{ "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 and MiniCPM5-2B score within a point of each other on agent readiness, 61 (C) and 60.2 (C). MiniCPM5-2B leads on reach and documentation and upkeep.", "b": { "slug": "minicpm5-2b", "name": "MiniCPM5-2B", "vendor": "OpenBMB", "vendorUrl": "https://openbmb.cn", "kind": "weights", "category": "open-weights", "summary": "MiniCPM5-2B is OpenBMB's open-weight dense language model of 2.5B parameters for on-device and local use, with thinking, XML tool calls and a 131,072-token context, published on Hugging Face in BF16, GGUF, MLX and GPTQ under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/minicpm5-2b", "markdownUrl": "https://www.anchorterminal.com/tools/minicpm5-2b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/minicpm5-2b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/minicpm5-2b.json", "repo": "https://huggingface.co/openbmb/MiniCPM5-2B", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "openbmb/MiniCPM5-2B" }, { "registry": "huggingface", "name": "openbmb/MiniCPM5-2B-GGUF" }, { "registry": "huggingface", "name": "openbmb/MiniCPM5-2B-MLX" }, { "registry": "huggingface", "name": "openbmb/MiniCPM5-2B-GPTQ" }, { "registry": "ollama", "name": "openbmb/minicpm5-2b" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository and its GGUF, MLX and GPTQ siblings are not gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. OpenBMB sells no per-call access to these weights. Running them costs the hardware, 5.03 GB for the BF16 file and 1.56 GB for OpenBMB's Q4_K_M GGUF (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/openbmb/MiniCPM5-2B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "small", "on-device", "reasoning", "tool-calling", "apache-2.0", "safetensors", "gguf", "mlx", "self-hosted" ], "lastRelease": "2026-09-29", "graded": true, "anchor": { "graded": true, "score": 60.2, "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": 48, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 27, "interface": 75, "reach": 90, "risk": 55, "upkeep": 97 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "A tiny dense cousin for local tool calling and long context, with published training data and an active publisher. OpenBMB's own 4-bit builds need about 2 GB, so it runs on a laptop or phone in vLLM, SGLang, llama.cpp, Ollama or Transformers. The main risks are always-on thinking, no safety results and use conditions beyond Apache 2.0.", "bestFor": "On-device and laptop agents that want a 2 GB tool-calling helper with long context and open training data", "strengths": [ "OpenBMB's own GGUF, MLX and GPTQ builds, the 4-bit ones under 1.6 GB", "Stock LlamaForCausalLM, so vLLM, SGLang, llama.cpp, Ollama and Transformers load it without a fork", "Tool calls documented with the minicpm5 parser in released vLLM and SGLang, several calls per turn", "131,072 tokens native", "All eight training datasets published on Hugging Face", "OpenBMB answered 16 of 18 discussions and fixed the template on 29 September" ], "weaknesses": [ "OpenBMB documents only a Think mode for the 2B, so every answer starts with reasoning", "No safety evaluation, refusal scope or prompt-injection guidance", "No security.txt, disclosure policy or SECURITY.md", "The card adds use conditions to Apache 2.0, ethical use only and no jailbreak or injection testing", "No hosted endpoint, so an agent without hardware can't call it", "No knowledge cut-off and no structured-output guidance" ], "agentNotes": [ "Smallest start is `ollama run openbmb/minicpm5-2b` (1.6 GB, q4_k_m)", "On vLLM 0.29.0 or later run `vllm serve openbmb/MiniCPM5-2B --max-model-len 131072 --enable-auto-tool-choice --tool-call-parser minicpm5`, or tool calls come back as XML text", "On SGLang 0.5.16 or later add `--tool-call-parser minicpm5 --reasoning-parser qwen3`; SGLang is OpenBMB's recommended backend for tool calls", "Tool calls are XML, `\u003cfunction name=...\u003e\u003cparam name=...\u003e`, not Hermes JSON", "Sample with temperature 1.0, top_p 0.95 and min_p 0.0; llama.cpp's default min_p 0.05 can loop, and repetition_penalty 1.05 helps if it does", "Expect a `\u003cthink\u003e` block before every answer and leave room in max_tokens; enable_thinking false is accepted by the template but OpenBMB doesn't support it for the 2B", "Validate JSON yourself or use the runtime's guided decoding, since structured output isn't documented", "Use the main repository's template; the GGUF, MLX and GPTQ copies predate the 29 September fix" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "C", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 60.2 } ], "editorialScores": { "capability": 27, "interface": 75, "reach": 90, "risk": 55, "upkeep": 97 }, "provenanceScore": 53, "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": "ollama run openbmb/minicpm5-2b\n# or, with vLLM 0.29.0 or later:\nvllm serve openbmb/MiniCPM5-2B --max-model-len 131072 \\\n --enable-auto-tool-choice --tool-call-parser minicpm5", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\n \"model\": \"openbmb/MiniCPM5-2B\",\n \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}],\n \"temperature\": 1.0, \"top_p\": 0.95,\n \"chat_template_kwargs\": {\"enable_thinking\": true}\n }'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/minicpm5-2b" }, "sameCompany": [ "minicpm-o-4-5" ], "weights": { "trainedBy": "OpenBMB", "builtOn": "openbmb/MiniCPM5-2B-Base", "params": "2.5B", "context": 131072, "goodFor": "Small on-device language model with thinking and XML tool calls, text only.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "MiniCPM5-2B", "url": "https://huggingface.co/openbmb/MiniCPM5-2B", "who": "publisher", "precision": "bf16", "sizeGB": 5, "memoryGB": 8, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] }, { "name": "MiniCPM5-2B-F16", "url": "https://huggingface.co/openbmb/MiniCPM5-2B-GGUF/blob/main/MiniCPM5-2B-F16.gguf", "who": "publisher", "precision": "f16", "sizeGB": 5, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)", "LM Studio" ] }, { "name": "MiniCPM5-2B-Q8_0", "url": "https://huggingface.co/openbmb/MiniCPM5-2B-GGUF/blob/main/MiniCPM5-2B-Q8_0.gguf", "who": "publisher", "precision": "q8_0", "sizeGB": 2.7, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)", "LM Studio" ] }, { "name": "MiniCPM5-2B-Q4_K_M", "url": "https://huggingface.co/openbmb/MiniCPM5-2B-GGUF/blob/main/MiniCPM5-2B-Q4_K_M.gguf", "who": "publisher", "precision": "q4_k_m", "sizeGB": 1.6, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)", "LM Studio" ] }, { "name": "MiniCPM5-2B-MLX", "url": "https://huggingface.co/openbmb/MiniCPM5-2B-MLX", "who": "publisher", "precision": "4bit", "sizeGB": 1.4, "memoryGB": 8, "format": "mlx", "note": "MLX runs on Apple silicon only" }, { "name": "MiniCPM5-2B-GPTQ", "url": "https://huggingface.co/openbmb/MiniCPM5-2B-GPTQ", "who": "publisher", "precision": "gptq-int4", "sizeGB": 2.1, "memoryGB": 8, "format": "safetensors", "note": "vLLM's quantisation table marks GPTQ unsupported on AMD GPUs" }, { "name": "ollama openbmb/minicpm5-2b:latest", "url": "https://ollama.com/openbmb/minicpm5-2b", "who": "publisher", "precision": "q4_k_m", "sizeGB": 1.6, "memoryGB": 8, "format": "ollama", "runsOn": [ "Ollama" ], "note": "In the openbmb namespace on ollama.com, taken as OpenBMB's. Size as Ollama's tags page shows it, rounded to whole GB above 10 GB; the Linux build is the llama.cpp variant" } ], "run": "ollama run openbmb/minicpm5-2b", "runBuild": "ollama openbmb/minicpm5-2b:latest", "checked": "2026-10-21", "minMemoryGB": 8 }, "family": { "lab": "OpenBMB (MiniCPM)", "family": "MiniCPM5", "url": "https://www.anchorterminal.com/open-weights/#openbmb", "others": [ { "name": "MiniCPM5-2B-SFT", "url": "https://huggingface.co/openbmb/MiniCPM5-2B-SFT", "params": "2B" }, { "name": "MiniCPM5-2B-Midtrain", "url": "https://huggingface.co/openbmb/MiniCPM5-2B-Midtrain", "params": "2B" }, { "name": "MiniCPM5-2B-Base", "url": "https://huggingface.co/openbmb/MiniCPM5-2B-Base", "params": "2B" }, { "name": "MiniCPM5-1B", "url": "https://huggingface.co/openbmb/MiniCPM5-1B", "params": "1B" }, { "name": "MiniCPM5-1B-SFT", "url": "https://huggingface.co/openbmb/MiniCPM5-1B-SFT", "params": "1B" }, { "name": "MiniCPM5-1B-Base", "url": "https://huggingface.co/openbmb/MiniCPM5-1B-Base", "params": "1B" } ], "olderCount": 22, "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "OpenBMB (named in the GitHub LICENSE's copyright line)", "domain": "openbmb.cn", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/openbmb/MiniCPM5-2B/commits/main", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The LICENSE in github.com/OpenBMB/MiniCPM reads Copyright [OpenBMB]. The card links its wiki to ModelBest's Feishu space, and no legal entity is named on the card. rdap.org has no RDAP service for .cn, so the domain's registration date wasn't checked.", "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "The changelog is the repository's commit history, whose messages say what changed (a template fix on 29 September referencing OpenBMB/MiniCPM#379, sampling advice on 12 September), and the GitHub README's dated changelog lists the release on 7 September 2026.", "openbmb.cn/.well-known/security.txt and modelbest.cn/.well-known/security.txt answered 404 on 21 October 2026, and the GitHub repository has no security policy." ], "score": 53 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/minicpm5-2b.json" }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Poolside", "b": "OpenBMB", "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": "Apache-2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-08-19", "b": "2026-09-29", "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": "3/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Laguna S 2.1 and MiniCPM5-2B score within a point of each other on agent readiness, 61 (C) and 60.2 (C). MiniCPM5-2B leads on reach and documentation and upkeep.", "question": "Which is better for AI agents, Laguna S 2.1 or MiniCPM5-2B?" } ], "goodFor": [ { "aheadOn": [ "Agent interface, 85 against 75" ], "also": null, "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": [ "Reach, 90 against 75", "Documentation and upkeep, 97 against 41" ], "also": [ "Rated higher by the reviewer agents, 3.0 against 2.3 out of 5" ], "goodFor": "On-device and laptop agents that want a 2 GB tool-calling helper with long context and open training data", "slug": "minicpm5-2b", "watchFor": "OpenBMB documents only a Think mode for the 2B, so every answer starts with reasoning" } ], "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, 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"json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-5-397b-a17b.json", "title": "Laguna S 2.1 vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-5-4b.json", "title": "Laguna S 2.1 vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-5-9b.json", "title": "Laguna S 2.1 vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-6-27b.json", "title": "Laguna S 2.1 vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-6-35b-a3b.json", "title": "Laguna S 2.1 vs Qwen3.6-35B-A3B", "url": 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vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-swift-1-5-qwen3-8-27b.json", "title": "Laguna S 2.1 vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Laguna S 2.1 vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-thinkingcap-qwen3-8-27b.json", "title": "Laguna S 2.1 vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-minicpm5-2b.json", "title": "Laguna XS 2.1 vs MiniCPM5-2B", "url": 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Ultra 550B A55B", "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-nemotron-3-ultra-550b-a55b" }, { "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-occamy-1-0.json", "title": "MiniCPM5-2B vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-olmo-3-7b-think.json", "title": "MiniCPM5-2B vs Olmo 3 7B Think", "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-olmo-3-7b-think" }, { "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-ornith-1-5-35b-a3b.json", "title": "MiniCPM5-2B vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-ornith-1-5-397b.json", "title": "MiniCPM5-2B vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-ornith-1-5-9b.json", "title": "MiniCPM5-2B vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-30b-a3b-instruct-2507.json", "title": "MiniCPM5-2B vs Qwen3-30B-A3B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-30b-a3b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-4b-instruct-2507.json", "title": "MiniCPM5-2B vs Qwen3-4B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-4b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-5-0-8b.json", "title": "MiniCPM5-2B vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-5-122b-a10b.json", "title": "MiniCPM5-2B vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-5-2b.json", "title": "MiniCPM5-2B vs Qwen3.5-2B", "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-5-2b" }, { "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-5-397b-a17b.json", "title": "MiniCPM5-2B vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-5-4b.json", "title": "MiniCPM5-2B vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-5-9b.json", "title": "MiniCPM5-2B vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-6-27b.json", "title": "MiniCPM5-2B vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-6-35b-a3b.json", "title": "MiniCPM5-2B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-8-27b.json", "title": "MiniCPM5-2B vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-8-flash-next.json", "title": "MiniCPM5-2B vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-coder-30b-a3b-instruct.json", "title": "MiniCPM5-2B vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-coder-next.json", "title": "MiniCPM5-2B vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-omni-30b-a3b-instruct.json", "title": "MiniCPM5-2B vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-vl-2b-instruct.json", "title": "MiniCPM5-2B vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-vl-30b-a3b-instruct.json", "title": "MiniCPM5-2B vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-vl-32b-instruct.json", "title": "MiniCPM5-2B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-vl-4b-instruct.json", "title": "MiniCPM5-2B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-vl-8b-instruct.json", "title": "MiniCPM5-2B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwopus3-8-27b-flash-v2.json", "title": "MiniCPM5-2B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-smollm3-3b.json", "title": "MiniCPM5-2B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-spark-x2-6-4b.json", "title": "MiniCPM5-2B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-swift-1-5-qwen3-8-27b.json", "title": "MiniCPM5-2B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "MiniCPM5-2B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-thinkingcap-qwen3-8-27b.json", "title": "MiniCPM5-2B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-thinkingcap-qwen3-8-27b" } ], "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 15, "edge": "minicpm5-2b", "key": "reach", "laguna-s-2-1": 75, "minicpm5-2b": 90, "name": "Reach", "weight": 25 }, { "by": 0, "edge": "", "key": "risk", "laguna-s-2-1": 55, "minicpm5-2b": 55, "name": "Risk", "weight": 20 }, { "by": 10, "edge": "laguna-s-2-1", "key": "interface", "laguna-s-2-1": 85, "minicpm5-2b": 75, "name": "Agent interface", "weight": 10 }, { "by": 56, "edge": "minicpm5-2b", "key": "upkeep", "laguna-s-2-1": 41, "minicpm5-2b": 97, "name": "Documentation and upkeep", "weight": 10 } ], "summary": "Laguna S 2.1 and MiniCPM5-2B score within a point of each other on agent readiness, 61 (C) and 60.2 (C). MiniCPM5-2B leads on reach and documentation and upkeep. 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.", "minicpm5-2b": "A tiny dense cousin for local tool calling and long context, with published training data and an active publisher. OpenBMB's own 4-bit builds need about 2 GB, so it runs on a laptop or phone in vLLM, SGLang, llama.cpp, Ollama or Transformers. The main risks are always-on thinking, no safety results and use conditions beyond Apache 2.0." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-minicpm5-2b", "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-minicpm5-2b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-minicpm5-2b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-minicpm5-2b.min.md" }, "markdown": "Laguna S 2.1 and MiniCPM5-2B score within a point of each other on agent readiness, 61 (C) and 60.2 (C). MiniCPM5-2B leads on reach and documentation and upkeep. 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- MiniCPM5-2B: grade C, 60.2/100, rank #56 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/minicpm5-2b.md · JSON https://www.anchorterminal.com/api/v1/tools/minicpm5-2b.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- Agent interface, 85 against 75\n\nWatch for: No reply from Poolside to any of 34 discussions, many of them reports about thinking and tool calls\n\n### MiniCPM5-2B (C)\n\nGood for: On-device and laptop agents that want a 2 GB tool-calling helper with long context and open training data\n\nAhead on:\n- Reach, 90 against 75\n- Documentation and upkeep, 97 against 41\n\nAlso in its favour:\n- Rated higher by the reviewer agents, 3.0 against 2.3 out of 5\n\nWatch for: OpenBMB documents only a Think mode for the 2B, so every answer starts with reasoning\n\n\n## Score by category\n\n| Category | Weight | Laguna S 2.1 | MiniCPM5-2B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 75 | 90 | MiniCPM5-2B +15 |\n| Risk | 20% (20 this run) | 55 | 55 | even |\n| Agent interface | 10% (10 this run) | 85 | 75 | Laguna S 2.1 +10 |\n| Documentation and upkeep | 10% (10 this run) | 41 | 97 | MiniCPM5-2B +56 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **60.2 · C** | |\n\n## Facts side by side\n\n| Fact | Laguna S 2.1 | MiniCPM5-2B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Poolside | OpenBMB |\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 | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-08-19 | 2026-09-29 |\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) | 3/5 (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**MiniCPM5-2B.** A tiny dense cousin for local tool calling and long context, with published training data and an active publisher. OpenBMB's own 4-bit builds need about 2 GB, so it runs on a laptop or phone in vLLM, SGLang, llama.cpp, Ollama or Transformers. The main risks are always-on thinking, no safety results and use conditions beyond Apache 2.0.\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### MiniCPM5-2B\n\n1. Smallest start is `ollama run openbmb/minicpm5-2b` (1.6 GB, q4_k_m)\n2. On vLLM 0.29.0 or later run `vllm serve openbmb/MiniCPM5-2B --max-model-len 131072 --enable-auto-tool-choice --tool-call-parser minicpm5`, or tool calls come back as XML text\n3. On SGLang 0.5.16 or later add `--tool-call-parser minicpm5 --reasoning-parser qwen3`; SGLang is OpenBMB's recommended backend for tool calls\n4. Tool calls are XML, `\u003cfunction name=...\u003e\u003cparam name=...\u003e`, not Hermes JSON\n5. Sample with temperature 1.0, top_p 0.95 and min_p 0.0; llama.cpp's default min_p 0.05 can loop, and repetition_penalty 1.05 helps if it does\n6. Expect a `\u003cthink\u003e` block before every answer and leave room in max_tokens; enable_thinking false is accepted by the template but OpenBMB doesn't support it for the 2B\n7. Validate JSON yourself or use the runtime's guided decoding, since structured output isn't documented\n8. Use the main repository's template; the GGUF, MLX and GPTQ copies predate the 29 September fix\n\n## Questions\n\n### Which is better for AI agents, Laguna S 2.1 or MiniCPM5-2B?\n\nLaguna S 2.1 and MiniCPM5-2B score within a point of each other on agent readiness, 61 (C) and 60.2 (C). MiniCPM5-2B leads on reach and documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/laguna-s-2-1-vs-minicpm5-2b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/laguna-s-2-1-vs-minicpm5-2b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"laguna-s-2-1\", \"b\": \"minicpm5-2b\"}`. From a terminal: `anchor compare laguna-s-2-1 minicpm5-2b`\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/minicpm5-2b.json\n\n## Other comparisons with Laguna S 2.1 or MiniCPM5-2B\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 MiniCPM5-2B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-minicpm5-2b.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 MiniCPM5-2B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minicpm5-2b.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 MiniCPM5-2B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minicpm5-2b.md)\n- [BigBang-v1 vs Laguna S 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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 MiniCPM5-2B", "url": "" } ], "description": "Laguna S 2.1 and MiniCPM5-2B score within a point of each other for open-weight models you run yourself, 61 and 60.2 out of 100. 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