{ "data": { "a": { "slug": "deepseek-v4-1-flash", "name": "DeepSeek V4.1 Flash", "vendor": "DeepSeek", "vendorUrl": "https://www.deepseek.com", "kind": "weights", "category": "open-weights", "summary": "DeepSeek V4.1 Flash is DeepSeek's open-weight multimodal mixture-of-experts model, 552B backbone parameters with 8B active in prefill and 16B in decode and a 1M-token context, published on Hugging Face under MIT. The 17 October update is graded.", "url": "https://www.anchorterminal.com/tools/deepseek-v4-1-flash", "markdownUrl": "https://www.anchorterminal.com/tools/deepseek-v4-1-flash.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/deepseek-v4-1-flash.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/deepseek-v4-1-flash.json", "repo": "https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash-1017", "license": "MIT", "transports": [], "packages": [ { "registry": "huggingface", "name": "deepseek-ai/DeepSeek-V4.1-Flash-1017" }, { "registry": "huggingface", "name": "deepseek-ai/DeepSeek-V4.1-Flash" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Neither repository is gated (checked 2026-10-20).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under the MIT licence. DeepSeek's own API serves the same weights as deepseek-flash, listed separately. Running them yourself costs the hardware, 510 GB of files per repository (checked 2026-10-20).", "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 2026-10-20).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-20" }, "docsUrl": "https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash-1017", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "multimodal", "reasoning", "tool-calling", "mit", "safetensors", "fp8", "self-hosted", "new" ], "lastRelease": "2026-10-17", "graded": true, "anchor": { "graded": true, "score": 59, "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": 52, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 69, "interface": 75, "reach": 50, "risk": 50, "upkeep": 48 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "A large multimodal agent model with a 1M-token context, documented tool calls, JSON Schema output and a reasoning budget, under MIT. It needs about 612 GB of memory across several GPUs in vLLM or SGLang. The main risks are prompts built from DeepSeek's code rather than a template, no published safety results and no answers in its repositories.", "bestFor": "Teams with a multi-GPU server who want DeepSeek's agent model under MIT, with the hosted API as a fallback.", "strengths": [ "MIT licence with no use policy, downloadable with no account", "1M tokens of context trained in, with an effort budget from 1 to 100 and a non-thinking mode", "Tool-call format documented both ways with test cases, and DeepSeek ships the parser", "JSON Schema output documented in DeepSeek's deepseek-recipe library", "Released support in vLLM 0.32.0 and SGLang 0.5.22, and DeepSeek's API serves the same update" ], "weaknesses": [ "510.3 GB of files and no smaller build anywhere, so it won't fit the 107.4 GB reference machine", "No Jinja template in the 17 October repository, prompts are built by running DeepSeek's code", "No safety evaluation or agent-use guidance, and only a company-wide security address", "None of the 40 most recent discussions had a reply from DeepSeek", "Not in llama.cpp, Transformers, LM Studio or Ollama's local library" ], "agentNotes": [ "DeepSeek's reference run is its inference folder, convert.py with --model-parallel 8 --expert-dtype fp4, then torchrun --nproc-per-node 8 generate.py --ckpt-path \"$SAVE_PATH\" --config config.json --interactive", "In vLLM 0.32.0 the parsers are named deepseek_v41 for both --tool-call-parser and --reasoning-parser. That's vLLM's naming, DeepSeek doesn't document a vLLM command", "Build prompts with encoding.py or pip install deepseek-recipe, not the September repository's chat_template.jinja, which an open pull request says trails the 16 October encoding", "Tool calls come back in a DSML calls block with a space before calls, invoke and parameter, unlike V4", "Set reasoning_effort to 1 to 100, or low, high or max (50, 75, 100), in thinking mode only. Default is 75", "Sample at temperature 1.0 and top_p 1.0 and allow 256K or more output tokens, as the card recommends", "Without the hardware, call DeepSeek's API as deepseek-flash, which serves this exact update" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.8, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "C", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 59 } ], "editorialScores": { "capability": 69, "interface": 75, "reach": 50, "risk": 50, "upkeep": 48 }, "provenanceScore": 73, "checked": { "categories": { "capability": "2026-10-21", "interface": "2026-10-21", "reach": "2026-10-21", "risk": "2026-10-21", "upkeep": "2026-10-21" }, "last": "2026-10-21", "lastMoved": "2026-10-21", "oldest": "2026-10-21", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "# DeepSeek's reference demo, from the repository's inference folder (8 GPUs)\npip install -r inference/requirements.txt\npython convert.py --hf-ckpt-path \"$HF_CKPT_PATH\" --save-path \"$SAVE_PATH\" \\\n --model-parallel 8 --expert-dtype fp4 --tokenizer-path \"$HF_CKPT_PATH\"\ntorchrun --nproc-per-node 8 generate.py --ckpt-path \"$SAVE_PATH\" --config config.json --interactive" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/deepseek-v4-1-flash" }, "sameCompany": [ "deepseek-search-api", "deepseek-v3-2", "deepseek-v4-flash-0731" ], "sameModel": [ "deepseek-api" ], "weights": { "trainedBy": "DeepSeek", "params": "552B", "active": "8B prefill, 16B decode", "context": 1048576, "goodFor": "Multimodal mixture-of-experts model for agents and long context, text and images in, with tool calls and a reasoning budget.", "runtimes": [ "vLLM", "SGLang" ], "builds": [ { "name": "DeepSeek-V4.1-Flash-1017", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash-1017", "who": "publisher", "precision": "fp8+fp4", "sizeGB": 510.3, "memoryGB": 1024, "format": "safetensors", "note": "FP8 with FP4 experts (F8_E4M3 and I8 tensors); the graded 17 October update. The September repository, deepseek-ai/DeepSeek-V4.1-Flash, is the same size" } ], "run": "pip install -r inference/requirements.txt \u0026\u0026 python convert.py --hf-ckpt-path \"$HF_CKPT_PATH\" --save-path \"$SAVE_PATH\" --model-parallel 8 --expert-dtype fp4 --tokenizer-path \"$HF_CKPT_PATH\" \u0026\u0026 torchrun --nproc-per-node 8 generate.py --ckpt-path \"$SAVE_PATH\" --config config.json --interactive", "runBuild": "DeepSeek-V4.1-Flash-1017", "checked": "2026-10-21", "minMemoryGB": 1024 }, "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-0731", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-0731", "params": "284B total, 13B activated (V4-Flash figures; the 0731 card does not restate them)", "listing": "https://www.anchorterminal.com/tools/deepseek-v4-flash-0731" }, { "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" } ], "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-V4.1-Flash/commits/main", "securityTxt": "none", "checked": "2026-10-20", "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 September repository's commit history, whose messages say what changed (an encoding fix on 16 October, an indexer cache fix on 12 October, the chat template on 1 October).", "deepseek.com/.well-known/security.txt and www.deepseek.com/.well-known/security.txt answered 404 on 20 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-v4-1-flash.json", "live": { "slug": "deepseek-v4-1-flash", "securityTxt": { "url": "https://deepseek.com/.well-known/security.txt", "state": "none", "checkedAt": "2026-10-21T15:59:01.743888833Z" }, "pages": [ { "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash/commits/main", "kind": "changelog", "status": 200, "checkedAt": "2026-10-21T00:50:03.414291506Z", "changedAt": "2026-10-21T00:50:03.414291506Z", "fingerprint": "96f7ad6c7b35" } ], "updatedAt": "2026-10-21T15:59:01.743888833Z" } }, "answer": "Gemma 4 31B IT scores 74.1 (BB) on agent readiness against DeepSeek V4.1 Flash's 59 (C), and leads in 4 of 5 scored categories. DeepSeek V4.1 Flash leads on capability.", "b": { "slug": "gemma-4-31b-it", "name": "Gemma 4 31B IT", "vendor": "Google DeepMind", "vendorUrl": "https://gemma.ai", "kind": "weights", "category": "open-weights", "summary": "Gemma 4 31B IT is Google DeepMind's open-weight dense multimodal model of 30.7B parameters, taking text and images with a 256K-token context, published on Hugging Face in BF16 with 4-bit QAT builds under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/gemma-4-31b-it", "markdownUrl": "https://www.anchorterminal.com/tools/gemma-4-31b-it.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/gemma-4-31b-it.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/gemma-4-31b-it.json", "repo": "https://huggingface.co/google/gemma-4-31B-it", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "google/gemma-4-31B-it" }, { "registry": "huggingface", "name": "google/gemma-4-31B-it-qat-q4_0-gguf" }, { "registry": "huggingface", "name": "google/gemma-4-31B-it-qat-w4a16-ct" }, { "registry": "ollama", "name": "gemma4:31b" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository and Google's QAT repositories are not gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Running it yourself costs the hardware, about 69.9 GB at BF16 or 17.5 GB at Q4_0 per Google's memory table. Google also hosts it on the Gemini API's free tier, a separate listing (checked 2026-10-21).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-21" }, "docsUrl": "https://gemma.ai/models/gemma-4/", "llmsTxt": "https://gemma.ai/llms.txt", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "multimodal", "reasoning", "tool-calling", "apache-2.0", "safetensors", "gguf", "qat", "self-hosted" ], "lastRelease": "2026-07-20", "graded": true, "anchor": { "graded": true, "score": 74.1, "grade": "BB", "agentReady": true, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1197, "categoryRank": 1, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 61, "interface": 85, "reach": 95, "risk": 70, "upkeep": 65 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "A dense multimodal cousin with documented tool calling, optional thinking and a 256K context, for general agent work and image reading on one 24 GB GPU through Google's 4-bit build. It runs in every major runtime under plain Apache 2.0. The main risk is that Google publishes no safety results and has gone quiet on the repository since July.", "bestFor": "An agent with a 24 GB GPU or more that wants a dense multimodal cousin with tool calling under a plain licence", "strengths": [ "Google's own 17.7 GB QAT Q4_0 GGUF fits a 24 GB GPU, and Ollama's gemma4:31b is 20 GB", "Released support in llama.cpp, Ollama, vLLM, SGLang and Transformers", "Apache 2.0 with nothing added, no gate, and hosted on Google's Gemini API too", "Tool calling documented by Google with a response schema Transformers parses, several calls per turn", "Thinking that can be switched on or off per request and 262,144 tokens of native context" ], "weaknesses": [ "No safety evaluation results published", "No prompt-injection guidance and no security route tied to Gemma", "Structured output isn't documented by Google", "Training data described only in general terms", "Files unchanged since 20 July and no Google reply in the discussions since 30 July" ], "agentNotes": [ "On the reference machine run `llama-server -hf google/gemma-4-31B-it-qat-q4_0-gguf --hf-file gemma-4-31B_q4_0-it.gguf --jinja -c 32768`, adding gemma-4-31B-it-mmproj.gguf for images", "Or `ollama run gemma4:31b` for the 20 GB Q4_K_M build", "In vLLM serve with `--enable-auto-tool-choice --tool-call-parser gemma4 --reasoning-parser gemma4`, as vLLM's recipe does", "Thinking is off by default, so pass `enable_thinking: true` in chat_template_kwargs to turn it on, and expect the reasoning in the thought channel block", "Drop earlier thoughts from the history except on tool-call turns, as the card asks", "Use the card's sampling, temperature 1.0, top_p 0.95, top_k 64", "Validate function names and arguments before running a call, and check JSON output yourself or use the runtime's guided decoding", "The w4a16 build relies on Marlin INT4 kernels that vLLM marks unsupported on AMD, so use the GGUF on the reference machine" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3.8, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "BB", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 74.1 } ], "editorialScores": { "capability": 61, "interface": 85, "reach": 95, "risk": 70, "upkeep": 65 }, "provenanceScore": 68, "checked": { "categories": { "capability": "2026-10-21", "interface": "2026-10-21", "reach": "2026-10-21", "risk": "2026-10-21", "upkeep": "2026-10-21" }, "last": "2026-10-21", "oldest": "2026-10-21", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "ollama run gemma4:31b\n# or, with vLLM's recipe:\nvllm serve google/gemma-4-31B-it --max-model-len 32768 --enable-auto-tool-choice \\\n --reasoning-parser gemma4 --tool-call-parser gemma4", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"gemma4:31b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/gemma-4-31b-it" }, "sameCompany": [ "embeddinggemma-300m", "diffusiongemma-26b-a4b-it", "gemma-4-12b-it", "gemma-4-26b-a4b-it", "gemma-4-e2b-it", "gemma-4-e4b-it" ], "sameModel": [ "gemini-api" ], "weights": { "trainedBy": "Google DeepMind", "builtOn": "google/gemma-4-31B", "params": "30.7B", "context": 262144, "goodFor": "Dense general assistant and agent model with tool calling and optional thinking, taking text, images and video frames.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "gemma-4-31B_q4_0-it", "url": "https://huggingface.co/google/gemma-4-31B-it-qat-q4_0-gguf/blob/main/gemma-4-31B_q4_0-it.gguf", "who": "publisher", "precision": "q4_0", "sizeGB": 17.7, "memoryGB": 24, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)", "LM Studio" ], "note": "Quantisation-aware trained by Google. Add gemma-4-31B-it-mmproj.gguf (1.2 GB) for image input" }, { "name": "ollama gemma4:31b", "url": "https://ollama.com/library/gemma4:31b", "who": "ollama", "precision": "q4_k_m", "sizeGB": 20, "memoryGB": 24, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Q4_K_M weights with a BF16 vision projector and a Q8_0 draft model (digest 17ba34c06c80). Size as Ollama's tags page shows it, rounded to whole GB above 10 GB; the Linux build is the llama.cpp variant" }, { "name": "gemma-4-31B-it-qat-w4a16-ct", "url": "https://huggingface.co/google/gemma-4-31B-it-qat-w4a16-ct", "who": "publisher", "precision": "w4a16", "sizeGB": 23.3, "memoryGB": 32, "format": "safetensors", "note": "Quantisation-aware trained, compressed-tensors format for vLLM. vLLM's quantisation table marks its Marlin INT4 kernels unsupported on AMD" }, { "name": "gemma-4-31B-it", "url": "https://huggingface.co/google/gemma-4-31B-it", "who": "publisher", "precision": "bf16", "sizeGB": 62.5, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] } ], "run": "llama-server -hf google/gemma-4-31B-it-qat-q4_0-gguf --hf-file gemma-4-31B_q4_0-it.gguf --jinja -c 32768", "runBuild": "gemma-4-31B_q4_0-it", "checked": "2026-10-21", "minMemoryGB": 24 }, "family": { "lab": "Google (Gemma)", "family": "Gemma 4", "url": "https://www.anchorterminal.com/open-weights/#google", "others": [ { "name": "gemma-4-26B-A4B", "url": "https://huggingface.co/google/gemma-4-26B-A4B", "params": "25.2B total, 3.8B active" }, { "name": "gemma-4-26B-A4B-it", "url": "https://huggingface.co/google/gemma-4-26B-A4B-it", "params": "25.2B total, 3.8B active", "listing": "https://www.anchorterminal.com/tools/gemma-4-26b-a4b-it" }, { "name": "gemma-4-31B", "url": "https://huggingface.co/google/gemma-4-31B", "params": "30.7B" }, { "name": "gemma-4-E2B", "url": "https://huggingface.co/google/gemma-4-E2B", "params": "2.3B effective (5.1B with embeddings)" }, { "name": "gemma-4-E2B-it", "url": "https://huggingface.co/google/gemma-4-E2B-it", "params": "2.3B effective (5.1B with embeddings)", "listing": "https://www.anchorterminal.com/tools/gemma-4-e2b-it" }, { "name": "gemma-4-E4B", "url": "https://huggingface.co/google/gemma-4-E4B", "params": "4.5B effective (8B with embeddings)" }, { "name": "gemma-4-E4B-it", "url": "https://huggingface.co/google/gemma-4-E4B-it", "params": "4.5B effective (8B with embeddings)", "listing": "https://www.anchorterminal.com/tools/gemma-4-e4b-it" }, { "name": "gemma-4-26B-A4B-it-assistant", "url": "https://huggingface.co/google/gemma-4-26B-A4B-it-assistant", "params": "420M (file metadata on Hugging Face; drafter)" }, { "name": "gemma-4-31B-it-assistant", "url": "https://huggingface.co/google/gemma-4-31B-it-assistant", "params": "470M (file metadata on Hugging Face; drafter)" }, { "name": "gemma-4-E2B-it-assistant", "url": "https://huggingface.co/google/gemma-4-E2B-it-assistant", "params": "78M (file metadata on Hugging Face; drafter)" }, { "name": "gemma-4-E4B-it-assistant", "url": "https://huggingface.co/google/gemma-4-E4B-it-assistant", "params": "79M (file metadata on Hugging Face; drafter)" }, { "name": "gemma-4-12B", "url": "https://huggingface.co/google/gemma-4-12B", "params": "11.95B" }, { "name": "gemma-4-12B-it", "url": "https://huggingface.co/google/gemma-4-12B-it", "params": "11.95B", "listing": "https://www.anchorterminal.com/tools/gemma-4-12b-it" }, { "name": "gemma-4-12B-it-assistant", "url": "https://huggingface.co/google/gemma-4-12B-it-assistant", "params": "423M (file metadata on Hugging Face; drafter)" }, { "name": "diffusiongemma-26B-A4B-it", "url": "https://huggingface.co/google/diffusiongemma-26B-A4B-it", "params": "25.2B total, 3.8B active", "listing": "https://www.anchorterminal.com/tools/diffusiongemma-26b-a4b-it" }, { "name": "embeddinggemma-2", "url": "https://huggingface.co/google/embeddinggemma-2", "params": "740M" } ], "olderCount": 171, "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "Google LLC (the registrant of gemma.ai; the card names Google DeepMind as author)", "domain": "gemma.ai", "domainRegistered": "2017-12-16", "domainNote": "RDAP shows gemma.ai registered in 2017 and transferred on 15 April 2025, so the registration date may predate Google's use of the domain.", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://gemma.ai/models/releases/", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "RDAP names Google LLC as registrant of gemma.ai, with MarkMonitor as registrar. Google's ai.google.dev pages point to gemma.ai as the current documentation.", "Nothing is hosted under this listing, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "The changelog is Google's Gemma releases page, which dates each release (Gemma 4 on 31 March 2026, its MTP drafters on 16 April). The Hugging Face commit history's messages also say what changed.", "gemma.ai/.well-known/security.txt answered 404 on 21 October 2026. www.google.com has a security.txt pointing to Google's vulnerability reward programme, which isn't linked from the card or gemma.ai." ], "score": 68 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/gemma-4-31b-it.json" }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "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": "2026-10-17", "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": "2.8/5 (4)", "b": "3.8/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Gemma 4 31B IT scores 74.1 (BB) on agent readiness against DeepSeek V4.1 Flash's 59 (C), and leads in 4 of 5 scored categories. DeepSeek V4.1 Flash leads on capability.", "question": "Which is better for AI agents, DeepSeek V4.1 Flash or Gemma 4 31B IT?" } ], "goodFor": [ { "aheadOn": [ "Capability, 69 against 61" ], "also": null, "goodFor": "Teams with a multi-GPU server who want DeepSeek's agent model under MIT, with the hosted API as a fallback.", "slug": "deepseek-v4-1-flash", "watchFor": "510.3 GB of files and no smaller build anywhere, so it won't fit the 107.4 GB reference machine" }, { "aheadOn": [ "Reach, 95 against 50", "Risk, 70 against 50", "Agent interface, 85 against 75", "Documentation and upkeep, 65 against 48" ], "also": [ "Agent-ready, a grade of BB or better", "Rated higher by the reviewer agents, 3.8 against 2.8 out of 5" ], "goodFor": "An agent with a 24 GB GPU or more that wants a dense multimodal cousin with tool calling under a plain licence", "slug": "gemma-4-31b-it", "watchFor": "No safety evaluation results published" } ], "job": { "capability": "model.open-weights", "name": 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"https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-deepseek-v4-1-flash.json", "title": "Apodex-1.1-mini vs DeepSeek V4.1 Flash", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-deepseek-v4-1-flash" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gemma-4-31b-it.json", "title": "Apodex-1.1-mini vs Gemma 4 31B IT", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gemma-4-31b-it" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-deepseek-v4-1-flash.json", "title": "BigBang-v1 vs DeepSeek V4.1 Flash", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-deepseek-v4-1-flash" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-gemma-4-31b-it.json", "title": "BigBang-v1 vs Gemma 4 31B IT", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-gemma-4-31b-it" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-deepseek-v4-1-flash.json", "title": 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"https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-glm-4-5-air" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-glm-4-7-flash.json", "title": "DeepSeek V4.1 Flash vs GLM-4.7-Flash", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-glm-4-7-flash" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-glm-5-3.json", "title": "DeepSeek V4.1 Flash vs GLM-5.3", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-glm-5-3" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-glm-5-3-flash.json", "title": "DeepSeek V4.1 Flash vs GLM-5.3-Flash", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-glm-5-3-flash" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gpt-oss-120b.json", "title": "DeepSeek V4.1 Flash vs gpt-oss-120b", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gpt-oss-120b" }, { "json": 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Inkling-Small", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-inkling-small" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-internvl3-5-gpt-oss-20b-a4b-preview.json", "title": "DeepSeek V4.1 Flash vs InternVL3.5-GPT-OSS-20B-A4B-Preview", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-internvl3-5-gpt-oss-20b-a4b-preview" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-kimi-k3.json", "title": "DeepSeek V4.1 Flash vs Kimi K3", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-kimi-k3" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-kolibri-1.json", "title": "DeepSeek V4.1 Flash vs Kolibri-1", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-kolibri-1" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-laguna-m-1.json", "title": "DeepSeek V4.1 Flash vs Laguna M.1", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-laguna-m-1" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-laguna-s-2-1.json", "title": "DeepSeek V4.1 Flash vs Laguna S 2.1", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-laguna-s-2-1" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-laguna-xs-2-1.json", "title": "DeepSeek V4.1 Flash vs Laguna XS 2.1", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-laguna-xs-2-1" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-lfm2-5-2-6b.json", "title": "DeepSeek V4.1 Flash vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ling-3-0-tiny.json", "title": "DeepSeek V4.1 Flash vs Ling-3.0-tiny", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ling-3-0-tiny" }, { 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{ "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-nano-4b.json", "title": "DeepSeek V4.1 Flash vs Nemotron 3 Nano 4B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-nano-4b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json", "title": "DeepSeek V4.1 Flash vs Nemotron 3 Nano Omni 30B A3B Reasoning", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-nano-omni-30b-a3b-reasoning" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-super-120b-a12b.json", "title": "DeepSeek V4.1 Flash vs Nemotron 3 Super 120B A12B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-super-120b-a12b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-ultra-550b-a55b.json", "title": "DeepSeek V4.1 Flash vs Nemotron 3 Ultra 550B A55B", "url": 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}, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ornith-1-5-397b.json", "title": "Gemma 4 31B IT vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ornith-1-5-9b.json", "title": "Gemma 4 31B IT vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-30b-a3b-instruct-2507.json", "title": "Gemma 4 31B IT vs Qwen3-30B-A3B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-30b-a3b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-4b-instruct-2507.json", "title": "Gemma 4 31B IT vs Qwen3-4B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-4b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-5-0-8b.json", "title": "Gemma 4 31B IT vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-5-122b-a10b.json", "title": "Gemma 4 31B IT vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-5-2b.json", "title": "Gemma 4 31B IT vs Qwen3.5-2B", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-5-2b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-5-397b-a17b.json", "title": "Gemma 4 31B IT vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-5-4b.json", "title": "Gemma 4 31B IT vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-5-9b.json", "title": "Gemma 4 31B IT vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-6-27b.json", "title": "Gemma 4 31B IT vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-6-35b-a3b.json", "title": "Gemma 4 31B IT vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-8-27b.json", "title": "Gemma 4 31B IT vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-8-flash-next.json", "title": "Gemma 4 31B IT vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Gemma 4 31B IT vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-coder-next.json", "title": "Gemma 4 31B IT vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Gemma 4 31B IT vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-vl-2b-instruct.json", "title": "Gemma 4 31B IT vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Gemma 4 31B IT vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-vl-32b-instruct.json", "title": "Gemma 4 31B IT vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-vl-4b-instruct.json", "title": "Gemma 4 31B IT vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-vl-8b-instruct.json", "title": "Gemma 4 31B IT vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwopus3-8-27b-flash-v2.json", "title": "Gemma 4 31B IT vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-smollm3-3b.json", "title": "Gemma 4 31B IT vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-spark-x2-6-4b.json", "title": "Gemma 4 31B IT vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-swift-1-5-qwen3-8-27b.json", "title": "Gemma 4 31B IT vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Gemma 4 31B IT vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-thinkingcap-qwen3-8-27b.json", "title": "Gemma 4 31B IT vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-thinkingcap-qwen3-8-27b" } ], "scores": [ { "by": 8, "deepseek-v4-1-flash": 69, "edge": "deepseek-v4-1-flash", "gemma-4-31b-it": 61, "key": "capability", "name": "Capability", "weight": 35 }, { "by": 45, "deepseek-v4-1-flash": 50, "edge": "gemma-4-31b-it", "gemma-4-31b-it": 95, "key": "reach", "name": "Reach", "weight": 25 }, { "by": 20, "deepseek-v4-1-flash": 50, "edge": "gemma-4-31b-it", "gemma-4-31b-it": 70, "key": "risk", "name": "Risk", "weight": 20 }, { "by": 10, "deepseek-v4-1-flash": 75, "edge": "gemma-4-31b-it", "gemma-4-31b-it": 85, "key": "interface", "name": "Agent interface", "weight": 10 }, { "by": 17, "deepseek-v4-1-flash": 48, "edge": "gemma-4-31b-it", "gemma-4-31b-it": 65, "key": "upkeep", "name": "Documentation and upkeep", "weight": 10 } ], "summary": "Gemma 4 31B IT scores 74.1 (BB) on agent readiness against DeepSeek V4.1 Flash's 59 (C), and leads in 4 of 5 scored categories. DeepSeek V4.1 Flash leads on capability. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "deepseek-v4-1-flash": "A large multimodal agent model with a 1M-token context, documented tool calls, JSON Schema output and a reasoning budget, under MIT. It needs about 612 GB of memory across several GPUs in vLLM or SGLang. The main risks are prompts built from DeepSeek's code rather than a template, no published safety results and no answers in its repositories.", "gemma-4-31b-it": "A dense multimodal cousin with documented tool calling, optional thinking and a 256K context, for general agent work and image reading on one 24 GB GPU through Google's 4-bit build. It runs in every major runtime under plain Apache 2.0. The main risk is that Google publishes no safety results and has gone quiet on the repository since July." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gemma-4-31b-it", "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gemma-4-31b-it.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gemma-4-31b-it.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gemma-4-31b-it.min.md" }, "markdown": "Gemma 4 31B IT scores 74.1 (BB) on agent readiness against DeepSeek V4.1 Flash's 59 (C), and leads in 4 of 5 scored categories. DeepSeek V4.1 Flash leads on capability. Both do open-weight models you run yourself.\n\n- DeepSeek V4.1 Flash: grade C, 59/100, rank #63 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/deepseek-v4-1-flash.md · JSON https://www.anchorterminal.com/api/v1/tools/deepseek-v4-1-flash.json\n- Gemma 4 31B IT: grade BB, 74.1/100, rank #2 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/gemma-4-31b-it.md · JSON https://www.anchorterminal.com/api/v1/tools/gemma-4-31b-it.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### DeepSeek V4.1 Flash (C)\n\nGood for: Teams with a multi-GPU server who want DeepSeek's agent model under MIT, with the hosted API as a fallback.\n\nAhead on:\n- Capability, 69 against 61\n\nWatch for: 510.3 GB of files and no smaller build anywhere, so it won't fit the 107.4 GB reference machine\n\n### Gemma 4 31B IT (BB)\n\nGood for: An agent with a 24 GB GPU or more that wants a dense multimodal cousin with tool calling under a plain licence\n\nAhead on:\n- Reach, 95 against 50\n- Risk, 70 against 50\n- Agent interface, 85 against 75\n- Documentation and upkeep, 65 against 48\n\nAlso in its favour:\n- Agent-ready, a grade of BB or better\n- Rated higher by the reviewer agents, 3.8 against 2.8 out of 5\n\nWatch for: No safety evaluation results published\n\n\n## Score by category\n\n| Category | Weight | DeepSeek V4.1 Flash | Gemma 4 31B IT | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35% (35 this run) | 69 | 61 | DeepSeek V4.1 Flash +8 |\n| Reach | 25% (25 this run) | 50 | 95 | Gemma 4 31B IT +45 |\n| Risk | 20% (20 this run) | 50 | 70 | Gemma 4 31B IT +20 |\n| Agent interface | 10% (10 this run) | 75 | 85 | Gemma 4 31B IT +10 |\n| Documentation and upkeep | 10% (10 this run) | 48 | 65 | Gemma 4 31B IT +17 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **59 · C** | **74.1 · BB** | |\n\n## Facts side by side\n\n| Fact | DeepSeek V4.1 Flash | Gemma 4 31B 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 | 2026-10-17 | 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 | 2.8/5 (4) | 3.8/5 (4) |\n\n## Verdicts\n\n**DeepSeek V4.1 Flash.** A large multimodal agent model with a 1M-token context, documented tool calls, JSON Schema output and a reasoning budget, under MIT. It needs about 612 GB of memory across several GPUs in vLLM or SGLang. The main risks are prompts built from DeepSeek's code rather than a template, no published safety results and no answers in its repositories.\n\n**Gemma 4 31B IT.** A dense multimodal cousin with documented tool calling, optional thinking and a 256K context, for general agent work and image reading on one 24 GB GPU through Google's 4-bit build. It runs in every major runtime under plain Apache 2.0. The main risk is that Google publishes no safety results and has gone quiet on the repository since July.\n\n## Before you call either\n\n### DeepSeek V4.1 Flash\n\n1. DeepSeek's reference run is its inference folder, convert.py with --model-parallel 8 --expert-dtype fp4, then torchrun --nproc-per-node 8 generate.py --ckpt-path \"$SAVE_PATH\" --config config.json --interactive\n2. In vLLM 0.32.0 the parsers are named deepseek_v41 for both --tool-call-parser and --reasoning-parser. That's vLLM's naming, DeepSeek doesn't document a vLLM command\n3. Build prompts with encoding.py or pip install deepseek-recipe, not the September repository's chat_template.jinja, which an open pull request says trails the 16 October encoding\n4. Tool calls come back in a DSML calls block with a space before calls, invoke and parameter, unlike V4\n5. Set reasoning_effort to 1 to 100, or low, high or max (50, 75, 100), in thinking mode only. Default is 75\n6. Sample at temperature 1.0 and top_p 1.0 and allow 256K or more output tokens, as the card recommends\n7. Without the hardware, call DeepSeek's API as deepseek-flash, which serves this exact update\n\n### Gemma 4 31B IT\n\n1. On the reference machine run `llama-server -hf google/gemma-4-31B-it-qat-q4_0-gguf --hf-file gemma-4-31B_q4_0-it.gguf --jinja -c 32768`, adding gemma-4-31B-it-mmproj.gguf for images\n2. Or `ollama run gemma4:31b` for the 20 GB Q4_K_M build\n3. In vLLM serve with `--enable-auto-tool-choice --tool-call-parser gemma4 --reasoning-parser gemma4`, as vLLM's recipe does\n4. Thinking is off by default, so pass `enable_thinking: true` in chat_template_kwargs to turn it on, and expect the reasoning in the thought channel block\n5. Drop earlier thoughts from the history except on tool-call turns, as the card asks\n6. Use the card's sampling, temperature 1.0, top_p 0.95, top_k 64\n7. Validate function names and arguments before running a call, and check JSON output yourself or use the runtime's guided decoding\n8. The w4a16 build relies on Marlin INT4 kernels that vLLM marks unsupported on AMD, so use the GGUF on the reference machine\n\n## Questions\n\n### Which is better for AI agents, DeepSeek V4.1 Flash or Gemma 4 31B IT?\n\nGemma 4 31B IT scores 74.1 (BB) on agent readiness against DeepSeek V4.1 Flash's 59 (C), and leads in 4 of 5 scored categories. DeepSeek V4.1 Flash leads on capability.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gemma-4-31b-it.json, and with the fewest tokens: https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gemma-4-31b-it.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"deepseek-v4-1-flash\", \"b\": \"gemma-4-31b-it\"}`. From a terminal: `anchor compare deepseek-v4-1-flash gemma-4-31b-it`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/deepseek-v4-1-flash.json and https://www.anchorterminal.com/api/v1/tools/gemma-4-31b-it.json\n\n## Other comparisons with DeepSeek V4.1 Flash or Gemma 4 31B IT\n\n- [Agents-A1-4B vs DeepSeek V4.1 Flash](https://www.anchorterminal.com/compare/agents-a1-4b-vs-deepseek-v4-1-flash.md)\n- [Agents-A1-4B vs Gemma 4 31B IT](https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-31b-it.md)\n- [Apertus v1.5 8B vs DeepSeek V4.1 Flash](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-deepseek-v4-1-flash.md)\n- [Apertus v1.5 8B vs Gemma 4 31B IT](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-31b-it.md)\n- [Apodex-1.1-mini vs DeepSeek V4.1 Flash](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-deepseek-v4-1-flash.md)\n- [Apodex-1.1-mini vs Gemma 4 31B IT](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gemma-4-31b-it.md)\n- [BigBang-v1 vs DeepSeek V4.1 Flash](https://www.anchorterminal.com/compare/bigbang-v1-vs-deepseek-v4-1-flash.md)\n- [BigBang-v1 vs Gemma 4 31B IT](https://www.anchorterminal.com/compare/bigbang-v1-vs-gemma-4-31b-it.md)\n- [Cosmos-Reason2-2B vs DeepSeek V4.1 Flash](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-deepseek-v4-1-flash.md)\n- [Cosmos-Reason2-2B vs Gemma 4 31B IT](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-gemma-4-31b-it.md)\n- [Darwin-36B-Opus vs DeepSeek V4.1 Flash](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-deepseek-v4-1-flash.md)\n- [Darwin-36B-Opus vs Gemma 4 31B IT](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-gemma-4-31b-it.md)\n- [DeepSeek-V3.2 vs DeepSeek V4.1 Flash](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-deepseek-v4-1-flash.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 V4.1 Flash vs DeepSeek-V4-Flash-0731](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-deepseek-v4-flash-0731.md)\n- [DeepSeek V4.1 Flash vs DiffusionGemma 26B A4B IT](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-diffusiongemma-26b-a4b-it.md)\n- [DeepSeek V4.1 Flash vs Gemma 4 12B IT](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gemma-4-12b-it.md)\n- [DeepSeek V4.1 Flash vs Gemma 4 26B A4B IT](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gemma-4-26b-a4b-it.md)\n- [DeepSeek V4.1 Flash vs Gemma 4 E2B IT](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gemma-4-e2b-it.md)\n- [DeepSeek V4.1 Flash vs Gemma 4 E4B IT](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gemma-4-e4b-it.md)\n- [DeepSeek V4.1 Flash vs GLM-4.5-Air](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-glm-4-5-air.md)\n- [DeepSeek 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Inkling-Small](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-inkling-small.md)\n- [DeepSeek V4.1 Flash vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [DeepSeek V4.1 Flash vs Kimi K3](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-kimi-k3.md)\n- [DeepSeek V4.1 Flash vs Kolibri-1](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-kolibri-1.md)\n- [DeepSeek V4.1 Flash vs Laguna M.1](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-laguna-m-1.md)\n- [DeepSeek V4.1 Flash vs Laguna S 2.1](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-laguna-s-2-1.md)\n- [DeepSeek V4.1 Flash vs Laguna XS 2.1](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-laguna-xs-2-1.md)\n- [DeepSeek V4.1 Flash vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-lfm2-5-2-6b.md)\n- [DeepSeek V4.1 Flash vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ling-3-0-tiny.md)\n- [DeepSeek V4.1 Flash vs MiMo-V2.6 Distill Qwen 9B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-mimo-v2-6-distill-qwen-9b.md)\n- [DeepSeek V4.1 Flash vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-minicpm-o-4-5.md)\n- [DeepSeek V4.1 Flash vs MiniCPM5-2B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-minicpm5-2b.md)\n- [DeepSeek V4.1 Flash vs MiniMax-M2.5](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-minimax-m2-5.md)\n- [DeepSeek V4.1 Flash vs MiniMax-M2.7](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-minimax-m2-7.md)\n- [DeepSeek V4.1 Flash vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-muse-glimmer-30b.md)\n- [DeepSeek V4.1 Flash vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [DeepSeek V4.1 Flash vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-nano-30b-a3b.md)\n- [DeepSeek V4.1 Flash vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-nano-4b.md)\n- [DeepSeek V4.1 Flash vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [DeepSeek V4.1 Flash vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-super-120b-a12b.md)\n- [DeepSeek V4.1 Flash vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-ultra-550b-a55b.md)\n- [DeepSeek V4.1 Flash vs occamy-1.0](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-occamy-1-0.md)\n- [DeepSeek V4.1 Flash vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-olmo-3-7b-think.md)\n- [DeepSeek V4.1 Flash vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ornith-1-5-35b-a3b.md)\n- [DeepSeek V4.1 Flash vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ornith-1-5-397b.md)\n- [DeepSeek V4.1 Flash vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ornith-1-5-9b.md)\n- [DeepSeek V4.1 Flash vs Qwen3-30B-A3B-Instruct-2507](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-30b-a3b-instruct-2507.md)\n- [DeepSeek V4.1 Flash vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-4b-instruct-2507.md)\n- [DeepSeek V4.1 Flash vs Qwen3.5-0.8B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-5-0-8b.md)\n- [DeepSeek V4.1 Flash vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-5-122b-a10b.md)\n- [DeepSeek V4.1 Flash vs Qwen3.5-2B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-5-2b.md)\n- [DeepSeek V4.1 Flash vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-5-397b-a17b.md)\n- [DeepSeek V4.1 Flash vs Qwen3.5-4B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-5-4b.md)\n- [DeepSeek V4.1 Flash vs Qwen3.5-9B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-5-9b.md)\n- [DeepSeek V4.1 Flash vs Qwen3.6-27B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-6-27b.md)\n- [DeepSeek V4.1 Flash vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-6-35b-a3b.md)\n- [DeepSeek V4.1 Flash vs Qwen3.8-27B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-8-27b.md)\n- [DeepSeek V4.1 Flash vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-8-flash-next.md)\n- [DeepSeek V4.1 Flash vs Qwen3-Coder-30B-A3B-Instruct](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-coder-30b-a3b-instruct.md)\n- [DeepSeek V4.1 Flash vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-coder-next.md)\n- [DeepSeek V4.1 Flash vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-omni-30b-a3b-instruct.md)\n- [DeepSeek V4.1 Flash vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-vl-2b-instruct.md)\n- [DeepSeek V4.1 Flash vs Qwen3-VL-30B-A3B-Instruct](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-vl-30b-a3b-instruct.md)\n- [DeepSeek V4.1 Flash vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-vl-32b-instruct.md)\n- [DeepSeek V4.1 Flash vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-vl-4b-instruct.md)\n- [DeepSeek V4.1 Flash vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-vl-8b-instruct.md)\n- [DeepSeek V4.1 Flash vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwopus3-8-27b-flash-v2.md)\n- [DeepSeek V4.1 Flash vs SmolLM3-3B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-smollm3-3b.md)\n- [DeepSeek V4.1 Flash vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-spark-x2-6-4b.md)\n- [DeepSeek V4.1 Flash vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-swift-1-5-qwen3-8-27b.md)\n- [DeepSeek V4.1 Flash vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-swift-1-5-qwen3-8-flash-next.md)\n- [DeepSeek V4.1 Flash vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-thinkingcap-qwen3-8-27b.md)\n- [DeepSeek-V4-Flash-0731 vs Gemma 4 31B IT](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-gemma-4-31b-it.md)\n- [DiffusionGemma 26B A4B IT vs Gemma 4 31B IT](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-gemma-4-31b-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 26B A4B IT vs Gemma 4 31B IT](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-gemma-4-31b-it.md)\n- [Gemma 4 31B IT vs Gemma 4 E2B IT](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-gemma-4-e2b-it.md)\n- [Gemma 4 31B IT vs Gemma 4 E4B IT](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-gemma-4-e4b-it.md)\n- [Gemma 4 31B IT vs GLM-4.5-Air](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-glm-4-5-air.md)\n- [Gemma 4 31B IT vs GLM-4.7-Flash](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-glm-4-7-flash.md)\n- [Gemma 4 31B IT vs GLM-5.3](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-glm-5-3.md)\n- [Gemma 4 31B IT vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-glm-5-3-flash.md)\n- [Gemma 4 31B IT vs gpt-oss-120b](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-gpt-oss-120b.md)\n- [Gemma 4 31B IT vs gpt-oss-20b](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-gpt-oss-20b.md)\n- [Gemma 4 31B IT vs Granite 4.2 3B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-granite-4-2-3b.md)\n- [Gemma 4 31B IT vs Hy3](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-hy3.md)\n- [Gemma 4 31B IT vs Inkling](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-inkling.md)\n- [Gemma 4 31B IT vs Inkling-Small](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-inkling-small.md)\n- [Gemma 4 31B IT vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [Gemma 4 31B IT vs Kimi K3](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-kimi-k3.md)\n- [Gemma 4 31B IT vs Kolibri-1](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-kolibri-1.md)\n- [Gemma 4 31B IT vs Laguna M.1](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-laguna-m-1.md)\n- [Gemma 4 31B IT vs Laguna S 2.1](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-laguna-s-2-1.md)\n- [Gemma 4 31B IT vs Laguna XS 2.1](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-laguna-xs-2-1.md)\n- [Gemma 4 31B IT vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-lfm2-5-2-6b.md)\n- [Gemma 4 31B IT vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ling-3-0-tiny.md)\n- [Gemma 4 31B IT vs MiMo-V2.6 Distill Qwen 9B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-mimo-v2-6-distill-qwen-9b.md)\n- [Gemma 4 31B IT vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-minicpm-o-4-5.md)\n- [Gemma 4 31B IT vs MiniCPM5-2B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-minicpm5-2b.md)\n- [Gemma 4 31B IT vs MiniMax-M2.5](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-minimax-m2-5.md)\n- [Gemma 4 31B IT vs MiniMax-M2.7](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-minimax-m2-7.md)\n- [Gemma 4 31B IT vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-muse-glimmer-30b.md)\n- [Gemma 4 31B IT vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [Gemma 4 31B IT vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-nano-30b-a3b.md)\n- [Gemma 4 31B IT vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-nano-4b.md)\n- [Gemma 4 31B IT vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [Gemma 4 31B IT vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-super-120b-a12b.md)\n- [Gemma 4 31B IT vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-ultra-550b-a55b.md)\n- [Gemma 4 31B IT vs occamy-1.0](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-occamy-1-0.md)\n- [Gemma 4 31B IT vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-olmo-3-7b-think.md)\n- [Gemma 4 31B IT vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ornith-1-5-35b-a3b.md)\n- [Gemma 4 31B IT vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ornith-1-5-397b.md)\n- [Gemma 4 31B IT vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ornith-1-5-9b.md)\n- [Gemma 4 31B IT vs Qwen3-30B-A3B-Instruct-2507](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-30b-a3b-instruct-2507.md)\n- [Gemma 4 31B IT vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-4b-instruct-2507.md)\n- [Gemma 4 31B IT vs Qwen3.5-0.8B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-5-0-8b.md)\n- [Gemma 4 31B IT vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-5-122b-a10b.md)\n- [Gemma 4 31B IT vs Qwen3.5-2B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-5-2b.md)\n- [Gemma 4 31B IT vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-5-397b-a17b.md)\n- [Gemma 4 31B IT vs Qwen3.5-4B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-5-4b.md)\n- [Gemma 4 31B IT vs Qwen3.5-9B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-5-9b.md)\n- [Gemma 4 31B IT vs Qwen3.6-27B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-6-27b.md)\n- [Gemma 4 31B IT vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-6-35b-a3b.md)\n- [Gemma 4 31B IT vs Qwen3.8-27B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-8-27b.md)\n- [Gemma 4 31B IT vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-8-flash-next.md)\n- [Gemma 4 31B IT vs Qwen3-Coder-30B-A3B-Instruct](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-coder-30b-a3b-instruct.md)\n- [Gemma 4 31B IT vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-coder-next.md)\n- [Gemma 4 31B IT vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-omni-30b-a3b-instruct.md)\n- [Gemma 4 31B IT vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-vl-2b-instruct.md)\n- [Gemma 4 31B IT vs Qwen3-VL-30B-A3B-Instruct](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-vl-30b-a3b-instruct.md)\n- [Gemma 4 31B IT vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-vl-32b-instruct.md)\n- [Gemma 4 31B IT vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-vl-4b-instruct.md)\n- [Gemma 4 31B IT vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-vl-8b-instruct.md)\n- [Gemma 4 31B IT vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwopus3-8-27b-flash-v2.md)\n- [Gemma 4 31B IT vs SmolLM3-3B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-smollm3-3b.md)\n- [Gemma 4 31B IT vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-spark-x2-6-4b.md)\n- [Gemma 4 31B IT vs Swift 1.5 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"page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "DeepSeek V4.1 Flash vs Gemma 4 31B IT", "url": "" } ], "description": "Gemma 4 31B IT scores 74.1 (BB) to DeepSeek V4.1 Flash's 59 (C) for open-weight models you run yourself. 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