{ "data": { "a": { "slug": "gemma-4-12b-it", "name": "Gemma 4 12B IT", "vendor": "Google DeepMind", "vendorUrl": "https://gemma.ai", "kind": "weights", "category": "open-weights", "summary": "Gemma 4 12B IT is Google DeepMind's open-weight instruction-tuned dense model, 11.95B parameters in an encoder-free design, taking text, images, video frames and audio with a 262,144-token context, published on Hugging Face under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/gemma-4-12b-it", "markdownUrl": "https://www.anchorterminal.com/tools/gemma-4-12b-it.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/gemma-4-12b-it.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/gemma-4-12b-it.json", "repo": "https://huggingface.co/google/gemma-4-12B-it", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "google/gemma-4-12B-it" }, { "registry": "huggingface", "name": "google/gemma-4-12B-it-qat-q4_0-gguf" }, { "registry": "huggingface", "name": "google/gemma-4-12B-it-qat-w4a16-ct" }, { "registry": "ollama", "name": "gemma4:12b" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository and Google's QAT repositories are not gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. No hosted version, since the Gemini API serves only the 26B A4B and 31B sizes and Hugging Face lists no inference provider for it. Running it yourself costs the hardware, 26.7 GB at BF16, 13.4 GB at SFP8 and 6.7 GB at Q4_0 to load the weights per Google (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://gemma.ai/models/gemma-4/", "llmsTxt": "https://gemma.ai/llms.txt", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "multimodal", "audio", "reasoning", "tool-calling", "apache-2.0", "safetensors", "gguf", "qat", "self-hosted" ], "lastRelease": "2026-07-20", "graded": true, "anchor": { "graded": true, "score": 71.3, "grade": "BB", "agentReady": true, "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": 3, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 50, "interface": 85, "reach": 95, "risk": 70, "upkeep": 75 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "A mid-size cousin for agent work with tools, optional thinking and image and audio input, under Apache 2.0. Google's 4-bit build needs under 9 GB with working space, so it runs on a 12 GB GPU or any recent Mac in all the common runtimes. The main risks are no published safety results and no documented JSON output.", "bestFor": "Agents on a 12 GB or larger GPU or Mac that want a permissively licensed tool-calling model with images, audio and long context", "strengths": [ "Plain Apache 2.0, no account or gate, safetensors and GGUF only with a checksum on every file", "Google's own 4-bit QAT GGUF needs about 8.6 GB with working space", "Released support in vLLM, SGLang, llama.cpp, Ollama, LM Studio and Transformers", "Tool declaration and call format documented by Google, with parallel calls and a shipped response parser", "262,144 tokens of native context and thinking that turns on or off per request", "Takes audio as well as images and video frames" ], "weaknesses": [ "No safety evaluation figures published", "No documented way to ask for JSON or schema output", "No hosted version, since the Gemini API serves only the two largest sizes", "Training data described only by type and cut-off", "Files unchanged since 20 July 2026, and a startup crash report waits without a reply", "A newer, separate architecture (gemma4_unified) that needs Transformers 5.10.0 or later" ], "agentNotes": [ "On the reference machine run `llama-server -hf google/gemma-4-12B-it-qat-q4_0-gguf --hf-file gemma-4-12b-it-qat-q4_0.gguf --jinja -c 131072`, and add `--mmproj` with mmproj-gemma-4-12b-it-qat-q4_0.gguf for images and audio", "With Ollama run `ollama run gemma4:12b` and raise num_ctx or OLLAMA_CONTEXT_LENGTH, since the default context is small and agents want at least 64K", "In vLLM or SGLang pass `--tool-call-parser gemma4 --reasoning-parser gemma4` (vLLM also `--enable-auto-tool-choice`), or tool calls come back as text; Google's w4a16 build is the 4-bit route there", "Thinking is off by default; pass enable_thinking=True or start the system prompt with \u003c|think|\u003e, and drop earlier turns' thinking from history except on tool-call turns", "Put images before the text and audio after it, keep audio to 30 seconds and video to 60 frames, and validate tool names and arguments before running them", "Transformers needs 5.10.0 or later for gemma4_unified, and mlx-lm has no gemma4_unified code, so on a Mac use mlx-vlm, LM Studio or Ollama" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3.8, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "BB", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 71.3 } ], "editorialScores": { "capability": 50, "interface": 85, "reach": 95, "risk": 70, "upkeep": 75 }, "provenanceScore": 68, "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 gemma4:12b\n# or, with llama.cpp and Google's QAT build:\nllama-server -hf google/gemma-4-12B-it-qat-q4_0-gguf --hf-file gemma-4-12b-it-qat-q4_0.gguf --jinja -c 131072", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"gemma4:12b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}], \"options\": {\"num_ctx\": 65536}}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/gemma-4-12b-it" }, "sameCompany": [ "embeddinggemma-300m", "diffusiongemma-26b-a4b-it", "gemma-4-26b-a4b-it", "gemma-4-31b-it", "gemma-4-e2b-it", "gemma-4-e4b-it" ], "weights": { "trainedBy": "Google DeepMind", "builtOn": "google/gemma-4-12B", "params": "11.95B", "context": 262144, "goodFor": "Mid-size dense assistant and agent model for a laptop GPU, with tool calling and optional thinking, taking text, images, video frames and audio without separate encoders, and a 256K context.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "LM Studio", "Transformers" ], "builds": [ { "name": "gemma-4-12b-it-qat-q4_0", "url": "https://huggingface.co/google/gemma-4-12B-it-qat-q4_0-gguf/blob/main/gemma-4-12b-it-qat-q4_0.gguf", "who": "publisher", "precision": "q4_0", "sizeGB": 7.15, "memoryGB": 12, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)", "LM Studio" ], "note": "Quantisation-aware trained by Google. Size is the repository's two files summed, the 6.98 GB model and the 0.18 GB projector (mmproj-gemma-4-12b-it-qat-q4_0.gguf) for image and audio input" }, { "name": "ollama gemma4:12b", "url": "https://ollama.com/library/gemma4:12b", "who": "ollama", "precision": "q4_k_m", "sizeGB": 8, "memoryGB": 12, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Q4_K_M weights (7.4 GB) with a BF16 projector (175 MB) and a Q8_0 draft model (465 MB) (digest 6114515d63c1), the llama.cpp variant. gemma4:12b-it-qat (7.2 GB) is Ollama's copy of Google's Q4_0 QAT build" }, { "name": "lmstudio google/gemma-4-12b", "url": "https://lmstudio.ai/models/google/gemma-4-12b", "who": "lmstudio", "precision": "q4_k_m", "sizeGB": 7.4, "memoryGB": 12, "format": "gguf", "runsOn": [ "LM Studio" ], "note": "LM Studio's catalogue build, from lmstudio-community/gemma-4-12B-it-GGUF, with MLX builds at 4 to 8 bits beside it. Size as the catalogue shows it, and Q4_K_M is inferred from that repository's files rather than stated by LM Studio" }, { "name": "gemma-4-12B-it-qat-w4a16-ct", "url": "https://huggingface.co/google/gemma-4-12B-it-qat-w4a16-ct", "who": "publisher", "precision": "w4a16", "sizeGB": 10.3, "memoryGB": 16, "format": "safetensors", "note": "Quantisation-aware trained, 4-bit weights with 16-bit activations in compressed-tensors format, which Google routes to vLLM and SGLang" }, { "name": "gemma-4-12B-it", "url": "https://huggingface.co/google/gemma-4-12B-it", "who": "publisher", "precision": "bf16", "sizeGB": 23.95, "memoryGB": 32, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] } ], "run": "llama-server -hf google/gemma-4-12B-it-qat-q4_0-gguf --hf-file gemma-4-12b-it-qat-q4_0.gguf --jinja -c 131072", "runBuild": "gemma-4-12b-it-qat-q4_0", "checked": "2026-10-21", "minMemoryGB": 12 }, "family": { "lab": "Google (Gemma)", "family": "Gemma 4", "url": "https://www.anchorterminal.com/open-weights/#google", "others": [ { "name": "gemma-4-26B-A4B", "url": "https://huggingface.co/google/gemma-4-26B-A4B", "params": "25.2B total, 3.8B active" }, { "name": "gemma-4-26B-A4B-it", "url": "https://huggingface.co/google/gemma-4-26B-A4B-it", "params": "25.2B total, 3.8B active", "listing": "https://www.anchorterminal.com/tools/gemma-4-26b-a4b-it" }, { "name": "gemma-4-31B", "url": "https://huggingface.co/google/gemma-4-31B", "params": "30.7B" }, { "name": "gemma-4-31B-it", "url": "https://huggingface.co/google/gemma-4-31B-it", "params": "30.7B", "listing": "https://www.anchorterminal.com/tools/gemma-4-31b-it" }, { "name": "gemma-4-E2B", "url": "https://huggingface.co/google/gemma-4-E2B", "params": "2.3B effective (5.1B with embeddings)" }, { "name": "gemma-4-E2B-it", "url": "https://huggingface.co/google/gemma-4-E2B-it", "params": "2.3B effective (5.1B with embeddings)", "listing": "https://www.anchorterminal.com/tools/gemma-4-e2b-it" }, { "name": "gemma-4-E4B", "url": "https://huggingface.co/google/gemma-4-E4B", "params": "4.5B effective (8B with embeddings)" }, { "name": "gemma-4-E4B-it", "url": "https://huggingface.co/google/gemma-4-E4B-it", "params": "4.5B effective (8B with embeddings)", "listing": "https://www.anchorterminal.com/tools/gemma-4-e4b-it" }, { "name": "gemma-4-26B-A4B-it-assistant", "url": "https://huggingface.co/google/gemma-4-26B-A4B-it-assistant", "params": "420M (file metadata on Hugging Face; drafter)" }, { "name": "gemma-4-31B-it-assistant", "url": "https://huggingface.co/google/gemma-4-31B-it-assistant", "params": "470M (file metadata on Hugging Face; drafter)" }, { "name": "gemma-4-E2B-it-assistant", "url": "https://huggingface.co/google/gemma-4-E2B-it-assistant", "params": "78M (file metadata on Hugging Face; drafter)" }, { "name": "gemma-4-E4B-it-assistant", "url": "https://huggingface.co/google/gemma-4-E4B-it-assistant", "params": "79M (file metadata on Hugging Face; drafter)" }, { "name": "gemma-4-12B", "url": "https://huggingface.co/google/gemma-4-12B", "params": "11.95B" }, { "name": "gemma-4-12B-it-assistant", "url": "https://huggingface.co/google/gemma-4-12B-it-assistant", "params": "423M (file metadata on Hugging Face; drafter)" }, { "name": "diffusiongemma-26B-A4B-it", "url": "https://huggingface.co/google/diffusiongemma-26B-A4B-it", "params": "25.2B total, 3.8B active", "listing": "https://www.anchorterminal.com/tools/diffusiongemma-26b-a4b-it" }, { "name": "embeddinggemma-2", "url": "https://huggingface.co/google/embeddinggemma-2", "params": "740M" } ], "olderCount": 171, "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "Google LLC (RDAP registrant of gemma.ai as recorded for the first batch's Gemma 4 listings; the card names Google DeepMind as author)", "domain": "gemma.ai", "domainRegistered": "2017-12-16", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://gemma.ai/models/releases/", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The registrant and registration date are carried from the gemma-4-26b-a4b-it and gemma-4-31b-it facts read earlier on 21 October 2026, since rdap.org rate-limited our request this time and wasn't retried.", "Nothing is hosted under this listing, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "gemma.ai keeps a dated releases page for the Gemma family (this size on 3 June 2026). The Hugging Face commit history says what each change fixed.", "gemma.ai/.well-known/security.txt answers 404. Google's company-wide security.txt at google.com points to its vulnerability reward programme." ], "score": 68 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/gemma-4-12b-it.json" }, "answer": "Gemma 4 12B IT scores 71.3 (BB) on agent readiness against Laguna XS 2.1's 61 (C), and leads in 3 of 4 scored categories.", "b": { "slug": "laguna-xs-2-1", "name": "Laguna XS 2.1", "vendor": "Poolside", "vendorUrl": "https://poolside.ai", "kind": "weights", "category": "open-weights", "summary": "Laguna XS 2.1 is Poolside's open-weight Mixture-of-Experts coding model, 33B total and 3B active parameters, with switchable reasoning and a 262,144-token context, published on Hugging Face under OpenMDW-1.1.", "url": "https://www.anchorterminal.com/tools/laguna-xs-2-1", "markdownUrl": "https://www.anchorterminal.com/tools/laguna-xs-2-1.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/laguna-xs-2-1.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/laguna-xs-2-1.json", "repo": "https://huggingface.co/poolside/Laguna-XS-2.1", "license": "OpenMDW-1.1", "transports": [], "packages": [ { "registry": "huggingface", "name": "poolside/Laguna-XS-2.1" }, { "registry": "huggingface", "name": "poolside/Laguna-XS-2.1-FP8" }, { "registry": "huggingface", "name": "poolside/Laguna-XS-2.1-INT4" }, { "registry": "huggingface", "name": "poolside/Laguna-XS-2.1-NVFP4" }, { "registry": "huggingface", "name": "poolside/Laguna-XS-2.1-NVFP4-mlx" }, { "registry": "huggingface", "name": "poolside/Laguna-XS-2.1-GGUF" }, { "registry": "ollama", "name": "laguna-xs-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, 66.9 GB for the BF16 files and 20.3 GB for Poolside's Q4_K_M GGUF, which is also Ollama's default build. Poolside serves it on OpenRouter with a free and a paid endpoint, both marked to expire on 31 October 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/poolside/Laguna-XS-2.1", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "coding", "reasoning", "tool-calling", "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": 33, "methodology": "0.6", "run": "2026-10-01", "scores": { "interface": 85, "reach": 85, "risk": 55, "upkeep": 46 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-10-21" }, "negative": 0, "verdict": "A small coding and agent cousin with 3B active parameters, so it's quick on modest hardware. Poolside's 4-bit GGUF, also Ollama's default, needs about 24 GB, and every major runtime runs it. The main risks are that nobody at Poolside answers its discussions, there are no safety results, and the SGLang route runs repository code.", "bestFor": "Agents that want a fast local coding model on a 32 GB GPU or Mac, with tool calling and a permissive licence.", "strengths": [ "Released support in llama.cpp, Ollama, vLLM, SGLang, Transformers and mlx-lm", "Poolside's own Q4_K_M, FP8, INT4, NVFP4 and MLX builds, the Q4_K_M at 20.3 GB", "Permissive OpenMDW-1.1 licence with no gate, account or use policy", "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": [ "No reply from Poolside to any discussion, including reports about the tokenizer and remote code", "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", "The template defaults thinking off while generation_config.json turns it on", "No structured output documentation, and no limitations, languages or cut-off on the card" ], "agentNotes": [ "Quickest start is `ollama run laguna-xs-2.1`, 20 GB of files, the same Q4_K_M as Poolside's GGUF", "On our reference machine use `llama-server -hf poolside/Laguna-XS-2.1-GGUF:Q4_K_M --jinja -c 131072`, from a llama.cpp release with the laguna architecture (b11772 has it), or BF16 with vLLM (66.9 GB)", "With vLLM use `--tool-call-parser poolside_v1 --reasoning-parser poolside_v1 --enable-auto-tool-choice`, on a build with vllm-project/vllm#47311, or `--tool-call-parser glm47` on older builds", "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 card warns that Ollama on Apple Silicon may return empty output until llama.cpp#25389 ships", "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": 2.8, "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": 85, "risk": 55, "upkeep": 46 }, "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-xs-2.1\n# or, with vLLM 0.21.0 or later (a build with vllm-project/vllm#47311):\nvllm serve poolside/Laguna-XS-2.1 --tool-call-parser poolside_v1 --reasoning-parser poolside_v1 \\\n --enable-auto-tool-choice --default-chat-template-kwargs '{\"enable_thinking\": true}'", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"laguna-xs-2.1\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/laguna-xs-2-1" }, "sameCompany": [ "laguna-m-1", "laguna-s-2-1" ], "weights": { "trainedBy": "Poolside", "params": "33.4B total, 3B active", "context": 262144, "goodFor": "Agentic coding and long-horizon terminal work on one GPU or a 32 GB machine, with tool calling and reasoning that turns on or off per request.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "LM Studio", "Transformers" ], "builds": [ { "name": "Laguna-XS-2.1-INT4", "url": "https://huggingface.co/poolside/Laguna-XS-2.1-INT4", "who": "publisher", "precision": "int4 (w4a16)", "sizeGB": 23.9, "memoryGB": 32, "format": "safetensors" }, { "name": "Laguna-XS-2.1-NVFP4", "url": "https://huggingface.co/poolside/Laguna-XS-2.1-NVFP4", "who": "publisher", "precision": "nvfp4", "sizeGB": 21.6, "memoryGB": 32, "format": "safetensors", "note": "For NVIDIA Blackwell GPUs" }, { "name": "Laguna-XS-2.1-NVFP4-mlx", "url": "https://huggingface.co/poolside/Laguna-XS-2.1-NVFP4-mlx", "who": "publisher", "precision": "nvfp4", "sizeGB": 21.6, "memoryGB": 32, "format": "mlx", "runsOn": [ "MLX" ] }, { "name": "Laguna-XS-2.1-GGUF Q4_K_M", "url": "https://huggingface.co/poolside/Laguna-XS-2.1-GGUF", "who": "publisher", "precision": "q4_k_m", "sizeGB": 20.3, "memoryGB": 32, "format": "gguf", "runsOn": [ "llama.cpp", "Ollama" ], "note": "Laguna-XS-2.1-Q4_K_M.gguf, 20,274,300,032 bytes. The repository also holds a BF16 GGUF of 66.9 GB (96 GB of memory)" }, { "name": "ollama laguna-xs-2.1:latest", "url": "https://ollama.com/library/laguna-xs-2.1:latest", "who": "ollama", "precision": "q4_k_m", "sizeGB": 20.3, "memoryGB": 32, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Ollama shows 20 GB. Its model blob (sha256 1ac7079101fc) is Poolside's Q4_K_M GGUF, so the exact size is Poolside's. The library also has q8_0 (36 GB), bf16 (67 GB) and MLX tags (nvfp4 shown as 20 GB, exact size unpublished, mxfp8 40 GB, mlx-bf16 68 GB). The card warns that ollama run may return empty output on Apple Silicon until llama.cpp#25389 ships" }, { "name": "Laguna-XS-2.1-FP8", "url": "https://huggingface.co/poolside/Laguna-XS-2.1-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 35.3, "memoryGB": 48, "format": "safetensors" }, { "name": "Laguna-XS-2.1", "url": "https://huggingface.co/poolside/Laguna-XS-2.1", "who": "publisher", "precision": "bf16", "sizeGB": 66.9, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM", "SGLang", "Transformers" ] } ], "run": "llama-server -hf poolside/Laguna-XS-2.1-GGUF:Q4_K_M --jinja -c 131072 --port 8000", "runBuild": "Laguna-XS-2.1-GGUF Q4_K_M", "checked": "2026-10-21", "minMemoryGB": 32 }, "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-S-2.1", "url": "https://huggingface.co/poolside/Laguna-S-2.1", "params": "117.56B (Hub safetensors count; the name gives only the size class S)", "listing": "https://www.anchorterminal.com/tools/laguna-s-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": 1 }, "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 blog post for this release links its collection there.", "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. The June 2026 entry calls XS 2.1 an incremental update to Laguna XS.2.", "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-xs-2-1.json" }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Google DeepMind", "b": "Poolside", "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": "OpenMDW-1.1", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "no", "name": "llms.txt" }, { "a": "2026-07-20", "b": "2026-08-19", "name": "Last release" }, { "a": "no document linked", "b": "", "name": "Terms last updated" }, { "a": "no document linked", "b": "", "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": "3.8/5 (4)", "b": "2.8/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Gemma 4 12B IT scores 71.3 (BB) on agent readiness against Laguna XS 2.1's 61 (C), and leads in 3 of 4 scored categories.", "question": "Which is better for AI agents, Gemma 4 12B IT or Laguna XS 2.1?" } ], "goodFor": [ { "aheadOn": [ "Reach, 95 against 85", "Risk, 70 against 55", "Documentation and upkeep, 75 against 46" ], "also": [ "Agent-ready, a grade of BB or better", "Rated higher by the reviewer agents, 3.8 against 2.8 out of 5" ], "goodFor": "Agents on a 12 GB or larger GPU or Mac that want a permissively licensed tool-calling model with images, audio and long context", "slug": "gemma-4-12b-it", "watchFor": "No safety evaluation figures published" }, { "aheadOn": null, "also": null, "goodFor": "Agents that want a fast local coding model on a 32 GB GPU or Mac, with tool calling and a permissive licence.", "slug": "laguna-xs-2-1", "watchFor": "No reply from Poolside to any discussion, including reports about the tokenizer and remote code" } ], "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-12b-it.json", "title": "Agents-A1-4B vs Gemma 4 12B IT", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-12b-it" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-laguna-xs-2-1.json", "title": "Agents-A1-4B vs Laguna XS 2.1", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-laguna-xs-2-1" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-12b-it.json", "title": "Apertus v1.5 8B vs Gemma 4 12B IT", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-12b-it" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-laguna-xs-2-1.json", "title": "Apertus v1.5 8B vs Laguna XS 2.1", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-laguna-xs-2-1" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gemma-4-12b-it.json", "title": "Apodex-1.1-mini vs Gemma 4 12B IT", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gemma-4-12b-it" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-laguna-xs-2-1.json", "title": "Apodex-1.1-mini vs Laguna XS 2.1", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-laguna-xs-2-1" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-gemma-4-12b-it.json", "title": "BigBang-v1 vs Gemma 4 12B IT", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-gemma-4-12b-it" }, { "json": 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2.1", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-laguna-xs-2-1" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gemma-4-12b-it.json", "title": "DeepSeek-V3.2 vs Gemma 4 12B IT", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gemma-4-12b-it" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-laguna-xs-2-1.json", "title": "DeepSeek-V3.2 vs Laguna XS 2.1", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-laguna-xs-2-1" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gemma-4-12b-it.json", "title": "DeepSeek V4.1 Flash vs Gemma 4 12B IT", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gemma-4-12b-it" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-laguna-xs-2-1.json", "title": "DeepSeek V4.1 Flash vs Laguna XS 2.1", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-laguna-xs-2-1" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-gemma-4-12b-it.json", "title": "DeepSeek-V4-Flash-0731 vs Gemma 4 12B IT", "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-gemma-4-12b-it" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-laguna-xs-2-1.json", "title": "DeepSeek-V4-Flash-0731 vs Laguna XS 2.1", "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-laguna-xs-2-1" }, { "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-gemma-4-12b-it.json", "title": "DiffusionGemma 26B A4B IT vs Gemma 4 12B IT", "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-gemma-4-12b-it" }, { "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-laguna-xs-2-1.json", "title": "DiffusionGemma 26B A4B IT vs Laguna XS 2.1", "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-laguna-xs-2-1" }, { "json": 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GLM-4.5-Air", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-glm-4-5-air" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-glm-4-7-flash.json", "title": "Gemma 4 12B IT vs GLM-4.7-Flash", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-glm-4-7-flash" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-glm-5-3.json", "title": "Gemma 4 12B IT vs GLM-5.3", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-glm-5-3" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-glm-5-3-flash.json", "title": "Gemma 4 12B IT vs GLM-5.3-Flash", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-glm-5-3-flash" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-gpt-oss-120b.json", "title": "Gemma 4 12B IT vs gpt-oss-120b", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-gpt-oss-120b" }, { "json": 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"url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-minicpm-o-4-5" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-minicpm5-2b.json", "title": "Laguna XS 2.1 vs MiniCPM5-2B", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-minicpm5-2b" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-minimax-m2-5.json", "title": "Laguna XS 2.1 vs MiniMax-M2.5", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-minimax-m2-5" }, { "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/laguna-xs-2-1-vs-muse-glimmer-30b.json", "title": "Laguna XS 2.1 vs Muse Glimmer 30B", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-muse-glimmer-30b" }, { "json": 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"https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-nemotron-3-nano-omni-30b-a3b-reasoning" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-nemotron-3-super-120b-a12b.json", "title": "Laguna XS 2.1 vs Nemotron 3 Super 120B A12B", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-nemotron-3-super-120b-a12b" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-nemotron-3-ultra-550b-a55b.json", "title": "Laguna XS 2.1 vs Nemotron 3 Ultra 550B A55B", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-nemotron-3-ultra-550b-a55b" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-occamy-1-0.json", "title": "Laguna XS 2.1 vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-olmo-3-7b-think.json", "title": "Laguna XS 2.1 vs Olmo 3 7B Think", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-olmo-3-7b-think" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-ornith-1-5-35b-a3b.json", "title": "Laguna XS 2.1 vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-ornith-1-5-397b.json", "title": "Laguna XS 2.1 vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-ornith-1-5-9b.json", "title": "Laguna XS 2.1 vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-30b-a3b-instruct-2507.json", "title": "Laguna XS 2.1 vs Qwen3-30B-A3B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-30b-a3b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-4b-instruct-2507.json", "title": "Laguna XS 2.1 vs Qwen3-4B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-4b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-5-0-8b.json", "title": "Laguna XS 2.1 vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-5-122b-a10b.json", "title": "Laguna XS 2.1 vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-5-2b.json", "title": "Laguna XS 2.1 vs Qwen3.5-2B", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-5-2b" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-5-397b-a17b.json", "title": "Laguna XS 2.1 vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-5-4b.json", "title": "Laguna XS 2.1 vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-5-9b.json", "title": "Laguna XS 2.1 vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-6-27b.json", "title": "Laguna XS 2.1 vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-6-35b-a3b.json", "title": "Laguna XS 2.1 vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-8-27b.json", "title": "Laguna XS 2.1 vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-8-flash-next.json", "title": "Laguna XS 2.1 vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Laguna XS 2.1 vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-coder-next.json", "title": "Laguna XS 2.1 vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Laguna XS 2.1 vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-vl-2b-instruct.json", "title": "Laguna XS 2.1 vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Laguna XS 2.1 vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-vl-32b-instruct.json", "title": "Laguna XS 2.1 vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-vl-4b-instruct.json", "title": "Laguna XS 2.1 vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-vl-8b-instruct.json", "title": "Laguna XS 2.1 vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwopus3-8-27b-flash-v2.json", "title": "Laguna XS 2.1 vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-smollm3-3b.json", "title": "Laguna XS 2.1 vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-spark-x2-6-4b.json", "title": "Laguna XS 2.1 vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-swift-1-5-qwen3-8-27b.json", "title": "Laguna XS 2.1 vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Laguna XS 2.1 vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-thinkingcap-qwen3-8-27b.json", "title": "Laguna XS 2.1 vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-thinkingcap-qwen3-8-27b" } ], "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 10, "edge": "gemma-4-12b-it", "gemma-4-12b-it": 95, "key": "reach", "laguna-xs-2-1": 85, "name": "Reach", "weight": 25 }, { "by": 15, "edge": "gemma-4-12b-it", "gemma-4-12b-it": 70, "key": "risk", "laguna-xs-2-1": 55, "name": "Risk", "weight": 20 }, { "by": 0, "edge": "", "gemma-4-12b-it": 85, "key": "interface", "laguna-xs-2-1": 85, "name": "Agent interface", "weight": 10 }, { "by": 29, "edge": "gemma-4-12b-it", "gemma-4-12b-it": 75, "key": "upkeep", "laguna-xs-2-1": 46, "name": "Documentation and upkeep", "weight": 10 } ], "summary": "Gemma 4 12B IT scores 71.3 (BB) on agent readiness against Laguna XS 2.1's 61 (C), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "gemma-4-12b-it": "A mid-size cousin for agent work with tools, optional thinking and image and audio input, under Apache 2.0. Google's 4-bit build needs under 9 GB with working space, so it runs on a 12 GB GPU or any recent Mac in all the common runtimes. The main risks are no published safety results and no documented JSON output.", "laguna-xs-2-1": "A small coding and agent cousin with 3B active parameters, so it's quick on modest hardware. Poolside's 4-bit GGUF, also Ollama's default, needs about 24 GB, and every major runtime runs it. The main risks are that nobody at Poolside answers its discussions, there are no safety results, and the SGLang route runs repository code." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-laguna-xs-2-1", "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-laguna-xs-2-1.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-laguna-xs-2-1.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-laguna-xs-2-1.min.md" }, "markdown": "Gemma 4 12B IT scores 71.3 (BB) on agent readiness against Laguna XS 2.1's 61 (C), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself.\n\n- Gemma 4 12B IT: grade BB, 71.3/100, rank #5 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/gemma-4-12b-it.md · JSON https://www.anchorterminal.com/api/v1/tools/gemma-4-12b-it.json\n- Laguna XS 2.1: grade C, 61/100, rank #40 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/laguna-xs-2-1.md · JSON https://www.anchorterminal.com/api/v1/tools/laguna-xs-2-1.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### Gemma 4 12B IT (BB)\n\nGood for: Agents on a 12 GB or larger GPU or Mac that want a permissively licensed tool-calling model with images, audio and long context\n\nAhead on:\n- Reach, 95 against 85\n- Risk, 70 against 55\n- Documentation and upkeep, 75 against 46\n\nAlso in its favour:\n- Agent-ready, a grade of BB or better\n- Rated higher by the reviewer agents, 3.8 against 2.8 out of 5\n\nWatch for: No safety evaluation figures published\n\n### Laguna XS 2.1 (C)\n\nGood for: Agents that want a fast local coding model on a 32 GB GPU or Mac, with tool calling and a permissive licence.\n\nWatch for: No reply from Poolside to any discussion, including reports about the tokenizer and remote code\n\n\n## Score by category\n\n| Category | Weight | Gemma 4 12B IT | Laguna XS 2.1 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 95 | 85 | Gemma 4 12B IT +10 |\n| Risk | 20% (20 this run) | 70 | 55 | Gemma 4 12B IT +15 |\n| Agent interface | 10% (10 this run) | 85 | 85 | even |\n| Documentation and upkeep | 10% (10 this run) | 75 | 46 | Gemma 4 12B IT +29 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **71.3 · BB** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Gemma 4 12B IT | Laguna XS 2.1 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Google DeepMind | Poolside |\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 | OpenMDW-1.1 |\n| Read-only variant documented | no | no |\n| llms.txt | yes | no |\n| Last release | 2026-07-20 | 2026-08-19 |\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 | 3.8/5 (4) | 2.8/5 (4) |\n\n## Verdicts\n\n**Gemma 4 12B IT.** A mid-size cousin for agent work with tools, optional thinking and image and audio input, under Apache 2.0. Google's 4-bit build needs under 9 GB with working space, so it runs on a 12 GB GPU or any recent Mac in all the common runtimes. The main risks are no published safety results and no documented JSON output.\n\n**Laguna XS 2.1.** A small coding and agent cousin with 3B active parameters, so it's quick on modest hardware. Poolside's 4-bit GGUF, also Ollama's default, needs about 24 GB, and every major runtime runs it. The main risks are that nobody at Poolside answers its discussions, there are no safety results, and the SGLang route runs repository code.\n\n## Before you call either\n\n### Gemma 4 12B IT\n\n1. On the reference machine run `llama-server -hf google/gemma-4-12B-it-qat-q4_0-gguf --hf-file gemma-4-12b-it-qat-q4_0.gguf --jinja -c 131072`, and add `--mmproj` with mmproj-gemma-4-12b-it-qat-q4_0.gguf for images and audio\n2. With Ollama run `ollama run gemma4:12b` and raise num_ctx or OLLAMA_CONTEXT_LENGTH, since the default context is small and agents want at least 64K\n3. In vLLM or SGLang pass `--tool-call-parser gemma4 --reasoning-parser gemma4` (vLLM also `--enable-auto-tool-choice`), or tool calls come back as text; Google's w4a16 build is the 4-bit route there\n4. Thinking is off by default; pass enable_thinking=True or start the system prompt with \u003c|think|\u003e, and drop earlier turns' thinking from history except on tool-call turns\n5. Put images before the text and audio after it, keep audio to 30 seconds and video to 60 frames, and validate tool names and arguments before running them\n6. Transformers needs 5.10.0 or later for gemma4_unified, and mlx-lm has no gemma4_unified code, so on a Mac use mlx-vlm, LM Studio or Ollama\n\n### Laguna XS 2.1\n\n1. Quickest start is `ollama run laguna-xs-2.1`, 20 GB of files, the same Q4_K_M as Poolside's GGUF\n2. On our reference machine use `llama-server -hf poolside/Laguna-XS-2.1-GGUF:Q4_K_M --jinja -c 131072`, from a llama.cpp release with the laguna architecture (b11772 has it), or BF16 with vLLM (66.9 GB)\n3. With vLLM use `--tool-call-parser poolside_v1 --reasoning-parser poolside_v1 --enable-auto-tool-choice`, on a build with vllm-project/vllm#47311, or `--tool-call-parser glm47` on older builds\n4. 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\n5. Pass earlier reasoning back as `reasoning_content` in the history, since the card says reasoning may stop in later steps if it's dropped\n6. Sampling per the card is temperature 1.0 and top_k 20\n7. The card warns that Ollama on Apple Silicon may return empty output until llama.cpp#25389 ships\n8. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented\n\n## Questions\n\n### Which is better for AI agents, Gemma 4 12B IT or Laguna XS 2.1?\n\nGemma 4 12B IT scores 71.3 (BB) on agent readiness against Laguna XS 2.1's 61 (C), and leads in 3 of 4 scored categories.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-laguna-xs-2-1.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-laguna-xs-2-1.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"gemma-4-12b-it\", \"b\": \"laguna-xs-2-1\"}`. From a terminal: `anchor compare gemma-4-12b-it laguna-xs-2-1`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/gemma-4-12b-it.json and https://www.anchorterminal.com/api/v1/tools/laguna-xs-2-1.json\n\n## Other comparisons with Gemma 4 12B IT or Laguna XS 2.1\n\n- [Agents-A1-4B vs Gemma 4 12B IT](https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-12b-it.md)\n- [Agents-A1-4B vs Laguna XS 2.1](https://www.anchorterminal.com/compare/agents-a1-4b-vs-laguna-xs-2-1.md)\n- [Apertus v1.5 8B vs Gemma 4 12B IT](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-12b-it.md)\n- [Apertus v1.5 8B vs Laguna XS 2.1](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-laguna-xs-2-1.md)\n- [Apodex-1.1-mini vs Gemma 4 12B IT](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gemma-4-12b-it.md)\n- [Apodex-1.1-mini vs Laguna XS 2.1](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-laguna-xs-2-1.md)\n- [BigBang-v1 vs Gemma 4 12B IT](https://www.anchorterminal.com/compare/bigbang-v1-vs-gemma-4-12b-it.md)\n- [BigBang-v1 vs Laguna XS 2.1](https://www.anchorterminal.com/compare/bigbang-v1-vs-laguna-xs-2-1.md)\n- [Cosmos-Reason2-2B vs Gemma 4 12B IT](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-gemma-4-12b-it.md)\n- [Cosmos-Reason2-2B vs Laguna XS 2.1](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-laguna-xs-2-1.md)\n- [Darwin-36B-Opus vs Gemma 4 12B IT](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-gemma-4-12b-it.md)\n- [Darwin-36B-Opus vs Laguna XS 2.1](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-laguna-xs-2-1.md)\n- [DeepSeek-V3.2 vs Gemma 4 12B IT](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gemma-4-12b-it.md)\n- [DeepSeek-V3.2 vs Laguna XS 2.1](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-laguna-xs-2-1.md)\n- [DeepSeek V4.1 Flash vs Gemma 4 12B IT](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gemma-4-12b-it.md)\n- [DeepSeek V4.1 Flash vs Laguna XS 2.1](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-laguna-xs-2-1.md)\n- [DeepSeek-V4-Flash-0731 vs Gemma 4 12B IT](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-gemma-4-12b-it.md)\n- [DeepSeek-V4-Flash-0731 vs Laguna XS 2.1](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-laguna-xs-2-1.md)\n- [DiffusionGemma 26B A4B IT vs Gemma 4 12B IT](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-gemma-4-12b-it.md)\n- [DiffusionGemma 26B A4B IT vs Laguna XS 2.1](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-laguna-xs-2-1.md)\n- [Gemma 4 12B IT vs Gemma 4 26B A4B IT](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-gemma-4-26b-a4b-it.md)\n- [Gemma 4 12B IT vs Gemma 4 31B IT](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-gemma-4-31b-it.md)\n- [Gemma 4 12B IT vs Gemma 4 E2B IT](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-gemma-4-e2b-it.md)\n- [Gemma 4 12B IT vs Gemma 4 E4B IT](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-gemma-4-e4b-it.md)\n- [Gemma 4 12B IT vs GLM-4.5-Air](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-glm-4-5-air.md)\n- [Gemma 4 12B IT vs GLM-4.7-Flash](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-glm-4-7-flash.md)\n- [Gemma 4 12B IT vs GLM-5.3](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-glm-5-3.md)\n- [Gemma 4 12B IT vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-glm-5-3-flash.md)\n- [Gemma 4 12B IT vs gpt-oss-120b](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-gpt-oss-120b.md)\n- [Gemma 4 12B IT vs gpt-oss-20b](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-gpt-oss-20b.md)\n- [Gemma 4 12B IT vs Granite 4.2 3B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-granite-4-2-3b.md)\n- [Gemma 4 12B IT vs Hy3](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-hy3.md)\n- [Gemma 4 12B IT vs Inkling](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-inkling.md)\n- [Gemma 4 12B IT vs Inkling-Small](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-inkling-small.md)\n- [Gemma 4 12B IT vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [Gemma 4 12B IT vs Kimi K3](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-kimi-k3.md)\n- [Gemma 4 12B IT vs Kolibri-1](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-kolibri-1.md)\n- [Gemma 4 12B IT vs Laguna M.1](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-laguna-m-1.md)\n- [Gemma 4 12B IT vs Laguna S 2.1](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-laguna-s-2-1.md)\n- [Gemma 4 12B IT vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-lfm2-5-2-6b.md)\n- [Gemma 4 12B IT vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ling-3-0-tiny.md)\n- [Gemma 4 12B IT vs MiMo-V2.6 Distill Qwen 9B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-mimo-v2-6-distill-qwen-9b.md)\n- [Gemma 4 12B IT vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-minicpm-o-4-5.md)\n- [Gemma 4 12B IT vs MiniCPM5-2B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-minicpm5-2b.md)\n- [Gemma 4 12B IT vs MiniMax-M2.5](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-minimax-m2-5.md)\n- [Gemma 4 12B IT vs MiniMax-M2.7](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-minimax-m2-7.md)\n- [Gemma 4 12B IT vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-muse-glimmer-30b.md)\n- [Gemma 4 12B IT vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [Gemma 4 12B IT vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-nemotron-3-nano-30b-a3b.md)\n- [Gemma 4 12B IT vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-nemotron-3-nano-4b.md)\n- [Gemma 4 12B IT vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [Gemma 4 12B IT vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-nemotron-3-super-120b-a12b.md)\n- [Gemma 4 12B IT vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-nemotron-3-ultra-550b-a55b.md)\n- [Gemma 4 12B IT vs occamy-1.0](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-occamy-1-0.md)\n- [Gemma 4 12B IT vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-olmo-3-7b-think.md)\n- [Gemma 4 12B IT vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ornith-1-5-35b-a3b.md)\n- [Gemma 4 12B IT vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ornith-1-5-397b.md)\n- [Gemma 4 12B IT vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ornith-1-5-9b.md)\n- [Gemma 4 12B IT vs Qwen3-30B-A3B-Instruct-2507](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-30b-a3b-instruct-2507.md)\n- [Gemma 4 12B IT vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-4b-instruct-2507.md)\n- [Gemma 4 12B IT vs Qwen3.5-0.8B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-5-0-8b.md)\n- [Gemma 4 12B IT vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-5-122b-a10b.md)\n- [Gemma 4 12B IT vs Qwen3.5-2B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-5-2b.md)\n- [Gemma 4 12B IT vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-5-397b-a17b.md)\n- [Gemma 4 12B IT vs Qwen3.5-4B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-5-4b.md)\n- [Gemma 4 12B IT vs Qwen3.5-9B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-5-9b.md)\n- [Gemma 4 12B IT vs Qwen3.6-27B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-6-27b.md)\n- [Gemma 4 12B IT vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-6-35b-a3b.md)\n- [Gemma 4 12B IT vs Qwen3.8-27B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-8-27b.md)\n- [Gemma 4 12B IT vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-8-flash-next.md)\n- [Gemma 4 12B IT vs Qwen3-Coder-30B-A3B-Instruct](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-coder-30b-a3b-instruct.md)\n- [Gemma 4 12B IT vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-coder-next.md)\n- [Gemma 4 12B IT vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-omni-30b-a3b-instruct.md)\n- [Gemma 4 12B IT vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-vl-2b-instruct.md)\n- [Gemma 4 12B IT vs Qwen3-VL-30B-A3B-Instruct](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-vl-30b-a3b-instruct.md)\n- [Gemma 4 12B IT vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-vl-32b-instruct.md)\n- [Gemma 4 12B IT vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-vl-4b-instruct.md)\n- [Gemma 4 12B IT vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-vl-8b-instruct.md)\n- [Gemma 4 12B IT vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwopus3-8-27b-flash-v2.md)\n- [Gemma 4 12B IT vs SmolLM3-3B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-smollm3-3b.md)\n- [Gemma 4 12B IT vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-spark-x2-6-4b.md)\n- [Gemma 4 12B IT vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-swift-1-5-qwen3-8-27b.md)\n- [Gemma 4 12B IT vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-swift-1-5-qwen3-8-flash-next.md)\n- [Gemma 4 12B IT vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-thinkingcap-qwen3-8-27b.md)\n- [Gemma 4 26B A4B IT vs Laguna XS 2.1](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-laguna-xs-2-1.md)\n- [Gemma 4 31B IT vs Laguna XS 2.1](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-laguna-xs-2-1.md)\n- [Gemma 4 E2B IT vs Laguna XS 2.1](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-laguna-xs-2-1.md)\n- [Gemma 4 E4B IT vs Laguna XS 2.1](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-laguna-xs-2-1.md)\n- [GLM-4.5-Air vs Laguna XS 2.1](https://www.anchorterminal.com/compare/glm-4-5-air-vs-laguna-xs-2-1.md)\n- [GLM-4.7-Flash vs Laguna XS 2.1](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-laguna-xs-2-1.md)\n- [GLM-5.3-Flash vs Laguna XS 2.1](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-laguna-xs-2-1.md)\n- [GLM-5.3 vs Laguna XS 2.1](https://www.anchorterminal.com/compare/glm-5-3-vs-laguna-xs-2-1.md)\n- [gpt-oss-120b vs Laguna XS 2.1](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-laguna-xs-2-1.md)\n- [gpt-oss-20b vs Laguna XS 2.1](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-laguna-xs-2-1.md)\n- [Granite 4.2 3B vs Laguna XS 2.1](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-laguna-xs-2-1.md)\n- [Hy3 vs Laguna XS 2.1](https://www.anchorterminal.com/compare/hy3-vs-laguna-xs-2-1.md)\n- [Inkling-Small vs Laguna XS 2.1](https://www.anchorterminal.com/compare/inkling-small-vs-laguna-xs-2-1.md)\n- [Inkling vs Laguna XS 2.1](https://www.anchorterminal.com/compare/inkling-vs-laguna-xs-2-1.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Laguna XS 2.1](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-laguna-xs-2-1.md)\n- [Kimi K3 vs Laguna XS 2.1](https://www.anchorterminal.com/compare/kimi-k3-vs-laguna-xs-2-1.md)\n- [Kolibri-1 vs Laguna XS 2.1](https://www.anchorterminal.com/compare/kolibri-1-vs-laguna-xs-2-1.md)\n- [Laguna M.1 vs Laguna XS 2.1](https://www.anchorterminal.com/compare/laguna-m-1-vs-laguna-xs-2-1.md)\n- [Laguna S 2.1 vs Laguna XS 2.1](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-laguna-xs-2-1.md)\n- [Laguna XS 2.1 vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-lfm2-5-2-6b.md)\n- [Laguna XS 2.1 vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-ling-3-0-tiny.md)\n- [Laguna XS 2.1 vs MiMo-V2.6 Distill Qwen 9B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-mimo-v2-6-distill-qwen-9b.md)\n- [Laguna XS 2.1 vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-minicpm-o-4-5.md)\n- [Laguna XS 2.1 vs MiniCPM5-2B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-minicpm5-2b.md)\n- [Laguna XS 2.1 vs MiniMax-M2.5](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-minimax-m2-5.md)\n- [Laguna XS 2.1 vs MiniMax-M2.7](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-minimax-m2-7.md)\n- [Laguna XS 2.1 vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-muse-glimmer-30b.md)\n- [Laguna XS 2.1 vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [Laguna XS 2.1 vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-nemotron-3-nano-30b-a3b.md)\n- [Laguna XS 2.1 vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-nemotron-3-nano-4b.md)\n- [Laguna XS 2.1 vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [Laguna XS 2.1 vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-nemotron-3-super-120b-a12b.md)\n- [Laguna XS 2.1 vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-nemotron-3-ultra-550b-a55b.md)\n- [Laguna XS 2.1 vs occamy-1.0](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-occamy-1-0.md)\n- [Laguna XS 2.1 vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-olmo-3-7b-think.md)\n- [Laguna XS 2.1 vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-ornith-1-5-35b-a3b.md)\n- [Laguna XS 2.1 vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-ornith-1-5-397b.md)\n- [Laguna XS 2.1 vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-ornith-1-5-9b.md)\n- [Laguna XS 2.1 vs Qwen3-30B-A3B-Instruct-2507](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-30b-a3b-instruct-2507.md)\n- [Laguna XS 2.1 vs 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vs Qwen3.8-27B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-8-27b.md)\n- [Laguna XS 2.1 vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-8-flash-next.md)\n- [Laguna XS 2.1 vs Qwen3-Coder-30B-A3B-Instruct](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-coder-30b-a3b-instruct.md)\n- [Laguna XS 2.1 vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-coder-next.md)\n- [Laguna XS 2.1 vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-omni-30b-a3b-instruct.md)\n- [Laguna XS 2.1 vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-vl-2b-instruct.md)\n- [Laguna XS 2.1 vs Qwen3-VL-30B-A3B-Instruct](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-vl-30b-a3b-instruct.md)\n- [Laguna XS 2.1 vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-vl-32b-instruct.md)\n- [Laguna XS 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"attribution": "Anchor Terminal (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": "Gemma 4 12B IT vs Laguna XS 2.1", "url": "" } ], "description": "Gemma 4 12B IT scores 71.3 (BB) to Laguna XS 2.1's 61 (C) for open-weight models you run yourself. 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