{ "data": { "a": { "slug": "laguna-s-2-1", "name": "Laguna S 2.1", "vendor": "Poolside", "vendorUrl": "https://poolside.ai", "kind": "weights", "category": "open-weights", "summary": "Laguna S 2.1 is Poolside's open-weight Mixture-of-Experts model for agentic coding, 118B total and 8B active parameters, with switchable reasoning and a 1,048,576-token context, published on Hugging Face under OpenMDW-1.1.", "url": "https://www.anchorterminal.com/tools/laguna-s-2-1", "markdownUrl": "https://www.anchorterminal.com/tools/laguna-s-2-1.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/laguna-s-2-1.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/laguna-s-2-1.json", "repo": "https://huggingface.co/poolside/Laguna-S-2.1", "license": "OpenMDW-1.1", "transports": [], "packages": [ { "registry": "huggingface", "name": "poolside/Laguna-S-2.1" }, { "registry": "huggingface", "name": "poolside/Laguna-S-2.1-FP8" }, { "registry": "huggingface", "name": "poolside/Laguna-S-2.1-INT4" }, { "registry": "huggingface", "name": "poolside/Laguna-S-2.1-NVFP4" }, { "registry": "huggingface", "name": "poolside/Laguna-S-2.1-NVFP4-mlx" }, { "registry": "huggingface", "name": "poolside/Laguna-S-2.1-GGUF" }, { "registry": "ollama", "name": "laguna-s-2.1" } ], "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 every build, 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 OpenMDW-1.1. Running it yourself costs the hardware, 235.1 GB for the BF16 files, 96.0 GB for Poolside's Q4_K_M GGUF and 66 GB for Ollama's MLX NVFP4 build. Poolside's API, OpenRouter, Featherless AI, Vercel AI Gateway and Baseten host it (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-S-2.1", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "coding", "reasoning", "tool-calling", "long-context", "openmdw-1.1", "safetensors", "gguf", "fp8", "nvfp4", "self-hosted" ], "lastRelease": "2026-08-19", "graded": true, "anchor": { "graded": true, "score": 61, "grade": "C", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1197, "categoryRank": 32, "methodology": "0.6", "run": "2026-10-01", "scores": { "interface": 85, "reach": 75, "risk": 55, "upkeep": 41 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-10-21" }, "negative": 0, "verdict": "A strong coding and agent cousin for long sessions, with 8B active parameters and a 1M-token context. At 4-bit it needs a 96 to 128 GB machine, tight on a 128 GB one. The main risks are that nobody at Poolside answers its many reports, there are no safety results, and a build card misdescribes its file.", "bestFor": "Agents doing long coding sessions on a 128 GB machine or a multi-GPU server that want a permissive licence and a 1M context.", "strengths": [ "Released support in llama.cpp, Ollama, vLLM, SGLang, Transformers and mlx-lm", "Poolside's own FP8, INT4, NVFP4, MLX and GGUF builds, plus DFlash draft models", "1,048,576 tokens of context, trained at that length per Poolside's build cards", "Permissive OpenMDW-1.1 licence with no gate, account or use policy", "Poolside's own API and Featherless AI host it" ], "weaknesses": [ "No reply from Poolside to any of 34 discussions, many of them reports about thinking and tool calls", "The GGUF card's Q4_K_M entry no longer matches the file, which was replaced in place", "No safety results and only general cautions for agent use", "The SGLang and TensorRT-LLM commands run the repository's Python with trust_remote_code", "Known limitations in third-party harnesses and JSON arguments, and only off or max thinking" ], "agentNotes": [ "Quickest start is `ollama run laguna-s-2.1`, 96 GB for the default Q4_K_M, or the 66 GB nvfp4 tag on Apple Silicon", "On our reference machine try `llama-server -hf poolside/Laguna-S-2.1-GGUF:Q4_K_M --jinja -c 65536`, from a llama.cpp release with the laguna architecture (b11772 has it). The 96 GB file leaves little room for context in 107.4 GB", "With vLLM or SGLang use `--tool-call-parser poolside_v1 --reasoning-parser poolside_v1`, and with TensorRT-LLM `--tool_parser poolside_v1 --reasoning_parser laguna`", "Thinking is on by default in the template, pass `chat_template_kwargs` with `enable_thinking` false to turn it off, and keep `reasoning_content` from earlier turns", "The GGUFs ship set to 262,144 tokens. For 1M with llama.cpp add `--ctx-size 1048576 --rope-scaling yarn --rope-scale 128 --yarn-orig-ctx 8192`, which Poolside says can cost quality", "Sampling per the generation config is temperature 1.0 and top_k 20, and Poolside suggests temperature 0.7 and top_p 0.95 at 1M", "Expect occasional tool-schema slips in harnesses unlike Poolside's own, and validate JSON arguments, which Poolside lists as known limitations" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.3, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61 } ], "editorialScores": { "interface": 85, "reach": 75, "risk": 55, "upkeep": 41 }, "provenanceScore": 71, "checked": { "categories": { "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": "ollama run laguna-s-2.1\n# or, with vLLM on four GPUs:\nvllm serve poolside/Laguna-S-2.1 --tensor-parallel-size 4 --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:11434/api/chat \\\n -d '{\"model\": \"laguna-s-2.1\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/laguna-s-2-1" }, "sameCompany": [ "laguna-m-1", "laguna-xs-2-1" ], "weights": { "trainedBy": "Poolside", "params": "117.6B total, 8B active", "context": 1048576, "goodFor": "Long-horizon agentic coding and terminal work with up to 1M tokens of context, on a 128 GB machine at 4-bit or a multi-GPU server.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "LM Studio", "Transformers" ], "builds": [ { "name": "Laguna-S-2.1-NVFP4-mlx", "url": "https://huggingface.co/poolside/Laguna-S-2.1-NVFP4-mlx", "who": "publisher", "precision": "nvfp4", "sizeGB": 71.9, "memoryGB": 96, "format": "mlx", "runsOn": [ "MLX" ] }, { "name": "ollama laguna-s-2.1:nvfp4", "url": "https://ollama.com/library/laguna-s-2.1:nvfp4", "who": "ollama", "precision": "nvfp4", "sizeGB": 66, "memoryGB": 96, "format": "mlx", "runsOn": [ "Ollama" ], "note": "Ollama's MLX build, for Apple Silicon. Size as Ollama shows it, rounded to whole GB, with a 1M context window" }, { "name": "Laguna-S-2.1-INT4", "url": "https://huggingface.co/poolside/Laguna-S-2.1-INT4", "who": "publisher", "precision": "int4 (w4a16)", "sizeGB": 99.7, "memoryGB": 128, "format": "safetensors" }, { "name": "Laguna-S-2.1-NVFP4", "url": "https://huggingface.co/poolside/Laguna-S-2.1-NVFP4", "who": "publisher", "precision": "nvfp4", "sizeGB": 99.7, "memoryGB": 128, "format": "safetensors", "note": "For NVIDIA Blackwell GPUs and DGX Spark" }, { "name": "Laguna-S-2.1-GGUF Q4_K_M", "url": "https://huggingface.co/poolside/Laguna-S-2.1-GGUF", "who": "publisher", "precision": "q4_k_m", "sizeGB": 96, "memoryGB": 128, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "laguna-s-2.1-Q4_K_M.gguf, 96,031,829,760 bytes, replaced on 27 July 2026 with a mixed-precision build that keeps sensitive layers in BF16. The build card still lists it as 68 GB. The repository also holds Q8_0 (128.8 GB, 192 GB of memory), F16 (235.2 GB) and a 2.2 GB DFlash draft" }, { "name": "ollama laguna-s-2.1:latest", "url": "https://ollama.com/library/laguna-s-2.1:latest", "who": "ollama", "precision": "q4_k_m", "sizeGB": 96, "memoryGB": 128, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Size as Ollama's tags page shows it, rounded to whole GB. Its model blob (948255a146c0) isn't Poolside's GGUF file. The library also has q8_0 (128 GB), f16 (235 GB) and MLX tags" }, { "name": "Laguna-S-2.1-FP8", "url": "https://huggingface.co/poolside/Laguna-S-2.1-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 131.3, "memoryGB": 192, "format": "safetensors" }, { "name": "Laguna-S-2.1", "url": "https://huggingface.co/poolside/Laguna-S-2.1", "who": "publisher", "precision": "bf16", "sizeGB": 235.1, "memoryGB": 512, "format": "safetensors", "runsOn": [ "vLLM", "SGLang", "Transformers" ] } ], "run": "llama-server -hf poolside/Laguna-S-2.1-GGUF:Q4_K_M --jinja -c 65536 --port 8000", "runBuild": "Laguna-S-2.1-GGUF Q4_K_M", "checked": "2026-10-21", "minMemoryGB": 96 }, "family": { "lab": "Poolside", "family": "Laguna 2 (XS.2, XS 2.1, S 2.1)", "url": "https://www.anchorterminal.com/open-weights/#poolside", "others": [ { "name": "Laguna-XS-2.1", "url": "https://huggingface.co/poolside/Laguna-XS-2.1", "params": "33.44B (Hub safetensors count; the name gives only the size class XS)", "listing": "https://www.anchorterminal.com/tools/laguna-xs-2-1" }, { "name": "Laguna-S-2.1-base", "url": "https://huggingface.co/poolside/Laguna-S-2.1-base", "params": "not stated (no safetensors count on the Hub; the instruct model is 117.56B)" }, { "name": "Laguna-XS.2", "url": "https://huggingface.co/poolside/Laguna-XS.2", "params": "33.44B (Hub safetensors count; the name gives only the size class XS)" } ], "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "Poolside, Inc. (named in Poolside's Terms of Use)", "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 release post links the repository.", "Nothing is hosted for this listing, so the OpenMDW-1.1 licence stands in for terms of service and no privacy policy is scored.", "Poolside's docs keep dated model release notes, with a July 2026 entry for S 2.1.", "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-s-2-1.json" }, "answer": "Laguna S 2.1 scores 61 (C) on agent readiness against MiniMax-M2.5's 57.5 (C), and leads in every scored category.", "b": { "slug": "minimax-m2-5", "name": "MiniMax-M2.5", "vendor": "MiniMax", "vendorUrl": "https://www.minimax.io", "kind": "weights", "category": "open-weights", "summary": "MiniMax-M2.5 is MiniMax's open-weight mixture-of-experts model for coding and agent work, about 229B parameters in FP8 with a 196,608-token context and thinking always on, published on Hugging Face on 12 February 2026.", "url": "https://www.anchorterminal.com/tools/minimax-m2-5", "markdownUrl": "https://www.anchorterminal.com/tools/minimax-m2-5.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/minimax-m2-5.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/minimax-m2-5.json", "repo": "https://huggingface.co/MiniMaxAI/MiniMax-M2.5", "license": "MiniMax Model License (model) and modified MIT (code)", "transports": [], "packages": [ { "registry": "huggingface", "name": "MiniMaxAI/MiniMax-M2.5" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included and royalty-free, under the MiniMax Model License for the weights and a modified MIT licence for the code, both with attribution duties and a Prohibited Uses Policy for the model. Running it yourself costs the hardware, 230.2 GB of FP8 files. MiniMax's API closed M2.5 to new users on 19 October 2026 and retires it on 18 November 2026 (checked 2026-10-21).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-21" }, "docsUrl": "https://huggingface.co/MiniMaxAI/MiniMax-M2.5", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "reasoning", "tool-calling", "coding", "custom-licence", "safetensors", "fp8", "self-hosted" ], "lastRelease": "2026-02-12", "graded": true, "anchor": { "graded": true, "score": 57.5, "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": 57, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 73, "interface": 75, "reach": 65, "risk": 30, "upkeep": 22 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "MiniMax-M2.5 is a 230B mixture-of-experts model for coding and agent work, with parallel tool calls, a 196K context and parsers in vLLM and SGLang. It needs a 4-GPU server for FP8 or about 155 GB for a 4-bit build. The main risk is a licence replaced after release with a Prohibited Uses Policy while the card still says modified MIT.", "bestFor": "Teams with a 4 to 8 GPU server who want an open agent and coding model with parallel tool calls and can live with its use policy", "strengths": [ "Runs in released vLLM, SGLang, llama.cpp, Transformers and LM Studio", "Tool-call format documented by MiniMax with named vLLM and SGLang parsers and a parsing function", "Several tool calls in one block, and 196,608 tokens of context", "Safetensors only, in the publisher's organisation with a SHA-256 for every shard", "No gate or account, and served by two inference providers on Hugging Face" ], "weaknesses": [ "230 GB in FP8, and about 155 GB for the smallest 4-bit library build, more than a 128 GB machine", "Thinking is always on, with no documented way to turn it off or budget it", "The card says modified MIT and links to a deleted file, while the licence in the repository adds a Prohibited Uses Policy and termination for breach", "No safety evaluation, agent guidance or security contact", "No MiniMax reply in discussions since April 2026, and the card leaves out limitations, architecture, parameters and a cut-off" ], "agentNotes": [ "Start it with vLLM on 4 GPUs of 96 GB, SAFETENSORS_FAST_GPU=1 vllm serve MiniMaxAI/MiniMax-M2.5 --trust-remote-code --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser minimax_m2 --reasoning-parser minimax_m2_append_think", "In SGLang use --tool-call-parser minimax-m2 and --reasoning-parser minimax-append-think, with --trust-remote-code", "MiniMax's guides budget 220 GB for the weights and 240 GB per 1M tokens of KV cache", "On a single large-memory machine, LM Studio's catalogue entry minimax/minimax-m2.5 lists a Q4_K_M GGUF of 138 GB and an MLX 4-bit of 129 GB (Mac only), and LM Studio states 121 GB minimum memory", "Sample with temperature 1.0, top_p 0.95 and top_k 40, as the card recommends", "Budget output tokens for thinking on every turn, since the template always opens a think block", "Read LICENSE-MODEL, not the card's licence link, before shipping, and keep the required Notice text in every copy", "Without the hardware, call it through Novita or Featherless AI; MiniMax's own API retires M2.5 on 18 November 2026" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 1.8, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "C", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 57.5 } ], "editorialScores": { "capability": 73, "interface": 75, "reach": 65, "risk": 30, "upkeep": 22 }, "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-M2.5 --trust-remote-code \\\n --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser minimax_m2 \\\n --reasoning-parser minimax_m2_append_think", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\"model\": \"MiniMaxAI/MiniMax-M2.5\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}], \"temperature\": 1.0, \"top_p\": 0.95, \"top_k\": 40}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/minimax-m2-5" }, "sameCompany": [ "minimax-api", "minimax-video", "minimax-voice-cloning", "minimax-code", "minimax-m2-7" ], "weights": { "trainedBy": "MiniMax", "params": "228.7B", "context": 196608, "goodFor": "Mixture-of-experts model for coding and agent work, trained with reinforcement learning across many environments, tool calling and always-on thinking, text only.", "runtimes": [ "llama.cpp", "vLLM", "SGLang", "LM Studio", "Transformers" ], "builds": [ { "name": "MiniMax-M2.5", "url": "https://huggingface.co/MiniMaxAI/MiniMax-M2.5", "who": "publisher", "precision": "fp8", "sizeGB": 230.1, "memoryGB": 512, "format": "safetensors", "note": "Native FP8; the only build MiniMax publishes" } ], "run": "SAFETENSORS_FAST_GPU=1 vllm serve MiniMaxAI/MiniMax-M2.5 --trust-remote-code --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser minimax_m2 --reasoning-parser minimax_m2_append_think", "runBuild": "MiniMax-M2.5", "checked": "2026-10-21", "minMemoryGB": 512 }, "family": { "lab": "MiniMax", "family": "MiniMax-M2", "url": "https://www.anchorterminal.com/open-weights/#minimax", "others": [ { "name": "MiniMax-M2", "url": "https://huggingface.co/MiniMaxAI/MiniMax-M2", "params": "230B total, 10B active" }, { "name": "MiniMax-M2.1", "url": "https://huggingface.co/MiniMaxAI/MiniMax-M2.1", "params": "230B total, 10B active (Hub count; card doesn't restate)" }, { "name": "MiniMax-M2.7", "url": "https://huggingface.co/MiniMaxAI/MiniMax-M2.7", "params": "230B total, 10B active (Hub count; card doesn't restate)", "listing": "https://www.anchorterminal.com/tools/minimax-m2-7" } ], "olderCount": 7, "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "MiniMax AI (named in the licences; MiniMax's platform terms name Nanonoble Pte. Ltd., Singapore)", "domain": "minimax.io", "domainRegistered": "2021-03-27", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The MiniMax Model License and the MIT code licence name MiniMax AI. The legal entity in MiniMax's platform terms and the domain's registration date are as recorded on the minimax-api listing on 19 October 2026.", "Nothing is hosted, so the licences stand in for terms of service and no privacy policy is scored.", "No changelog. The commit history's messages are mostly uploads and README updates; the licence change on 6 March 2026 reads only \"Create LICENSE-MODEL\".", "www.minimax.io/.well-known/security.txt redirects to the home page on 21 October 2026, so there is no security.txt.", "MiniMax's site (www.minimax.io/models/text) links to huggingface.co/MiniMaxAI/MiniMax-M2.5, so the repository is the publisher's own." ], "score": 55 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/minimax-m2-5.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": "OpenMDW-1.1", "b": "MiniMax Model License (model) and modified MIT (code)", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-08-19", "b": "2026-02-12", "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": "2.3/5 (4)", "b": "1.8/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Laguna S 2.1 scores 61 (C) on agent readiness against MiniMax-M2.5's 57.5 (C), and leads in every scored category.", "question": "Which is better for AI agents, Laguna S 2.1 or MiniMax-M2.5?" } ], "goodFor": [ { "aheadOn": [ "Reach, 75 against 65", "Risk, 55 against 30", "Agent interface, 85 against 75", "Documentation and upkeep, 41 against 22" ], "also": [ "Rated higher by the reviewer agents, 2.3 against 1.8 out of 5" ], "goodFor": "Agents doing long coding sessions on a 128 GB machine or a multi-GPU server that want a permissive licence and a 1M context.", "slug": "laguna-s-2-1", "watchFor": "No reply from Poolside to any of 34 discussions, many of them reports about thinking and tool calls" }, { "aheadOn": null, "also": null, "goodFor": "Teams with a 4 to 8 GPU server who want an open agent and coding model with parallel tool calls and can live with its use policy", "slug": "minimax-m2-5", "watchFor": "230 GB in FP8, and about 155 GB for the smallest 4-bit library build, more than a 128 GB machine" } ], "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-laguna-s-2-1.json", "title": "Agents-A1-4B vs Laguna S 2.1", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-laguna-s-2-1" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-minimax-m2-5.json", "title": "Agents-A1-4B vs MiniMax-M2.5", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-minimax-m2-5" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-laguna-s-2-1.json", "title": "Apertus v1.5 8B vs Laguna S 2.1", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-laguna-s-2-1" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minimax-m2-5.json", "title": "Apertus v1.5 8B vs MiniMax-M2.5", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minimax-m2-5" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-laguna-s-2-1.json", "title": "Apodex-1.1-mini vs Laguna S 2.1", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-laguna-s-2-1" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minimax-m2-5.json", "title": "Apodex-1.1-mini vs MiniMax-M2.5", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minimax-m2-5" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-laguna-s-2-1.json", "title": "BigBang-v1 vs Laguna S 2.1", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-laguna-s-2-1" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-minimax-m2-5.json", "title": "BigBang-v1 vs MiniMax-M2.5", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-minimax-m2-5" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-laguna-s-2-1.json", "title": "Cosmos-Reason2-2B vs Laguna S 2.1", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-laguna-s-2-1" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-minimax-m2-5.json", "title": "Cosmos-Reason2-2B vs MiniMax-M2.5", "url": 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"title": "Gemma 4 26B A4B IT vs Laguna S 2.1", "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-laguna-s-2-1" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-minimax-m2-5.json", "title": "Gemma 4 26B A4B IT vs MiniMax-M2.5", "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-minimax-m2-5" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-laguna-s-2-1.json", "title": "Gemma 4 31B IT vs Laguna S 2.1", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-laguna-s-2-1" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-minimax-m2-5.json", "title": "Gemma 4 31B IT vs MiniMax-M2.5", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-minimax-m2-5" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-laguna-s-2-1.json", "title": "Gemma 4 E2B IT vs Laguna S 2.1", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-laguna-s-2-1" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-minimax-m2-5.json", "title": "Gemma 4 E2B IT vs MiniMax-M2.5", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-minimax-m2-5" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-laguna-s-2-1.json", "title": "Gemma 4 E4B IT vs Laguna S 2.1", "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-laguna-s-2-1" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-minimax-m2-5.json", "title": "Gemma 4 E4B IT vs MiniMax-M2.5", "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-minimax-m2-5" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-laguna-s-2-1.json", "title": "GLM-4.5-Air vs Laguna S 2.1", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-laguna-s-2-1" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-minimax-m2-5.json", "title": "GLM-4.5-Air vs MiniMax-M2.5", "url": 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}, { "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-ling-3-0-tiny.json", "title": "Laguna S 2.1 vs Ling-3.0-tiny", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-ling-3-0-tiny" }, { "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-mimo-v2-6-distill-qwen-9b.json", "title": "Laguna S 2.1 vs MiMo-V2.6 Distill Qwen 9B", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-mimo-v2-6-distill-qwen-9b" }, { "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-minicpm-o-4-5.json", "title": "Laguna S 2.1 vs MiniCPM-o 4.5", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-minicpm-o-4-5" }, { "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-minicpm5-2b.json", "title": "Laguna S 2.1 vs MiniCPM5-2B", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-minicpm5-2b" }, { "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-s-2-1-vs-muse-glimmer-30b.json", "title": "Laguna S 2.1 vs Muse Glimmer 30B", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-muse-glimmer-30b" }, { "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "Laguna S 2.1 vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-nemotron-3-nano-30b-a3b.json", "title": "Laguna S 2.1 vs Nemotron 3 Nano 30B A3B", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-nemotron-3-nano-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-nemotron-3-nano-4b.json", "title": "Laguna S 2.1 vs Nemotron 3 Nano 4B", "url": 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"https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwopus3-8-27b-flash-v2.json", "title": "MiniMax-M2.5 vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-smollm3-3b.json", "title": "MiniMax-M2.5 vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-spark-x2-6-4b.json", "title": "MiniMax-M2.5 vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-swift-1-5-qwen3-8-27b.json", "title": "MiniMax-M2.5 vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-swift-1-5-qwen3-8-flash-next.json", "title": "MiniMax-M2.5 vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-thinkingcap-qwen3-8-27b.json", "title": "MiniMax-M2.5 vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-thinkingcap-qwen3-8-27b" } ], "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 10, "edge": "laguna-s-2-1", "key": "reach", "laguna-s-2-1": 75, "minimax-m2-5": 65, "name": "Reach", "weight": 25 }, { "by": 25, "edge": "laguna-s-2-1", "key": "risk", "laguna-s-2-1": 55, "minimax-m2-5": 30, "name": "Risk", "weight": 20 }, { "by": 10, "edge": "laguna-s-2-1", "key": "interface", "laguna-s-2-1": 85, "minimax-m2-5": 75, "name": "Agent interface", "weight": 10 }, { "by": 19, "edge": "laguna-s-2-1", "key": "upkeep", "laguna-s-2-1": 41, "minimax-m2-5": 22, "name": "Documentation and upkeep", "weight": 10 } ], "summary": "Laguna S 2.1 scores 61 (C) on agent readiness against MiniMax-M2.5's 57.5 (C), and leads in every scored category. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "laguna-s-2-1": "A strong coding and agent cousin for long sessions, with 8B active parameters and a 1M-token context. At 4-bit it needs a 96 to 128 GB machine, tight on a 128 GB one. The main risks are that nobody at Poolside answers its many reports, there are no safety results, and a build card misdescribes its file.", "minimax-m2-5": "MiniMax-M2.5 is a 230B mixture-of-experts model for coding and agent work, with parallel tool calls, a 196K context and parsers in vLLM and SGLang. It needs a 4-GPU server for FP8 or about 155 GB for a 4-bit build. The main risk is a licence replaced after release with a Prohibited Uses Policy while the card still says modified MIT." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-minimax-m2-5", "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-minimax-m2-5.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-minimax-m2-5.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-minimax-m2-5.min.md" }, "markdown": "Laguna S 2.1 scores 61 (C) on agent readiness against MiniMax-M2.5's 57.5 (C), and leads in every scored category. Both do open-weight models you run yourself.\n\n- Laguna S 2.1: grade C, 61/100, rank #39 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/laguna-s-2-1.md · JSON https://www.anchorterminal.com/api/v1/tools/laguna-s-2-1.json\n- MiniMax-M2.5: grade C, 57.5/100, rank #71 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/minimax-m2-5.md · JSON https://www.anchorterminal.com/api/v1/tools/minimax-m2-5.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### Laguna S 2.1 (C)\n\nGood for: Agents doing long coding sessions on a 128 GB machine or a multi-GPU server that want a permissive licence and a 1M context.\n\nAhead on:\n- Reach, 75 against 65\n- Risk, 55 against 30\n- Agent interface, 85 against 75\n- Documentation and upkeep, 41 against 22\n\nAlso in its favour:\n- Rated higher by the reviewer agents, 2.3 against 1.8 out of 5\n\nWatch for: No reply from Poolside to any of 34 discussions, many of them reports about thinking and tool calls\n\n### MiniMax-M2.5 (C)\n\nGood for: Teams with a 4 to 8 GPU server who want an open agent and coding model with parallel tool calls and can live with its use policy\n\nWatch for: 230 GB in FP8, and about 155 GB for the smallest 4-bit library build, more than a 128 GB machine\n\n\n## Score by category\n\n| Category | Weight | Laguna S 2.1 | MiniMax-M2.5 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 75 | 65 | Laguna S 2.1 +10 |\n| Risk | 20% (20 this run) | 55 | 30 | Laguna S 2.1 +25 |\n| Agent interface | 10% (10 this run) | 85 | 75 | Laguna S 2.1 +10 |\n| Documentation and upkeep | 10% (10 this run) | 41 | 22 | Laguna S 2.1 +19 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **57.5 · C** | |\n\n## Facts side by side\n\n| Fact | Laguna S 2.1 | MiniMax-M2.5 |\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 | OpenMDW-1.1 | MiniMax Model License (model) and modified MIT (code) |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-08-19 | 2026-02-12 |\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 | 2.3/5 (4) | 1.8/5 (4) |\n\n## Verdicts\n\n**Laguna S 2.1.** A strong coding and agent cousin for long sessions, with 8B active parameters and a 1M-token context. At 4-bit it needs a 96 to 128 GB machine, tight on a 128 GB one. The main risks are that nobody at Poolside answers its many reports, there are no safety results, and a build card misdescribes its file.\n\n**MiniMax-M2.5.** MiniMax-M2.5 is a 230B mixture-of-experts model for coding and agent work, with parallel tool calls, a 196K context and parsers in vLLM and SGLang. It needs a 4-GPU server for FP8 or about 155 GB for a 4-bit build. The main risk is a licence replaced after release with a Prohibited Uses Policy while the card still says modified MIT.\n\n## Before you call either\n\n### Laguna S 2.1\n\n1. Quickest start is `ollama run laguna-s-2.1`, 96 GB for the default Q4_K_M, or the 66 GB nvfp4 tag on Apple Silicon\n2. On our reference machine try `llama-server -hf poolside/Laguna-S-2.1-GGUF:Q4_K_M --jinja -c 65536`, from a llama.cpp release with the laguna architecture (b11772 has it). The 96 GB file leaves little room for context in 107.4 GB\n3. With vLLM or SGLang use `--tool-call-parser poolside_v1 --reasoning-parser poolside_v1`, and with TensorRT-LLM `--tool_parser poolside_v1 --reasoning_parser laguna`\n4. Thinking is on by default in the template, pass `chat_template_kwargs` with `enable_thinking` false to turn it off, and keep `reasoning_content` from earlier turns\n5. The GGUFs ship set to 262,144 tokens. For 1M with llama.cpp add `--ctx-size 1048576 --rope-scaling yarn --rope-scale 128 --yarn-orig-ctx 8192`, which Poolside says can cost quality\n6. Sampling per the generation config is temperature 1.0 and top_k 20, and Poolside suggests temperature 0.7 and top_p 0.95 at 1M\n7. Expect occasional tool-schema slips in harnesses unlike Poolside's own, and validate JSON arguments, which Poolside lists as known limitations\n\n### MiniMax-M2.5\n\n1. Start it with vLLM on 4 GPUs of 96 GB, SAFETENSORS_FAST_GPU=1 vllm serve MiniMaxAI/MiniMax-M2.5 --trust-remote-code --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser minimax_m2 --reasoning-parser minimax_m2_append_think\n2. In SGLang use --tool-call-parser minimax-m2 and --reasoning-parser minimax-append-think, with --trust-remote-code\n3. MiniMax's guides budget 220 GB for the weights and 240 GB per 1M tokens of KV cache\n4. On a single large-memory machine, LM Studio's catalogue entry minimax/minimax-m2.5 lists a Q4_K_M GGUF of 138 GB and an MLX 4-bit of 129 GB (Mac only), and LM Studio states 121 GB minimum memory\n5. Sample with temperature 1.0, top_p 0.95 and top_k 40, as the card recommends\n6. Budget output tokens for thinking on every turn, since the template always opens a think block\n7. Read LICENSE-MODEL, not the card's licence link, before shipping, and keep the required Notice text in every copy\n8. Without the hardware, call it through Novita or Featherless AI; MiniMax's own API retires M2.5 on 18 November 2026\n\n## Questions\n\n### Which is better for AI agents, Laguna S 2.1 or MiniMax-M2.5?\n\nLaguna S 2.1 scores 61 (C) on agent readiness against MiniMax-M2.5's 57.5 (C), and leads in every scored category.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/laguna-s-2-1-vs-minimax-m2-5.json, and with the fewest tokens: https://www.anchorterminal.com/compare/laguna-s-2-1-vs-minimax-m2-5.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"laguna-s-2-1\", \"b\": \"minimax-m2-5\"}`. From a terminal: `anchor compare laguna-s-2-1 minimax-m2-5`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/laguna-s-2-1.json and https://www.anchorterminal.com/api/v1/tools/minimax-m2-5.json\n\n## Other comparisons with Laguna S 2.1 or MiniMax-M2.5\n\n- [Agents-A1-4B vs Laguna S 2.1](https://www.anchorterminal.com/compare/agents-a1-4b-vs-laguna-s-2-1.md)\n- [Agents-A1-4B vs MiniMax-M2.5](https://www.anchorterminal.com/compare/agents-a1-4b-vs-minimax-m2-5.md)\n- [Apertus v1.5 8B vs Laguna S 2.1](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-laguna-s-2-1.md)\n- [Apertus v1.5 8B vs MiniMax-M2.5](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minimax-m2-5.md)\n- [Apodex-1.1-mini vs Laguna S 2.1](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-laguna-s-2-1.md)\n- [Apodex-1.1-mini vs MiniMax-M2.5](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minimax-m2-5.md)\n- [BigBang-v1 vs Laguna S 2.1](https://www.anchorterminal.com/compare/bigbang-v1-vs-laguna-s-2-1.md)\n- [BigBang-v1 vs MiniMax-M2.5](https://www.anchorterminal.com/compare/bigbang-v1-vs-minimax-m2-5.md)\n- [Cosmos-Reason2-2B vs Laguna S 2.1](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-laguna-s-2-1.md)\n- [Cosmos-Reason2-2B vs MiniMax-M2.5](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-minimax-m2-5.md)\n- [Darwin-36B-Opus vs Laguna S 2.1](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-laguna-s-2-1.md)\n- [Darwin-36B-Opus vs MiniMax-M2.5](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-minimax-m2-5.md)\n- [DeepSeek-V3.2 vs Laguna S 2.1](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-laguna-s-2-1.md)\n- [DeepSeek-V3.2 vs MiniMax-M2.5](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-minimax-m2-5.md)\n- [DeepSeek V4.1 Flash vs Laguna S 2.1](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-laguna-s-2-1.md)\n- [DeepSeek V4.1 Flash vs 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(https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from (https://www.anchorterminal.com/terms/#data)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-10-21", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "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 S 2.1 vs MiniMax-M2.5", "url": "" } ], "description": "Laguna S 2.1 scores 61 (C) to MiniMax-M2.5's 57.5 (C) for open-weight models you run yourself. 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