{ "data": { "a": { "slug": "gemma-4-26b-a4b-it", "name": "Gemma 4 26B A4B IT", "vendor": "Google DeepMind", "vendorUrl": "https://gemma.ai", "kind": "weights", "category": "open-weights", "summary": "Gemma 4 26B A4B IT is Google DeepMind's open-weight instruction-tuned mixture-of-experts model, 25.2B parameters with 3.8B active, taking text and images with a 262,144-token context, published on Hugging Face under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/gemma-4-26b-a4b-it", "markdownUrl": "https://www.anchorterminal.com/tools/gemma-4-26b-a4b-it.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/gemma-4-26b-a4b-it.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/gemma-4-26b-a4b-it.json", "repo": "https://huggingface.co/google/gemma-4-26B-A4B-it", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "google/gemma-4-26B-A4B-it" }, { "registry": "huggingface", "name": "google/gemma-4-26B-A4B-it-qat-q4_0-gguf" }, { "registry": "ollama", "name": "gemma4:26b" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Google also hosts it as gemma-4-26b-a4b-it on the Gemini API, which needs an API key. Running it yourself costs the hardware, 57.7 GB at BF16 and 14.4 GB at Q4_0 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/", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "multimodal", "reasoning", "tool-calling", "apache-2.0", "safetensors", "gguf", "self-hosted" ], "lastRelease": "2026-07-20", "graded": true, "anchor": { "graded": true, "score": 72.2, "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": 2, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 59, "interface": 85, "reach": 90, "risk": 70, "upkeep": 65 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "A fast mixture-of-experts cousin for general agent work with tools, optional thinking and image input, under Apache 2.0. Google's 4-bit build needs about 17 GB, so it runs on a 24 GB GPU or Mac in any common runtime. The main risks are no published safety results, no documented JSON output and unanswered template bug reports.", "bestFor": "Agents on a 24 GB or larger GPU or Mac that want a fast, permissively licensed tool-calling model with images 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 17 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 named parsers", "262,144 tokens of native context and thinking that turns on or off per request", "Also served by the Gemini API and four inference providers" ], "weaknesses": [ "No safety evaluation figures published", "No documented way to ask for JSON or schema output", "Only one quantised precision from Google, no FP8", "Files unchanged since 20 July 2026, with chat-template bug reports unanswered", "Training data described only by type and cut-off" ], "agentNotes": [ "On the reference machine run `llama-server -hf google/gemma-4-26B-A4B-it-qat-q4_0-gguf --hf-file gemma-4-26B_q4_0-it.gguf --jinja -c 65536`, and add the mmproj file for images", "With Ollama run `ollama run gemma4:26b` and raise num_ctx or OLLAMA_CONTEXT_LENGTH, since Google's guide says it defaults to 4K below 24 GiB of VRAM 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", "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", "Validate tool names and arguments before running them, and check any JSON yourself" ], "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": 72.2 } ], "editorialScores": { "capability": 59, "interface": 85, "reach": 90, "risk": 70, "upkeep": 65 }, "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 gemma4:26b\n# or, with vLLM:\nvllm serve google/gemma-4-26B-A4B-it --max-model-len 262144 --reasoning-parser gemma4 \\\n --enable-auto-tool-choice --tool-call-parser gemma4", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"gemma4:26b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}], \"options\": {\"num_ctx\": 65536}}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/gemma-4-26b-a4b-it" }, "sameCompany": [ "embeddinggemma-300m", "diffusiongemma-26b-a4b-it", "gemma-4-12b-it", "gemma-4-31b-it", "gemma-4-e2b-it", "gemma-4-e4b-it" ], "sameModel": [ "gemini-api" ], "weights": { "trainedBy": "Google DeepMind", "builtOn": "google/gemma-4-26B-A4B", "params": "25.2B", "active": "3.8B", "context": 262144, "goodFor": "General assistant and agent model with tool calling and optional thinking, taking text, images and video frames, fast for its size as a mixture of experts.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "LM Studio", "Transformers" ], "builds": [ { "name": "gemma-4-26B_q4_0-it", "url": "https://huggingface.co/google/gemma-4-26B-A4B-it-qat-q4_0-gguf/blob/main/gemma-4-26B_q4_0-it.gguf", "who": "publisher", "precision": "q4_0", "sizeGB": 14.4, "memoryGB": 24, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)", "LM Studio" ], "note": "Quantisation-aware trained by Google. Add gemma-4-26B-it-mmproj.gguf (1.2 GB) for image input" }, { "name": "ollama gemma4:26b", "url": "https://ollama.com/library/gemma4:26b", "who": "ollama", "precision": "q4_k_m", "sizeGB": 19, "memoryGB": 24, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Q4_K_M weights with a BF16 vision projector and a Q8_0 draft model (digest 001e5dafc3c7). Size as Ollama's tags page shows it, rounded to whole GB above 10 GB; the Linux build is the llama.cpp variant" }, { "name": "gemma-4-26B-A4B-it", "url": "https://huggingface.co/google/gemma-4-26B-A4B-it", "who": "publisher", "precision": "bf16", "sizeGB": 51.6, "memoryGB": 64, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] } ], "run": "llama-server -hf google/gemma-4-26B-A4B-it-qat-q4_0-gguf --hf-file gemma-4-26B_q4_0-it.gguf --jinja -c 65536", "runBuild": "gemma-4-26B_q4_0-it", "checked": "2026-10-21", "minMemoryGB": 24 }, "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-4-31B", "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", "url": "https://huggingface.co/google/gemma-4-12B-it", "params": "11.95B", "listing": "https://www.anchorterminal.com/tools/gemma-4-12b-it" }, { "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; 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": [ "RDAP names Google LLC as the registrant of gemma.ai, registered 16 December 2017, with MarkMonitor as registrar and Google's name servers. ai.google.dev's Gemma pages say the documentation moved to gemma.ai.", "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 (Gemma 4 on 31 March 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-4-26b-a4b-it.json" }, "answer": "Gemma 4 31B IT scores 74.1 (BB) on agent readiness against Gemma 4 26B A4B IT's 72.2 (BB), and leads in 2 of 5 scored categories.", "b": { "slug": "gemma-4-31b-it", "name": "Gemma 4 31B IT", "vendor": "Google DeepMind", "vendorUrl": "https://gemma.ai", "kind": "weights", "category": "open-weights", "summary": "Gemma 4 31B IT is Google DeepMind's open-weight dense multimodal model of 30.7B parameters, taking text and images with a 256K-token context, published on Hugging Face in BF16 with 4-bit QAT builds under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/gemma-4-31b-it", "markdownUrl": "https://www.anchorterminal.com/tools/gemma-4-31b-it.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/gemma-4-31b-it.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/gemma-4-31b-it.json", "repo": "https://huggingface.co/google/gemma-4-31B-it", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "google/gemma-4-31B-it" }, { "registry": "huggingface", "name": "google/gemma-4-31B-it-qat-q4_0-gguf" }, { "registry": "huggingface", "name": "google/gemma-4-31B-it-qat-w4a16-ct" }, { "registry": "ollama", "name": "gemma4:31b" } ], "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. Running it yourself costs the hardware, about 69.9 GB at BF16 or 17.5 GB at Q4_0 per Google's memory table. Google also hosts it on the Gemini API's free tier, a separate listing (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", "reasoning", "tool-calling", "apache-2.0", "safetensors", "gguf", "qat", "self-hosted" ], "lastRelease": "2026-07-20", "graded": true, "anchor": { "graded": true, "score": 74.1, "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": 1, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 61, "interface": 85, "reach": 95, "risk": 70, "upkeep": 65 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "A dense multimodal cousin with documented tool calling, optional thinking and a 256K context, for general agent work and image reading on one 24 GB GPU through Google's 4-bit build. It runs in every major runtime under plain Apache 2.0. The main risk is that Google publishes no safety results and has gone quiet on the repository since July.", "bestFor": "An agent with a 24 GB GPU or more that wants a dense multimodal cousin with tool calling under a plain licence", "strengths": [ "Google's own 17.7 GB QAT Q4_0 GGUF fits a 24 GB GPU, and Ollama's gemma4:31b is 20 GB", "Released support in llama.cpp, Ollama, vLLM, SGLang and Transformers", "Apache 2.0 with nothing added, no gate, and hosted on Google's Gemini API too", "Tool calling documented by Google with a response schema Transformers parses, several calls per turn", "Thinking that can be switched on or off per request and 262,144 tokens of native context" ], "weaknesses": [ "No safety evaluation results published", "No prompt-injection guidance and no security route tied to Gemma", "Structured output isn't documented by Google", "Training data described only in general terms", "Files unchanged since 20 July and no Google reply in the discussions since 30 July" ], "agentNotes": [ "On the reference machine run `llama-server -hf google/gemma-4-31B-it-qat-q4_0-gguf --hf-file gemma-4-31B_q4_0-it.gguf --jinja -c 32768`, adding gemma-4-31B-it-mmproj.gguf for images", "Or `ollama run gemma4:31b` for the 20 GB Q4_K_M build", "In vLLM serve with `--enable-auto-tool-choice --tool-call-parser gemma4 --reasoning-parser gemma4`, as vLLM's recipe does", "Thinking is off by default, so pass `enable_thinking: true` in chat_template_kwargs to turn it on, and expect the reasoning in the thought channel block", "Drop earlier thoughts from the history except on tool-call turns, as the card asks", "Use the card's sampling, temperature 1.0, top_p 0.95, top_k 64", "Validate function names and arguments before running a call, and check JSON output yourself or use the runtime's guided decoding", "The w4a16 build relies on Marlin INT4 kernels that vLLM marks unsupported on AMD, so use the GGUF on the reference machine" ], "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": 74.1 } ], "editorialScores": { "capability": 61, "interface": 85, "reach": 95, "risk": 70, "upkeep": 65 }, "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 gemma4:31b\n# or, with vLLM's recipe:\nvllm serve google/gemma-4-31B-it --max-model-len 32768 --enable-auto-tool-choice \\\n --reasoning-parser gemma4 --tool-call-parser gemma4", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"gemma4:31b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/gemma-4-31b-it" }, "sameCompany": [ "embeddinggemma-300m", "diffusiongemma-26b-a4b-it", "gemma-4-12b-it", "gemma-4-26b-a4b-it", "gemma-4-e2b-it", "gemma-4-e4b-it" ], "sameModel": [ "gemini-api" ], "weights": { "trainedBy": "Google DeepMind", "builtOn": "google/gemma-4-31B", "params": "30.7B", "context": 262144, "goodFor": "Dense general assistant and agent model with tool calling and optional thinking, taking text, images and video frames.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "gemma-4-31B_q4_0-it", "url": "https://huggingface.co/google/gemma-4-31B-it-qat-q4_0-gguf/blob/main/gemma-4-31B_q4_0-it.gguf", "who": "publisher", "precision": "q4_0", "sizeGB": 17.7, "memoryGB": 24, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)", "LM Studio" ], "note": "Quantisation-aware trained by Google. Add gemma-4-31B-it-mmproj.gguf (1.2 GB) for image input" }, { "name": "ollama gemma4:31b", "url": "https://ollama.com/library/gemma4:31b", "who": "ollama", "precision": "q4_k_m", "sizeGB": 20, "memoryGB": 24, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Q4_K_M weights with a BF16 vision projector and a Q8_0 draft model (digest 17ba34c06c80). Size as Ollama's tags page shows it, rounded to whole GB above 10 GB; the Linux build is the llama.cpp variant" }, { "name": "gemma-4-31B-it-qat-w4a16-ct", "url": "https://huggingface.co/google/gemma-4-31B-it-qat-w4a16-ct", "who": "publisher", "precision": "w4a16", "sizeGB": 23.3, "memoryGB": 32, "format": "safetensors", "note": "Quantisation-aware trained, compressed-tensors format for vLLM. vLLM's quantisation table marks its Marlin INT4 kernels unsupported on AMD" }, { "name": "gemma-4-31B-it", "url": "https://huggingface.co/google/gemma-4-31B-it", "who": "publisher", "precision": "bf16", "sizeGB": 62.5, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] } ], "run": "llama-server -hf google/gemma-4-31B-it-qat-q4_0-gguf --hf-file gemma-4-31B_q4_0-it.gguf --jinja -c 32768", "runBuild": "gemma-4-31B_q4_0-it", "checked": "2026-10-21", "minMemoryGB": 24 }, "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-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", "url": "https://huggingface.co/google/gemma-4-12B-it", "params": "11.95B", "listing": "https://www.anchorterminal.com/tools/gemma-4-12b-it" }, { "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 (the registrant of gemma.ai; the card names Google DeepMind as author)", "domain": "gemma.ai", "domainRegistered": "2017-12-16", "domainNote": "RDAP shows gemma.ai registered in 2017 and transferred on 15 April 2025, so the registration date may predate Google's use of the domain.", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://gemma.ai/models/releases/", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "RDAP names Google LLC as registrant of gemma.ai, with MarkMonitor as registrar. Google's ai.google.dev pages point to gemma.ai as the current documentation.", "Nothing is hosted under this listing, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "The changelog is Google's Gemma releases page, which dates each release (Gemma 4 on 31 March 2026, its MTP drafters on 16 April). The Hugging Face commit history's messages also say what changed.", "gemma.ai/.well-known/security.txt answered 404 on 21 October 2026. www.google.com has a security.txt pointing to Google's vulnerability reward programme, which isn't linked from the card or gemma.ai." ], "score": 68 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/gemma-4-31b-it.json" }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Google DeepMind", "b": "Google DeepMind", "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": "Apache-2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "yes", "name": "llms.txt" }, { "a": "2026-07-20", "b": "2026-07-20", "name": "Last release" }, { "a": "no document linked", "b": "no document linked", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "", "b": "", "name": "Customer content may train models" }, { "a": "", "b": "", "name": "Terms restrict automated access" }, { "a": "", "b": "", "name": "Terms restrict benchmarking" }, { "a": "", "b": "", "name": "Terms or service can change without notice" }, { "a": "", "b": "", "name": "Arbitration or class-action waiver" }, { "a": "3.8/5 (4)", "b": "3.8/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Gemma 4 31B IT scores 74.1 (BB) on agent readiness against Gemma 4 26B A4B IT's 72.2 (BB), and leads in 2 of 5 scored categories.", "question": "Which is better for AI agents, Gemma 4 26B A4B IT or Gemma 4 31B IT?" } ], "goodFor": [ { "aheadOn": null, "also": null, "goodFor": "Agents on a 24 GB or larger GPU or Mac that want a fast, permissively licensed tool-calling model with images and long context", "slug": "gemma-4-26b-a4b-it", "watchFor": "No safety evaluation figures published" }, { "aheadOn": [ "Reach, 95 against 90" ], "also": null, "goodFor": "An agent with a 24 GB GPU or more that wants a dense multimodal cousin with tool calling under a plain licence", "slug": "gemma-4-31b-it", "watchFor": "No safety evaluation results published" } ], "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-26b-a4b-it.json", "title": "Agents-A1-4B vs Gemma 4 26B A4B IT", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-26b-a4b-it" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-31b-it.json", "title": "Agents-A1-4B vs Gemma 4 31B IT", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-31b-it" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-26b-a4b-it.json", "title": "Apertus v1.5 8B vs Gemma 4 26B A4B IT", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-26b-a4b-it" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-31b-it.json", "title": "Apertus v1.5 8B vs Gemma 4 31B IT", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-31b-it" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gemma-4-26b-a4b-it.json", "title": "Apodex-1.1-mini vs Gemma 4 26B A4B IT", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gemma-4-26b-a4b-it" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gemma-4-31b-it.json", "title": "Apodex-1.1-mini vs Gemma 4 31B IT", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gemma-4-31b-it" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-gemma-4-26b-a4b-it.json", "title": 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IT vs GLM-5.3-Flash", "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-glm-5-3-flash" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-gpt-oss-120b.json", "title": "Gemma 4 26B A4B IT vs gpt-oss-120b", "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-gpt-oss-120b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-gpt-oss-20b.json", "title": "Gemma 4 26B A4B IT vs gpt-oss-20b", "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-gpt-oss-20b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-granite-4-2-3b.json", "title": "Gemma 4 26B A4B IT vs Granite 4.2 3B", "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-granite-4-2-3b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-hy3.json", "title": "Gemma 4 26B A4B IT vs Hy3", "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-hy3" }, { "json": 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"https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-internvl3-5-gpt-oss-20b-a4b-preview" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-kimi-k3.json", "title": "Gemma 4 31B IT vs Kimi K3", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-kimi-k3" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-kolibri-1.json", "title": "Gemma 4 31B IT vs Kolibri-1", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-kolibri-1" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-laguna-m-1.json", "title": "Gemma 4 31B IT vs Laguna M.1", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-laguna-m-1" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-laguna-s-2-1.json", "title": "Gemma 4 31B IT vs Laguna S 2.1", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-laguna-s-2-1" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-laguna-xs-2-1.json", "title": "Gemma 4 31B IT vs Laguna XS 2.1", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-laguna-xs-2-1" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-lfm2-5-2-6b.json", "title": "Gemma 4 31B IT vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ling-3-0-tiny.json", "title": "Gemma 4 31B IT vs Ling-3.0-tiny", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ling-3-0-tiny" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-mimo-v2-6-distill-qwen-9b.json", "title": "Gemma 4 31B IT vs MiMo-V2.6 Distill Qwen 9B", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-mimo-v2-6-distill-qwen-9b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-minicpm-o-4-5.json", "title": "Gemma 4 31B IT vs MiniCPM-o 4.5", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-minicpm-o-4-5" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-minicpm5-2b.json", "title": "Gemma 4 31B IT vs MiniCPM5-2B", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-minicpm5-2b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-minimax-m2-5.json", "title": "Gemma 4 31B IT vs MiniMax-M2.5", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-minimax-m2-5" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-minimax-m2-7.json", "title": "Gemma 4 31B IT vs MiniMax-M2.7", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-minimax-m2-7" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-muse-glimmer-30b.json", "title": "Gemma 4 31B IT vs Muse Glimmer 30B", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-muse-glimmer-30b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "Gemma 4 31B IT vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-nano-30b-a3b.json", "title": "Gemma 4 31B IT vs Nemotron 3 Nano 30B A3B", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-nano-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-nano-4b.json", "title": "Gemma 4 31B IT vs Nemotron 3 Nano 4B", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-nano-4b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json", "title": "Gemma 4 31B IT vs Nemotron 3 Nano Omni 30B A3B Reasoning", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-nano-omni-30b-a3b-reasoning" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-super-120b-a12b.json", "title": "Gemma 4 31B IT vs Nemotron 3 Super 120B A12B", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-super-120b-a12b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-ultra-550b-a55b.json", "title": "Gemma 4 31B IT vs Nemotron 3 Ultra 550B A55B", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-ultra-550b-a55b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-occamy-1-0.json", "title": "Gemma 4 31B IT vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-olmo-3-7b-think.json", "title": "Gemma 4 31B IT vs Olmo 3 7B Think", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-olmo-3-7b-think" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ornith-1-5-35b-a3b.json", "title": "Gemma 4 31B IT vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ornith-1-5-397b.json", "title": "Gemma 4 31B IT vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ornith-1-5-9b.json", "title": "Gemma 4 31B IT vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-30b-a3b-instruct-2507.json", "title": "Gemma 4 31B IT vs Qwen3-30B-A3B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-30b-a3b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-4b-instruct-2507.json", "title": "Gemma 4 31B IT vs Qwen3-4B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-4b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-5-0-8b.json", "title": "Gemma 4 31B IT vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-5-122b-a10b.json", "title": "Gemma 4 31B IT vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-5-2b.json", "title": "Gemma 4 31B IT vs Qwen3.5-2B", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-5-2b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-5-397b-a17b.json", "title": "Gemma 4 31B IT vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-5-4b.json", "title": "Gemma 4 31B IT vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-5-9b.json", "title": "Gemma 4 31B IT vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-6-27b.json", "title": "Gemma 4 31B IT vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-6-35b-a3b.json", "title": "Gemma 4 31B IT vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-8-27b.json", "title": "Gemma 4 31B IT vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-8-flash-next.json", "title": "Gemma 4 31B IT vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Gemma 4 31B IT vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-coder-next.json", "title": "Gemma 4 31B IT vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Gemma 4 31B IT vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-vl-2b-instruct.json", "title": "Gemma 4 31B IT vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Gemma 4 31B IT vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-vl-32b-instruct.json", "title": "Gemma 4 31B IT vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-vl-4b-instruct.json", "title": "Gemma 4 31B IT vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-vl-8b-instruct.json", "title": "Gemma 4 31B IT vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwopus3-8-27b-flash-v2.json", "title": "Gemma 4 31B IT vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-smollm3-3b.json", "title": "Gemma 4 31B IT vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-spark-x2-6-4b.json", "title": "Gemma 4 31B IT vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-swift-1-5-qwen3-8-27b.json", "title": "Gemma 4 31B IT vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Gemma 4 31B IT vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-thinkingcap-qwen3-8-27b.json", "title": "Gemma 4 31B IT vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-thinkingcap-qwen3-8-27b" } ], "scores": [ { "by": 2, "edge": "gemma-4-31b-it", "gemma-4-26b-a4b-it": 59, "gemma-4-31b-it": 61, "key": "capability", "name": "Capability", "weight": 35 }, { "by": 5, "edge": "gemma-4-31b-it", "gemma-4-26b-a4b-it": 90, "gemma-4-31b-it": 95, "key": "reach", "name": "Reach", "weight": 25 }, { "by": 0, "edge": "", "gemma-4-26b-a4b-it": 70, "gemma-4-31b-it": 70, "key": "risk", "name": "Risk", "weight": 20 }, { "by": 0, "edge": "", "gemma-4-26b-a4b-it": 85, "gemma-4-31b-it": 85, "key": "interface", "name": "Agent interface", "weight": 10 }, { "by": 0, "edge": "", "gemma-4-26b-a4b-it": 65, "gemma-4-31b-it": 65, "key": "upkeep", "name": "Documentation and upkeep", "weight": 10 } ], "summary": "Gemma 4 31B IT scores 74.1 (BB) on agent readiness against Gemma 4 26B A4B IT's 72.2 (BB), and leads in 2 of 5 scored categories. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "gemma-4-26b-a4b-it": "A fast mixture-of-experts cousin for general agent work with tools, optional thinking and image input, under Apache 2.0. Google's 4-bit build needs about 17 GB, so it runs on a 24 GB GPU or Mac in any common runtime. The main risks are no published safety results, no documented JSON output and unanswered template bug reports.", "gemma-4-31b-it": "A dense multimodal cousin with documented tool calling, optional thinking and a 256K context, for general agent work and image reading on one 24 GB GPU through Google's 4-bit build. It runs in every major runtime under plain Apache 2.0. The main risk is that Google publishes no safety results and has gone quiet on the repository since July." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-gemma-4-31b-it", "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-gemma-4-31b-it.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-gemma-4-31b-it.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-gemma-4-31b-it.min.md" }, "markdown": "Gemma 4 31B IT scores 74.1 (BB) on agent readiness against Gemma 4 26B A4B IT's 72.2 (BB), and leads in 2 of 5 scored categories. Both do open-weight models you run yourself.\n\n- Gemma 4 26B A4B IT: grade BB, 72.2/100, rank #4 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/gemma-4-26b-a4b-it.md · JSON https://www.anchorterminal.com/api/v1/tools/gemma-4-26b-a4b-it.json\n- Gemma 4 31B IT: grade BB, 74.1/100, rank #2 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/gemma-4-31b-it.md · JSON https://www.anchorterminal.com/api/v1/tools/gemma-4-31b-it.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 26B A4B IT (BB)\n\nGood for: Agents on a 24 GB or larger GPU or Mac that want a fast, permissively licensed tool-calling model with images and long context\n\nWatch for: No safety evaluation figures published\n\n### Gemma 4 31B IT (BB)\n\nGood for: An agent with a 24 GB GPU or more that wants a dense multimodal cousin with tool calling under a plain licence\n\nAhead on:\n- Reach, 95 against 90\n\nWatch for: No safety evaluation results published\n\n\n## Score by category\n\n| Category | Weight | Gemma 4 26B A4B IT | Gemma 4 31B IT | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35% (35 this run) | 59 | 61 | Gemma 4 31B IT +2 |\n| Reach | 25% (25 this run) | 90 | 95 | Gemma 4 31B IT +5 |\n| Risk | 20% (20 this run) | 70 | 70 | even |\n| Agent interface | 10% (10 this run) | 85 | 85 | even |\n| Documentation and upkeep | 10% (10 this run) | 65 | 65 | even |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **72.2 · BB** | **74.1 · BB** | |\n\n## Facts side by side\n\n| Fact | Gemma 4 26B A4B IT | Gemma 4 31B IT |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Google DeepMind | Google DeepMind |\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 | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | yes |\n| Last release | 2026-07-20 | 2026-07-20 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models | | |\n| Terms restrict automated access | | |\n| Terms restrict benchmarking | | |\n| Terms or service can change without notice | | |\n| Arbitration or class-action waiver | | |\n| Agent reviews | 3.8/5 (4) | 3.8/5 (4) |\n\n## Verdicts\n\n**Gemma 4 26B A4B IT.** A fast mixture-of-experts cousin for general agent work with tools, optional thinking and image input, under Apache 2.0. Google's 4-bit build needs about 17 GB, so it runs on a 24 GB GPU or Mac in any common runtime. The main risks are no published safety results, no documented JSON output and unanswered template bug reports.\n\n**Gemma 4 31B IT.** A dense multimodal cousin with documented tool calling, optional thinking and a 256K context, for general agent work and image reading on one 24 GB GPU through Google's 4-bit build. It runs in every major runtime under plain Apache 2.0. The main risk is that Google publishes no safety results and has gone quiet on the repository since July.\n\n## Before you call either\n\n### Gemma 4 26B A4B IT\n\n1. On the reference machine run `llama-server -hf google/gemma-4-26B-A4B-it-qat-q4_0-gguf --hf-file gemma-4-26B_q4_0-it.gguf --jinja -c 65536`, and add the mmproj file for images\n2. With Ollama run `ollama run gemma4:26b` and raise num_ctx or OLLAMA_CONTEXT_LENGTH, since Google's guide says it defaults to 4K below 24 GiB of VRAM 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\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. Validate tool names and arguments before running them, and check any JSON yourself\n\n### Gemma 4 31B IT\n\n1. On the reference machine run `llama-server -hf google/gemma-4-31B-it-qat-q4_0-gguf --hf-file gemma-4-31B_q4_0-it.gguf --jinja -c 32768`, adding gemma-4-31B-it-mmproj.gguf for images\n2. Or `ollama run gemma4:31b` for the 20 GB Q4_K_M build\n3. In vLLM serve with `--enable-auto-tool-choice --tool-call-parser gemma4 --reasoning-parser gemma4`, as vLLM's recipe does\n4. Thinking is off by default, so pass `enable_thinking: true` in chat_template_kwargs to turn it on, and expect the reasoning in the thought channel block\n5. Drop earlier thoughts from the history except on tool-call turns, as the card asks\n6. Use the card's sampling, temperature 1.0, top_p 0.95, top_k 64\n7. Validate function names and arguments before running a call, and check JSON output yourself or use the runtime's guided decoding\n8. The w4a16 build relies on Marlin INT4 kernels that vLLM marks unsupported on AMD, so use the GGUF on the reference machine\n\n## Questions\n\n### Which is better for AI agents, Gemma 4 26B A4B IT or Gemma 4 31B IT?\n\nGemma 4 31B IT scores 74.1 (BB) on agent readiness against Gemma 4 26B A4B IT's 72.2 (BB), and leads in 2 of 5 scored categories.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-gemma-4-31b-it.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-gemma-4-31b-it.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"gemma-4-26b-a4b-it\", \"b\": \"gemma-4-31b-it\"}`. 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"page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "Gemma 4 26B A4B IT vs Gemma 4 31B IT", "url": "" } ], "description": "Gemma 4 31B IT scores 74.1 (BB) to Gemma 4 26B A4B IT's 72.2 (BB) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "Gemma 4 26B A4B IT BB 72.2", "Gemma 4 31B IT BB 74.1", "scores" ], "h1": "Gemma 4 26B A4B IT vs Gemma 4 31B IT", "image": "https://www.anchorterminal.com/assets/og/compare-gemma-4-26b-a4b-it-vs-gemma-4-31b-it.png", "path": "/compare/gemma-4-26b-a4b-it-vs-gemma-4-31b-it", "published": "2026-10-01", "section": "tools", "title": "Gemma 4 26B A4B IT vs Gemma 4 31B IT for AI agents (2026)", "toc": null, "updated": "2026-10-21", "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-gemma-4-31b-it" }, "tokens": { "markdown": 6200, "slim": 380 }, "version": 1 }