{ "data": { "a": { "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" }, "answer": "Gemma 4 31B IT scores 74.1 (BB) on agent readiness against gpt-oss-20b's 69.9 (B), and leads in 3 of 5 scored categories. gpt-oss-20b leads on risk and agent interface.", "b": { "slug": "gpt-oss-20b", "name": "gpt-oss-20b", "vendor": "OpenAI", "vendorUrl": "https://openai.com", "kind": "weights", "category": "open-weights", "summary": "gpt-oss-20b is OpenAI's open-weight mixture-of-experts reasoning model, 21B parameters with 3.6B active and a 131,072-token context, published on Hugging Face in MXFP4 under Apache 2.0 and trained on the harmony prompt format.", "url": "https://www.anchorterminal.com/tools/gpt-oss-20b", "markdownUrl": "https://www.anchorterminal.com/tools/gpt-oss-20b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/gpt-oss-20b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/gpt-oss-20b.json", "repo": "https://huggingface.co/openai/gpt-oss-20b", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "openai/gpt-oss-20b" }, { "registry": "ollama", "name": "gpt-oss:20b" }, { "registry": "pypi", "name": "gpt-oss" }, { "registry": "pypi", "name": "openai-harmony" } ], "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 with a one-line usage policy that asks users to comply with applicable law. Running it yourself costs the hardware, about 13.8 GB of weights, and OpenAI says it runs within 16 GB of memory (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/openai/gpt-oss-20b", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "reasoning", "tool-calling", "apache-2.0", "safetensors", "mxfp4", "self-hosted" ], "lastRelease": "2025-08-26", "graded": true, "anchor": { "graded": true, "score": 69.9, "grade": "B", "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": 6, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 49, "interface": 90, "reach": 90, "risk": 85, "upkeep": 42 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "gpt-oss-20b is a cousin for reasoning, coding and single tool calls with structured output, under plain Apache 2.0. It needs about 17 GB with a working context, and five inference providers serve it. The main risks are that it trails o4-mini at keeping system instructions above user ones, and OpenAI hasn't answered a report since August 2025.", "bestFor": "Local agents on a 24 GB GPU or Mac that want a permissive licence, wide runtime support and documented structured output, one tool call at a time", "strengths": [ "Model code in released Transformers, vLLM, SGLang and llama.cpp, plus a 14 GB build in Ollama's library", "Fits a 24 GB machine at OpenAI's own MXFP4 precision, the one every evaluation used", "Apache 2.0 with no gate, no account and a usage policy that only asks users to follow the law", "Harmony guide documents tool declarations, call parsing, JSON Schema output and three reasoning levels, with OpenAI's own parser", "Trained on an instruction hierarchy against prompt injection, with OpenAI's results for it published" ], "weaknesses": [ "No reply from OpenAI to any discussion since August 2025, and template reports wait unanswered", "One tool call per message", "Reasoning can be lowered but not switched off", "Only one quantised build from OpenAI, and its weights and template haven't changed since August 2025", "Safety results for this size are OpenAI's own, and it trails o4-mini on the instruction hierarchy" ], "agentNotes": [ "Start it with `ollama run gpt-oss:20b` (the build that fits the reference machine), or `vllm serve openai/gpt-oss-20b` with a current vLLM release rather than the card's pinned vllm==0.10.1+gptoss wheel", "Use the harmony format through the chat template or openai-harmony; OpenAI says the model doesn't work correctly without it", "Parse tool calls and reasoning with openai-harmony; calls come on the commentary channel ending in `\u003c|call|\u003e`, reasoning on the analysis channel", "Set effort with `Reasoning: high` (or low, medium) in the system message, or `reasoning_effort` in the template; medium is the default", "Ask for JSON by putting a JSON Schema under `# Response Formats` in the developer message", "Drop earlier reasoning after a final answer but pass it back after tool calls, and don't show the analysis channel to users", "Expect one tool call per turn; run several in sequence rather than in parallel" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3.5, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "B", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 69.9 } ], "editorialScores": { "capability": 49, "interface": 90, "reach": 90, "risk": 85, "upkeep": 42 }, "provenanceScore": 87, "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 gpt-oss:20b\n# or with vLLM:\nvllm serve openai/gpt-oss-20b", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"gpt-oss:20b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/gpt-oss-20b" }, "sameCompany": [ "openai-api", "openai-embeddings", "openai-guardrails", "openai-moderation", "openai-image-api", "openai-sora", "openai-speech-to-text", "gpt-live", "openai-realtime", "openai-agents-sdk", "openai-decisions-api", "openai-codex", "gpt-oss-120b" ], "weights": { "trainedBy": "OpenAI", "params": "20.91B", "active": "3.61B", "context": 131072, "goodFor": "Text reasoning model for agents and coding, with function calling, built-in browser and Python tools and low, medium or high reasoning, in the harmony format.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "gpt-oss-20b", "url": "https://huggingface.co/openai/gpt-oss-20b", "who": "publisher", "precision": "mxfp4", "sizeGB": 13.8, "memoryGB": 24, "format": "safetensors", "note": "MoE weights in MXFP4, the rest BF16, the precision OpenAI evaluated. original/ and metal/ hold the same weights for OpenAI's reference code. vLLM's gpt-oss recipe lists MI300x, MI325x, MI355x and Radeon AI PRO R9700 on AMD, not gfx1151" }, { "name": "ollama gpt-oss:20b", "url": "https://ollama.com/library/gpt-oss:20b", "who": "ollama", "precision": "mxfp4", "sizeGB": 14, "memoryGB": 24, "format": "ollama", "runsOn": [ "Ollama" ], "note": "OpenAI's MXFP4 weights; the card links it. Size as Ollama's tags page shows it, rounded to whole GB above 10 GB; the Linux build is the llama.cpp variant" } ], "run": "ollama run gpt-oss:20b", "runBuild": "ollama gpt-oss:20b", "checked": "2026-10-21", "minMemoryGB": 24 }, "family": { "lab": "OpenAI", "family": "gpt-oss", "url": "https://www.anchorterminal.com/open-weights/#openai", "others": [ { "name": "gpt-oss-120b", "url": "https://huggingface.co/openai/gpt-oss-120b", "params": "117B total, 5.1B active (MoE)", "listing": "https://www.anchorterminal.com/tools/gpt-oss-120b" }, { "name": "gpt-oss-safeguard-120b", "url": "https://huggingface.co/openai/gpt-oss-safeguard-120b", "params": "117B total, 5.1B active (MoE)" }, { "name": "gpt-oss-safeguard-20b", "url": "https://huggingface.co/openai/gpt-oss-safeguard-20b", "params": "21B total, 3.6B active (MoE)" } ], "olderCount": 6, "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "OpenAI (named in USAGE_POLICY and the model card; the LICENSE file's copyright line is the unfilled Apache template)", "domain": "openai.com", "domainRegistered": "2007-01-19", "domainNote": "openai.com was registered in 2007, years before the company was founded in 2015, so domain age flatters it.", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/openai/gpt-oss-20b/commits/main", "securityTxt": "valid", "checked": "2026-10-21", "notes": [ "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "The changelog is the repository's commit history, whose messages say what changed for some commits (the chat template on 7 and 8 August 2025, `\u003c|call|\u003e` as an end token on 13 August 2025) and not for others (README updates).", "openai.com/.well-known/security.txt answered 200, signed, with a Bugcrowd contact and a coordinated disclosure policy, but no Expires field, which RFC 9116 requires. Recorded as valid, as on the openai-api listing, since the data has no value for a file without one.", "openai.com/index/introducing-gpt-oss/ and openai.com/open-models/ answered 403 to our reader on 21 October 2026, so OpenAI's announcement wasn't read." ], "score": 87 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/gpt-oss-20b.json" }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Google DeepMind", "b": "OpenAI", "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": "yes", "b": "no", "name": "llms.txt" }, { "a": "2026-07-20", "b": "2025-08-26", "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": "3.5/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Gemma 4 31B IT scores 74.1 (BB) on agent readiness against gpt-oss-20b's 69.9 (B), and leads in 3 of 5 scored categories. gpt-oss-20b leads on risk and agent interface.", "question": "Which is better for AI agents, Gemma 4 31B IT or gpt-oss-20b?" } ], "goodFor": [ { "aheadOn": [ "Capability, 61 against 49", "Reach, 95 against 90", "Documentation and upkeep, 65 against 42" ], "also": [ 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"https://www.anchorterminal.com/compare/agents-a1-4b-vs-gpt-oss-20b.json", "title": "Agents-A1-4B vs gpt-oss-20b", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gpt-oss-20b" }, { "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/apertus-v1-5-8b-vs-gpt-oss-20b.json", "title": "Apertus v1.5 8B vs gpt-oss-20b", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gpt-oss-20b" }, { "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/apodex-1-1-mini-vs-gpt-oss-20b.json", "title": "Apodex-1.1-mini vs gpt-oss-20b", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gpt-oss-20b" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-gemma-4-31b-it.json", "title": "BigBang-v1 vs Gemma 4 31B IT", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-gemma-4-31b-it" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-gpt-oss-20b.json", "title": "BigBang-v1 vs gpt-oss-20b", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-gpt-oss-20b" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-gemma-4-31b-it.json", "title": "Cosmos-Reason2-2B vs Gemma 4 31B IT", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-gemma-4-31b-it" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-gpt-oss-20b.json", "title": "Cosmos-Reason2-2B vs gpt-oss-20b", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-gpt-oss-20b" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-gemma-4-31b-it.json", "title": "Darwin-36B-Opus vs Gemma 4 31B IT", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-gemma-4-31b-it" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-gpt-oss-20b.json", "title": "Darwin-36B-Opus vs gpt-oss-20b", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-gpt-oss-20b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gemma-4-31b-it.json", "title": "DeepSeek-V3.2 vs Gemma 4 31B IT", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gemma-4-31b-it" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gpt-oss-20b.json", "title": "DeepSeek-V3.2 vs gpt-oss-20b", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gpt-oss-20b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gemma-4-31b-it.json", "title": "DeepSeek V4.1 Flash vs Gemma 4 31B IT", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gemma-4-31b-it" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gpt-oss-20b.json", "title": "DeepSeek V4.1 Flash vs gpt-oss-20b", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gpt-oss-20b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-gemma-4-31b-it.json", "title": "DeepSeek-V4-Flash-0731 vs Gemma 4 31B IT", "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-gemma-4-31b-it" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-gpt-oss-20b.json", "title": "DeepSeek-V4-Flash-0731 vs gpt-oss-20b", "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-gpt-oss-20b" }, { "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-gemma-4-31b-it.json", "title": "DiffusionGemma 26B A4B IT vs Gemma 4 31B IT", "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-gemma-4-31b-it" }, { "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-gpt-oss-20b.json", "title": "DiffusionGemma 26B A4B IT vs gpt-oss-20b", "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-gpt-oss-20b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-gemma-4-31b-it.json", "title": "Gemma 4 12B IT vs Gemma 4 31B IT", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-gemma-4-31b-it" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-gpt-oss-20b.json", "title": "Gemma 4 12B IT vs gpt-oss-20b", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-gpt-oss-20b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-gemma-4-31b-it.json", "title": "Gemma 4 26B A4B IT vs Gemma 4 31B IT", "url": "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-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-4-31b-it-vs-gemma-4-e2b-it.json", "title": "Gemma 4 31B IT vs Gemma 4 E2B IT", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-gemma-4-e2b-it" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-gemma-4-e4b-it.json", "title": "Gemma 4 31B IT vs Gemma 4 E4B IT", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-gemma-4-e4b-it" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-glm-4-5-air.json", "title": "Gemma 4 31B IT vs GLM-4.5-Air", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-glm-4-5-air" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-glm-4-7-flash.json", "title": "Gemma 4 31B IT vs GLM-4.7-Flash", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-glm-4-7-flash" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-glm-5-3.json", "title": "Gemma 4 31B IT vs GLM-5.3", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-glm-5-3" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-glm-5-3-flash.json", "title": "Gemma 4 31B IT vs GLM-5.3-Flash", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-glm-5-3-flash" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-gpt-oss-120b.json", "title": "Gemma 4 31B IT vs gpt-oss-120b", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-gpt-oss-120b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-granite-4-2-3b.json", "title": "Gemma 4 31B IT vs Granite 4.2 3B", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-granite-4-2-3b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-hy3.json", "title": "Gemma 4 31B IT vs Hy3", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-hy3" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-inkling.json", "title": "Gemma 4 31B IT vs Inkling", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-inkling" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-inkling-small.json", "title": "Gemma 4 31B IT vs Inkling-Small", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-inkling-small" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-internvl3-5-gpt-oss-20b-a4b-preview.json", "title": "Gemma 4 31B IT vs InternVL3.5-GPT-OSS-20B-A4B-Preview", "url": "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": 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}, { "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": 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"https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwopus3-8-27b-flash-v2.json", "title": "gpt-oss-20b vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-smollm3-3b.json", "title": "gpt-oss-20b vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-spark-x2-6-4b.json", "title": "gpt-oss-20b vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-swift-1-5-qwen3-8-27b.json", "title": "gpt-oss-20b vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "gpt-oss-20b vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-thinkingcap-qwen3-8-27b.json", "title": "gpt-oss-20b vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-thinkingcap-qwen3-8-27b" } ], "scores": [ { "by": 12, "edge": "gemma-4-31b-it", "gemma-4-31b-it": 61, "gpt-oss-20b": 49, "key": "capability", "name": "Capability", "weight": 35 }, { "by": 5, "edge": "gemma-4-31b-it", "gemma-4-31b-it": 95, "gpt-oss-20b": 90, "key": "reach", "name": "Reach", "weight": 25 }, { "by": 15, "edge": "gpt-oss-20b", "gemma-4-31b-it": 70, "gpt-oss-20b": 85, "key": "risk", "name": "Risk", "weight": 20 }, { "by": 5, "edge": "gpt-oss-20b", "gemma-4-31b-it": 85, "gpt-oss-20b": 90, "key": "interface", "name": "Agent interface", "weight": 10 }, { "by": 23, "edge": "gemma-4-31b-it", "gemma-4-31b-it": 65, "gpt-oss-20b": 42, "key": "upkeep", "name": "Documentation and upkeep", "weight": 10 } ], "summary": "Gemma 4 31B IT scores 74.1 (BB) on agent readiness against gpt-oss-20b's 69.9 (B), and leads in 3 of 5 scored categories. gpt-oss-20b leads on risk and agent interface. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "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.", "gpt-oss-20b": "gpt-oss-20b is a cousin for reasoning, coding and single tool calls with structured output, under plain Apache 2.0. It needs about 17 GB with a working context, and five inference providers serve it. The main risks are that it trails o4-mini at keeping system instructions above user ones, and OpenAI hasn't answered a report since August 2025." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-gpt-oss-20b", "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-gpt-oss-20b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-gpt-oss-20b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-gpt-oss-20b.min.md" }, "markdown": "Gemma 4 31B IT scores 74.1 (BB) on agent readiness against gpt-oss-20b's 69.9 (B), and leads in 3 of 5 scored categories. gpt-oss-20b leads on risk and agent interface. Both do open-weight models you run yourself.\n\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- gpt-oss-20b: grade B, 69.9/100, rank #8 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/gpt-oss-20b.md · JSON https://www.anchorterminal.com/api/v1/tools/gpt-oss-20b.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 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- Capability, 61 against 49\n- Reach, 95 against 90\n- Documentation and upkeep, 65 against 42\n\nAlso in its favour:\n- Agent-ready, a grade of BB or better\n\nWatch for: No safety evaluation results published\n\n### gpt-oss-20b (B)\n\nGood for: Local agents on a 24 GB GPU or Mac that want a permissive licence, wide runtime support and documented structured output, one tool call at a time\n\nAhead on:\n- Risk, 85 against 70\n- Agent interface, 90 against 85\n\nWatch for: No reply from OpenAI to any discussion since August 2025, and template reports wait unanswered\n\n\n## Score by category\n\n| Category | Weight | Gemma 4 31B IT | gpt-oss-20b | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35% (35 this run) | 61 | 49 | Gemma 4 31B IT +12 |\n| Reach | 25% (25 this run) | 95 | 90 | Gemma 4 31B IT +5 |\n| Risk | 20% (20 this run) | 70 | 85 | gpt-oss-20b +15 |\n| Agent interface | 10% (10 this run) | 85 | 90 | gpt-oss-20b +5 |\n| Documentation and upkeep | 10% (10 this run) | 65 | 42 | Gemma 4 31B IT +23 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **74.1 · BB** | **69.9 · B** | |\n\n## Facts side by side\n\n| Fact | Gemma 4 31B IT | gpt-oss-20b |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Google DeepMind | OpenAI |\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 | yes | no |\n| Last release | 2026-07-20 | 2025-08-26 |\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) | 3.5/5 (4) |\n\n## Verdicts\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**gpt-oss-20b.** gpt-oss-20b is a cousin for reasoning, coding and single tool calls with structured output, under plain Apache 2.0. It needs about 17 GB with a working context, and five inference providers serve it. The main risks are that it trails o4-mini at keeping system instructions above user ones, and OpenAI hasn't answered a report since August 2025.\n\n## Before you call either\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### gpt-oss-20b\n\n1. Start it with `ollama run gpt-oss:20b` (the build that fits the reference machine), or `vllm serve openai/gpt-oss-20b` with a current vLLM release rather than the card's pinned vllm==0.10.1+gptoss wheel\n2. Use the harmony format through the chat template or openai-harmony; OpenAI says the model doesn't work correctly without it\n3. Parse tool calls and reasoning with openai-harmony; calls come on the commentary channel ending in `\u003c|call|\u003e`, reasoning on the analysis channel\n4. Set effort with `Reasoning: high` (or low, medium) in the system message, or `reasoning_effort` in the template; medium is the default\n5. Ask for JSON by putting a JSON Schema under `# Response Formats` in the developer message\n6. Drop earlier reasoning after a final answer but pass it back after tool calls, and don't show the analysis channel to users\n7. Expect one tool call per turn; run several in sequence rather than in parallel\n\n## Questions\n\n### Which is better for AI agents, Gemma 4 31B IT or gpt-oss-20b?\n\nGemma 4 31B IT scores 74.1 (BB) on agent readiness against gpt-oss-20b's 69.9 (B), and leads in 3 of 5 scored categories. gpt-oss-20b leads on risk and agent interface.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-gpt-oss-20b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-gpt-oss-20b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"gemma-4-31b-it\", \"b\": \"gpt-oss-20b\"}`. 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27B)](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-thinkingcap-qwen3-8-27b.md)\n", "meta": { "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 31B IT vs gpt-oss-20b", "url": "" } ], "description": "Gemma 4 31B IT scores 74.1 (BB) to gpt-oss-20b's 69.9 (B) for open-weight models you run yourself. 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