{ "data": { "a": { "slug": "lfm2-5-2-6b", "name": "LFM2.5-2.6B", "vendor": "Liquid AI", "vendorUrl": "https://www.liquid.ai", "kind": "weights", "category": "open-weights", "summary": "LFM2.5-2.6B is Liquid AI's open-weight 2.6B hybrid model of convolution and attention layers for on-device agents, with tool calling, thinking always on and a 131,072-token context, published on 4 August 2026 with Liquid's own GGUF, MLX and ONNX builds.", "url": "https://www.anchorterminal.com/tools/lfm2-5-2-6b", "markdownUrl": "https://www.anchorterminal.com/tools/lfm2-5-2-6b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/lfm2-5-2-6b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/lfm2-5-2-6b.json", "repo": "https://huggingface.co/LiquidAI/LFM2.5-2.6B", "license": "LFM Open License v1.0 (free commercial use below $10M annual revenue)", "transports": [], "packages": [ { "registry": "huggingface", "name": "LiquidAI/LFM2.5-2.6B" }, { "registry": "huggingface", "name": "LiquidAI/LFM2.5-2.6B-GGUF" }, { "registry": "huggingface", "name": "LiquidAI/LFM2.5-2.6B-MLX" }, { "registry": "ollama", "name": "LiquidAI/lfm2.5-2.6b" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository and Liquid's GGUF, MLX and ONNX repositories are not gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use under the LFM Open License v1.0, commercial use included for organisations with under $10M in annual revenue; above that, commercial use isn't licensed. Running it yourself costs little hardware, 1.6 GB for Liquid's Q4_0 GGUF and 5.4 GB in BF16 (checked 2026-10-21).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-21" }, "docsUrl": "https://docs.liquid.ai/lfm/models/lfm25-2.6b", "llmsTxt": "https://docs.liquid.ai/llms.txt", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "small", "edge", "reasoning", "tool-calling", "custom-licence", "safetensors", "gguf", "mlx", "self-hosted" ], "lastRelease": "2026-09-22", "graded": true, "anchor": { "graded": true, "score": 51.5, "grade": "D", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1210, "categoryRank": 75, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 13, "interface": 75, "reach": 95, "risk": 50, "upkeep": 57 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "A small, fast cousin for tool calls, extraction and long-context steps on a laptop or phone. Liquid's own 4-bit build is 1.6 GB, and every common runtime runs it. It always thinks, isn't meant for coding or knowledge-heavy work, and organisations over $10M of revenue need another licence. There are no safety results or security contact.", "bestFor": "Agents that want a small local helper for tool calls, extraction and RAG on a laptop, phone or CPU, at organisations under the licence's revenue threshold", "strengths": [ "Liquid's own 4-bit GGUF and MLX builds are 1.6 GB, under 2 GB of memory with the fifth added", "Runs in released llama.cpp, Ollama, vLLM, SGLang and Transformers, with an lfm2 tool parser in vLLM and SGLang", "Tool-call format documented by Liquid, several calls per turn, and 131,072 tokens of trained context", "Safetensors and GGUF only, no repository code, a checksum on every file and no gate", "Builds at many precisions from Liquid itself, refreshed in September 2026" ], "weaknesses": [ "Commercial use isn't licensed for organisations with $10M or more of annual revenue", "Thinking is always on, with no way to turn it off or budget it", "No safety evaluation, agent guidance or security contact", "No structured-output documentation and no knowledge cut-off on the card", "Liquid answered 2 of 16 discussions" ], "agentNotes": [ "Quickest start is `ollama run LiquidAI/lfm2.5-2.6b`, 1.7 GB, or `llama-cli -hf LiquidAI/LFM2.5-2.6B-GGUF`", "With vLLM or SGLang pass `--tool-call-parser lfm2`; for vLLM add `--reasoning-parser qwen3` so the thinking comes back separately", "Sample at temperature 0.1, top_k 50 and repetition_penalty 1.1, as the card and generation_config.json set", "Put tools in the system prompt as `List of tools: [...]` JSON, or pass tools to apply_chat_template; calls come back as a Python list of function calls, and the system prompt can ask for JSON calls instead", "Leave max_tokens unset or generous, since a low cap cuts the reasoning off mid-thought", "Earlier thinking is dropped from the prompt unless `preserve_thinking` is true", "Liquid's 328M LFM2.5-2.6B-DSpark drafter speeds decoding about 2.6 times in SGLang and on Apple silicon, per Liquid", "Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.5, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "D", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 51.5 } ], "editorialScores": { "capability": 13, "interface": 75, "reach": 95, "risk": 50, "upkeep": 57 }, "provenanceScore": 55, "checked": { "categories": { "capability": "2026-10-21", "interface": "2026-10-21", "reach": "2026-10-21", "risk": "2026-10-21", "upkeep": "2026-10-21" }, "last": "2026-10-21", "oldest": "2026-10-21", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "llama-cli -hf LiquidAI/LFM2.5-2.6B-GGUF --conversation --temp 0.1 --top-k 50 --repeat-penalty 1.1\n# or\nollama run LiquidAI/lfm2.5-2.6b\n# or, with vLLM:\nvllm serve LiquidAI/LFM2.5-2.6B --enable-auto-tool-choice --tool-call-parser lfm2 --reasoning-parser qwen3", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"LiquidAI/lfm2.5-2.6b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/lfm2-5-2-6b" }, "sameCompany": [ "liquid-d1" ], "weights": { "trainedBy": "Liquid AI", "builtOn": "LiquidAI/LFM2.5-2.6B-Base", "params": "2.69B", "context": 131072, "goodFor": "Small on-device agent model for tool use, data extraction, RAG and long-context work on a laptop or phone, always thinking, not for agentic coding or knowledge-heavy tasks.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "LFM2.5-2.6B", "url": "https://huggingface.co/LiquidAI/LFM2.5-2.6B", "who": "publisher", "precision": "bf16", "sizeGB": 5.4, "memoryGB": 8, "format": "safetensors", "runsOn": [ "vLLM", "SGLang", "Transformers" ] }, { "name": "LFM2.5-2.6B-GGUF Q4_0", "url": "https://huggingface.co/LiquidAI/LFM2.5-2.6B-GGUF", "who": "publisher", "precision": "q4_0", "sizeGB": 1.6, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp", "LM Studio" ], "note": "LFM2.5-2.6B-Q4_0.gguf, 1.59 GB from the Hub's file list. The repository also holds Q4_K_M (1.67 GB), Q5_K_M, Q6_K, Q8_0 (2.87 GB), F16 and BF16, and a quantisation-aware LFM2.5-2.6B-QAD-Q4_0.gguf added on 19 August 2026" }, { "name": "LFM2.5-2.6B-MLX 4bit", "url": "https://huggingface.co/LiquidAI/LFM2.5-2.6B-MLX", "who": "publisher", "precision": "4bit", "sizeGB": 1.6, "memoryGB": 8, "format": "mlx", "runsOn": [ "MLX" ], "note": "4bit/model.safetensors (1.58 GB) and tokenizer, 1.60 GB summed. The repository also holds 5, 6 and 8-bit, BF16, MXFP4, MXFP8 and NVFP4 folders, each also published as its own LFM2.5-2.6B-MLX-* repository" }, { "name": "ollama LiquidAI/lfm2.5-2.6b:q4_k_m", "url": "https://ollama.com/LiquidAI/lfm2.5-2.6b", "who": "publisher", "precision": "q4_k_m", "sizeGB": 1.7, "memoryGB": 8, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Liquid's own namespace in Ollama's registry, latest and q4_k_m share digest 83f596831da1; q4_0 is 1.6 GB. Sizes as Ollama's tags page shows them" } ], "run": "llama-server -hf LiquidAI/LFM2.5-2.6B-GGUF:Q4_0 --jinja -c 131072 --temp 0.1 --top-k 50 --repeat-penalty 1.1", "runBuild": "LFM2.5-2.6B-GGUF Q4_0", "checked": "2026-10-21", "minMemoryGB": 8 }, "family": { "lab": "Liquid AI", "family": "LFM2 (LFM2 and LFM2.5)", "url": "https://www.anchorterminal.com/open-weights/#liquid", "others": [ { "name": "LFM2.5-24B-A2B", "url": "https://huggingface.co/LiquidAI/LFM2.5-24B-A2B", "params": "24B total, 2B active (MoE) (Hub safetensors count 23.97B)" }, { "name": "LFM2.5-24B-A2B-Base", "url": "https://huggingface.co/LiquidAI/LFM2.5-24B-A2B-Base", "params": "24B total, 2B active (MoE) (Hub safetensors count 23.97B)" }, { "name": "LFM2.5-VL-3B", "url": "https://huggingface.co/LiquidAI/LFM2.5-VL-3B", "params": "3B (Hub safetensors count 3.12B)" }, { "name": "LFM2.5-2.6B-Base", "url": "https://huggingface.co/LiquidAI/LFM2.5-2.6B-Base", "params": "2.6B (Hub safetensors count 2.70B)" }, { "name": "LFM2.5-Encoder-350M", "url": "https://huggingface.co/LiquidAI/LFM2.5-Encoder-350M", "params": "350M (Hub safetensors count 354M)" }, { "name": "LFM2.5-Encoder-230M", "url": "https://huggingface.co/LiquidAI/LFM2.5-Encoder-230M", "params": "230M (Hub safetensors count 230M)" }, { "name": "LFM2.5-Encoder-350M-Prompt-Router", "url": "https://huggingface.co/LiquidAI/LFM2.5-Encoder-350M-Prompt-Router", "params": "350M (Hub safetensors count 355M)" }, { "name": "LFM2.5-Encoder-350M-Policy-Linter", "url": "https://huggingface.co/LiquidAI/LFM2.5-Encoder-350M-Policy-Linter", "params": "350M (Hub safetensors count 355M)" }, { "name": "LFM2.5-Encoder-350M-Diffusion", "url": "https://huggingface.co/LiquidAI/LFM2.5-Encoder-350M-Diffusion", "params": "350M (Hub safetensors count 354M)" }, { "name": "LFM2.5-Encoder-350M-Spellchecker", "url": "https://huggingface.co/LiquidAI/LFM2.5-Encoder-350M-Spellchecker", "params": "350M (Hub safetensors count 357M)" }, { "name": "LFM2.5-230M", "url": "https://huggingface.co/LiquidAI/LFM2.5-230M", "params": "230M (Hub safetensors count 230M)" }, { "name": "LFM2.5-230M-Base", "url": "https://huggingface.co/LiquidAI/LFM2.5-230M-Base", "params": "230M (Hub safetensors count 230M)" }, { "name": "LFM2.5-Encoder-350M-PII-Detector", "url": "https://huggingface.co/LiquidAI/LFM2.5-Encoder-350M-PII-Detector", "params": "350M (Hub safetensors count 355M)" }, { "name": "LFM2.5-8B-A1B-Base", "url": "https://huggingface.co/LiquidAI/LFM2.5-8B-A1B-Base", "params": "8B total, 1B active (MoE) (Hub safetensors count 8.47B)" }, { "name": "LFM2.5-8B-A1B", "url": "https://huggingface.co/LiquidAI/LFM2.5-8B-A1B", "params": "8B total, 1B active (MoE) (Hub safetensors count 8.47B)" }, { "name": "LFM2.5-VL-450M-Extract", "url": "https://huggingface.co/LiquidAI/LFM2.5-VL-450M-Extract", "params": "450M (Hub safetensors count 449M)" }, { "name": "LFM2.5-VL-1.6B-Extract", "url": "https://huggingface.co/LiquidAI/LFM2.5-VL-1.6B-Extract", "params": "1.6B (Hub safetensors count 1.60B)" }, { "name": "LFM2.5-Audio-1.5B-JP", "url": "https://huggingface.co/LiquidAI/LFM2.5-Audio-1.5B-JP", "params": "1.5B (Hub safetensors count 1.47B)" }, { "name": "LFM2.5-1.2B-JP-202606", "url": "https://huggingface.co/LiquidAI/LFM2.5-1.2B-JP-202606", "params": "1.2B (Hub safetensors count 1.17B)" }, { "name": "LFM2-2.6B-Longevity", "url": "https://huggingface.co/LiquidAI/LFM2-2.6B-Longevity", "params": "2.6B (Hub safetensors count 2.57B)" }, { "name": "LFM2-1.2B-Longevity", "url": "https://huggingface.co/LiquidAI/LFM2-1.2B-Longevity", "params": "1.2B (Hub safetensors count 1.17B)" }, { "name": "LFM2.5-ColBERT-350M", "url": "https://huggingface.co/LiquidAI/LFM2.5-ColBERT-350M", "params": "350M (Hub safetensors count 353M)" }, { "name": "LFM2.5-Embedding-350M", "url": "https://huggingface.co/LiquidAI/LFM2.5-Embedding-350M", "params": "350M (Hub safetensors count 354M)" }, { "name": "LFM2.5-VL-450M", "url": "https://huggingface.co/LiquidAI/LFM2.5-VL-450M", "params": "450M (Hub safetensors count 449M)" }, { "name": "LFM2.5-350M-Base", "url": "https://huggingface.co/LiquidAI/LFM2.5-350M-Base", "params": "350M (Hub safetensors count 354M)" }, { "name": "LFM2.5-350M", "url": "https://huggingface.co/LiquidAI/LFM2.5-350M", "params": "350M (Hub safetensors count 354M)" }, { "name": "LFM2-24B-A2B", "url": "https://huggingface.co/LiquidAI/LFM2-24B-A2B", "params": "24B total, 2B active (MoE) (Hub safetensors count 23.84B)" }, { "name": "LFM2.5-1.2B-Thinking", "url": "https://huggingface.co/LiquidAI/LFM2.5-1.2B-Thinking", "params": "1.2B (Hub safetensors count 1.17B)" }, { "name": "LFM2.5-1.2B-Instruct", "url": "https://huggingface.co/LiquidAI/LFM2.5-1.2B-Instruct", "params": "1.2B (Hub safetensors count 1.17B)" }, { "name": "LFM2.5-VL-1.6B", "url": "https://huggingface.co/LiquidAI/LFM2.5-VL-1.6B", "params": "1.6B (Hub safetensors count 1.60B)" }, { "name": "LFM2.5-1.2B-Base", "url": "https://huggingface.co/LiquidAI/LFM2.5-1.2B-Base", "params": "1.2B (Hub safetensors count 1.17B)" }, { "name": "LFM2-2.6B-Transcript", "url": "https://huggingface.co/LiquidAI/LFM2-2.6B-Transcript", "params": "2.6B (Hub safetensors count 2.57B)" }, { "name": "LFM2.5-1.2B-JP", "url": "https://huggingface.co/LiquidAI/LFM2.5-1.2B-JP", "params": "1.2B (Hub safetensors count 1.17B)" }, { "name": "LFM2-2.6B-Exp", "url": "https://huggingface.co/LiquidAI/LFM2-2.6B-Exp", "params": "2.6B (Hub safetensors count 2.57B)" }, { "name": "LFM2.5-Audio-1.5B", "url": "https://huggingface.co/LiquidAI/LFM2.5-Audio-1.5B", "params": "1.5B (Hub safetensors count 1.47B)" }, { "name": "LFM2-ColBERT-350M", "url": "https://huggingface.co/LiquidAI/LFM2-ColBERT-350M", "params": "350M (Hub safetensors count 353M)" }, { "name": "LFM2-VL-3B", "url": "https://huggingface.co/LiquidAI/LFM2-VL-3B", "params": "3B (Hub safetensors count 3.00B)" }, { "name": "LFM2-8B-A1B", "url": "https://huggingface.co/LiquidAI/LFM2-8B-A1B", "params": "8B total, 1B active (MoE) (Hub safetensors count 8.34B)" }, { "name": "LFM2-350M-PII-Extract-JP", "url": "https://huggingface.co/LiquidAI/LFM2-350M-PII-Extract-JP", "params": "350M (Hub safetensors count 354M)" }, { "name": "LFM2-2.6B", "url": "https://huggingface.co/LiquidAI/LFM2-2.6B", "params": "2.6B (Hub safetensors count 2.57B)" }, { "name": "LFM2-350M-Extract", "url": "https://huggingface.co/LiquidAI/LFM2-350M-Extract", "params": "350M (Hub safetensors count 354M)" }, { "name": "LFM2-350M-ENJP-MT", "url": "https://huggingface.co/LiquidAI/LFM2-350M-ENJP-MT", "params": "350M (Hub safetensors count 354M)" }, { "name": "LFM2-1.2B-Tool", "url": "https://huggingface.co/LiquidAI/LFM2-1.2B-Tool", "params": "1.2B (Hub safetensors count 1.17B)" }, { "name": "LFM2-1.2B-RAG", "url": "https://huggingface.co/LiquidAI/LFM2-1.2B-RAG", "params": "1.2B (Hub safetensors count 1.17B)" }, { "name": "LFM2-Audio-1.5B", "url": "https://huggingface.co/LiquidAI/LFM2-Audio-1.5B", "params": "1.5B (Hub safetensors count 1.47B)" }, { "name": "LFM2-350M-Math", "url": "https://huggingface.co/LiquidAI/LFM2-350M-Math", "params": "350M (Hub safetensors count 354M)" }, { "name": "LFM2-1.2B-Extract", "url": "https://huggingface.co/LiquidAI/LFM2-1.2B-Extract", "params": "1.2B (Hub safetensors count 1.17B)" }, { "name": "LFM2-VL-450M", "url": "https://huggingface.co/LiquidAI/LFM2-VL-450M", "params": "450M (Hub safetensors count 451M)" }, { "name": "LFM2-VL-1.6B", "url": "https://huggingface.co/LiquidAI/LFM2-VL-1.6B", "params": "1.6B (Hub safetensors count 1.58B)" }, { "name": "LFM2-700M", "url": "https://huggingface.co/LiquidAI/LFM2-700M", "params": "700M (Hub safetensors count 742M)" }, { "name": "LFM2-350M", "url": "https://huggingface.co/LiquidAI/LFM2-350M", "params": "350M (Hub safetensors count 354M)" }, { "name": "LFM2-1.2B", "url": "https://huggingface.co/LiquidAI/LFM2-1.2B", "params": "1.2B (Hub safetensors count 1.17B)" } ], "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "Liquid AI, Inc. (the licensor named in the LFM Open License)", "domain": "liquid.ai", "domainRegistered": "2017-12-16", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-21", "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.", "Nothing is hosted for this listing, so the licence stands in for terms of service and no privacy policy is scored.", "No changelog. The commits are README edits, a config fix and the DSpark link; Liquid's deprecations page records which earlier models this one replaces, without dates.", "/.well-known/security.txt answered 404 on www.liquid.ai on 21 October 2026, and Liquid4All/.github has no SECURITY.md.", "Liquid's site and docs link to huggingface.co/LiquidAI, so the repositories are the publisher's own." ], "score": 55 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/lfm2-5-2-6b.json", "live": { "slug": "lfm2-5-2-6b", "securityTxt": { "url": "https://liquid.ai/.well-known/security.txt", "state": "none", "checkedAt": "2026-10-25T15:27:49.412118793Z" }, "llmsTxt": { "url": "https://docs.liquid.ai/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-10-25T14:52:24.663143087Z" }, "domain": { "domain": "liquid.ai", "registered": "2017-12-16", "source": "https://rdap.identitydigital.services/rdap/domain/liquid.ai", "checkedAt": "2026-10-25T14:01:59.325890117Z" }, "updatedAt": "2026-10-25T15:27:49.412118793Z" } }, "answer": "LFM2.5-2.6B scores 51.5 (D) on agent readiness against MiniMax-M3.1-Flash's 50.5 (D), and leads in 4 of 5 scored categories. MiniMax-M3.1-Flash leads on capability.", "b": { "slug": "minimax-m3-1-flash", "name": "MiniMax-M3.1-Flash", "vendor": "MiniMax", "vendorUrl": "https://www.minimax.io", "kind": "weights", "category": "open-weights", "summary": "MiniMax-M3.1-Flash is MiniMax's open-weight mixture-of-experts model for coding, agent work, computer use and cybersecurity, about 428B parameters with 23B active, taking text, images and video with up to 1M tokens of context.", "url": "https://www.anchorterminal.com/tools/minimax-m3-1-flash", "markdownUrl": "https://www.anchorterminal.com/tools/minimax-m3-1-flash.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/minimax-m3-1-flash.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/minimax-m3-1-flash.json", "repo": "https://huggingface.co/MiniMaxAI/MiniMax-M3.1-Flash", "license": "MiniMax Community License (custom; commercial use needs a \"Built with MiniMax M3.1-Flash\" line and a one-time notice to MiniMax, and MiniMax's written authorisation above $20M yearly revenue)", "transports": [], "packages": [ { "registry": "huggingface", "name": "MiniMaxAI/MiniMax-M3.1-Flash" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The Hub reports gated false for MiniMaxAI/MiniMax-M3.1-Flash (checked 2026-10-24).", "pricing": "free", "pricingNotes": "Free to download. The MiniMax Community License allows commercial use if the product shows \"Built with MiniMax M3.1-Flash\" and MiniMax gets a one-time notice by email; products earning more than $20M a year need MiniMax's prior written authorisation. Running it yourself costs the hardware, 250.1 GB of NVFP4 and MXFP8 weights plus a 2.3 GB DSpark draft. MiniMax's API sells the same-named model at $0.15 in and $0.60 out per million tokens (checked 2026-10-24).", "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 2026-10-24).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-24" }, "docsUrl": "https://huggingface.co/MiniMaxAI/MiniMax-M3.1-Flash", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "multimodal", "vision", "video", "reasoning", "tool-calling", "coding", "nvfp4", "mxfp8", "safetensors", "self-hosted", "new" ], "lastRelease": "2026-10-23", "graded": true, "anchor": { "graded": true, "score": 50.5, "grade": "D", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1210, "categoryRank": 77, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 64, "interface": 70, "reach": 40, "risk": 35, "upkeep": 41 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-24" }, "negative": 0, "verdict": "A 428B mixture-of-experts model, 23B active, for agent work, coding and computer use, with image and video input, about 1M tokens of context and parallel tool calls. It needs a 4 to 8 GPU server for 250 GB of NVFP4 and MXFP8 weights, and vLLM and SGLang support was pre-release on 24 October 2026. Commercial use needs attribution and notice.", "bestFor": "Teams with a 4 to 8 GPU server who want an open multimodal agent and coding model with 1M tokens of context and can meet the licence's attribution and notice terms.", "strengths": [ "Quantisation-aware NVFP4 and MXFP8 release, about 250 GB for 428B parameters, with a DSpark draft included", "1,048,576 tokens of context in the config, with sparse attention in every layer", "Several tool calls in one block, and reasoning_effort from low to max per request", "Text, image and video input in one model", "No gate or account, files in MiniMax's own organisation with a SHA-256 for every shard" ], "weaknesses": [ "No released vLLM or SGLang ran it on 24 October 2026; only llama.cpp b11861 from that day did", "About 300 GB with a fifth added, more than any 128 GB or 256 GB machine", "Commercial use needs a Built with line and a notice to MiniMax, and authorisation above $20M yearly revenue", "No safety evaluation, agent guidance or security contact, alongside strong claimed offensive security capability", "The card omits limitations, languages, training data and a knowledge cut-off, and no discussion has a MiniMax reply" ], "agentNotes": [ "Check the use case first; commercial products must show \"Built with MiniMax M3.1-Flash\" and notify MiniMax once, or get written authorisation above $20M yearly revenue", "On 8 H100 or H200 GPUs use vLLM's nightly image vllm/vllm-openai:minimax-m31-flash with --tensor-parallel-size 8 --block-size 128 --kv-cache-dtype fp8 --moe-backend marlin --max-model-len 262144", "Pass --reasoning-parser minimax_m3 --tool-call-parser minimax_m3 --enable-auto-tool-choice in vLLM, or minimax-m3 for both in SGLang with --trust-remote-code", "--block-size 128 is mandatory in vLLM, since MSA's sparse block size is 128", "Set effort per request with chat_template_kwargs reasoning_effort (low, medium, high, xhigh, max); sample at temperature 1.0 and top_p 0.95", "On Blackwell or MI355X, 4 GPUs serve it with the DSpark draft from dspark/; vLLM's Hopper command leaves DSpark off and turns video input off", "For GGUF builds by others, use llama.cpp b11861 or later; earlier builds mis-scale the NVFP4 experts and produce garbage", "Without the hardware, call MiniMax-M3.1-Flash on MiniMax's API (minimax-api), whose thinking defaults to max effort" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.3, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "D", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 50.5 } ], "editorialScores": { "capability": 64, "interface": 70, "reach": 40, "risk": 35, "upkeep": 41 }, "provenanceScore": 55, "checked": { "categories": { "capability": "2026-10-24", "interface": "2026-10-24", "reach": "2026-10-24", "risk": "2026-10-24", "upkeep": "2026-10-24" }, "last": "2026-10-24", "oldest": "2026-10-24", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "# vLLM's recipe, 8 x H100 or H200 (nightly image; no pip release supports it on 24 October 2026)\ndocker run --gpus all --ipc=host -p 8000:8000 -v ~/.cache/huggingface:/root/.cache/huggingface \\\n vllm/vllm-openai:minimax-m31-flash MiniMaxAI/MiniMax-M3.1-Flash \\\n --tensor-parallel-size 8 --block-size 128 --disable-hybrid-kv-cache-manager \\\n --kv-cache-dtype fp8 --attention-config '{\"indexer_kv_dtype\": \"fp8\"}' --moe-backend marlin \\\n --max-model-len 262144 --reasoning-parser minimax_m3 \\\n --enable-auto-tool-choice --tool-call-parser minimax_m3", "http": "curl http://localhost:8000/v1/chat/completions -H 'Content-Type: application/json' \\\n -d '{\"model\": \"MiniMaxAI/MiniMax-M3.1-Flash\", \"temperature\": 1.0, \"top_p\": 0.95, \"chat_template_kwargs\": {\"reasoning_effort\": \"medium\"}, \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/minimax-m3-1-flash" }, "sameCompany": [ "minimax-api", "minimax-video", "minimax-voice-cloning", "minimax-code", "minimax-m2-5", "minimax-m2-7" ], "weights": { "trainedBy": "MiniMax", "builtOn": "MiniMaxAI/MiniMax-M3 (the card says M3.1-Flash builds on M3 and keeps its mixture-of-experts architecture; not declared as base_model in the metadata; M3 carries the same MiniMax Community License)", "params": "428B", "context": 1048576, "goodFor": "Mixture-of-experts model for coding, agent harnesses, computer use and security work, with images and video in, about 1M tokens of context and parallel tool calls, on a 4 to 8 GPU server.", "runtimes": [ "llama.cpp" ], "builds": [ { "name": "MiniMax-M3.1-Flash", "url": "https://huggingface.co/MiniMaxAI/MiniMax-M3.1-Flash", "who": "publisher", "precision": "nvfp4", "sizeGB": 250.12, "memoryGB": 512, "format": "safetensors", "runsOn": [ "vLLM", "SGLang" ], "note": "Quantisation-aware trained release, routed experts in NVFP4 (group 16) and dense and attention layers in MXFP8, 250,116,419,880 bytes in 48 shards summed from the Hub's file list, plus a 2.30 GB DSpark draft. The only build MiniMax publishes. vLLM and SGLang ran it only from pre-release images on 24 October 2026" } ], "run": "docker run --gpus all --ipc=host -p 8000:8000 -v ~/.cache/huggingface:/root/.cache/huggingface vllm/vllm-openai:minimax-m31-flash MiniMaxAI/MiniMax-M3.1-Flash --tensor-parallel-size 8 --block-size 128 --disable-hybrid-kv-cache-manager --kv-cache-dtype fp8 --attention-config '{\"indexer_kv_dtype\": \"fp8\"}' --moe-backend marlin --max-model-len 262144 --reasoning-parser minimax_m3 --enable-auto-tool-choice --tool-call-parser minimax_m3", "runBuild": "MiniMax-M3.1-Flash", "checked": "2026-10-24", "minMemoryGB": 512 }, "family": { "lab": "MiniMax", "family": "MiniMax-M3", "url": "https://www.anchorterminal.com/open-weights/#minimax", "others": [ { "name": "MiniMax-M3", "url": "https://huggingface.co/MiniMaxAI/MiniMax-M3", "params": "~428B total, ~23B activated" } ], "olderCount": 7 }, "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": "2026-10-24", "notes": [ "The licence reads Copyright (c) 2026 MiniMax. The platform terms' legal entity and the domain's registration date are as recorded on the minimax-api and minimax-m2-7 listings.", "Nothing is hosted, so the licence stands in for terms of service and no privacy policy is scored.", "No changelog. The repository's history is one commit, \"Init\", on 23 October 2026.", "www.minimax.io/.well-known/security.txt redirects to the home page on 24 October 2026, and MiniMax has no public GitHub repository for M3.1 Flash, so there is no security.txt or SECURITY.md.", "MiniMax's site (www.minimax.io/models/text) links to huggingface.co/MiniMaxAI, and the card links MiniMax's site, API and GitHub organisation, so the repository is the publisher's own." ], "score": 55 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/minimax-m3-1-flash.json", "live": { "slug": "minimax-m3-1-flash", "securityTxt": { "url": "https://minimax.io/.well-known/security.txt", "state": "none", "checkedAt": "2026-10-25T15:28:08.450653621Z" }, "updatedAt": "2026-10-25T15:28:08.450653621Z" } }, "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 Community License (custom; commercial use needs a \"Built with MiniMax M3.1-Flash\" line and a one-time notice to MiniMax, and MiniMax's written authorisation above $20M yearly revenue)", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "no", "name": "llms.txt" }, { "a": "2026-09-22", "b": "2026-10-23", "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": "", "b": "", "name": "Customer content may train models" }, { "a": "", "b": "", "name": "Terms restrict automated access" }, { "a": "", "b": "", "name": "Terms restrict benchmarking" }, { "a": "", "b": "", "name": "Terms or service can change without notice" }, { "a": "", "b": "", "name": "Arbitration or class-action waiver" }, { "a": "2.5/5 (4)", "b": "2.3/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "LFM2.5-2.6B scores 51.5 (D) on agent readiness against MiniMax-M3.1-Flash's 50.5 (D), and leads in 4 of 5 scored categories. MiniMax-M3.1-Flash leads on capability.", "question": "Which is better for AI agents, LFM2.5-2.6B or MiniMax-M3.1-Flash?" } ], "goodFor": [ { "aheadOn": [ "Reach, 95 against 40", "Risk, 50 against 35", "Agent interface, 75 against 70", "Documentation and upkeep, 57 against 41" ], "also": null, "goodFor": "Agents that want a small local helper for tool calls, extraction and RAG on a laptop, phone or CPU, at organisations under the licence's revenue threshold", "slug": "lfm2-5-2-6b", "watchFor": "Commercial use isn't licensed for organisations with $10M or more of annual revenue" }, { "aheadOn": [ "Capability, 64 against 13" ], "also": null, "goodFor": "Teams with a 4 to 8 GPU server who want an open multimodal agent and coding model with 1M tokens of context and can meet the licence's attribution and notice terms.", "slug": "minimax-m3-1-flash", "watchFor": "No released vLLM or SGLang ran it on 24 October 2026; only llama.cpp b11861 from that day did" } ], "job": { "capability": 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"https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-30b-a3b-instruct.json", "title": "LFM2.5-2.6B vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-32b-instruct.json", "title": "LFM2.5-2.6B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-4b-instruct.json", "title": "LFM2.5-2.6B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-8b-instruct.json", "title": "LFM2.5-2.6B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwopus3-8-27b-flash-v2.json", "title": "LFM2.5-2.6B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-recursive-flint.json", "title": "LFM2.5-2.6B vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-smollm3-3b.json", "title": "LFM2.5-2.6B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-spark-x2-6-4b.json", "title": "LFM2.5-2.6B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-swift-1-5-qwen3-8-27b.json", "title": "LFM2.5-2.6B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "LFM2.5-2.6B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-thinkingcap-qwen3-8-27b.json", "title": "LFM2.5-2.6B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-minimax-m3-1-flash.json", "title": "Ling-3.0-tiny vs MiniMax-M3.1-Flash", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-minimax-m3-1-flash" }, { "json": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-minimax-m3-1-flash.json", "title": "MiMo-V2.6 Distill Qwen 9B vs MiniMax-M3.1-Flash", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-minimax-m3-1-flash" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-minimax-m3-1-flash.json", "title": "MiniCPM-o 4.5 vs MiniMax-M3.1-Flash", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-minimax-m3-1-flash" }, { "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-minimax-m3-1-flash.json", "title": "MiniCPM5-2B vs MiniMax-M3.1-Flash", "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-minimax-m3-1-flash" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-minimax-m3-1-flash.json", "title": "MiniMax-M2.5 vs MiniMax-M3.1-Flash", "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-minimax-m3-1-flash" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-minimax-m3-1-flash.json", "title": "MiniMax-M2.7 vs MiniMax-M3.1-Flash", "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-minimax-m3-1-flash" }, { "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-muse-glimmer-30b.json", "title": "MiniMax-M3.1-Flash vs Muse Glimmer 30B", "url": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-muse-glimmer-30b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "MiniMax-M3.1-Flash vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-nemotron-3-5-bolt-e4b-4gb" }, { "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "MiniMax-M3.1-Flash vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-nemotron-3-nano-30b-a3b.json", "title": "MiniMax-M3.1-Flash vs Nemotron 3 Nano 30B A3B", "url": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-nemotron-3-nano-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-nemotron-3-nano-4b.json", "title": "MiniMax-M3.1-Flash vs Nemotron 3 Nano 4B", "url": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-nemotron-3-nano-4b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json", "title": "MiniMax-M3.1-Flash vs Nemotron 3 Nano Omni 30B A3B Reasoning", "url": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-nemotron-3-nano-omni-30b-a3b-reasoning" }, { "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-nemotron-3-super-120b-a12b.json", "title": "MiniMax-M3.1-Flash vs Nemotron 3 Super 120B A12B", "url": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-nemotron-3-super-120b-a12b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-nemotron-3-ultra-550b-a55b.json", "title": "MiniMax-M3.1-Flash vs Nemotron 3 Ultra 550B A55B", "url": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-nemotron-3-ultra-550b-a55b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-occamy-1-0.json", "title": "MiniMax-M3.1-Flash vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-olmo-3-7b-think.json", "title": "MiniMax-M3.1-Flash vs Olmo 3 7B Think", "url": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-olmo-3-7b-think" }, { "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-ornith-1-5-35b-a3b.json", "title": "MiniMax-M3.1-Flash vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-ornith-1-5-397b.json", "title": "MiniMax-M3.1-Flash vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-ornith-1-5-9b.json", "title": "MiniMax-M3.1-Flash vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-30b-a3b-instruct-2507.json", "title": "MiniMax-M3.1-Flash vs Qwen3-30B-A3B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-30b-a3b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-4b-instruct-2507.json", "title": "MiniMax-M3.1-Flash vs Qwen3-4B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-4b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-5-0-8b.json", "title": "MiniMax-M3.1-Flash vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-5-122b-a10b.json", "title": "MiniMax-M3.1-Flash vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-5-2b.json", "title": "MiniMax-M3.1-Flash vs Qwen3.5-2B", "url": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-5-2b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-5-397b-a17b.json", "title": "MiniMax-M3.1-Flash vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-5-4b.json", "title": "MiniMax-M3.1-Flash vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-5-9b.json", "title": "MiniMax-M3.1-Flash vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-6-27b.json", "title": "MiniMax-M3.1-Flash vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-6-35b-a3b.json", "title": "MiniMax-M3.1-Flash vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-8-27b.json", "title": "MiniMax-M3.1-Flash vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-8-flash-next.json", "title": "MiniMax-M3.1-Flash vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-coder-30b-a3b-instruct.json", "title": "MiniMax-M3.1-Flash vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-coder-next.json", "title": "MiniMax-M3.1-Flash vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-omni-30b-a3b-instruct.json", "title": "MiniMax-M3.1-Flash vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-vl-2b-instruct.json", "title": "MiniMax-M3.1-Flash vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-vl-30b-a3b-instruct.json", "title": "MiniMax-M3.1-Flash vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-vl-32b-instruct.json", "title": "MiniMax-M3.1-Flash vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-vl-4b-instruct.json", "title": "MiniMax-M3.1-Flash vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-vl-8b-instruct.json", "title": "MiniMax-M3.1-Flash vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwopus3-8-27b-flash-v2.json", "title": "MiniMax-M3.1-Flash vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-recursive-flint.json", "title": "MiniMax-M3.1-Flash vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-smollm3-3b.json", "title": "MiniMax-M3.1-Flash vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-spark-x2-6-4b.json", "title": "MiniMax-M3.1-Flash vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-swift-1-5-qwen3-8-27b.json", "title": "MiniMax-M3.1-Flash vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-swift-1-5-qwen3-8-flash-next.json", "title": "MiniMax-M3.1-Flash vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-thinkingcap-qwen3-8-27b.json", "title": "MiniMax-M3.1-Flash vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-thinkingcap-qwen3-8-27b" } ], "scores": [ { "by": 51, "edge": "minimax-m3-1-flash", "key": "capability", "lfm2-5-2-6b": 13, "minimax-m3-1-flash": 64, "name": "Capability", "weight": 35 }, { "by": 55, "edge": "lfm2-5-2-6b", "key": "reach", "lfm2-5-2-6b": 95, "minimax-m3-1-flash": 40, "name": "Reach", "weight": 25 }, { "by": 15, "edge": "lfm2-5-2-6b", "key": "risk", "lfm2-5-2-6b": 50, "minimax-m3-1-flash": 35, "name": "Risk", "weight": 20 }, { "by": 5, "edge": "lfm2-5-2-6b", "key": "interface", "lfm2-5-2-6b": 75, "minimax-m3-1-flash": 70, "name": "Agent interface", "weight": 10 }, { "by": 16, "edge": "lfm2-5-2-6b", "key": "upkeep", "lfm2-5-2-6b": 57, "minimax-m3-1-flash": 41, "name": "Documentation and upkeep", "weight": 10 } ], "summary": "LFM2.5-2.6B scores 51.5 (D) on agent readiness against MiniMax-M3.1-Flash's 50.5 (D), and leads in 4 of 5 scored categories. MiniMax-M3.1-Flash leads on capability. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "lfm2-5-2-6b": "A small, fast cousin for tool calls, extraction and long-context steps on a laptop or phone. Liquid's own 4-bit build is 1.6 GB, and every common runtime runs it. It always thinks, isn't meant for coding or knowledge-heavy work, and organisations over $10M of revenue need another licence. There are no safety results or security contact.", "minimax-m3-1-flash": "A 428B mixture-of-experts model, 23B active, for agent work, coding and computer use, with image and video input, about 1M tokens of context and parallel tool calls. It needs a 4 to 8 GPU server for 250 GB of NVFP4 and MXFP8 weights, and vLLM and SGLang support was pre-release on 24 October 2026. Commercial use needs attribution and notice." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minimax-m3-1-flash", "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minimax-m3-1-flash.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minimax-m3-1-flash.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minimax-m3-1-flash.min.md" }, "markdown": "LFM2.5-2.6B scores 51.5 (D) on agent readiness against MiniMax-M3.1-Flash's 50.5 (D), and leads in 4 of 5 scored categories. MiniMax-M3.1-Flash leads on capability. Both do open-weight models you run yourself.\n\n- LFM2.5-2.6B: grade D, 51.5/100, rank #96 of 110 open-weight models. Markdown https://www.anchorterminal.com/tools/lfm2-5-2-6b.md · JSON https://www.anchorterminal.com/api/v1/tools/lfm2-5-2-6b.json\n- MiniMax-M3.1-Flash: grade D, 50.5/100, rank #99 of 110 open-weight models. Markdown https://www.anchorterminal.com/tools/minimax-m3-1-flash.md · JSON https://www.anchorterminal.com/api/v1/tools/minimax-m3-1-flash.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 3260 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### LFM2.5-2.6B (D)\n\nGood for: Agents that want a small local helper for tool calls, extraction and RAG on a laptop, phone or CPU, at organisations under the licence's revenue threshold\n\nAhead on:\n- Reach, 95 against 40\n- Risk, 50 against 35\n- Agent interface, 75 against 70\n- Documentation and upkeep, 57 against 41\n\nWatch for: Commercial use isn't licensed for organisations with $10M or more of annual revenue\n\n### MiniMax-M3.1-Flash (D)\n\nGood for: Teams with a 4 to 8 GPU server who want an open multimodal agent and coding model with 1M tokens of context and can meet the licence's attribution and notice terms.\n\nAhead on:\n- Capability, 64 against 13\n\nWatch for: No released vLLM or SGLang ran it on 24 October 2026; only llama.cpp b11861 from that day did\n\n\n## Score by category\n\n| Category | Weight | LFM2.5-2.6B | MiniMax-M3.1-Flash | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35% (35 this run) | 13 | 64 | MiniMax-M3.1-Flash +51 |\n| Reach | 25% (25 this run) | 95 | 40 | LFM2.5-2.6B +55 |\n| Risk | 20% (20 this run) | 50 | 35 | LFM2.5-2.6B +15 |\n| Agent interface | 10% (10 this run) | 75 | 70 | LFM2.5-2.6B +5 |\n| Documentation and upkeep | 10% (10 this run) | 57 | 41 | LFM2.5-2.6B +16 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **51.5 · D** | **50.5 · D** | |\n\n## Facts side by side\n\n| Fact | LFM2.5-2.6B | MiniMax-M3.1-Flash |\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 Community License (custom; commercial use needs a \"Built with MiniMax M3.1-Flash\" line and a one-time notice to MiniMax, and MiniMax's written authorisation above $20M yearly revenue) |\n| Read-only variant documented | no | no |\n| llms.txt | yes | no |\n| Last release | 2026-09-22 | 2026-10-23 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models | | |\n| Terms restrict automated access | | |\n| Terms restrict benchmarking | | |\n| Terms or service can change without notice | | |\n| Arbitration or class-action waiver | | |\n| Agent reviews | 2.5/5 (4) | 2.3/5 (4) |\n\n## Verdicts\n\n**LFM2.5-2.6B.** A small, fast cousin for tool calls, extraction and long-context steps on a laptop or phone. Liquid's own 4-bit build is 1.6 GB, and every common runtime runs it. It always thinks, isn't meant for coding or knowledge-heavy work, and organisations over $10M of revenue need another licence. There are no safety results or security contact.\n\n**MiniMax-M3.1-Flash.** A 428B mixture-of-experts model, 23B active, for agent work, coding and computer use, with image and video input, about 1M tokens of context and parallel tool calls. It needs a 4 to 8 GPU server for 250 GB of NVFP4 and MXFP8 weights, and vLLM and SGLang support was pre-release on 24 October 2026. Commercial use needs attribution and notice.\n\n## Before you call either\n\n### LFM2.5-2.6B\n\n1. Quickest start is `ollama run LiquidAI/lfm2.5-2.6b`, 1.7 GB, or `llama-cli -hf LiquidAI/LFM2.5-2.6B-GGUF`\n2. With vLLM or SGLang pass `--tool-call-parser lfm2`; for vLLM add `--reasoning-parser qwen3` so the thinking comes back separately\n3. Sample at temperature 0.1, top_k 50 and repetition_penalty 1.1, as the card and generation_config.json set\n4. Put tools in the system prompt as `List of tools: [...]` JSON, or pass tools to apply_chat_template; calls come back as a Python list of function calls, and the system prompt can ask for JSON calls instead\n5. Leave max_tokens unset or generous, since a low cap cuts the reasoning off mid-thought\n6. Earlier thinking is dropped from the prompt unless `preserve_thinking` is true\n7. Liquid's 328M LFM2.5-2.6B-DSpark drafter speeds decoding about 2.6 times in SGLang and on Apple silicon, per Liquid\n8. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented\n\n### MiniMax-M3.1-Flash\n\n1. Check the use case first; commercial products must show \"Built with MiniMax M3.1-Flash\" and notify MiniMax once, or get written authorisation above $20M yearly revenue\n2. On 8 H100 or H200 GPUs use vLLM's nightly image vllm/vllm-openai:minimax-m31-flash with --tensor-parallel-size 8 --block-size 128 --kv-cache-dtype fp8 --moe-backend marlin --max-model-len 262144\n3. Pass --reasoning-parser minimax_m3 --tool-call-parser minimax_m3 --enable-auto-tool-choice in vLLM, or minimax-m3 for both in SGLang with --trust-remote-code\n4. --block-size 128 is mandatory in vLLM, since MSA's sparse block size is 128\n5. Set effort per request with chat_template_kwargs reasoning_effort (low, medium, high, xhigh, max); sample at temperature 1.0 and top_p 0.95\n6. On Blackwell or MI355X, 4 GPUs serve it with the DSpark draft from dspark/; vLLM's Hopper command leaves DSpark off and turns video input off\n7. For GGUF builds by others, use llama.cpp b11861 or later; earlier builds mis-scale the NVFP4 experts and produce garbage\n8. Without the hardware, call MiniMax-M3.1-Flash on MiniMax's API (minimax-api), whose thinking defaults to max effort\n\n## Questions\n\n### Which is better for AI agents, LFM2.5-2.6B or MiniMax-M3.1-Flash?\n\nLFM2.5-2.6B scores 51.5 (D) on agent readiness against MiniMax-M3.1-Flash's 50.5 (D), and leads in 4 of 5 scored categories. MiniMax-M3.1-Flash leads on capability.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minimax-m3-1-flash.json, and with the fewest tokens: https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minimax-m3-1-flash.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"lfm2-5-2-6b\", \"b\": \"minimax-m3-1-flash\"}`. From a terminal: `anchor compare lfm2-5-2-6b minimax-m3-1-flash`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/lfm2-5-2-6b.json and https://www.anchorterminal.com/api/v1/tools/minimax-m3-1-flash.json\n\n## Other comparisons with LFM2.5-2.6B or MiniMax-M3.1-Flash\n\n- [Agents-A1-4B vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-lfm2-5-2-6b.md)\n- [Agents-A1-4B vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/agents-a1-4b-vs-minimax-m3-1-flash.md)\n- [Apertus v1.5 8B vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-lfm2-5-2-6b.md)\n- [Apertus v1.5 8B vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minimax-m3-1-flash.md)\n- [Apodex-1.1-mini vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-lfm2-5-2-6b.md)\n- [Apodex-1.1-mini vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minimax-m3-1-flash.md)\n- [BigBang-v1 vs 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