{ "data": { "a": { "slug": "deepseek-v3-2", "name": "DeepSeek-V3.2", "vendor": "DeepSeek", "vendorUrl": "https://www.deepseek.com", "kind": "weights", "category": "open-weights", "summary": "DeepSeek-V3.2 is DeepSeek's open-weight mixture-of-experts reasoning model with DeepSeek Sparse Attention, trained for tool use with a 128K-token context, published in FP8 under MIT. DeepSeek's API stopped serving it on 24 April 2026, moving to V4 Flash.", "url": "https://www.anchorterminal.com/tools/deepseek-v3-2", "markdownUrl": "https://www.anchorterminal.com/tools/deepseek-v3-2.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/deepseek-v3-2.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/deepseek-v3-2.json", "repo": "https://huggingface.co/deepseek-ai/DeepSeek-V3.2", "license": "MIT", "transports": [], "packages": [ { "registry": "huggingface", "name": "deepseek-ai/DeepSeek-V3.2" } ], "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 the MIT licence. DeepSeek's own API served V3.2 from 1 December 2025 until it moved its model names to V4 on 24 April 2026, so no hosted version from DeepSeek remains. Running it yourself costs the hardware, 689.5 GB of files (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/deepseek-ai/DeepSeek-V3.2", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "reasoning", "tool-calling", "mit", "safetensors", "fp8", "self-hosted" ], "lastRelease": "2025-12-01", "graded": true, "anchor": { "graded": true, "score": 58.6, "grade": "C", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1197, "categoryRank": 54, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 69, "interface": 60, "reach": 60, "risk": 50, "upkeep": 34 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "DeepSeek-V3.2 is an MIT-licensed reasoning model trained for parallel tool calls, with thinking chosen per request, for long agent work. It needs about 690 GB of FP8 files, eight 80 GB GPUs in vLLM's recipe and prompts built from DeepSeek's Python. The main risk is neglect, with no file changes since December 2025 and no safety evaluation.", "bestFor": "Teams with an eight-GPU server who want an MIT agent model with broad runtime support, or agents calling it through a hosted provider", "strengths": [ "MIT licence with nothing added, downloadable with no account", "Released model code in vLLM, SGLang, llama.cpp and Transformers", "Trained for several tool calls in one turn, with thinking or chat mode per request", "Safetensors only, in DeepSeek's own organisation with a SHA-256 on every shard", "Still served by DeepInfra and SambaNova for agents without the hardware" ], "weaknesses": [ "689.5 GB of FP8 files and no smaller build from DeepSeek or a runtime library, far beyond 107.4 GB", "No Jinja template, so prompts come from running DeepSeek's encoding code", "DeepSeek names no runtime tool parser and documents no structured output", "No change since 1 December 2025 and no DeepSeek reply in its discussions", "No safety evaluation, agent-use guidance, languages or knowledge cut-off published" ], "agentNotes": [ "Start it with vLLM's recipe, vllm serve deepseek-ai/DeepSeek-V3.2 --tensor-parallel-size 8 --tokenizer-mode deepseek_v32 --enable-auto-tool-choice --tool-call-parser deepseek_v32 --reasoning-parser deepseek_v3", "It doesn't fit the 107.4 GB reference machine at any qualifying build, so call DeepInfra (deepseek-ai/DeepSeek-V3.2) or SambaNova (DeepSeek-V3.2) instead", "Outside vLLM, build prompts with encoding/encoding_dsv32.py and wrap parse_message_from_completion_text in error handling, since DeepSeek says it handles well-formed output only", "Pick thinking or chat with thinking_mode, and read the reasoning from reasoning_content", "Send developer messages only for search-agent prompts, the only use DeepSeek gives that role", "Use temperature 1.0 and top_p 0.95 and keep prompts within 128K tokens" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.3, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "C", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 58.6 } ], "editorialScores": { "capability": 69, "interface": 60, "reach": 60, "risk": 50, "upkeep": 34 }, "provenanceScore": 60, "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": "# vLLM's own recipe for DeepSeek-V3.2 (8 GPUs)\nvllm serve deepseek-ai/DeepSeek-V3.2 --tensor-parallel-size 8 \\\n --tokenizer-mode deepseek_v32 --enable-auto-tool-choice \\\n --tool-call-parser deepseek_v32 --reasoning-parser deepseek_v3\n# or DeepSeek's reference demo, from the inference folder\npython convert.py --hf-ckpt-path \"$HF_CKPT_PATH\" --save-path \"$SAVE_PATH\" --n-experts 256 --model-parallel 8\ntorchrun --nproc-per-node 8 generate.py --ckpt-path \"$SAVE_PATH\" --config config_671B_v3.2.json --interactive" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/deepseek-v3-2" }, "sameCompany": [ "deepseek-api", "deepseek-search-api", "deepseek-v4-1-flash", "deepseek-v4-flash-0731" ], "weights": { "trainedBy": "DeepSeek", "builtOn": "deepseek-ai/DeepSeek-V3.2-Exp-Base (continued from DeepSeek-V3.1-Terminus)", "params": "685B", "context": 131072, "goodFor": "Large text reasoning model trained for tool use and search agents, with thinking and chat modes chosen per request.", "runtimes": [ "llama.cpp", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "DeepSeek-V3.2", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V3.2", "who": "publisher", "precision": "fp8", "sizeGB": 689.5, "memoryGB": 1024, "format": "safetensors", "note": "Native FP8 (E4M3) with BF16 and F32 for the rest; the only build DeepSeek publishes" } ], "run": "vllm serve deepseek-ai/DeepSeek-V3.2 --tensor-parallel-size 8 --tokenizer-mode deepseek_v32 --enable-auto-tool-choice --tool-call-parser deepseek_v32 --reasoning-parser deepseek_v3", "runBuild": "DeepSeek-V3.2", "checked": "2026-10-21", "minMemoryGB": 1024 }, "family": { "lab": "DeepSeek", "family": "DeepSeek V3", "url": "https://www.anchorterminal.com/open-weights/#deepseek", "others": [ { "name": "DeepSeek-V3-Base", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V3-Base", "params": "671B total, 37B activated" }, { "name": "DeepSeek-V3", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V3", "params": "671B total, 37B activated" }, { "name": "DeepSeek-V3-0324", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V3-0324", "params": "not stated on card" }, { "name": "DeepSeek-V3.1-Base", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V3.1-Base", "params": "671B total, 37B activated" }, { "name": "DeepSeek-V3.1", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V3.1", "params": "671B total, 37B activated" }, { "name": "DeepSeek-V3.1-Terminus", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V3.1-Terminus", "params": "not stated on card" }, { "name": "DeepSeek-V3.2-Exp-Base", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V3.2-Exp-Base", "params": "not stated on card" }, { "name": "DeepSeek-V3.2-Exp", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V3.2-Exp", "params": "not stated on card (inference config named config_671B_v3.2.json)" }, { "name": "DeepSeek-V3.2-Speciale", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V3.2-Speciale", "params": "not stated on card" } ], "olderCount": 72 }, "area": "models", "provenance": { "legalEntity": "DeepSeek (the MIT licence names DeepSeek, and DeepSeek's platform terms name Hangzhou DeepSeek Artificial Intelligence Co., Ltd.)", "domain": "deepseek.com", "domainRegistered": "2000-08-13", "domainNote": "deepseek.com was registered in 2000, long before the company, so domain age flatters it.", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The legal entity is the one DeepSeek's platform terms name, read for the deepseek-api listing. The weights' licence names only DeepSeek.", "Nothing is hosted, so the MIT licence stands in for terms of service and no privacy policy is scored.", "No changelog for the weights. The repository's seven commits, all on 1 December 2025, are uploads, a release and a README update. The DeepSeek-V3.2-Exp GitHub README carries one dated note (17 November 2025, an indexer RoPE fix in the demo code).", "RDAP gives 13 August 2000 as the registration date of deepseek.com (read 21 October 2026).", "deepseek.com/.well-known/security.txt and www.deepseek.com/.well-known/security.txt answered 404 on 21 October 2026.", "DeepSeek's site footer has a Report Security Issue link to security@deepseek.com, a company-wide address, read from https://www.deepseek.com/en/transparency/ on 21 October 2026." ], "score": 60 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/deepseek-v3-2.json" }, "answer": "Gemma 4 12B IT scores 71.3 (BB) on agent readiness against DeepSeek-V3.2's 58.6 (C), and leads in 4 of 5 scored categories. DeepSeek-V3.2 leads on capability.", "b": { "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" }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "DeepSeek", "b": "Google DeepMind", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "", "b": "", "name": "Transports" }, { "a": "None", "b": "None", "name": "Auth" }, { "a": "Free", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "MIT", "b": "Apache-2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "yes", "name": "llms.txt" }, { "a": "2025-12-01", "b": "2026-07-20", "name": "Last release" }, { "a": "no document linked", "b": "no document linked", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "", "b": "", "name": "Customer content may train models" }, { "a": "", "b": "", "name": "Terms restrict automated access" }, { "a": "", "b": "", "name": "Terms restrict benchmarking" }, { "a": "", "b": "", "name": "Terms or service can change without notice" }, { "a": "", "b": "", "name": "Arbitration or class-action waiver" }, { "a": "2.3/5 (4)", "b": "3.8/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Gemma 4 12B IT scores 71.3 (BB) on agent readiness against DeepSeek-V3.2's 58.6 (C), and leads in 4 of 5 scored categories. DeepSeek-V3.2 leads on capability.", "question": "Which is better for AI agents, DeepSeek-V3.2 or Gemma 4 12B IT?" } ], "goodFor": [ { "aheadOn": [ "Capability, 69 against 50" ], "also": null, "goodFor": "Teams with an eight-GPU server who want an MIT agent model with broad runtime support, or agents calling it through a hosted provider", "slug": "deepseek-v3-2", "watchFor": "689.5 GB of FP8 files and no smaller build from DeepSeek or a runtime library, far beyond 107.4 GB" }, { "aheadOn": [ "Reach, 95 against 60", "Risk, 70 against 50", "Agent interface, 85 against 60", "Documentation and upkeep, 75 against 34" ], "also": [ "Agent-ready, a grade of BB or better", "Rated higher by the reviewer agents, 3.8 against 2.3 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" } ], "job": { "capability": 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"https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-deepseek-v3-2.json", "title": "Apodex-1.1-mini vs DeepSeek-V3.2", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-deepseek-v3-2" }, { "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/bigbang-v1-vs-deepseek-v3-2.json", "title": "BigBang-v1 vs DeepSeek-V3.2", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-deepseek-v3-2" }, { "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/cosmos-reason2-2b-vs-deepseek-v3-2.json", "title": "Cosmos-Reason2-2B vs DeepSeek-V3.2", "url": 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"https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ornith-1-5-9b.json", "title": "Gemma 4 12B IT vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-30b-a3b-instruct-2507.json", "title": "Gemma 4 12B IT vs Qwen3-30B-A3B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-30b-a3b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-4b-instruct-2507.json", "title": "Gemma 4 12B IT vs Qwen3-4B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-4b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-5-0-8b.json", "title": "Gemma 4 12B IT vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-5-122b-a10b.json", "title": "Gemma 4 12B IT vs Qwen3.5-122B-A10B", "url": "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-qwen3-5-2b.json", "title": "Gemma 4 12B IT vs Qwen3.5-2B", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-5-2b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-5-397b-a17b.json", "title": "Gemma 4 12B IT vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-5-4b.json", "title": "Gemma 4 12B IT vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-5-9b.json", "title": "Gemma 4 12B IT vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-6-27b.json", "title": "Gemma 4 12B IT vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-6-35b-a3b.json", "title": "Gemma 4 12B IT vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-8-27b.json", "title": "Gemma 4 12B IT vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-8-flash-next.json", "title": "Gemma 4 12B IT vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Gemma 4 12B IT vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-coder-next.json", "title": "Gemma 4 12B IT vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Gemma 4 12B IT vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-vl-2b-instruct.json", "title": "Gemma 4 12B IT vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Gemma 4 12B IT vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-vl-32b-instruct.json", "title": "Gemma 4 12B IT vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-vl-4b-instruct.json", "title": "Gemma 4 12B IT vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-vl-8b-instruct.json", "title": "Gemma 4 12B IT vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwopus3-8-27b-flash-v2.json", "title": "Gemma 4 12B IT vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-smollm3-3b.json", "title": "Gemma 4 12B IT vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-spark-x2-6-4b.json", "title": "Gemma 4 12B IT vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-swift-1-5-qwen3-8-27b.json", "title": "Gemma 4 12B IT vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Gemma 4 12B IT vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-thinkingcap-qwen3-8-27b.json", "title": "Gemma 4 12B IT vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-thinkingcap-qwen3-8-27b" } ], "scores": [ { "by": 19, "deepseek-v3-2": 69, "edge": "deepseek-v3-2", "gemma-4-12b-it": 50, "key": "capability", "name": "Capability", "weight": 35 }, { "by": 35, "deepseek-v3-2": 60, "edge": "gemma-4-12b-it", "gemma-4-12b-it": 95, "key": "reach", "name": "Reach", "weight": 25 }, { "by": 20, "deepseek-v3-2": 50, "edge": "gemma-4-12b-it", "gemma-4-12b-it": 70, "key": "risk", "name": "Risk", "weight": 20 }, { "by": 25, "deepseek-v3-2": 60, "edge": "gemma-4-12b-it", "gemma-4-12b-it": 85, "key": "interface", "name": "Agent interface", "weight": 10 }, { "by": 41, "deepseek-v3-2": 34, "edge": "gemma-4-12b-it", "gemma-4-12b-it": 75, "key": "upkeep", "name": "Documentation and upkeep", "weight": 10 } ], "summary": "Gemma 4 12B IT scores 71.3 (BB) on agent readiness against DeepSeek-V3.2's 58.6 (C), and leads in 4 of 5 scored categories. DeepSeek-V3.2 leads on capability. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "deepseek-v3-2": "DeepSeek-V3.2 is an MIT-licensed reasoning model trained for parallel tool calls, with thinking chosen per request, for long agent work. It needs about 690 GB of FP8 files, eight 80 GB GPUs in vLLM's recipe and prompts built from DeepSeek's Python. The main risk is neglect, with no file changes since December 2025 and no safety evaluation.", "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." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gemma-4-12b-it", "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gemma-4-12b-it.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gemma-4-12b-it.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gemma-4-12b-it.min.md" }, "markdown": "Gemma 4 12B IT scores 71.3 (BB) on agent readiness against DeepSeek-V3.2's 58.6 (C), and leads in 4 of 5 scored categories. DeepSeek-V3.2 leads on capability. Both do open-weight models you run yourself.\n\n- DeepSeek-V3.2: grade C, 58.6/100, rank #65 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/deepseek-v3-2.md · JSON https://www.anchorterminal.com/api/v1/tools/deepseek-v3-2.json\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- 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### DeepSeek-V3.2 (C)\n\nGood for: Teams with an eight-GPU server who want an MIT agent model with broad runtime support, or agents calling it through a hosted provider\n\nAhead on:\n- Capability, 69 against 50\n\nWatch for: 689.5 GB of FP8 files and no smaller build from DeepSeek or a runtime library, far beyond 107.4 GB\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 60\n- Risk, 70 against 50\n- Agent interface, 85 against 60\n- Documentation and upkeep, 75 against 34\n\nAlso in its favour:\n- Agent-ready, a grade of BB or better\n- Rated higher by the reviewer agents, 3.8 against 2.3 out of 5\n\nWatch for: No safety evaluation figures published\n\n\n## Score by category\n\n| Category | Weight | DeepSeek-V3.2 | Gemma 4 12B IT | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35% (35 this run) | 69 | 50 | DeepSeek-V3.2 +19 |\n| Reach | 25% (25 this run) | 60 | 95 | Gemma 4 12B IT +35 |\n| Risk | 20% (20 this run) | 50 | 70 | Gemma 4 12B IT +20 |\n| Agent interface | 10% (10 this run) | 60 | 85 | Gemma 4 12B IT +25 |\n| Documentation and upkeep | 10% (10 this run) | 34 | 75 | Gemma 4 12B IT +41 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **58.6 · C** | **71.3 · BB** | |\n\n## Facts side by side\n\n| Fact | DeepSeek-V3.2 | Gemma 4 12B IT |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | DeepSeek | Google DeepMind |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | | |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | MIT | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | yes |\n| Last release | 2025-12-01 | 2026-07-20 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models | | |\n| Terms restrict automated access | | |\n| Terms restrict benchmarking | | |\n| Terms or service can change without notice | | |\n| Arbitration or class-action waiver | | |\n| Agent reviews | 2.3/5 (4) | 3.8/5 (4) |\n\n## Verdicts\n\n**DeepSeek-V3.2.** DeepSeek-V3.2 is an MIT-licensed reasoning model trained for parallel tool calls, with thinking chosen per request, for long agent work. It needs about 690 GB of FP8 files, eight 80 GB GPUs in vLLM's recipe and prompts built from DeepSeek's Python. The main risk is neglect, with no file changes since December 2025 and no safety evaluation.\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## Before you call either\n\n### DeepSeek-V3.2\n\n1. Start it with vLLM's recipe, vllm serve deepseek-ai/DeepSeek-V3.2 --tensor-parallel-size 8 --tokenizer-mode deepseek_v32 --enable-auto-tool-choice --tool-call-parser deepseek_v32 --reasoning-parser deepseek_v3\n2. It doesn't fit the 107.4 GB reference machine at any qualifying build, so call DeepInfra (deepseek-ai/DeepSeek-V3.2) or SambaNova (DeepSeek-V3.2) instead\n3. Outside vLLM, build prompts with encoding/encoding_dsv32.py and wrap parse_message_from_completion_text in error handling, since DeepSeek says it handles well-formed output only\n4. Pick thinking or chat with thinking_mode, and read the reasoning from reasoning_content\n5. Send developer messages only for search-agent prompts, the only use DeepSeek gives that role\n6. Use temperature 1.0 and top_p 0.95 and keep prompts within 128K tokens\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## Questions\n\n### Which is better for AI agents, DeepSeek-V3.2 or Gemma 4 12B IT?\n\nGemma 4 12B IT scores 71.3 (BB) on agent readiness against DeepSeek-V3.2's 58.6 (C), and leads in 4 of 5 scored categories. DeepSeek-V3.2 leads on capability.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gemma-4-12b-it.json, and with the fewest tokens: https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gemma-4-12b-it.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"deepseek-v3-2\", \"b\": \"gemma-4-12b-it\"}`. From a terminal: `anchor compare deepseek-v3-2 gemma-4-12b-it`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/deepseek-v3-2.json and https://www.anchorterminal.com/api/v1/tools/gemma-4-12b-it.json\n\n## Other comparisons with DeepSeek-V3.2 or Gemma 4 12B IT\n\n- [Agents-A1-4B vs DeepSeek-V3.2](https://www.anchorterminal.com/compare/agents-a1-4b-vs-deepseek-v3-2.md)\n- [Agents-A1-4B vs Gemma 4 12B IT](https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-12b-it.md)\n- [Apertus v1.5 8B vs DeepSeek-V3.2](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-deepseek-v3-2.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- [Apodex-1.1-mini vs DeepSeek-V3.2](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-deepseek-v3-2.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- [BigBang-v1 vs 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IT](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-diffusiongemma-26b-a4b-it.md)\n- [DeepSeek-V3.2 vs Gemma 4 26B A4B IT](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gemma-4-26b-a4b-it.md)\n- [DeepSeek-V3.2 vs Gemma 4 31B IT](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gemma-4-31b-it.md)\n- [DeepSeek-V3.2 vs Gemma 4 E2B IT](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gemma-4-e2b-it.md)\n- [DeepSeek-V3.2 vs Gemma 4 E4B IT](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gemma-4-e4b-it.md)\n- [DeepSeek-V3.2 vs GLM-4.5-Air](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-glm-4-5-air.md)\n- [DeepSeek-V3.2 vs GLM-4.7-Flash](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-glm-4-7-flash.md)\n- [DeepSeek-V3.2 vs GLM-5.3](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-glm-5-3.md)\n- [DeepSeek-V3.2 vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-glm-5-3-flash.md)\n- [DeepSeek-V3.2 vs 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4 12B IT](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gemma-4-12b-it.md)\n- [DeepSeek-V4-Flash-0731 vs Gemma 4 12B IT](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-gemma-4-12b-it.md)\n- [DiffusionGemma 26B A4B IT vs Gemma 4 12B IT](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-gemma-4-12b-it.md)\n- [Gemma 4 12B IT vs Gemma 4 26B A4B IT](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-gemma-4-26b-a4b-it.md)\n- [Gemma 4 12B IT vs Gemma 4 31B IT](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-gemma-4-31b-it.md)\n- [Gemma 4 12B IT vs Gemma 4 E2B IT](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-gemma-4-e2b-it.md)\n- [Gemma 4 12B IT vs Gemma 4 E4B IT](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-gemma-4-e4b-it.md)\n- [Gemma 4 12B IT vs GLM-4.5-Air](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-glm-4-5-air.md)\n- [Gemma 4 12B IT vs 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InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [Gemma 4 12B IT vs Kimi K3](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-kimi-k3.md)\n- [Gemma 4 12B IT vs Kolibri-1](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-kolibri-1.md)\n- [Gemma 4 12B IT vs Laguna M.1](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-laguna-m-1.md)\n- [Gemma 4 12B IT vs Laguna S 2.1](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-laguna-s-2-1.md)\n- [Gemma 4 12B IT vs Laguna XS 2.1](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-laguna-xs-2-1.md)\n- [Gemma 4 12B IT vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-lfm2-5-2-6b.md)\n- [Gemma 4 12B IT vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ling-3-0-tiny.md)\n- [Gemma 4 12B IT vs MiMo-V2.6 Distill Qwen 9B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-mimo-v2-6-distill-qwen-9b.md)\n- [Gemma 4 12B IT vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-minicpm-o-4-5.md)\n- [Gemma 4 12B IT vs MiniCPM5-2B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-minicpm5-2b.md)\n- [Gemma 4 12B IT vs MiniMax-M2.5](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-minimax-m2-5.md)\n- [Gemma 4 12B IT vs MiniMax-M2.7](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-minimax-m2-7.md)\n- [Gemma 4 12B IT vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-muse-glimmer-30b.md)\n- [Gemma 4 12B IT vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [Gemma 4 12B IT vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-nemotron-3-nano-30b-a3b.md)\n- [Gemma 4 12B IT vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-nemotron-3-nano-4b.md)\n- [Gemma 4 12B IT vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [Gemma 4 12B IT vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-nemotron-3-super-120b-a12b.md)\n- [Gemma 4 12B IT vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-nemotron-3-ultra-550b-a55b.md)\n- [Gemma 4 12B IT vs occamy-1.0](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-occamy-1-0.md)\n- [Gemma 4 12B IT vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-olmo-3-7b-think.md)\n- [Gemma 4 12B IT vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ornith-1-5-35b-a3b.md)\n- [Gemma 4 12B IT vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ornith-1-5-397b.md)\n- [Gemma 4 12B IT vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ornith-1-5-9b.md)\n- [Gemma 4 12B IT vs Qwen3-30B-A3B-Instruct-2507](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-30b-a3b-instruct-2507.md)\n- [Gemma 4 12B IT vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-4b-instruct-2507.md)\n- [Gemma 4 12B IT vs Qwen3.5-0.8B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-5-0-8b.md)\n- [Gemma 4 12B IT vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-5-122b-a10b.md)\n- [Gemma 4 12B IT vs Qwen3.5-2B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-5-2b.md)\n- [Gemma 4 12B IT vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-5-397b-a17b.md)\n- [Gemma 4 12B IT vs Qwen3.5-4B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-5-4b.md)\n- [Gemma 4 12B IT vs 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"page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "DeepSeek-V3.2 vs Gemma 4 12B IT", "url": "" } ], "description": "Gemma 4 12B IT scores 71.3 (BB) to DeepSeek-V3.2's 58.6 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "DeepSeek-V3.2 C 58.6", "Gemma 4 12B IT BB 71.3", "scores" ], "h1": "DeepSeek-V3.2 vs Gemma 4 12B IT", "image": "https://www.anchorterminal.com/assets/og/compare-deepseek-v3-2-vs-gemma-4-12b-it.png", "path": "/compare/deepseek-v3-2-vs-gemma-4-12b-it", "published": "2026-10-01", "section": "tools", "title": "DeepSeek-V3.2 vs Gemma 4 12B IT for AI agents (2026) | Anchor Terminal", "toc": null, "updated": "2026-10-21", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gemma-4-12b-it" }, "tokens": { "markdown": 6150, "slim": 480 }, "version": 1 }