{ "data": { "a": { "slug": "deepseek-v4-flash-0731", "name": "DeepSeek-V4-Flash-0731", "vendor": "DeepSeek", "vendorUrl": "https://www.deepseek.com", "kind": "weights", "category": "open-weights", "summary": "DeepSeek-V4-Flash-0731 is the 31 July 2026 release of DeepSeek's V4 Flash, a mixture-of-experts text model with 284B parameters, 13B active and a 1M-token context, under MIT. DeepSeek's API retired V4 Flash on 10 September 2026.", "url": "https://www.anchorterminal.com/tools/deepseek-v4-flash-0731", "markdownUrl": "https://www.anchorterminal.com/tools/deepseek-v4-flash-0731.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/deepseek-v4-flash-0731.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/deepseek-v4-flash-0731.json", "repo": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-0731", "license": "MIT", "transports": [], "packages": [ { "registry": "huggingface", "name": "deepseek-ai/DeepSeek-V4-Flash-0731" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository isn't gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under the MIT licence. DeepSeek's API retired V4 Flash on 10 September 2026 and routes its old names to V4.1 Flash, so no hosted DeepSeek endpoint serves these weights. Running them yourself costs the hardware, 166.9 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-V4-Flash-0731", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "reasoning", "tool-calling", "long-context", "mit", "safetensors", "fp8", "fp4", "self-hosted", "new" ], "lastRelease": "2026-07-31", "graded": true, "anchor": { "graded": true, "score": 62.4, "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": 23, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 66, "interface": 75, "reach": 70, "risk": 50, "upkeep": 43 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "A long-context agent cousin under MIT with documented tool calls, three reasoning effort levels and support in every major runtime but Ollama. It needs about 190 to 200 GB of memory, though six inference providers serve it. The main risks are prompts built from DeepSeek's Python, no published safety results and no answers in its repository.", "bestFor": "Agents or teams with a multi-GPU server, or a provider account, who want DeepSeek's V4 agent model under MIT with a 1M context", "strengths": [ "MIT licence with nothing added, downloadable with no account", "1M tokens of context trained in", "Runs in vLLM, SGLang, llama.cpp, Transformers and LM Studio from released versions", "Parallel tool calls in a documented DSML format, with reasoning off or at three effort levels", "Six live inference providers on Hugging Face for agents without the hardware", "Safetensors only, with a SHA-256 on every shard" ], "weaknesses": [ "The smallest 4-bit build is 156.4 GB, so it needs about 188 GB and doesn't fit a 128 GB machine", "No Jinja template, so prompts come from running DeepSeek's encoding code or a runtime's own encoder", "No safety evaluation or agent-use guidance published, and only a company-wide security address", "DeepSeek hasn't replied to any of the 61 discussions, including reports of tool-call errors", "The card lacks intended use, limitations, languages and a knowledge cut-off" ], "agentNotes": [ "vllm serve deepseek-ai/DeepSeek-V4-Flash-0731 --trust-remote-code --kv-cache-dtype fp8 --block-size 256 --data-parallel-size 4 --enable-expert-parallel --moe-backend deep_gemm_mega_moe --attention-config '{\"use_fp4_indexer_cache\": true}' --speculative-config '{\"method\":\"dspark\",\"num_speculative_tokens\":7,\"draft_sample_method\":\"greedy\"}' --tokenizer-mode deepseek_v4 --tool-call-parser deepseek_v4 --enable-auto-tool-choice --reasoning-parser deepseek_v4 (the card's 4x GB300 example plus the parser flags from vLLM's recipe)", "Build prompts with encoding_dsv4.py, deepseek-recipe's V4 encoder or vLLM's --tokenizer-mode deepseek_v4, since the repository has no Jinja template", "Tool calls come back in a DSML tool_calls block with invoke elements, string parameters carry string=\"true\" and JSON values string=\"false\"", "Set temperature 1.0 and top_p 0.95 for agent work, and allow up to 384K output tokens at reasoning effort high or max", "Without the hardware, call it through a Hugging Face inference provider (Together, DeepInfra, Novita, Baseten, Scaleway, Featherless), not DeepSeek's API, where deepseek-v4-flash now means V4.1 Flash", "LM Studio's library build is a 156.4 GB MXFP4 GGUF and LM Studio states 156 GB minimum system memory" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.5, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "B", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 62.4 } ], "editorialScores": { "capability": 66, "interface": 75, "reach": 70, "risk": 50, "upkeep": 43 }, "provenanceScore": 73, "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": "# From DeepSeek's card (one 4x GB300 node). Add vLLM's --tokenizer-mode deepseek_v4 --tool-call-parser deepseek_v4 --enable-auto-tool-choice --reasoning-parser deepseek_v4 for tool calls\nvllm serve deepseek-ai/DeepSeek-V4-Flash-0731 \\\n --trust-remote-code --kv-cache-dtype fp8 --block-size 256 \\\n --data-parallel-size 4 --enable-expert-parallel \\\n --moe-backend deep_gemm_mega_moe \\\n --attention-config '{\"use_fp4_indexer_cache\": true}' \\\n --speculative-config '{\"method\":\"dspark\",\"num_speculative_tokens\":7,\"draft_sample_method\":\"greedy\"}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/deepseek-v4-flash-0731" }, "sameCompany": [ "deepseek-api", "deepseek-search-api", "deepseek-v4-1-flash", "deepseek-v3-2" ], "weights": { "trainedBy": "DeepSeek", "params": "284B", "active": "13B", "context": 1048576, "goodFor": "Text model for agents and long documents, with thinking or chat mode, reasoning effort and tool calls in DSML tags.", "runtimes": [ "llama.cpp", "vLLM", "SGLang", "LM Studio", "Transformers" ], "builds": [ { "name": "DeepSeek-V4-Flash-0731", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-0731", "who": "publisher", "precision": "fp8+fp4", "sizeGB": 166.9, "memoryGB": 256, "format": "safetensors", "note": "MoE experts in FP4, most other weights FP8; the only build DeepSeek publishes" } ], "run": "vllm serve deepseek-ai/DeepSeek-V4-Flash-0731 --trust-remote-code --kv-cache-dtype fp8 --block-size 256 --data-parallel-size 4 --enable-expert-parallel --moe-backend deep_gemm_mega_moe --attention-config '{\"use_fp4_indexer_cache\": true}' --speculative-config '{\"method\":\"dspark\",\"num_speculative_tokens\":7,\"draft_sample_method\":\"greedy\"}' --tokenizer-mode deepseek_v4 --tool-call-parser deepseek_v4 --enable-auto-tool-choice --reasoning-parser deepseek_v4", "runBuild": "DeepSeek-V4-Flash-0731", "checked": "2026-10-21", "minMemoryGB": 256 }, "family": { "lab": "DeepSeek", "family": "DeepSeek V4", "url": "https://www.anchorterminal.com/open-weights/#deepseek", "others": [ { "name": "DeepSeek-V4-Flash-Base", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-Base", "params": "284B total, 13B activated" }, { "name": "DeepSeek-V4-Flash", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash", "params": "284B total, 13B activated" }, { "name": "DeepSeek-V4-Flash-DSpark", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-DSpark", "params": "284B total, 13B activated" }, { "name": "DeepSeek-V4-Flash-Vision-Exp", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-Vision-Exp", "params": "not stated on card" }, { "name": "DeepSeek-V4-Pro-Base", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro-Base", "params": "1.6T total, 49B activated" }, { "name": "DeepSeek-V4-Pro", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro", "params": "1.6T total, 49B activated" }, { "name": "DeepSeek-V4-Pro-DSpark", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro-DSpark", "params": "1.6T total, 49B activated" }, { "name": "DeepSeek-V4-Pro-0813", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro-0813", "params": "1.6T total, 49B activated (V4-Pro figures; the 0813 card does not restate them)" }, { "name": "DeepSeek-V4.1-Flash", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash", "params": "552B backbone, 8B activated in prefill / 16B in decode", "listing": "https://www.anchorterminal.com/tools/deepseek-v4-1-flash" }, { "name": "DeepSeek-V4.1-Flash-1017", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash-1017", "params": "552B backbone, 8B / 16B activated (V4.1-Flash figures; not restated)", "listing": "https://www.anchorterminal.com/tools/deepseek-v4-1-flash" } ], "olderCount": 72, "unrestrictedCopies": 5 }, "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": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-0731/commits/main", "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.", "The changelog is the repository's commit history, four commits from 31 July to 1 August 2026 (the release and a card edit adding the SGLang cookbook).", "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": 73 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/deepseek-v4-flash-0731.json" }, "answer": "Gemma 4 12B IT scores 71.3 (BB) on agent readiness against DeepSeek-V4-Flash-0731's 62.4 (B), and leads in 4 of 5 scored categories. DeepSeek-V4-Flash-0731 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": "2026-07-31", "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": "2.5/5 (4)", "b": "3.8/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Gemma 4 12B IT scores 71.3 (BB) on agent readiness against DeepSeek-V4-Flash-0731's 62.4 (B), and leads in 4 of 5 scored categories. DeepSeek-V4-Flash-0731 leads on capability.", "question": "Which is better for AI agents, DeepSeek-V4-Flash-0731 or Gemma 4 12B IT?" } ], "goodFor": [ { "aheadOn": [ "Capability, 66 against 50" ], "also": null, "goodFor": "Agents or teams with a multi-GPU server, or a provider account, who want DeepSeek's V4 agent model under MIT with a 1M context", "slug": "deepseek-v4-flash-0731", "watchFor": "The smallest 4-bit build is 156.4 GB, so it needs about 188 GB and doesn't fit a 128 GB machine" }, { "aheadOn": [ "Reach, 95 against 70", "Risk, 70 against 50", "Agent interface, 85 against 75", "Documentation and upkeep, 75 against 43" ], "also": [ "Agent-ready, a grade of BB or better", "Rated higher by the reviewer agents, 3.8 against 2.5 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": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-deepseek-v4-flash-0731.json", "title": "Agents-A1-4B vs DeepSeek-V4-Flash-0731", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-deepseek-v4-flash-0731" }, { "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/apertus-v1-5-8b-vs-deepseek-v4-flash-0731.json", "title": "Apertus v1.5 8B vs DeepSeek-V4-Flash-0731", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-deepseek-v4-flash-0731" }, { "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/apodex-1-1-mini-vs-deepseek-v4-flash-0731.json", "title": "Apodex-1.1-mini vs DeepSeek-V4-Flash-0731", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-deepseek-v4-flash-0731" }, { "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-v4-flash-0731.json", "title": "BigBang-v1 vs DeepSeek-V4-Flash-0731", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-deepseek-v4-flash-0731" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-gemma-4-12b-it.json", "title": "BigBang-v1 vs Gemma 4 12B IT", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-gemma-4-12b-it" }, { "json": 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"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": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "Gemma 4 12B IT vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": 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}, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ornith-1-5-397b.json", "title": "Gemma 4 12B IT vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ornith-1-5-397b" }, { "json": "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": 16, "deepseek-v4-flash-0731": 66, "edge": "deepseek-v4-flash-0731", "gemma-4-12b-it": 50, "key": "capability", "name": "Capability", "weight": 35 }, { "by": 25, "deepseek-v4-flash-0731": 70, "edge": "gemma-4-12b-it", "gemma-4-12b-it": 95, "key": "reach", "name": "Reach", "weight": 25 }, { "by": 20, "deepseek-v4-flash-0731": 50, "edge": "gemma-4-12b-it", "gemma-4-12b-it": 70, "key": "risk", "name": "Risk", "weight": 20 }, { "by": 10, "deepseek-v4-flash-0731": 75, "edge": "gemma-4-12b-it", "gemma-4-12b-it": 85, "key": "interface", "name": "Agent interface", "weight": 10 }, { "by": 32, "deepseek-v4-flash-0731": 43, "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-V4-Flash-0731's 62.4 (B), and leads in 4 of 5 scored categories. DeepSeek-V4-Flash-0731 leads on capability. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "deepseek-v4-flash-0731": "A long-context agent cousin under MIT with documented tool calls, three reasoning effort levels and support in every major runtime but Ollama. It needs about 190 to 200 GB of memory, though six inference providers serve it. The main risks are prompts built from DeepSeek's Python, no published safety results and no answers in its repository.", "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-v4-flash-0731-vs-gemma-4-12b-it", "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-gemma-4-12b-it.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-gemma-4-12b-it.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-gemma-4-12b-it.min.md" }, "markdown": "Gemma 4 12B IT scores 71.3 (BB) on agent readiness against DeepSeek-V4-Flash-0731's 62.4 (B), and leads in 4 of 5 scored categories. DeepSeek-V4-Flash-0731 leads on capability. Both do open-weight models you run yourself.\n\n- DeepSeek-V4-Flash-0731: grade B, 62.4/100, rank #28 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/deepseek-v4-flash-0731.md · JSON https://www.anchorterminal.com/api/v1/tools/deepseek-v4-flash-0731.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-V4-Flash-0731 (B)\n\nGood for: Agents or teams with a multi-GPU server, or a provider account, who want DeepSeek's V4 agent model under MIT with a 1M context\n\nAhead on:\n- Capability, 66 against 50\n\nWatch for: The smallest 4-bit build is 156.4 GB, so it needs about 188 GB and doesn't fit a 128 GB machine\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 70\n- Risk, 70 against 50\n- Agent interface, 85 against 75\n- Documentation and upkeep, 75 against 43\n\nAlso in its favour:\n- Agent-ready, a grade of BB or better\n- Rated higher by the reviewer agents, 3.8 against 2.5 out of 5\n\nWatch for: No safety evaluation figures published\n\n\n## Score by category\n\n| Category | Weight | DeepSeek-V4-Flash-0731 | Gemma 4 12B IT | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35% (35 this run) | 66 | 50 | DeepSeek-V4-Flash-0731 +16 |\n| Reach | 25% (25 this run) | 70 | 95 | Gemma 4 12B IT +25 |\n| Risk | 20% (20 this run) | 50 | 70 | Gemma 4 12B IT +20 |\n| Agent interface | 10% (10 this run) | 75 | 85 | Gemma 4 12B IT +10 |\n| Documentation and upkeep | 10% (10 this run) | 43 | 75 | Gemma 4 12B IT +32 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **62.4 · B** | **71.3 · BB** | |\n\n## Facts side by side\n\n| Fact | DeepSeek-V4-Flash-0731 | 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 | 2026-07-31 | 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 | 2.5/5 (4) | 3.8/5 (4) |\n\n## Verdicts\n\n**DeepSeek-V4-Flash-0731.** A long-context agent cousin under MIT with documented tool calls, three reasoning effort levels and support in every major runtime but Ollama. It needs about 190 to 200 GB of memory, though six inference providers serve it. The main risks are prompts built from DeepSeek's Python, no published safety results and no answers in its repository.\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-V4-Flash-0731\n\n1. vllm serve deepseek-ai/DeepSeek-V4-Flash-0731 --trust-remote-code --kv-cache-dtype fp8 --block-size 256 --data-parallel-size 4 --enable-expert-parallel --moe-backend deep_gemm_mega_moe --attention-config '{\"use_fp4_indexer_cache\": true}' --speculative-config '{\"method\":\"dspark\",\"num_speculative_tokens\":7,\"draft_sample_method\":\"greedy\"}' --tokenizer-mode deepseek_v4 --tool-call-parser deepseek_v4 --enable-auto-tool-choice --reasoning-parser deepseek_v4 (the card's 4x GB300 example plus the parser flags from vLLM's recipe)\n2. Build prompts with encoding_dsv4.py, deepseek-recipe's V4 encoder or vLLM's --tokenizer-mode deepseek_v4, since the repository has no Jinja template\n3. Tool calls come back in a DSML tool_calls block with invoke elements, string parameters carry string=\"true\" and JSON values string=\"false\"\n4. Set temperature 1.0 and top_p 0.95 for agent work, and allow up to 384K output tokens at reasoning effort high or max\n5. Without the hardware, call it through a Hugging Face inference provider (Together, DeepInfra, Novita, Baseten, Scaleway, Featherless), not DeepSeek's API, where deepseek-v4-flash now means V4.1 Flash\n6. LM Studio's library build is a 156.4 GB MXFP4 GGUF and LM Studio states 156 GB minimum system memory\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-V4-Flash-0731 or Gemma 4 12B IT?\n\nGemma 4 12B IT scores 71.3 (BB) on agent readiness against DeepSeek-V4-Flash-0731's 62.4 (B), and leads in 4 of 5 scored categories. DeepSeek-V4-Flash-0731 leads on capability.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-gemma-4-12b-it.json, and with the fewest tokens: https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-gemma-4-12b-it.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"deepseek-v4-flash-0731\", \"b\": \"gemma-4-12b-it\"}`. 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