{ "data": { "a": { "slug": "gemma-4-12b-it", "name": "Gemma 4 12B IT", "vendor": "Google DeepMind", "vendorUrl": "https://gemma.ai", "kind": "weights", "category": "open-weights", "summary": "Gemma 4 12B IT is Google DeepMind's open-weight instruction-tuned dense model, 11.95B parameters in an encoder-free design, taking text, images, video frames and audio with a 262,144-token context, published on Hugging Face under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/gemma-4-12b-it", "markdownUrl": "https://www.anchorterminal.com/tools/gemma-4-12b-it.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/gemma-4-12b-it.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/gemma-4-12b-it.json", "repo": "https://huggingface.co/google/gemma-4-12B-it", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "google/gemma-4-12B-it" }, { "registry": "huggingface", "name": "google/gemma-4-12B-it-qat-q4_0-gguf" }, { "registry": "huggingface", "name": "google/gemma-4-12B-it-qat-w4a16-ct" }, { "registry": "ollama", "name": "gemma4:12b" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository and Google's QAT repositories are not gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. No hosted version, since the Gemini API serves only the 26B A4B and 31B sizes and Hugging Face lists no inference provider for it. Running it yourself costs the hardware, 26.7 GB at BF16, 13.4 GB at SFP8 and 6.7 GB at Q4_0 to load the weights per Google (checked 2026-10-21).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-21" }, "docsUrl": "https://gemma.ai/models/gemma-4/", "llmsTxt": "https://gemma.ai/llms.txt", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "multimodal", "audio", "reasoning", "tool-calling", "apache-2.0", "safetensors", "gguf", "qat", "self-hosted" ], "lastRelease": "2026-07-20", "graded": true, "anchor": { "graded": true, "score": 71.3, "grade": "BB", "agentReady": true, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1197, "categoryRank": 3, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 50, "interface": 85, "reach": 95, "risk": 70, "upkeep": 75 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "A mid-size cousin for agent work with tools, optional thinking and image and audio input, under Apache 2.0. Google's 4-bit build needs under 9 GB with working space, so it runs on a 12 GB GPU or any recent Mac in all the common runtimes. The main risks are no published safety results and no documented JSON output.", "bestFor": "Agents on a 12 GB or larger GPU or Mac that want a permissively licensed tool-calling model with images, audio and long context", "strengths": [ "Plain Apache 2.0, no account or gate, safetensors and GGUF only with a checksum on every file", "Google's own 4-bit QAT GGUF needs about 8.6 GB with working space", "Released support in vLLM, SGLang, llama.cpp, Ollama, LM Studio and Transformers", "Tool declaration and call format documented by Google, with parallel calls and a shipped response parser", "262,144 tokens of native context and thinking that turns on or off per request", "Takes audio as well as images and video frames" ], "weaknesses": [ "No safety evaluation figures published", "No documented way to ask for JSON or schema output", "No hosted version, since the Gemini API serves only the two largest sizes", "Training data described only by type and cut-off", "Files unchanged since 20 July 2026, and a startup crash report waits without a reply", "A newer, separate architecture (gemma4_unified) that needs Transformers 5.10.0 or later" ], "agentNotes": [ "On the reference machine run `llama-server -hf google/gemma-4-12B-it-qat-q4_0-gguf --hf-file gemma-4-12b-it-qat-q4_0.gguf --jinja -c 131072`, and add `--mmproj` with mmproj-gemma-4-12b-it-qat-q4_0.gguf for images and audio", "With Ollama run `ollama run gemma4:12b` and raise num_ctx or OLLAMA_CONTEXT_LENGTH, since the default context is small and agents want at least 64K", "In vLLM or SGLang pass `--tool-call-parser gemma4 --reasoning-parser gemma4` (vLLM also `--enable-auto-tool-choice`), or tool calls come back as text; Google's w4a16 build is the 4-bit route there", "Thinking is off by default; pass enable_thinking=True or start the system prompt with \u003c|think|\u003e, and drop earlier turns' thinking from history except on tool-call turns", "Put images before the text and audio after it, keep audio to 30 seconds and video to 60 frames, and validate tool names and arguments before running them", "Transformers needs 5.10.0 or later for gemma4_unified, and mlx-lm has no gemma4_unified code, so on a Mac use mlx-vlm, LM Studio or Ollama" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3.8, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "BB", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 71.3 } ], "editorialScores": { "capability": 50, "interface": 85, "reach": 95, "risk": 70, "upkeep": 75 }, "provenanceScore": 68, "checked": { "categories": { "capability": "2026-10-21", "interface": "2026-10-21", "reach": "2026-10-21", "risk": "2026-10-21", "upkeep": "2026-10-21" }, "last": "2026-10-21", "oldest": "2026-10-21", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "ollama run gemma4:12b\n# or, with llama.cpp and Google's QAT build:\nllama-server -hf google/gemma-4-12B-it-qat-q4_0-gguf --hf-file gemma-4-12b-it-qat-q4_0.gguf --jinja -c 131072", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"gemma4:12b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}], \"options\": {\"num_ctx\": 65536}}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/gemma-4-12b-it" }, "sameCompany": [ "embeddinggemma-300m", "diffusiongemma-26b-a4b-it", "gemma-4-26b-a4b-it", "gemma-4-31b-it", "gemma-4-e2b-it", "gemma-4-e4b-it" ], "weights": { "trainedBy": "Google DeepMind", "builtOn": "google/gemma-4-12B", "params": "11.95B", "context": 262144, "goodFor": "Mid-size dense assistant and agent model for a laptop GPU, with tool calling and optional thinking, taking text, images, video frames and audio without separate encoders, and a 256K context.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "LM Studio", "Transformers" ], "builds": [ { "name": "gemma-4-12b-it-qat-q4_0", "url": "https://huggingface.co/google/gemma-4-12B-it-qat-q4_0-gguf/blob/main/gemma-4-12b-it-qat-q4_0.gguf", "who": "publisher", "precision": "q4_0", "sizeGB": 7.15, "memoryGB": 12, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)", "LM Studio" ], "note": "Quantisation-aware trained by Google. Size is the repository's two files summed, the 6.98 GB model and the 0.18 GB projector (mmproj-gemma-4-12b-it-qat-q4_0.gguf) for image and audio input" }, { "name": "ollama gemma4:12b", "url": "https://ollama.com/library/gemma4:12b", "who": "ollama", "precision": "q4_k_m", "sizeGB": 8, "memoryGB": 12, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Q4_K_M weights (7.4 GB) with a BF16 projector (175 MB) and a Q8_0 draft model (465 MB) (digest 6114515d63c1), the llama.cpp variant. gemma4:12b-it-qat (7.2 GB) is Ollama's copy of Google's Q4_0 QAT build" }, { "name": "lmstudio google/gemma-4-12b", "url": "https://lmstudio.ai/models/google/gemma-4-12b", "who": "lmstudio", "precision": "q4_k_m", "sizeGB": 7.4, "memoryGB": 12, "format": "gguf", "runsOn": [ "LM Studio" ], "note": "LM Studio's catalogue build, from lmstudio-community/gemma-4-12B-it-GGUF, with MLX builds at 4 to 8 bits beside it. Size as the catalogue shows it, and Q4_K_M is inferred from that repository's files rather than stated by LM Studio" }, { "name": "gemma-4-12B-it-qat-w4a16-ct", "url": "https://huggingface.co/google/gemma-4-12B-it-qat-w4a16-ct", "who": "publisher", "precision": "w4a16", "sizeGB": 10.3, "memoryGB": 16, "format": "safetensors", "note": "Quantisation-aware trained, 4-bit weights with 16-bit activations in compressed-tensors format, which Google routes to vLLM and SGLang" }, { "name": "gemma-4-12B-it", "url": "https://huggingface.co/google/gemma-4-12B-it", "who": "publisher", "precision": "bf16", "sizeGB": 23.95, "memoryGB": 32, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] } ], "run": "llama-server -hf google/gemma-4-12B-it-qat-q4_0-gguf --hf-file gemma-4-12b-it-qat-q4_0.gguf --jinja -c 131072", "runBuild": "gemma-4-12b-it-qat-q4_0", "checked": "2026-10-21", "minMemoryGB": 12 }, "family": { "lab": "Google (Gemma)", "family": "Gemma 4", "url": "https://www.anchorterminal.com/open-weights/#google", "others": [ { "name": "gemma-4-26B-A4B", "url": "https://huggingface.co/google/gemma-4-26B-A4B", "params": "25.2B total, 3.8B active" }, { "name": "gemma-4-26B-A4B-it", "url": "https://huggingface.co/google/gemma-4-26B-A4B-it", "params": "25.2B total, 3.8B active", "listing": "https://www.anchorterminal.com/tools/gemma-4-26b-a4b-it" }, { "name": "gemma-4-31B", "url": "https://huggingface.co/google/gemma-4-31B", "params": "30.7B" }, { "name": "gemma-4-31B-it", "url": "https://huggingface.co/google/gemma-4-31B-it", "params": "30.7B", "listing": "https://www.anchorterminal.com/tools/gemma-4-31b-it" }, { "name": "gemma-4-E2B", "url": "https://huggingface.co/google/gemma-4-E2B", "params": "2.3B effective (5.1B with embeddings)" }, { "name": "gemma-4-E2B-it", "url": "https://huggingface.co/google/gemma-4-E2B-it", "params": "2.3B effective (5.1B with embeddings)", "listing": "https://www.anchorterminal.com/tools/gemma-4-e2b-it" }, { "name": "gemma-4-E4B", "url": "https://huggingface.co/google/gemma-4-E4B", "params": "4.5B effective (8B with embeddings)" }, { "name": "gemma-4-E4B-it", "url": "https://huggingface.co/google/gemma-4-E4B-it", "params": "4.5B effective (8B with embeddings)", "listing": "https://www.anchorterminal.com/tools/gemma-4-e4b-it" }, { "name": "gemma-4-26B-A4B-it-assistant", "url": "https://huggingface.co/google/gemma-4-26B-A4B-it-assistant", "params": "420M (file metadata on Hugging Face; drafter)" }, { "name": "gemma-4-31B-it-assistant", "url": "https://huggingface.co/google/gemma-4-31B-it-assistant", "params": "470M (file metadata on Hugging Face; drafter)" }, { "name": "gemma-4-E2B-it-assistant", "url": "https://huggingface.co/google/gemma-4-E2B-it-assistant", "params": "78M (file metadata on Hugging Face; drafter)" }, { "name": "gemma-4-E4B-it-assistant", "url": "https://huggingface.co/google/gemma-4-E4B-it-assistant", "params": "79M (file metadata on Hugging Face; drafter)" }, { "name": "gemma-4-12B", "url": "https://huggingface.co/google/gemma-4-12B", "params": "11.95B" }, { "name": "gemma-4-12B-it-assistant", "url": "https://huggingface.co/google/gemma-4-12B-it-assistant", "params": "423M (file metadata on Hugging Face; drafter)" }, { "name": "diffusiongemma-26B-A4B-it", "url": "https://huggingface.co/google/diffusiongemma-26B-A4B-it", "params": "25.2B total, 3.8B active", "listing": "https://www.anchorterminal.com/tools/diffusiongemma-26b-a4b-it" }, { "name": "embeddinggemma-2", "url": "https://huggingface.co/google/embeddinggemma-2", "params": "740M" } ], "olderCount": 171, "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "Google LLC (RDAP registrant of gemma.ai as recorded for the first batch's Gemma 4 listings; the card names Google DeepMind as author)", "domain": "gemma.ai", "domainRegistered": "2017-12-16", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://gemma.ai/models/releases/", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The registrant and registration date are carried from the gemma-4-26b-a4b-it and gemma-4-31b-it facts read earlier on 21 October 2026, since rdap.org rate-limited our request this time and wasn't retried.", "Nothing is hosted under this listing, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "gemma.ai keeps a dated releases page for the Gemma family (this size on 3 June 2026). The Hugging Face commit history says what each change fixed.", "gemma.ai/.well-known/security.txt answers 404. Google's company-wide security.txt at google.com points to its vulnerability reward programme." ], "score": 68 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/gemma-4-12b-it.json" }, "answer": "Gemma 4 12B IT scores 71.3 (BB) on agent readiness against Qwen3.5-122B-A10B's 63.6 (B), and leads in 4 of 5 scored categories. Qwen3.5-122B-A10B leads on capability.", "b": { "slug": "qwen3-5-122b-a10b", "name": "Qwen3.5-122B-A10B", "vendor": "Alibaba (Qwen team)", "vendorUrl": "https://qwen.ai", "kind": "weights", "category": "open-weights", "summary": "Qwen3.5-122B-A10B is Alibaba's mid-size open-weight mixture-of-experts vision-language model, 122B parameters with 10B active, thinking on by default and a 262,144-token context, published on Hugging Face in BF16, FP8 and GPTQ 4-bit under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/qwen3-5-122b-a10b", "markdownUrl": "https://www.anchorterminal.com/tools/qwen3-5-122b-a10b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/qwen3-5-122b-a10b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/qwen3-5-122b-a10b.json", "repo": "https://huggingface.co/Qwen/Qwen3.5-122B-A10B", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "Qwen/Qwen3.5-122B-A10B" }, { "registry": "huggingface", "name": "Qwen/Qwen3.5-122B-A10B-FP8" }, { "registry": "huggingface", "name": "Qwen/Qwen3.5-122B-A10B-GPTQ-Int4" }, { "registry": "ollama", "name": "qwen3.5:122b" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. None of the three repositories is gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Alibaba Cloud Model Studio and several inference providers also serve it per token. Running it yourself costs the hardware, 250.2 GB for the BF16 files, 78.9 GB for Alibaba's GPTQ 4-bit build and 81 GB for Ollama's build (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/Qwen/Qwen3.5-122B-A10B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "multimodal", "reasoning", "tool-calling", "apache-2.0", "safetensors", "fp8", "gptq", "self-hosted" ], "lastRelease": "2026-02-24", "graded": true, "anchor": { "graded": true, "score": 63.6, "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": 18, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 62, "interface": 70, "reach": 80, "risk": 55, "upkeep": 39 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "A strong mid-size cousin for coding, agent steps and reading images or video, with thinking you can switch off per request. Alibaba's 4-bit build needs about 95 GB, which fits one 128 GB unified-memory machine, and several providers host it. The main risk is silence, with no safety results, agent guidance or answers from Alibaba.", "bestFor": "Agents with a 128 GB unified-memory machine or two large GPUs that want a capable vision-language model with a plain licence.", "strengths": [ "Released support in llama.cpp, Ollama, vLLM, SGLang and Transformers", "Alibaba's own FP8 and GPTQ 4-bit builds, the 4-bit one about 95 GB in use", "Plain Apache 2.0 with no gate, no account and no use policy", "Safetensors only, with a checksum on every file in the publisher's own organisation", "262,144 tokens native, several tool calls per turn and thinking that turns off per request", "Served by Alibaba's Model Studio and at least four other providers" ], "weaknesses": [ "Too big for a single 64 GB machine even at 4 bits", "No safety results, agent-use guidance or reporting route for flaws", "No reply from Qwen to any discussion, and a template fix waits since April", "Tool format is only in the template and structured output isn't documented", "No limitations, knowledge cut-off or training data sources on the card" ], "agentNotes": [ "Quickest start on a 128 GB machine is `ollama run qwen3.5:122b` (Q4_K_M, 81 GB of files, about 97 GB with context)", "Alibaba's 4-bit build serves with `vllm serve Qwen/Qwen3.5-122B-A10B-GPTQ-Int4 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder --quantization moe_wna16`, though that kernel isn't documented for AMD gfx1151", "Use the qwen3_coder tool parser and the qwen3 reasoning parser, as the card's commands do", "Turn thinking off with `\"chat_template_kwargs\": {\"enable_thinking\": false}` in the request body, since it's on by default and `/think` and `/nothink` aren't supported", "Sampling per the card is temperature 0.6, top_p 0.95 and top_k 20 when thinking, and temperature 0.7, top_p 0.8, top_k 20 and presence_penalty 1.5 without", "Keep the context at 128K or more if you rely on thinking, and turn on YaRN only past 262,144 tokens", "Add `--language-model-only` in vLLM to skip the vision encoder and free memory for the KV cache when you only need text", "Validate any JSON it returns, since structured output isn't documented" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "B", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 63.6 } ], "editorialScores": { "capability": 62, "interface": 70, "reach": 80, "risk": 55, "upkeep": 39 }, "provenanceScore": 49, "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 qwen3.5:122b\n# or, with the card's vLLM command for Alibaba's 4-bit build:\nvllm serve Qwen/Qwen3.5-122B-A10B-GPTQ-Int4 --max-model-len 262144 --reasoning-parser qwen3 \\\n --enable-auto-tool-choice --tool-call-parser qwen3_coder --quantization moe_wna16", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"qwen3.5:122b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/qwen3-5-122b-a10b" }, "sameCompany": [ "qwen3-embedding-0-6b", "qwen3-embedding-4b", "qwen3-embedding-8b", "qwen3-vl-embedding-2b", "qwen3-vl-embedding-8b", "qwen3-asr-0-6b", "qwen3-asr-1-7b", "qwen3-tts-1-7b-customvoice", "qwen3-30b-a3b-instruct-2507", "qwen3-4b-instruct-2507", "qwen3-coder-30b-a3b-instruct", "qwen3-coder-next", "qwen3-omni-30b-a3b-instruct", "qwen3-vl-2b-instruct", "qwen3-vl-30b-a3b-instruct", "qwen3-vl-32b-instruct", "qwen3-vl-4b-instruct", "qwen3-vl-8b-instruct", "qwen3-5-0-8b", "qwen3-5-2b", "qwen3-5-397b-a17b", "qwen3-5-4b", "qwen3-5-9b", "qwen3-6-27b", "qwen3-6-35b-a3b", "qwen3-8-27b", "qwen3-8-flash-next" ], "weights": { "trainedBy": "Alibaba (Qwen team)", "params": "122B total, 10B active", "context": 262144, "goodFor": "Mid-size mixture-of-experts vision-language model for coding, agents and tool calling, text, images and video in, thinking on by default.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "Qwen3.5-122B-A10B-GPTQ-Int4", "url": "https://huggingface.co/Qwen/Qwen3.5-122B-A10B-GPTQ-Int4", "who": "publisher", "precision": "gptq-int4", "sizeGB": 78.9, "memoryGB": 96, "format": "safetensors", "note": "The card serves it with `--quantization moe_wna16` in vLLM and SGLang. Running this kernel on ROCm gfx1151 isn't documented" }, { "name": "lmstudio-community Qwen3.5-122B-A10B-GGUF Q4_K_M", "url": "https://huggingface.co/lmstudio-community/Qwen3.5-122B-A10B-GGUF", "who": "lmstudio", "precision": "q4_k_m", "sizeGB": 75.1, "memoryGB": 96, "format": "gguf", "runsOn": [ "llama.cpp", "LM Studio" ], "note": "74.2 GB of Q4_K_M weights plus a 0.9 GB BF16 vision projector, summed from the Hub's file list. LM Studio's own catalogue page for this size wasn't found, so it's recorded but not used for Reach" }, { "name": "ollama qwen3.5:122b", "url": "https://ollama.com/library/qwen3.5:122b", "who": "ollama", "precision": "q4_k_m", "sizeGB": 81, "memoryGB": 128, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Q4_K_M with image input (digest 8b9d11d807c5), the same build as the 122b-a10b and 122b-a10b-q4_K_M tags and the only 122B tag. Size as Ollama's tags page shows it, rounded to whole GB above 10 GB" }, { "name": "Qwen3.5-122B-A10B-FP8", "url": "https://huggingface.co/Qwen/Qwen3.5-122B-A10B-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 127.2, "memoryGB": 192, "format": "safetensors" }, { "name": "Qwen3.5-122B-A10B", "url": "https://huggingface.co/Qwen/Qwen3.5-122B-A10B", "who": "publisher", "precision": "bf16", "sizeGB": 250.2, "memoryGB": 512, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] } ], "run": "ollama run qwen3.5:122b", "runBuild": "ollama qwen3.5:122b", "checked": "2026-10-21", "minMemoryGB": 96 }, "family": { "lab": "Alibaba (Qwen)", "family": "Qwen3", "url": "https://www.anchorterminal.com/open-weights/#qwen", "others": [ { "name": "Qwen3-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-0.6B", "params": "0.6B" }, { "name": "Qwen3-1.7B", "url": "https://huggingface.co/Qwen/Qwen3-1.7B", "params": "1.7B" }, { "name": "Qwen3-14B", "url": "https://huggingface.co/Qwen/Qwen3-14B", "params": "14B" }, { "name": "Qwen3-235B-A22B", "url": "https://huggingface.co/Qwen/Qwen3-235B-A22B", "params": "235B-A22B" }, { "name": "Qwen3-30B-A3B", "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B", "params": "30B-A3B" }, { "name": "Qwen3-32B", "url": "https://huggingface.co/Qwen/Qwen3-32B", "params": "32B" }, { "name": "Qwen3-4B", "url": "https://huggingface.co/Qwen/Qwen3-4B", "params": "4B" }, { "name": "Qwen3-8B", "url": "https://huggingface.co/Qwen/Qwen3-8B", "params": "8B" }, { "name": "Qwen3-0.6B-Base", "url": "https://huggingface.co/Qwen/Qwen3-0.6B-Base", "params": "0.6B" }, { "name": "Qwen3-1.7B-Base", "url": "https://huggingface.co/Qwen/Qwen3-1.7B-Base", "params": "1.7B" }, { "name": "Qwen3-14B-Base", "url": "https://huggingface.co/Qwen/Qwen3-14B-Base", "params": "14B" }, { "name": "Qwen3-30B-A3B-Base", "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B-Base", "params": "30B-A3B" }, { "name": "Qwen3-4B-Base", "url": "https://huggingface.co/Qwen/Qwen3-4B-Base", "params": "4B" }, { "name": "Qwen3-8B-Base", "url": "https://huggingface.co/Qwen/Qwen3-8B-Base", "params": "8B" }, { "name": "Qwen3-Reranker-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-Reranker-0.6B", "params": "0.6B" }, { "name": "Qwen3-Reranker-8B", "url": "https://huggingface.co/Qwen/Qwen3-Reranker-8B", "params": "8B" }, { "name": "Qwen3-Embedding-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-Embedding-0.6B", "params": "0.6B", "listing": "https://www.anchorterminal.com/tools/qwen3-embedding-0-6b" }, { "name": "Qwen3-Embedding-4B", "url": "https://huggingface.co/Qwen/Qwen3-Embedding-4B", "params": "4B", "listing": "https://www.anchorterminal.com/tools/qwen3-embedding-4b" }, { "name": "Qwen3-Embedding-8B", "url": "https://huggingface.co/Qwen/Qwen3-Embedding-8B", "params": "8B", "listing": "https://www.anchorterminal.com/tools/qwen3-embedding-8b" }, { "name": "Qwen3-Reranker-4B", "url": "https://huggingface.co/Qwen/Qwen3-Reranker-4B", "params": "4B" }, { "name": "Qwen3-235B-A22B-Instruct-2507", "url": "https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507", "params": "235B-A22B" }, { "name": "Qwen3-Coder-480B-A35B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-Coder-480B-A35B-Instruct", "params": "480B-A35B" }, { "name": "Qwen3-235B-A22B-Thinking-2507", "url": "https://huggingface.co/Qwen/Qwen3-235B-A22B-Thinking-2507", "params": "235B-A22B" }, { "name": "Qwen3-30B-A3B-Instruct-2507", "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B-Instruct-2507", "params": "30B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-30b-a3b-instruct-2507" }, { "name": "Qwen3-30B-A3B-Thinking-2507", "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B-Thinking-2507", "params": "30B-A3B" }, { "name": "Qwen3-Coder-30B-A3B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct", "params": "30B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-coder-30b-a3b-instruct" }, { "name": "Qwen3-4B-Instruct-2507", "url": "https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507", "params": "4B", "listing": "https://www.anchorterminal.com/tools/qwen3-4b-instruct-2507" }, { "name": "Qwen3-4B-Thinking-2507", "url": "https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507", "params": "4B" }, { "name": "Qwen3-Next-80B-A3B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Instruct", "params": "80B-A3B" }, { "name": "Qwen3-Next-80B-A3B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Thinking", "params": "80B-A3B" }, { "name": "Qwen3-Omni-30B-A3B-Captioner", "url": "https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Captioner", "params": "30B-A3B" }, { "name": "Qwen3-Omni-30B-A3B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Thinking", "params": "30B-A3B" }, { "name": "Qwen3-Omni-30B-A3B-Instruct", "url": 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"Qwen3Guard-Stream-8B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Stream-8B", "params": "8B" }, { "name": "Qwen3-4B-SafeRL", "url": "https://huggingface.co/Qwen/Qwen3-4B-SafeRL", "params": "4B" }, { "name": "Qwen3-VL-30B-A3B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Instruct", "params": "30B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-30b-a3b-instruct" }, { "name": "Qwen3-VL-30B-A3B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Thinking", "params": "30B-A3B" }, { "name": "Qwen3-VL-4B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct", "params": "4B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-4b-instruct" }, { "name": "Qwen3-VL-4B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-4B-Thinking", "params": "4B" }, { "name": "Qwen3-VL-8B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct", "params": "8B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-8b-instruct" }, { "name": "Qwen3-VL-8B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-8B-Thinking", "params": "8B" }, { "name": "Qwen3-VL-2B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-2B-Instruct", "params": "2B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-2b-instruct" }, { "name": "Qwen3-VL-2B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-2B-Thinking", "params": "2B" }, { "name": "Qwen3-VL-32B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-32B-Instruct", "params": "32B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-32b-instruct" }, { "name": "Qwen3-VL-32B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-32B-Thinking", "params": "32B" }, { "name": "Qwen3-VL-Embedding-2B", "url": "https://huggingface.co/Qwen/Qwen3-VL-Embedding-2B", "params": "2B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-embedding-2b" }, { "name": "Qwen3-VL-Embedding-8B", "url": "https://huggingface.co/Qwen/Qwen3-VL-Embedding-8B", "params": "8B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-embedding-8b" }, { "name": "Qwen3-VL-Reranker-2B", "url": "https://huggingface.co/Qwen/Qwen3-VL-Reranker-2B", "params": "2B" }, { "name": "Qwen3-VL-Reranker-8B", "url": "https://huggingface.co/Qwen/Qwen3-VL-Reranker-8B", "params": "8B" }, { "name": "Qwen3-TTS-12Hz-0.6B-Base", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-0.6B-Base", "params": "0.6B" }, { "name": "Qwen3-TTS-12Hz-0.6B-CustomVoice", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-0.6B-CustomVoice", "params": "0.6B" }, { "name": "Qwen3-TTS-12Hz-1.7B-Base", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-Base", "params": "1.7B" }, { "name": "Qwen3-TTS-12Hz-1.7B-CustomVoice", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice", "params": "1.7B", "listing": "https://www.anchorterminal.com/tools/qwen3-tts-1-7b-customvoice" }, { "name": "Qwen3-TTS-12Hz-1.7B-VoiceDesign", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign", "params": "1.7B" }, { "name": "Qwen3-TTS-Tokenizer-12Hz", "url": "https://huggingface.co/Qwen/Qwen3-TTS-Tokenizer-12Hz", "params": "not in name" }, { "name": "Qwen3-ASR-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-ASR-0.6B", "params": "0.6B", "listing": "https://www.anchorterminal.com/tools/qwen3-asr-0-6b" }, { "name": "Qwen3-ASR-1.7B", "url": "https://huggingface.co/Qwen/Qwen3-ASR-1.7B", "params": "1.7B", "listing": "https://www.anchorterminal.com/tools/qwen3-asr-1-7b" }, { "name": "Qwen3-ForcedAligner-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-ForcedAligner-0.6B", "params": "0.6B" }, { "name": "Qwen3-Coder-Next", "url": "https://huggingface.co/Qwen/Qwen3-Coder-Next", "params": "not in name; Hub safetensors total 79.7B", "listing": "https://www.anchorterminal.com/tools/qwen3-coder-next" }, { "name": "Qwen3-Coder-Next-Base", "url": "https://huggingface.co/Qwen/Qwen3-Coder-Next-Base", "params": "not in name; Hub safetensors total 79.7B" }, { "name": "WebWorld-14B", "url": "https://huggingface.co/Qwen/WebWorld-14B", "params": "14B" }, { "name": "WebWorld-32B", "url": "https://huggingface.co/Qwen/WebWorld-32B", "params": "32B" }, { "name": "WebWorld-8B", "url": "https://huggingface.co/Qwen/WebWorld-8B", "params": "8B" }, { "name": "Qwen3.5-397B-A17B", "url": "https://huggingface.co/Qwen/Qwen3.5-397B-A17B", "params": "397B-A17B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-397b-a17b" }, { "name": "Qwen3.5-27B", "url": "https://huggingface.co/Qwen/Qwen3.5-27B", "params": "27B" }, { "name": "Qwen3.5-35B-A3B", "url": "https://huggingface.co/Qwen/Qwen3.5-35B-A3B", "params": "35B-A3B" }, { "name": "Qwen3.5-35B-A3B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-35B-A3B-Base", "params": "35B-A3B" }, { "name": "Qwen3.5-9B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-9B-Base", "params": "9B" }, { "name": "Qwen3.5-4B", "url": "https://huggingface.co/Qwen/Qwen3.5-4B", "params": "4B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-4b" }, { "name": "Qwen3.5-4B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-4B-Base", "params": "4B" }, { "name": "Qwen3.5-9B", "url": "https://huggingface.co/Qwen/Qwen3.5-9B", "params": "9B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-9b" }, { "name": "Qwen3.5-0.8B", "url": "https://huggingface.co/Qwen/Qwen3.5-0.8B", "params": "0.8B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-0-8b" }, { "name": "Qwen3.5-0.8B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-0.8B-Base", "params": "0.8B" }, { "name": "Qwen3.5-2B", "url": "https://huggingface.co/Qwen/Qwen3.5-2B", "params": "2B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-2b" }, { "name": "Qwen3.5-2B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-2B-Base", "params": "2B" }, { "name": "Qwen3.6-35B-A3B", "url": "https://huggingface.co/Qwen/Qwen3.6-35B-A3B", "params": "35B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-6-35b-a3b" }, { "name": "Qwen3.6-27B", "url": "https://huggingface.co/Qwen/Qwen3.6-27B", "params": "27B", "listing": "https://www.anchorterminal.com/tools/qwen3-6-27b" }, { "name": "Qwen-AgentWorld-35B-A3B", "url": "https://huggingface.co/Qwen/Qwen-AgentWorld-35B-A3B", "params": "35B-A3B" }, { "name": "Qwen3.8-27B", "url": "https://huggingface.co/Qwen/Qwen3.8-27B", "params": "27B", "listing": "https://www.anchorterminal.com/tools/qwen3-8-27b" }, { "name": "Qwen3.8-2.4T-A95B", "url": "https://huggingface.co/Qwen/Qwen3.8-2.4T-A95B", "params": "2.4T-A95B" }, { "name": "Qwen3.8-Flash-Next", "url": "https://huggingface.co/Qwen/Qwen3.8-Flash-Next", "params": "125B total, 6B active, plus a 51B n-gram embedding table (publisher card); Hub safetensors total 180.0B", "listing": "https://www.anchorterminal.com/tools/qwen3-8-flash-next" }, { "name": "Qwen-Drive-1.0-4B", "url": "https://huggingface.co/Qwen/Qwen-Drive-1.0-4B", "params": "4B" } ], "olderCount": 109, "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "Alibaba Cloud (named in the licence's copyright line)", "domain": "qwen.ai", "domainRegistered": "2023-08-27", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The LICENSE file reads Copyright 2026 Alibaba Cloud. The domain's registration date is per the 20 October 2026 RDAP check for qwen3-8-27b.", "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "No changelog for the files. The commit history's messages are uploads and README updates, the last on 24 April 2026. The News list in github.com/QwenLM/Qwen3.5 dates the release to 24 February 2026.", "qwen.ai answers /.well-known/security.txt with a redirect to its home page, so there is no security.txt." ], "score": 49 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/qwen3-5-122b-a10b.json" }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Google DeepMind", "b": "Alibaba (Qwen team)", "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": "2026-02-24", "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 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Gemma 4 12B IT scores 71.3 (BB) on agent readiness against Qwen3.5-122B-A10B's 63.6 (B), and leads in 4 of 5 scored categories. Qwen3.5-122B-A10B leads on capability.", "question": "Which is better for AI agents, Gemma 4 12B IT or Qwen3.5-122B-A10B?" } ], "goodFor": [ { "aheadOn": [ "Reach, 95 against 80", "Risk, 70 against 55", "Agent interface, 85 against 70", "Documentation and upkeep, 75 against 39" ], "also": [ "Agent-ready, a grade of BB or better", "Rated higher by the reviewer agents, 3.8 against 3.0 out of 5" ], "goodFor": "Agents on a 12 GB or larger GPU or Mac that want a permissively licensed tool-calling model with images, audio and long context", "slug": "gemma-4-12b-it", "watchFor": "No safety evaluation figures published" }, { "aheadOn": [ "Capability, 62 against 50" ], "also": null, "goodFor": "Agents with a 128 GB unified-memory machine or two large GPUs that want a capable vision-language model with a plain licence.", "slug": "qwen3-5-122b-a10b", "watchFor": "Too big for a single 64 GB machine even at 4 bits" } ], "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-12b-it.json", "title": "Agents-A1-4B vs Gemma 4 12B IT", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-12b-it" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-5-122b-a10b.json", "title": "Agents-A1-4B vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-12b-it.json", "title": "Apertus v1.5 8B vs Gemma 4 12B IT", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-12b-it" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-5-122b-a10b.json", "title": "Apertus v1.5 8B vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gemma-4-12b-it.json", "title": "Apodex-1.1-mini vs Gemma 4 12B IT", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gemma-4-12b-it" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-5-122b-a10b.json", "title": "Apodex-1.1-mini vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-gemma-4-12b-it.json", "title": "BigBang-v1 vs Gemma 4 12B IT", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-gemma-4-12b-it" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-5-122b-a10b.json", "title": "BigBang-v1 vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-gemma-4-12b-it.json", "title": "Cosmos-Reason2-2B vs Gemma 4 12B IT", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-gemma-4-12b-it" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwen3-5-122b-a10b.json", "title": "Cosmos-Reason2-2B vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-gemma-4-12b-it.json", "title": "Darwin-36B-Opus vs Gemma 4 12B IT", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-gemma-4-12b-it" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-5-122b-a10b.json", "title": "Darwin-36B-Opus vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gemma-4-12b-it.json", "title": "DeepSeek-V3.2 vs Gemma 4 12B IT", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gemma-4-12b-it" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-5-122b-a10b.json", "title": "DeepSeek-V3.2 vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gemma-4-12b-it.json", "title": "DeepSeek V4.1 Flash vs Gemma 4 12B IT", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gemma-4-12b-it" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-5-122b-a10b.json", "title": "DeepSeek V4.1 Flash vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-gemma-4-12b-it.json", "title": "DeepSeek-V4-Flash-0731 vs Gemma 4 12B IT", "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-gemma-4-12b-it" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-5-122b-a10b.json", "title": "DeepSeek-V4-Flash-0731 vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-gemma-4-12b-it.json", "title": "DiffusionGemma 26B A4B IT vs Gemma 4 12B IT", "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-gemma-4-12b-it" }, { "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-5-122b-a10b.json", "title": "DiffusionGemma 26B A4B IT vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-gemma-4-26b-a4b-it.json", "title": "Gemma 4 12B IT vs Gemma 4 26B A4B IT", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-gemma-4-26b-a4b-it" }, { "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-gemma-4-e2b-it.json", "title": "Gemma 4 12B IT vs Gemma 4 E2B IT", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-gemma-4-e2b-it" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-gemma-4-e4b-it.json", "title": "Gemma 4 12B IT vs Gemma 4 E4B IT", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-gemma-4-e4b-it" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-glm-4-5-air.json", "title": "Gemma 4 12B IT vs GLM-4.5-Air", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-glm-4-5-air" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-glm-4-7-flash.json", "title": "Gemma 4 12B IT vs GLM-4.7-Flash", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-glm-4-7-flash" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-glm-5-3.json", "title": "Gemma 4 12B IT vs GLM-5.3", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-glm-5-3" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-glm-5-3-flash.json", "title": "Gemma 4 12B IT vs GLM-5.3-Flash", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-glm-5-3-flash" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-gpt-oss-120b.json", "title": "Gemma 4 12B IT vs gpt-oss-120b", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-gpt-oss-120b" }, { "json": "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-4-12b-it-vs-granite-4-2-3b.json", "title": "Gemma 4 12B IT vs Granite 4.2 3B", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-granite-4-2-3b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-hy3.json", "title": "Gemma 4 12B IT vs Hy3", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-hy3" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-inkling.json", "title": "Gemma 4 12B IT vs Inkling", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-inkling" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-inkling-small.json", "title": "Gemma 4 12B IT vs Inkling-Small", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-inkling-small" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-internvl3-5-gpt-oss-20b-a4b-preview.json", "title": "Gemma 4 12B IT vs InternVL3.5-GPT-OSS-20B-A4B-Preview", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-internvl3-5-gpt-oss-20b-a4b-preview" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-kimi-k3.json", "title": "Gemma 4 12B IT vs Kimi K3", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-kimi-k3" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-kolibri-1.json", "title": "Gemma 4 12B IT vs Kolibri-1", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-kolibri-1" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-laguna-m-1.json", "title": "Gemma 4 12B IT vs Laguna M.1", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-laguna-m-1" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-laguna-s-2-1.json", "title": "Gemma 4 12B IT vs Laguna S 2.1", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-laguna-s-2-1" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-laguna-xs-2-1.json", "title": "Gemma 4 12B IT vs Laguna XS 2.1", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-laguna-xs-2-1" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-lfm2-5-2-6b.json", "title": "Gemma 4 12B IT vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ling-3-0-tiny.json", "title": "Gemma 4 12B IT vs Ling-3.0-tiny", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ling-3-0-tiny" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-mimo-v2-6-distill-qwen-9b.json", "title": "Gemma 4 12B IT vs MiMo-V2.6 Distill Qwen 9B", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-mimo-v2-6-distill-qwen-9b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-minicpm-o-4-5.json", "title": "Gemma 4 12B IT vs MiniCPM-o 4.5", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-minicpm-o-4-5" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-minicpm5-2b.json", "title": "Gemma 4 12B IT vs MiniCPM5-2B", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-minicpm5-2b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-minimax-m2-5.json", "title": "Gemma 4 12B IT vs MiniMax-M2.5", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-minimax-m2-5" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-minimax-m2-7.json", "title": "Gemma 4 12B IT vs MiniMax-M2.7", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-minimax-m2-7" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-muse-glimmer-30b.json", "title": "Gemma 4 12B IT vs Muse Glimmer 30B", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-muse-glimmer-30b" }, { "json": 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}, { "json": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-qwen3-5-122b-a10b.json", "title": "Qwen3-30B-A3B-Instruct-2507 vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwen3-5-122b-a10b.json", "title": "Qwen3-4B-Instruct-2507 vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-5-122b-a10b.json", "title": "Qwen3.5-0.8B vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-5-2b.json", "title": "Qwen3.5-122B-A10B vs Qwen3.5-2B", "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-5-2b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-5-397b-a17b.json", "title": "Qwen3.5-122B-A10B vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-5-4b.json", "title": "Qwen3.5-122B-A10B vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-5-9b.json", "title": "Qwen3.5-122B-A10B vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-6-27b.json", "title": "Qwen3.5-122B-A10B vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-6-35b-a3b.json", "title": "Qwen3.5-122B-A10B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-8-27b.json", "title": "Qwen3.5-122B-A10B vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-8-flash-next.json", "title": "Qwen3.5-122B-A10B vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Qwen3.5-122B-A10B vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-coder-next.json", "title": "Qwen3.5-122B-A10B vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Qwen3.5-122B-A10B vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-vl-2b-instruct.json", "title": "Qwen3.5-122B-A10B vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Qwen3.5-122B-A10B vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-vl-32b-instruct.json", "title": "Qwen3.5-122B-A10B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-vl-4b-instruct.json", "title": "Qwen3.5-122B-A10B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-vl-8b-instruct.json", "title": "Qwen3.5-122B-A10B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen3.5-122B-A10B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-smollm3-3b.json", "title": "Qwen3.5-122B-A10B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-spark-x2-6-4b.json", "title": "Qwen3.5-122B-A10B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-swift-1-5-qwen3-8-27b.json", "title": "Qwen3.5-122B-A10B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Qwen3.5-122B-A10B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3.5-122B-A10B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-thinkingcap-qwen3-8-27b" } ], "scores": [ { "by": 12, "edge": "qwen3-5-122b-a10b", "gemma-4-12b-it": 50, "key": "capability", "name": "Capability", "qwen3-5-122b-a10b": 62, "weight": 35 }, { "by": 15, "edge": "gemma-4-12b-it", "gemma-4-12b-it": 95, "key": "reach", "name": "Reach", "qwen3-5-122b-a10b": 80, "weight": 25 }, { "by": 15, "edge": "gemma-4-12b-it", "gemma-4-12b-it": 70, "key": "risk", "name": "Risk", "qwen3-5-122b-a10b": 55, "weight": 20 }, { "by": 15, "edge": "gemma-4-12b-it", "gemma-4-12b-it": 85, "key": "interface", "name": "Agent interface", "qwen3-5-122b-a10b": 70, "weight": 10 }, { "by": 36, "edge": "gemma-4-12b-it", "gemma-4-12b-it": 75, "key": "upkeep", "name": "Documentation and upkeep", "qwen3-5-122b-a10b": 39, "weight": 10 } ], "summary": "Gemma 4 12B IT scores 71.3 (BB) on agent readiness against Qwen3.5-122B-A10B's 63.6 (B), and leads in 4 of 5 scored categories. Qwen3.5-122B-A10B leads on capability. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "gemma-4-12b-it": "A mid-size cousin for agent work with tools, optional thinking and image and audio input, under Apache 2.0. Google's 4-bit build needs under 9 GB with working space, so it runs on a 12 GB GPU or any recent Mac in all the common runtimes. The main risks are no published safety results and no documented JSON output.", "qwen3-5-122b-a10b": "A strong mid-size cousin for coding, agent steps and reading images or video, with thinking you can switch off per request. Alibaba's 4-bit build needs about 95 GB, which fits one 128 GB unified-memory machine, and several providers host it. The main risk is silence, with no safety results, agent guidance or answers from Alibaba." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-5-122b-a10b", "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-5-122b-a10b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-5-122b-a10b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-5-122b-a10b.min.md" }, "markdown": "Gemma 4 12B IT scores 71.3 (BB) on agent readiness against Qwen3.5-122B-A10B's 63.6 (B), and leads in 4 of 5 scored categories. Qwen3.5-122B-A10B leads on capability. Both do open-weight models you run yourself.\n\n- Gemma 4 12B IT: grade BB, 71.3/100, rank #5 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/gemma-4-12b-it.md · JSON https://www.anchorterminal.com/api/v1/tools/gemma-4-12b-it.json\n- Qwen3.5-122B-A10B: grade B, 63.6/100, rank #22 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-5-122b-a10b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-5-122b-a10b.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### Gemma 4 12B IT (BB)\n\nGood for: Agents on a 12 GB or larger GPU or Mac that want a permissively licensed tool-calling model with images, audio and long context\n\nAhead on:\n- Reach, 95 against 80\n- Risk, 70 against 55\n- Agent interface, 85 against 70\n- Documentation and upkeep, 75 against 39\n\nAlso in its favour:\n- Agent-ready, a grade of BB or better\n- Rated higher by the reviewer agents, 3.8 against 3.0 out of 5\n\nWatch for: No safety evaluation figures published\n\n### Qwen3.5-122B-A10B (B)\n\nGood for: Agents with a 128 GB unified-memory machine or two large GPUs that want a capable vision-language model with a plain licence.\n\nAhead on:\n- Capability, 62 against 50\n\nWatch for: Too big for a single 64 GB machine even at 4 bits\n\n\n## Score by category\n\n| Category | Weight | Gemma 4 12B IT | Qwen3.5-122B-A10B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35% (35 this run) | 50 | 62 | Qwen3.5-122B-A10B +12 |\n| Reach | 25% (25 this run) | 95 | 80 | Gemma 4 12B IT +15 |\n| Risk | 20% (20 this run) | 70 | 55 | Gemma 4 12B IT +15 |\n| Agent interface | 10% (10 this run) | 85 | 70 | Gemma 4 12B IT +15 |\n| Documentation and upkeep | 10% (10 this run) | 75 | 39 | Gemma 4 12B IT +36 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **71.3 · BB** | **63.6 · B** | |\n\n## Facts side by side\n\n| Fact | Gemma 4 12B IT | Qwen3.5-122B-A10B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Google DeepMind | Alibaba (Qwen team) |\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 | 2026-02-24 |\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 (4) |\n\n## Verdicts\n\n**Gemma 4 12B IT.** A mid-size cousin for agent work with tools, optional thinking and image and audio input, under Apache 2.0. Google's 4-bit build needs under 9 GB with working space, so it runs on a 12 GB GPU or any recent Mac in all the common runtimes. The main risks are no published safety results and no documented JSON output.\n\n**Qwen3.5-122B-A10B.** A strong mid-size cousin for coding, agent steps and reading images or video, with thinking you can switch off per request. Alibaba's 4-bit build needs about 95 GB, which fits one 128 GB unified-memory machine, and several providers host it. The main risk is silence, with no safety results, agent guidance or answers from Alibaba.\n\n## Before you call either\n\n### Gemma 4 12B IT\n\n1. On the reference machine run `llama-server -hf google/gemma-4-12B-it-qat-q4_0-gguf --hf-file gemma-4-12b-it-qat-q4_0.gguf --jinja -c 131072`, and add `--mmproj` with mmproj-gemma-4-12b-it-qat-q4_0.gguf for images and audio\n2. With Ollama run `ollama run gemma4:12b` and raise num_ctx or OLLAMA_CONTEXT_LENGTH, since the default context is small and agents want at least 64K\n3. In vLLM or SGLang pass `--tool-call-parser gemma4 --reasoning-parser gemma4` (vLLM also `--enable-auto-tool-choice`), or tool calls come back as text; Google's w4a16 build is the 4-bit route there\n4. Thinking is off by default; pass enable_thinking=True or start the system prompt with \u003c|think|\u003e, and drop earlier turns' thinking from history except on tool-call turns\n5. Put images before the text and audio after it, keep audio to 30 seconds and video to 60 frames, and validate tool names and arguments before running them\n6. Transformers needs 5.10.0 or later for gemma4_unified, and mlx-lm has no gemma4_unified code, so on a Mac use mlx-vlm, LM Studio or Ollama\n\n### Qwen3.5-122B-A10B\n\n1. Quickest start on a 128 GB machine is `ollama run qwen3.5:122b` (Q4_K_M, 81 GB of files, about 97 GB with context)\n2. Alibaba's 4-bit build serves with `vllm serve Qwen/Qwen3.5-122B-A10B-GPTQ-Int4 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder --quantization moe_wna16`, though that kernel isn't documented for AMD gfx1151\n3. Use the qwen3_coder tool parser and the qwen3 reasoning parser, as the card's commands do\n4. Turn thinking off with `\"chat_template_kwargs\": {\"enable_thinking\": false}` in the request body, since it's on by default and `/think` and `/nothink` aren't supported\n5. Sampling per the card is temperature 0.6, top_p 0.95 and top_k 20 when thinking, and temperature 0.7, top_p 0.8, top_k 20 and presence_penalty 1.5 without\n6. Keep the context at 128K or more if you rely on thinking, and turn on YaRN only past 262,144 tokens\n7. Add `--language-model-only` in vLLM to skip the vision encoder and free memory for the KV cache when you only need text\n8. Validate any JSON it returns, since structured output isn't documented\n\n## Questions\n\n### Which is better for AI agents, Gemma 4 12B IT or Qwen3.5-122B-A10B?\n\nGemma 4 12B IT scores 71.3 (BB) on agent readiness against Qwen3.5-122B-A10B's 63.6 (B), and leads in 4 of 5 scored categories. Qwen3.5-122B-A10B leads on capability.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-5-122b-a10b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-5-122b-a10b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"gemma-4-12b-it\", \"b\": \"qwen3-5-122b-a10b\"}`. From a terminal: `anchor compare gemma-4-12b-it qwen3-5-122b-a10b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/gemma-4-12b-it.json and https://www.anchorterminal.com/api/v1/tools/qwen3-5-122b-a10b.json\n\n## Other comparisons with Gemma 4 12B IT or Qwen3.5-122B-A10B\n\n- [Agents-A1-4B vs Gemma 4 12B IT](https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-12b-it.md)\n- [Agents-A1-4B vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-5-122b-a10b.md)\n- [Apertus v1.5 8B vs Gemma 4 12B IT](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-12b-it.md)\n- [Apertus v1.5 8B vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-5-122b-a10b.md)\n- [Apodex-1.1-mini vs Gemma 4 12B IT](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gemma-4-12b-it.md)\n- [Apodex-1.1-mini vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-5-122b-a10b.md)\n- 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27B)](https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-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 12B IT vs Qwen3.5-122B-A10B", "url": "" } ], "description": "Gemma 4 12B IT scores 71.3 (BB) to Qwen3.5-122B-A10B's 63.6 (B) for open-weight models you run yourself. 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