimax-m2-7.json, and with the fewest tokens: https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-minimax-m2-7.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"lfm3-omni-2b\", \"b\": \"minimax-m2-7\"}`. From a terminal: `anchor compare lfm3-omni-2b minimax-m2-7`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/lfm3-omni-2b.json and https://www.anchorterminal.com/api/v1/tools/minimax-m2-7.json\n\n## Other comparisons with LFM3-Omni-2B or MiniMax-M2.7\n\n- [Agents-A1-4B vs LFM3-Omni-2B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-lfm3-omni-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-Omni-2B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-lfm3-omni-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-Omni-2B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-lfm3-omni-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 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"https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run", "terms": "https://www.anchorterminal.com/terms/#data" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "LFM3-Omni-2B vs MiniMax-M2.7", "url": "" } ], "description": "LFM3-Omni-2B scores 61 (C) to MiniMax-M2.7's 57.6 (C) for open-weight models you run yourself. 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