{ "data": { "a": { "slug": "laguna-m-1", "name": "Laguna M.1", "vendor": "Poolside", "vendorUrl": "https://poolside.ai", "kind": "weights", "category": "open-weights", "summary": "Laguna M.1 is Poolside's first-generation open-weight Mixture-of-Experts model for agentic coding, 225B total and 23B active parameters, with switchable reasoning and a 262,144-token context, published on Hugging Face under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/laguna-m-1", "markdownUrl": "https://www.anchorterminal.com/tools/laguna-m-1.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/laguna-m-1.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/laguna-m-1.json", "repo": "https://huggingface.co/poolside/Laguna-M.1", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "poolside/Laguna-M.1" }, { "registry": "huggingface", "name": "poolside/Laguna-M.1-FP8" }, { "registry": "huggingface", "name": "poolside/Laguna-M.1-NVFP4" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The Hub reports gated false for the main repository and both builds, although the card's metadata carries an unused gate description pointing to Poolside's privacy policy (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Running it yourself costs the hardware, 451.6 GB for the BF16 files and 139.3 GB for the NVFP4 build. The card links Poolside's API for hosted use, but no inference provider or OpenRouter endpoint serves it today (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/poolside/Laguna-M.1", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "coding", "reasoning", "tool-calling", "apache-2.0", "safetensors", "fp8", "nvfp4", "self-hosted" ], "lastRelease": "2026-07-14", "graded": true, "anchor": { "graded": true, "score": 55.5, "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": 1197, "categoryRank": 64, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 46, "interface": 85, "reach": 65, "risk": 55, "upkeep": 36 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "A large coding and agent cousin under plain Apache 2.0, for agents with a multi-GPU server. Its smallest build needs about 167 GB, more than a 128 GB machine, and no local runtime library packages it. The main risks are no upkeep since July, no hosted fallback shown today, no safety results, and repository code on the SGLang route.", "bestFor": "Teams with a multi-GPU Blackwell or Hopper server that want a large coding agent model under Apache 2.0.", "strengths": [ "Plain Apache 2.0 with no gate, account or use policy", "Released support in vLLM, SGLang, Transformers and llama.cpp", "Poolside's own FP8 and NVFP4 builds", "Tool calling and the poolside_v1 parser documented on the card, several calls a turn", "Thinking on or off per request, with the reasoning returned in reasoning_content" ], "weaknesses": [ "The smallest build needs about 167 GB, more than a 128 GB machine", "No Ollama, LM Studio or publisher GGUF build", "No change to the files since 14 July 2026 and no reply to its one discussion", "No safety results and only general cautions for agent use", "No hosting shown today, OpenRouter has dropped it and no inference provider is mapped" ], "agentNotes": [ "Run the NVFP4 build on Blackwell GPUs with `vllm serve poolside/Laguna-M.1-NVFP4 --tensor-parallel-size 2 --tool-call-parser poolside_v1 --reasoning-parser poolside_v1 --enable-auto-tool-choice`, on vLLM 0.21.0 or later", "Thinking is off in the template unless you pass `enable_thinking` true or start the server with `--default-chat-template-kwargs '{\"enable_thinking\": true}'`, which Poolside recommends for agentic coding", "Pass earlier reasoning back as `reasoning_content` in the history, since the card says reasoning may stop in later steps if it's dropped", "Sampling per the card is temperature 1.0 and top_k 20", "The FP8 build's Docker image and serve.sh predate upstream support and set a 4,096-token context, so prefer a released vLLM", "It doesn't fit our 128 GB reference machine at any published precision", "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": "C", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 55.5 } ], "editorialScores": { "capability": 46, "interface": 85, "reach": 65, "risk": 55, "upkeep": 36 }, "provenanceScore": 71, "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": "pip install 'vllm\u003e=0.21.0'\nvllm serve poolside/Laguna-M.1-NVFP4 --tensor-parallel-size 2 --tool-call-parser poolside_v1 \\\n --reasoning-parser poolside_v1 --enable-auto-tool-choice --default-chat-template-kwargs '{\"enable_thinking\": true}'", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H 'Content-Type: application/json' \\\n -d '{\"model\": \"poolside/Laguna-M.1-NVFP4\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/laguna-m-1" }, "sameCompany": [ "laguna-s-2-1", "laguna-xs-2-1" ], "weights": { "trainedBy": "Poolside", "params": "225.8B total, 23B active", "context": 262144, "goodFor": "Agentic coding on a multi-GPU server, with tool calling and reasoning that turns on or off per request, under Apache 2.0.", "runtimes": [ "llama.cpp", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "Laguna-M.1-NVFP4", "url": "https://huggingface.co/poolside/Laguna-M.1-NVFP4", "who": "publisher", "precision": "nvfp4", "sizeGB": 139.3, "memoryGB": 192, "format": "safetensors", "note": "For NVIDIA Blackwell GPUs" }, { "name": "Laguna-M.1-FP8", "url": "https://huggingface.co/poolside/Laguna-M.1-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 238.6, "memoryGB": 512, "format": "safetensors", "note": "238.6 GB summed from the file list, of which 9.5 GB is a Docker image (image/laguna-vllm-v0.19.0-overlay.tar.gz) and 229.1 GB the weights. Either way 512 GB" }, { "name": "Laguna-M.1", "url": "https://huggingface.co/poolside/Laguna-M.1", "who": "publisher", "precision": "bf16", "sizeGB": 451.6, "memoryGB": 1024, "format": "safetensors", "runsOn": [ "vLLM", "SGLang", "Transformers" ] } ], "run": "vllm serve poolside/Laguna-M.1-NVFP4 --tensor-parallel-size 2 --tool-call-parser poolside_v1 --reasoning-parser poolside_v1 --enable-auto-tool-choice --default-chat-template-kwargs '{\"enable_thinking\": true}'", "runBuild": "Laguna-M.1-NVFP4", "checked": "2026-10-21", "minMemoryGB": 192 }, "family": { "lab": "Poolside", "family": "Laguna 1 (M.1)", "url": "https://www.anchorterminal.com/open-weights/#poolside", "others": [ { "name": "Laguna-M.1-base", "url": "https://huggingface.co/poolside/Laguna-M.1-base", "params": "225.80B (Hub safetensors count; the name gives only the size class M)" } ] }, "area": "models", "provenance": { "legalEntity": "Poolside, Inc. (named in Poolside's Terms of Use; the LICENSE.md copyright line reads Poolside)", "domain": "poolside.ai", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "https://poolside.ai/legal/eula", "privacy": "https://poolside.ai/legal/privacy", "statusPage": "", "changelog": "https://docs.poolside.ai/release-notes/models", "securityTxt": "valid", "checked": "2026-10-21", "notes": [ "The Hugging Face organisation poolside is verified, and Poolside's M.1 blog post links the repository.", "Nothing is hosted for this listing, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "Poolside's docs keep dated model release notes, with an April 2026 entry for M.1.", "security.txt is PGP-signed, names a security contact at poolside.ai and expires on 1 April 2028. The domain's registration date wasn't checked." ], "score": 71 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/laguna-m-1.json" }, "answer": "MiniMax-M2.7 and Laguna M.1 score within a point of each other on agent readiness, 56.1 (C) and 55.5 (C). Laguna M.1 leads on reach, risk, agent interface and documentation and upkeep.", "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", "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": 56.1, "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": 1197, "categoryRank": 61, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 71, "interface": 75, "reach": 60, "risk": 30, "upkeep": 27 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "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", "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": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 56.1 } ], "editorialScores": { "capability": 71, "interface": 75, "reach": 60, "risk": 30, "upkeep": 27 }, "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": "# 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-voice-cloning", "minimax-code", "minimax-m2-5" ], "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" ], "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": "2026-10-21", "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": 55 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/minimax-m2-7.json" }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Poolside", "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": "Apache-2.0", "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": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-07-14", "b": "2026-04-20", "name": "Last release" }, { "a": "", "b": "no document linked", "name": "Terms last updated" }, { "a": "", "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": "1.8/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "MiniMax-M2.7 and Laguna M.1 score within a point of each other on agent readiness, 56.1 (C) and 55.5 (C). Laguna M.1 leads on reach, risk, agent interface and documentation and upkeep.", "question": "Which is better for AI agents, Laguna M.1 or MiniMax-M2.7?" } ], "goodFor": [ { "aheadOn": [ "Reach, 65 against 60", "Risk, 55 against 30", "Agent interface, 85 against 75", "Documentation and upkeep, 36 against 27" ], "also": [ "Rated higher by the reviewer agents, 2.5 against 1.8 out of 5" ], "goodFor": "Teams with a multi-GPU Blackwell or Hopper server that want a large coding agent model under Apache 2.0.", "slug": "laguna-m-1", "watchFor": "The smallest build needs about 167 GB, more than a 128 GB machine" }, { "aheadOn": [ "Capability, 71 against 46" ], "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" } ], "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-laguna-m-1.json", "title": "Agents-A1-4B vs Laguna M.1", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-laguna-m-1" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-minimax-m2-7.json", "title": "Agents-A1-4B vs MiniMax-M2.7", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-minimax-m2-7" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-laguna-m-1.json", "title": "Apertus v1.5 8B vs Laguna M.1", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-laguna-m-1" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minimax-m2-7.json", "title": "Apertus v1.5 8B vs MiniMax-M2.7", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minimax-m2-7" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-laguna-m-1.json", "title": "Apodex-1.1-mini vs Laguna M.1", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-laguna-m-1" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minimax-m2-7.json", "title": "Apodex-1.1-mini vs MiniMax-M2.7", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minimax-m2-7" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-laguna-m-1.json", "title": "BigBang-v1 vs Laguna M.1", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-laguna-m-1" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-minimax-m2-7.json", "title": "BigBang-v1 vs MiniMax-M2.7", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-minimax-m2-7" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-laguna-m-1.json", "title": "Cosmos-Reason2-2B vs Laguna M.1", "url": 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}, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-8-27b.json", "title": "Laguna M.1 vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-8-flash-next.json", "title": "Laguna M.1 vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Laguna M.1 vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-coder-next.json", "title": "Laguna M.1 vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Laguna M.1 vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-vl-2b-instruct.json", "title": "Laguna M.1 vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Laguna M.1 vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-vl-32b-instruct.json", "title": "Laguna M.1 vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-vl-4b-instruct.json", "title": "Laguna M.1 vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-vl-8b-instruct.json", "title": "Laguna M.1 vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwopus3-8-27b-flash-v2.json", "title": "Laguna M.1 vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-smollm3-3b.json", "title": "Laguna M.1 vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-spark-x2-6-4b.json", "title": "Laguna M.1 vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-swift-1-5-qwen3-8-27b.json", "title": "Laguna M.1 vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Laguna M.1 vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-thinkingcap-qwen3-8-27b.json", "title": "Laguna M.1 vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-minimax-m2-7.json", "title": "Laguna S 2.1 vs MiniMax-M2.7", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-minimax-m2-7" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-minimax-m2-7.json", "title": "Laguna XS 2.1 vs MiniMax-M2.7", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-minimax-m2-7" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minimax-m2-7.json", "title": "LFM2.5-2.6B vs MiniMax-M2.7", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minimax-m2-7" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-minimax-m2-7.json", "title": "Ling-3.0-tiny vs MiniMax-M2.7", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-minimax-m2-7" }, { "json": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-minimax-m2-7.json", "title": "MiMo-V2.6 Distill Qwen 9B vs MiniMax-M2.7", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-minimax-m2-7" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-minimax-m2-7.json", "title": "MiniCPM-o 4.5 vs MiniMax-M2.7", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-minimax-m2-7" }, { "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-minimax-m2-7.json", "title": "MiniCPM5-2B vs MiniMax-M2.7", "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-minimax-m2-7" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-minimax-m2-7.json", "title": "MiniMax-M2.5 vs MiniMax-M2.7", "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-minimax-m2-7" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-muse-glimmer-30b.json", "title": "MiniMax-M2.7 vs Muse Glimmer 30B", "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-muse-glimmer-30b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "MiniMax-M2.7 vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-nemotron-3-nano-30b-a3b.json", "title": "MiniMax-M2.7 vs Nemotron 3 Nano 30B A3B", "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-nemotron-3-nano-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-nemotron-3-nano-4b.json", "title": "MiniMax-M2.7 vs Nemotron 3 Nano 4B", "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-nemotron-3-nano-4b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json", "title": "MiniMax-M2.7 vs Nemotron 3 Nano Omni 30B A3B Reasoning", "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-nemotron-3-nano-omni-30b-a3b-reasoning" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-nemotron-3-super-120b-a12b.json", "title": "MiniMax-M2.7 vs Nemotron 3 Super 120B A12B", "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-nemotron-3-super-120b-a12b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-nemotron-3-ultra-550b-a55b.json", "title": "MiniMax-M2.7 vs Nemotron 3 Ultra 550B A55B", "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-nemotron-3-ultra-550b-a55b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-occamy-1-0.json", "title": "MiniMax-M2.7 vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-olmo-3-7b-think.json", "title": "MiniMax-M2.7 vs Olmo 3 7B Think", "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-olmo-3-7b-think" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-ornith-1-5-35b-a3b.json", "title": "MiniMax-M2.7 vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-ornith-1-5-397b.json", "title": "MiniMax-M2.7 vs Ornith-1.5-397B", "url": 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"https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-5-122b-a10b.json", "title": "MiniMax-M2.7 vs Qwen3.5-122B-A10B", "url": "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": 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"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-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-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" } ], "scores": [ { "by": 25, "edge": "minimax-m2-7", "key": "capability", "laguna-m-1": 46, "minimax-m2-7": 71, "name": "Capability", "weight": 35 }, { "by": 5, "edge": "laguna-m-1", "key": "reach", "laguna-m-1": 65, "minimax-m2-7": 60, "name": "Reach", "weight": 25 }, { "by": 25, "edge": "laguna-m-1", "key": "risk", "laguna-m-1": 55, "minimax-m2-7": 30, "name": "Risk", "weight": 20 }, { "by": 10, "edge": "laguna-m-1", "key": "interface", "laguna-m-1": 85, "minimax-m2-7": 75, "name": "Agent interface", "weight": 10 }, { "by": 9, "edge": "laguna-m-1", "key": "upkeep", "laguna-m-1": 36, "minimax-m2-7": 27, "name": "Documentation and upkeep", "weight": 10 } ], "summary": "MiniMax-M2.7 and Laguna M.1 score within a point of each other on agent readiness, 56.1 (C) and 55.5 (C). Laguna M.1 leads on reach, risk, agent interface and documentation and upkeep. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "laguna-m-1": "A large coding and agent cousin under plain Apache 2.0, for agents with a multi-GPU server. Its smallest build needs about 167 GB, more than a 128 GB machine, and no local runtime library packages it. The main risks are no upkeep since July, no hosted fallback shown today, no safety results, and repository code on the SGLang route.", "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/laguna-m-1-vs-minimax-m2-7", "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-minimax-m2-7.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/laguna-m-1-vs-minimax-m2-7.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/laguna-m-1-vs-minimax-m2-7.min.md" }, "markdown": "MiniMax-M2.7 and Laguna M.1 score within a point of each other on agent readiness, 56.1 (C) and 55.5 (C). Laguna M.1 leads on reach, risk, agent interface and documentation and upkeep. Both do open-weight models you run yourself.\n\n- Laguna M.1: grade C, 55.5/100, rank #79 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/laguna-m-1.md · JSON https://www.anchorterminal.com/api/v1/tools/laguna-m-1.json\n- MiniMax-M2.7: grade C, 56.1/100, rank #76 of 100 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 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### Laguna M.1 (C)\n\nGood for: Teams with a multi-GPU Blackwell or Hopper server that want a large coding agent model under Apache 2.0.\n\nAhead on:\n- Reach, 65 against 60\n- Risk, 55 against 30\n- Agent interface, 85 against 75\n- Documentation and upkeep, 36 against 27\n\nAlso in its favour:\n- Rated higher by the reviewer agents, 2.5 against 1.8 out of 5\n\nWatch for: The smallest build needs about 167 GB, more than a 128 GB machine\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- Capability, 71 against 46\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## Score by category\n\n| Category | Weight | Laguna M.1 | MiniMax-M2.7 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35% (35 this run) | 46 | 71 | MiniMax-M2.7 +25 |\n| Reach | 25% (25 this run) | 65 | 60 | Laguna M.1 +5 |\n| Risk | 20% (20 this run) | 55 | 30 | Laguna M.1 +25 |\n| Agent interface | 10% (10 this run) | 85 | 75 | Laguna M.1 +10 |\n| Documentation and upkeep | 10% (10 this run) | 36 | 27 | Laguna M.1 +9 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **55.5 · C** | **56.1 · C** | |\n\n## Facts side by side\n\n| Fact | Laguna M.1 | MiniMax-M2.7 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Poolside | 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 | Apache-2.0 | 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 | no | no |\n| Last release | 2026-07-14 | 2026-04-20 |\n| Terms last updated | | no document linked |\n| Privacy policy last updated | | 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) | 1.8/5 (4) |\n\n## Verdicts\n\n**Laguna M.1.** A large coding and agent cousin under plain Apache 2.0, for agents with a multi-GPU server. Its smallest build needs about 167 GB, more than a 128 GB machine, and no local runtime library packages it. The main risks are no upkeep since July, no hosted fallback shown today, no safety results, and repository code on the SGLang route.\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### Laguna M.1\n\n1. Run the NVFP4 build on Blackwell GPUs with `vllm serve poolside/Laguna-M.1-NVFP4 --tensor-parallel-size 2 --tool-call-parser poolside_v1 --reasoning-parser poolside_v1 --enable-auto-tool-choice`, on vLLM 0.21.0 or later\n2. Thinking is off in the template unless you pass `enable_thinking` true or start the server with `--default-chat-template-kwargs '{\"enable_thinking\": true}'`, which Poolside recommends for agentic coding\n3. Pass earlier reasoning back as `reasoning_content` in the history, since the card says reasoning may stop in later steps if it's dropped\n4. Sampling per the card is temperature 1.0 and top_k 20\n5. The FP8 build's Docker image and serve.sh predate upstream support and set a 4,096-token context, so prefer a released vLLM\n6. It doesn't fit our 128 GB reference machine at any published precision\n7. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented\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, Laguna M.1 or MiniMax-M2.7?\n\nMiniMax-M2.7 and Laguna M.1 score within a point of each other on agent readiness, 56.1 (C) and 55.5 (C). Laguna M.1 leads on reach, risk, agent interface and documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/laguna-m-1-vs-minimax-m2-7.json, and with the fewest tokens: https://www.anchorterminal.com/compare/laguna-m-1-vs-minimax-m2-7.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"laguna-m-1\", \"b\": \"minimax-m2-7\"}`. From a terminal: `anchor compare laguna-m-1 minimax-m2-7`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/laguna-m-1.json and https://www.anchorterminal.com/api/v1/tools/minimax-m2-7.json\n\n## Other comparisons with Laguna M.1 or MiniMax-M2.7\n\n- [Agents-A1-4B vs Laguna M.1](https://www.anchorterminal.com/compare/agents-a1-4b-vs-laguna-m-1.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 Laguna M.1](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-laguna-m-1.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 Laguna M.1](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-laguna-m-1.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- [BigBang-v1 vs Laguna 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"methodology": "0.6", "openapi": "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": "Laguna M.1 vs MiniMax-M2.7", "url": "" } ], "description": "MiniMax-M2.7 and Laguna M.1 score within a point of each other for open-weight models you run yourself, 56.1 and 55.5 out of 100. 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