{ "data": { "a": { "slug": "gemma-4-31b-it", "name": "Gemma 4 31B IT", "vendor": "Google DeepMind", "vendorUrl": "https://gemma.ai", "kind": "weights", "category": "open-weights", "summary": "Gemma 4 31B IT is Google DeepMind's open-weight dense multimodal model of 30.7B parameters, taking text and images with a 256K-token context, published on Hugging Face in BF16 with 4-bit QAT builds under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/gemma-4-31b-it", "markdownUrl": "https://www.anchorterminal.com/tools/gemma-4-31b-it.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/gemma-4-31b-it.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/gemma-4-31b-it.json", "repo": "https://huggingface.co/google/gemma-4-31B-it", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "google/gemma-4-31B-it" }, { "registry": "huggingface", "name": "google/gemma-4-31B-it-qat-q4_0-gguf" }, { "registry": "huggingface", "name": "google/gemma-4-31B-it-qat-w4a16-ct" }, { "registry": "ollama", "name": "gemma4:31b" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository and Google's QAT repositories are not gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Running it yourself costs the hardware, about 69.9 GB at BF16 or 17.5 GB at Q4_0 per Google's memory table. Google also hosts it on the Gemini API's free tier, a separate listing (checked 2026-10-21).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-21" }, "docsUrl": "https://gemma.ai/models/gemma-4/", "llmsTxt": "https://gemma.ai/llms.txt", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "multimodal", "reasoning", "tool-calling", "apache-2.0", "safetensors", "gguf", "qat", "self-hosted" ], "lastRelease": "2026-07-20", "graded": true, "anchor": { "graded": true, "score": 74.1, "grade": "BB", "agentReady": true, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1197, "categoryRank": 1, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 61, "interface": 85, "reach": 95, "risk": 70, "upkeep": 65 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "A dense multimodal cousin with documented tool calling, optional thinking and a 256K context, for general agent work and image reading on one 24 GB GPU through Google's 4-bit build. It runs in every major runtime under plain Apache 2.0. The main risk is that Google publishes no safety results and has gone quiet on the repository since July.", "bestFor": "An agent with a 24 GB GPU or more that wants a dense multimodal cousin with tool calling under a plain licence", "strengths": [ "Google's own 17.7 GB QAT Q4_0 GGUF fits a 24 GB GPU, and Ollama's gemma4:31b is 20 GB", "Released support in llama.cpp, Ollama, vLLM, SGLang and Transformers", "Apache 2.0 with nothing added, no gate, and hosted on Google's Gemini API too", "Tool calling documented by Google with a response schema Transformers parses, several calls per turn", "Thinking that can be switched on or off per request and 262,144 tokens of native context" ], "weaknesses": [ "No safety evaluation results published", "No prompt-injection guidance and no security route tied to Gemma", "Structured output isn't documented by Google", "Training data described only in general terms", "Files unchanged since 20 July and no Google reply in the discussions since 30 July" ], "agentNotes": [ "On the reference machine run `llama-server -hf google/gemma-4-31B-it-qat-q4_0-gguf --hf-file gemma-4-31B_q4_0-it.gguf --jinja -c 32768`, adding gemma-4-31B-it-mmproj.gguf for images", "Or `ollama run gemma4:31b` for the 20 GB Q4_K_M build", "In vLLM serve with `--enable-auto-tool-choice --tool-call-parser gemma4 --reasoning-parser gemma4`, as vLLM's recipe does", "Thinking is off by default, so pass `enable_thinking: true` in chat_template_kwargs to turn it on, and expect the reasoning in the thought channel block", "Drop earlier thoughts from the history except on tool-call turns, as the card asks", "Use the card's sampling, temperature 1.0, top_p 0.95, top_k 64", "Validate function names and arguments before running a call, and check JSON output yourself or use the runtime's guided decoding", "The w4a16 build relies on Marlin INT4 kernels that vLLM marks unsupported on AMD, so use the GGUF on the reference machine" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3.8, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "BB", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 74.1 } ], "editorialScores": { "capability": 61, "interface": 85, "reach": 95, "risk": 70, "upkeep": 65 }, "provenanceScore": 68, "checked": { "categories": { "capability": "2026-10-21", "interface": "2026-10-21", "reach": "2026-10-21", "risk": "2026-10-21", "upkeep": "2026-10-21" }, "last": "2026-10-21", "oldest": "2026-10-21", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "ollama run gemma4:31b\n# or, with vLLM's recipe:\nvllm serve google/gemma-4-31B-it --max-model-len 32768 --enable-auto-tool-choice \\\n --reasoning-parser gemma4 --tool-call-parser gemma4", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"gemma4:31b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/gemma-4-31b-it" }, "sameCompany": [ "embeddinggemma-300m", "diffusiongemma-26b-a4b-it", "gemma-4-12b-it", "gemma-4-26b-a4b-it", "gemma-4-e2b-it", "gemma-4-e4b-it" ], "sameModel": [ "gemini-api" ], "weights": { "trainedBy": "Google DeepMind", "builtOn": "google/gemma-4-31B", "params": "30.7B", "context": 262144, "goodFor": "Dense general assistant and agent model with tool calling and optional thinking, taking text, images and video frames.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "gemma-4-31B_q4_0-it", "url": "https://huggingface.co/google/gemma-4-31B-it-qat-q4_0-gguf/blob/main/gemma-4-31B_q4_0-it.gguf", "who": "publisher", "precision": "q4_0", "sizeGB": 17.7, "memoryGB": 24, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)", "LM Studio" ], "note": "Quantisation-aware trained by Google. Add gemma-4-31B-it-mmproj.gguf (1.2 GB) for image input" }, { "name": "ollama gemma4:31b", "url": "https://ollama.com/library/gemma4:31b", "who": "ollama", "precision": "q4_k_m", "sizeGB": 20, "memoryGB": 24, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Q4_K_M weights with a BF16 vision projector and a Q8_0 draft model (digest 17ba34c06c80). Size as Ollama's tags page shows it, rounded to whole GB above 10 GB; the Linux build is the llama.cpp variant" }, { "name": "gemma-4-31B-it-qat-w4a16-ct", "url": "https://huggingface.co/google/gemma-4-31B-it-qat-w4a16-ct", "who": "publisher", "precision": "w4a16", "sizeGB": 23.3, "memoryGB": 32, "format": "safetensors", "note": "Quantisation-aware trained, compressed-tensors format for vLLM. vLLM's quantisation table marks its Marlin INT4 kernels unsupported on AMD" }, { "name": "gemma-4-31B-it", "url": "https://huggingface.co/google/gemma-4-31B-it", "who": "publisher", "precision": "bf16", "sizeGB": 62.5, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] } ], "run": "llama-server -hf google/gemma-4-31B-it-qat-q4_0-gguf --hf-file gemma-4-31B_q4_0-it.gguf --jinja -c 32768", "runBuild": "gemma-4-31B_q4_0-it", "checked": "2026-10-21", "minMemoryGB": 24 }, "family": { "lab": "Google (Gemma)", "family": "Gemma 4", "url": "https://www.anchorterminal.com/open-weights/#google", "others": [ { "name": "gemma-4-26B-A4B", "url": "https://huggingface.co/google/gemma-4-26B-A4B", "params": "25.2B total, 3.8B active" }, { "name": "gemma-4-26B-A4B-it", "url": "https://huggingface.co/google/gemma-4-26B-A4B-it", "params": "25.2B total, 3.8B active", "listing": "https://www.anchorterminal.com/tools/gemma-4-26b-a4b-it" }, { "name": "gemma-4-31B", "url": "https://huggingface.co/google/gemma-4-31B", "params": "30.7B" }, { "name": "gemma-4-E2B", "url": "https://huggingface.co/google/gemma-4-E2B", "params": "2.3B effective (5.1B with embeddings)" }, { "name": "gemma-4-E2B-it", "url": "https://huggingface.co/google/gemma-4-E2B-it", "params": "2.3B effective (5.1B with embeddings)", "listing": "https://www.anchorterminal.com/tools/gemma-4-e2b-it" }, { "name": "gemma-4-E4B", "url": "https://huggingface.co/google/gemma-4-E4B", "params": "4.5B effective (8B with embeddings)" }, { "name": "gemma-4-E4B-it", "url": "https://huggingface.co/google/gemma-4-E4B-it", "params": "4.5B effective (8B with embeddings)", "listing": "https://www.anchorterminal.com/tools/gemma-4-e4b-it" }, { "name": "gemma-4-26B-A4B-it-assistant", "url": "https://huggingface.co/google/gemma-4-26B-A4B-it-assistant", "params": "420M (file metadata on Hugging Face; drafter)" }, { "name": "gemma-4-31B-it-assistant", "url": "https://huggingface.co/google/gemma-4-31B-it-assistant", "params": "470M (file metadata on Hugging Face; drafter)" }, { "name": "gemma-4-E2B-it-assistant", "url": "https://huggingface.co/google/gemma-4-E2B-it-assistant", "params": "78M (file metadata on Hugging Face; drafter)" }, { "name": "gemma-4-E4B-it-assistant", "url": "https://huggingface.co/google/gemma-4-E4B-it-assistant", "params": "79M (file metadata on Hugging Face; drafter)" }, { "name": "gemma-4-12B", "url": "https://huggingface.co/google/gemma-4-12B", "params": "11.95B" }, { "name": "gemma-4-12B-it", "url": "https://huggingface.co/google/gemma-4-12B-it", "params": "11.95B", "listing": "https://www.anchorterminal.com/tools/gemma-4-12b-it" }, { "name": "gemma-4-12B-it-assistant", "url": "https://huggingface.co/google/gemma-4-12B-it-assistant", "params": "423M (file metadata on Hugging Face; drafter)" }, { "name": "diffusiongemma-26B-A4B-it", "url": "https://huggingface.co/google/diffusiongemma-26B-A4B-it", "params": "25.2B total, 3.8B active", "listing": "https://www.anchorterminal.com/tools/diffusiongemma-26b-a4b-it" }, { "name": "embeddinggemma-2", "url": "https://huggingface.co/google/embeddinggemma-2", "params": "740M" } ], "olderCount": 171, "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "Google LLC (the registrant of gemma.ai; the card names Google DeepMind as author)", "domain": "gemma.ai", "domainRegistered": "2017-12-16", "domainNote": "RDAP shows gemma.ai registered in 2017 and transferred on 15 April 2025, so the registration date may predate Google's use of the domain.", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://gemma.ai/models/releases/", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "RDAP names Google LLC as registrant of gemma.ai, with MarkMonitor as registrar. Google's ai.google.dev pages point to gemma.ai as the current documentation.", "Nothing is hosted under this listing, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "The changelog is Google's Gemma releases page, which dates each release (Gemma 4 on 31 March 2026, its MTP drafters on 16 April). The Hugging Face commit history's messages also say what changed.", "gemma.ai/.well-known/security.txt answered 404 on 21 October 2026. www.google.com has a security.txt pointing to Google's vulnerability reward programme, which isn't linked from the card or gemma.ai." ], "score": 68 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/gemma-4-31b-it.json" }, "answer": "Gemma 4 31B IT scores 74.1 (BB) on agent readiness against Nemotron 3 Ultra 550B A55B's 68.8 (B), and leads in 1 of 5 scored categories. Nemotron 3 Ultra 550B A55B leads on capability and agent interface.", "b": { "slug": "nemotron-3-ultra-550b-a55b", "name": "Nemotron 3 Ultra 550B A55B", "vendor": "NVIDIA", "vendorUrl": "https://developer.nvidia.com/nemotron", "kind": "weights", "category": "open-weights", "summary": "Nemotron 3 Ultra is NVIDIA's largest open-weight hybrid Mamba-2 and latent mixture-of-experts reasoning model for agents, 550B parameters with 55B active and up to 1M tokens of context, published on Hugging Face in BF16 and NVFP4 under OpenMDW-1.1.", "url": "https://www.anchorterminal.com/tools/nemotron-3-ultra-550b-a55b", "markdownUrl": "https://www.anchorterminal.com/tools/nemotron-3-ultra-550b-a55b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/nemotron-3-ultra-550b-a55b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/nemotron-3-ultra-550b-a55b.json", "repo": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16", "license": "OpenMDW-1.1", "transports": [], "packages": [ { "registry": "huggingface", "name": "nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16" }, { "registry": "huggingface", "name": "nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-NVFP4" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The BF16 and NVFP4 repositories are not gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under OpenMDW-1.1. Running it yourself costs the hardware, 1,121 GB for the BF16 repository (8 × B200 or 16 × H100 at least, per the card) or 352 GB for NVIDIA's NVFP4 build (4 × B200 or 8 × H100). Ollama lists it only as a cloud model (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/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "mamba", "hybrid", "frontier", "reasoning", "tool-calling", "long-context", "multilingual", "safetensors", "nvfp4", "self-hosted" ], "lastRelease": "2026-06-04", "graded": true, "anchor": { "graded": true, "score": 68.8, "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": 7, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 73, "interface": 90, "reach": 55, "risk": 70, "upkeep": 65 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "NVIDIA's frontier-scale agent cousin with trained parallel tool calls, up to a 1M-token context and training against prompt injection through tool output. It needs at least four Blackwell GPUs for the 352 GB NVFP4 build, so most agents will use NVIDIA's API or a provider. The main risks are serving commands that run repository code and no published safety results.", "bestFor": "Agents with a multi-GPU Blackwell or Hopper node, or using a hosted endpoint, that want NVIDIA's strongest open model for long agent runs", "strengths": [ "Trained against prompt injection in tool output, as NVIDIA's technical report documents", "Tool calls documented with a named parser, several per turn, and thinking off or medium effort per request", "Up to 1M tokens of context, with RULER at 1M in the report", "Training data listed with token counts and cut-offs, much of it published", "OpenMDW-1.1 with no gate, no threshold and no use policy, and NVIDIA's API plus two providers serve it" ], "weaknesses": [ "The smallest build is 352 GB and needs at least four B200-class GPUs", "The card's serving commands pass --trust-remote-code", "No safety evaluation results published", "No NVIDIA reply to any of nine questions on its two repositories", "No LICENSE file, and the BF16 card's SGLang command names a reasoning parser SGLang doesn't register" ], "agentNotes": [ "It doesn't fit the 128 GB reference machine at any precision. Call NVIDIA's API (model nvidia/nemotron-3-ultra-550b-a55b) or a provider instead", "With vLLM serve the NVFP4 build with `--trust-remote-code --reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder` on 4 × B200 or more", "With SGLang use `--reasoning-parser nemotron_3`, not the BF16 card's `nemotron_v3`, which SGLang doesn't register", "On SGLang with tools, pass `chat_template_kwargs` with `enable_thinking` true and `force_nonempty_content` true, as the card says, or reasoning and calls won't both parse", "Thinking is on by default. Pass `enable_thinking: false` for short tasks or `medium_effort: true` to cut reasoning tokens", "Use `temperature=1.0` and `top_p=0.95`, as the generation config sets" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.8, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "B", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 68.8 } ], "editorialScores": { "capability": 73, "interface": 90, "reach": 55, "risk": 70, "upkeep": 65 }, "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": "# NVFP4 on 4 × B200 or more, with the card's vLLM command (abridged):\nvllm serve nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-NVFP4 --served-model-name nvidia/nemotron-3-ultra \\\n --tensor-parallel-size 4 --trust-remote-code --kv-cache-dtype fp8 --max-model-len 262144 \\\n --reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder \\\n --speculative-config '{\"method\": \"nemotron_h_mtp\", \"num_speculative_tokens\": 5}'", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\n \"model\": \"nvidia/nemotron-3-ultra\",\n \"messages\": [{\"role\": \"user\", \"content\": \"What is the capital of Japan?\"}],\n \"temperature\": 1.0, \"top_p\": 0.95,\n \"chat_template_kwargs\": {\"enable_thinking\": false}\n }'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/nemotron-3-ultra-550b-a55b" }, "sameCompany": [ "nemotron-3-5-super-vl", "nvidia-nemo-retriever", "nemo-guardrails", "nemotron-3-5-asr-streaming-0-6b", "parakeet-tdt-0-6b-v3", "cosmos-reason2-2b", "nemotron-3-nano-30b-a3b", "nemotron-3-nano-4b", "nemotron-3-nano-omni-30b-a3b-reasoning", "nemotron-3-super-120b-a12b", "nemotron-3-5-lightning-30b-a3b" ], "weights": { "trainedBy": "NVIDIA", "builtOn": "nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-Base-BF16", "params": "550B", "active": "55B", "context": 1048576, "goodFor": "Frontier-scale hybrid Mamba and latent mixture-of-experts reasoning model for long-running agents, text only, in ten languages, with tool calling, a medium-effort mode and up to 1M tokens of context.", "runtimes": [ "llama.cpp", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "NVIDIA-Nemotron-3-Ultra-550B-A55B-NVFP4", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-NVFP4", "who": "publisher", "precision": "nvfp4", "sizeGB": 352.38, "memoryGB": 512, "format": "safetensors", "note": "4 × B200, GB200, GB300 or B300, or 8 × H100, per its card. NVFP4 needs NVIDIA GPUs" }, { "name": "NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16", "who": "publisher", "precision": "bf16", "sizeGB": 1121.08, "memoryGB": 2048, "format": "safetensors", "note": "8 × B200 single node or 16 × H100 across nodes, per the card" } ], "run": "vllm serve nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-NVFP4 --served-model-name nvidia/nemotron-3-ultra --tensor-parallel-size 4 --trust-remote-code --kv-cache-dtype fp8 --max-model-len 262144 --reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder", "runBuild": "NVIDIA-Nemotron-3-Ultra-550B-A55B-NVFP4", "checked": "2026-10-21", "minMemoryGB": 512 }, "family": { "lab": "NVIDIA (Nemotron)", "family": "Nemotron 3", "url": "https://www.anchorterminal.com/open-weights/#nvidia", "others": [ { "name": "Nemotron 3 Nano 30B-A3B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16", "params": "30B total, 3B active (31.6B per the safetensors index)", "listing": "https://www.anchorterminal.com/tools/nemotron-3-nano-30b-a3b" }, { "name": "Nemotron 3 Nano 30B-A3B Base", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-Base-BF16", "params": "30B total, 3B active" }, { "name": "Nemotron 3 Super 120B-A12B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16", "params": "120B total, 12B active", "listing": "https://www.anchorterminal.com/tools/nemotron-3-super-120b-a12b" }, { "name": "Nemotron 3 Super 120B-A12B Base", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-Base-BF16", "params": "120B total, 12B active" }, { "name": "Nemotron 3 Nano 4B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16", "params": "4B", "listing": "https://www.anchorterminal.com/tools/nemotron-3-nano-4b" }, { "name": "Nemotron 3 Nano Omni 30B-A3B Reasoning", "url": "https://huggingface.co/nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16", "params": "30B total, 3B active", "listing": "https://www.anchorterminal.com/tools/nemotron-3-nano-omni-30b-a3b-reasoning" }, { "name": "Nemotron 3 Ultra 550B-A55B Base", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-Base-BF16", "params": "550B total, 55B active" }, { "name": "Nemotron 3 Ultra 550B-A55B GenRM", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-GenRM", "params": "550B total, 55B active" }, { "name": "Nemotron 3.5 Lightning 30B-A3B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16", "params": "30B total, 3B active", "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-lightning-30b-a3b" }, { "name": "Nemotron 3.5 Lightning 30B-A3B Base", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-Base-BF16", "params": "30B total, 3B active" }, { "name": "Nemotron 3.5 Super VL 120B-A12B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16", "params": "120B total, 12B active", "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-super-vl" }, { "name": "Nemotron 3.5 Super VL 120B-A12B Base", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-Base-BF16", "params": "120B total, 12B active" }, { "name": "Nemotron 3.5 Bolt 74B-A9B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Bolt-74B-A9B-BF16", "params": "74B total, 9B active" }, { "name": "Nemotron Labs 3 Elastic 30B-A3B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-Labs-3-Elastic-30B-A3B-BF16", "params": "30B total, 3B active" }, { "name": "Nemotron Labs 3 Puzzle 75B-A9B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-Labs-3-Puzzle-75B-A9B-BF16", "params": "75B total, 9B active" }, { "name": "Nemotron 3 Labs Ultra Math SFT", "url": "https://huggingface.co/nvidia/Nemotron-3-Labs-Ultra-Math-SFT", "params": "550B total, 55B active" }, { "name": "Nemotron 3 Labs Ultra Math RL", "url": "https://huggingface.co/nvidia/Nemotron-3-Labs-Ultra-Math-RL", "params": "550B total, 55B active" }, { "name": "Nemotron Labs 3 Competitive Coding 550B-A55B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-Labs-3-Competitive-Coding-550B-A55B-NVFP4", "params": "550B total, 55B active" }, { "name": "nemotron-3.5-asr-streaming-0.6b", "url": "https://huggingface.co/nvidia/nemotron-3.5-asr-streaming-0.6b", "params": "0.6B (638M per the safetensors count)", "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-asr-streaming-0-6b" } ], "olderCount": 43, "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "NVIDIA Corporation", "domain": "nvidia.com", "domainRegistered": "1993-04-20", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16/commits/main", "securityTxt": "unknown", "checked": "2026-10-21", "notes": [ "The card names NVIDIA Corporation as model developer. The Hugging Face organisation nvidia is the one developer.nvidia.com links to. Domain date as recorded for the other Nemotron listings.", "Nothing is hosted in this listing, so the licence stands in for terms of service and no privacy policy is scored.", "The changelog is the repository's commit history. Since release on 4 June 2026 the BF16 repository has changed the reasoning parser (5 June), the benchmark table and plot (10 June) and the card (24 August).", "www.nvidia.com/.well-known/security.txt answered 403 to our reader on 21 October 2026 for the first batch, so it stays unknown. The card links NVIDIA's vulnerability submission page." ], "score": 73 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/nemotron-3-ultra-550b-a55b.json" }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Google DeepMind", "b": "NVIDIA", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "", "b": "", "name": "Transports" }, { "a": "None", "b": "None", "name": "Auth" }, { "a": "Free", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Apache-2.0", "b": "OpenMDW-1.1", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "no", "name": "llms.txt" }, { "a": "2026-07-20", "b": "2026-06-04", "name": "Last release" }, { "a": "no document linked", "b": "no document linked", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "", "b": "", "name": "Customer content may train models" }, { "a": "", "b": "", "name": "Terms restrict automated access" }, { "a": "", "b": "", "name": "Terms restrict benchmarking" }, { "a": "", "b": "", "name": "Terms or service can change without notice" }, { "a": "", "b": "", "name": "Arbitration or class-action waiver" }, { "a": "3.8/5 (4)", "b": "2.8/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Gemma 4 31B IT scores 74.1 (BB) on agent readiness against Nemotron 3 Ultra 550B A55B's 68.8 (B), and leads in 1 of 5 scored categories. Nemotron 3 Ultra 550B A55B leads on capability and agent interface.", "question": "Which is better for AI agents, Gemma 4 31B IT or Nemotron 3 Ultra 550B A55B?" } ], "goodFor": [ { "aheadOn": [ "Reach, 95 against 55" ], "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" }, { "aheadOn": [ "Capability, 73 against 61", "Agent interface, 90 against 85" ], "also": null, "goodFor": "Agents with a multi-GPU Blackwell or Hopper node, or using a hosted endpoint, that want NVIDIA's strongest open model for long agent runs", "slug": "nemotron-3-ultra-550b-a55b", "watchFor": "The smallest build is 352 GB and needs at least four B200-class GPUs" } ], "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-31b-it.json", "title": "Agents-A1-4B vs Gemma 4 31B IT", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-31b-it" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-ultra-550b-a55b.json", "title": "Agents-A1-4B vs Nemotron 3 Ultra 550B A55B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-ultra-550b-a55b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-31b-it.json", "title": "Apertus v1.5 8B vs Gemma 4 31B IT", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-31b-it" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-ultra-550b-a55b.json", "title": "Apertus v1.5 8B vs Nemotron 3 Ultra 550B A55B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-ultra-550b-a55b" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gemma-4-31b-it.json", "title": "Apodex-1.1-mini vs Gemma 4 31B IT", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gemma-4-31b-it" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nemotron-3-ultra-550b-a55b.json", "title": "Apodex-1.1-mini vs Nemotron 3 Ultra 550B A55B", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nemotron-3-ultra-550b-a55b" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-gemma-4-31b-it.json", "title": "BigBang-v1 vs Gemma 4 31B IT", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-gemma-4-31b-it" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-nemotron-3-ultra-550b-a55b.json", "title": "BigBang-v1 vs Nemotron 3 Ultra 550B A55B", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-nemotron-3-ultra-550b-a55b" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-gemma-4-31b-it.json", "title": "Cosmos-Reason2-2B vs Gemma 4 31B IT", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-gemma-4-31b-it" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nemotron-3-ultra-550b-a55b.json", "title": "Cosmos-Reason2-2B vs Nemotron 3 Ultra 550B A55B", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nemotron-3-ultra-550b-a55b" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-gemma-4-31b-it.json", "title": "Darwin-36B-Opus vs Gemma 4 31B IT", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-gemma-4-31b-it" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-nemotron-3-ultra-550b-a55b.json", "title": "Darwin-36B-Opus vs Nemotron 3 Ultra 550B A55B", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-nemotron-3-ultra-550b-a55b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gemma-4-31b-it.json", "title": "DeepSeek-V3.2 vs Gemma 4 31B IT", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gemma-4-31b-it" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-nemotron-3-ultra-550b-a55b.json", "title": "DeepSeek-V3.2 vs Nemotron 3 Ultra 550B A55B", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-nemotron-3-ultra-550b-a55b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gemma-4-31b-it.json", "title": "DeepSeek V4.1 Flash vs Gemma 4 31B IT", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gemma-4-31b-it" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-ultra-550b-a55b.json", "title": "DeepSeek V4.1 Flash vs Nemotron 3 Ultra 550B A55B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-ultra-550b-a55b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-gemma-4-31b-it.json", "title": "DeepSeek-V4-Flash-0731 vs Gemma 4 31B IT", "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-gemma-4-31b-it" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-nemotron-3-ultra-550b-a55b.json", "title": "DeepSeek-V4-Flash-0731 vs Nemotron 3 Ultra 550B A55B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-nemotron-3-ultra-550b-a55b" }, { "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-gemma-4-31b-it.json", "title": "DiffusionGemma 26B A4B IT vs Gemma 4 31B IT", "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-gemma-4-31b-it" }, { "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-nemotron-3-ultra-550b-a55b.json", "title": "DiffusionGemma 26B A4B IT vs Nemotron 3 Ultra 550B A55B", "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-nemotron-3-ultra-550b-a55b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-gemma-4-31b-it.json", "title": "Gemma 4 12B IT vs Gemma 4 31B IT", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-gemma-4-31b-it" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-nemotron-3-ultra-550b-a55b.json", "title": "Gemma 4 12B IT vs Nemotron 3 Ultra 550B A55B", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-nemotron-3-ultra-550b-a55b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-gemma-4-31b-it.json", "title": "Gemma 4 26B A4B IT vs Gemma 4 31B IT", "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-gemma-4-31b-it" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-nemotron-3-ultra-550b-a55b.json", "title": "Gemma 4 26B A4B IT vs Nemotron 3 Ultra 550B A55B", "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-nemotron-3-ultra-550b-a55b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-gemma-4-e2b-it.json", "title": "Gemma 4 31B IT vs Gemma 4 E2B IT", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-gemma-4-e2b-it" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-gemma-4-e4b-it.json", "title": "Gemma 4 31B IT vs Gemma 4 E4B IT", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-gemma-4-e4b-it" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-glm-4-5-air.json", "title": "Gemma 4 31B IT vs GLM-4.5-Air", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-glm-4-5-air" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-glm-4-7-flash.json", "title": "Gemma 4 31B IT vs GLM-4.7-Flash", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-glm-4-7-flash" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-glm-5-3.json", "title": "Gemma 4 31B IT vs GLM-5.3", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-glm-5-3" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-glm-5-3-flash.json", "title": "Gemma 4 31B IT vs GLM-5.3-Flash", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-glm-5-3-flash" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-gpt-oss-120b.json", "title": "Gemma 4 31B IT vs gpt-oss-120b", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-gpt-oss-120b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-gpt-oss-20b.json", "title": "Gemma 4 31B IT vs gpt-oss-20b", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-gpt-oss-20b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-granite-4-2-3b.json", "title": "Gemma 4 31B IT vs Granite 4.2 3B", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-granite-4-2-3b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-hy3.json", "title": "Gemma 4 31B IT vs Hy3", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-hy3" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-inkling.json", "title": "Gemma 4 31B IT vs Inkling", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-inkling" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-inkling-small.json", "title": "Gemma 4 31B IT vs Inkling-Small", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-inkling-small" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-internvl3-5-gpt-oss-20b-a4b-preview.json", "title": "Gemma 4 31B IT vs InternVL3.5-GPT-OSS-20B-A4B-Preview", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-internvl3-5-gpt-oss-20b-a4b-preview" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-kimi-k3.json", "title": "Gemma 4 31B IT vs Kimi K3", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-kimi-k3" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-kolibri-1.json", "title": "Gemma 4 31B IT vs Kolibri-1", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-kolibri-1" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-laguna-m-1.json", "title": "Gemma 4 31B IT vs Laguna M.1", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-laguna-m-1" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-laguna-s-2-1.json", "title": "Gemma 4 31B IT vs Laguna S 2.1", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-laguna-s-2-1" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-laguna-xs-2-1.json", "title": "Gemma 4 31B IT vs Laguna XS 2.1", "url": 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{ "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-nemotron-3-ultra-550b-a55b.json", "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs Nemotron 3 Ultra 550B A55B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-nemotron-3-ultra-550b-a55b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-nemotron-3-ultra-550b-a55b.json", "title": "Nemotron 3 Super 120B A12B vs Nemotron 3 Ultra 550B A55B", "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-nemotron-3-ultra-550b-a55b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-occamy-1-0.json", "title": "Nemotron 3 Ultra 550B A55B vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-olmo-3-7b-think.json", "title": "Nemotron 3 Ultra 550B A55B vs Olmo 3 7B Think", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-olmo-3-7b-think" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-ornith-1-5-35b-a3b.json", "title": "Nemotron 3 Ultra 550B A55B vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-ornith-1-5-397b.json", "title": "Nemotron 3 Ultra 550B A55B vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-ornith-1-5-9b.json", "title": "Nemotron 3 Ultra 550B A55B vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-30b-a3b-instruct-2507.json", "title": "Nemotron 3 Ultra 550B A55B vs Qwen3-30B-A3B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-30b-a3b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-4b-instruct-2507.json", "title": "Nemotron 3 Ultra 550B A55B vs Qwen3-4B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-4b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-5-0-8b.json", "title": "Nemotron 3 Ultra 550B A55B vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-5-122b-a10b.json", "title": "Nemotron 3 Ultra 550B A55B vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-5-2b.json", "title": "Nemotron 3 Ultra 550B A55B vs Qwen3.5-2B", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-5-2b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-5-397b-a17b.json", "title": "Nemotron 3 Ultra 550B A55B vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-5-4b.json", "title": "Nemotron 3 Ultra 550B A55B vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-5-9b.json", "title": "Nemotron 3 Ultra 550B A55B vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-6-27b.json", "title": "Nemotron 3 Ultra 550B A55B vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-6-35b-a3b.json", "title": "Nemotron 3 Ultra 550B A55B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-8-27b.json", "title": "Nemotron 3 Ultra 550B A55B vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-8-flash-next.json", "title": "Nemotron 3 Ultra 550B A55B vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Nemotron 3 Ultra 550B A55B vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-coder-next.json", "title": "Nemotron 3 Ultra 550B A55B vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Nemotron 3 Ultra 550B A55B vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-vl-2b-instruct.json", "title": "Nemotron 3 Ultra 550B A55B vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Nemotron 3 Ultra 550B A55B vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-vl-32b-instruct.json", "title": "Nemotron 3 Ultra 550B A55B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-vl-4b-instruct.json", "title": "Nemotron 3 Ultra 550B A55B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-vl-8b-instruct.json", "title": "Nemotron 3 Ultra 550B A55B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwopus3-8-27b-flash-v2.json", "title": "Nemotron 3 Ultra 550B A55B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-smollm3-3b.json", "title": "Nemotron 3 Ultra 550B A55B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-spark-x2-6-4b.json", "title": "Nemotron 3 Ultra 550B A55B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-swift-1-5-qwen3-8-27b.json", "title": "Nemotron 3 Ultra 550B A55B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Nemotron 3 Ultra 550B A55B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-thinkingcap-qwen3-8-27b.json", "title": "Nemotron 3 Ultra 550B A55B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-thinkingcap-qwen3-8-27b" } ], "scores": [ { "by": 12, "edge": "nemotron-3-ultra-550b-a55b", "gemma-4-31b-it": 61, "key": "capability", "name": "Capability", "nemotron-3-ultra-550b-a55b": 73, "weight": 35 }, { "by": 40, "edge": "gemma-4-31b-it", "gemma-4-31b-it": 95, "key": "reach", "name": "Reach", "nemotron-3-ultra-550b-a55b": 55, "weight": 25 }, { "by": 0, "edge": "", "gemma-4-31b-it": 70, "key": "risk", "name": "Risk", "nemotron-3-ultra-550b-a55b": 70, "weight": 20 }, { "by": 5, "edge": "nemotron-3-ultra-550b-a55b", "gemma-4-31b-it": 85, "key": "interface", "name": "Agent interface", "nemotron-3-ultra-550b-a55b": 90, "weight": 10 }, { "by": 0, "edge": "", "gemma-4-31b-it": 65, "key": "upkeep", "name": "Documentation and upkeep", "nemotron-3-ultra-550b-a55b": 65, "weight": 10 } ], "summary": "Gemma 4 31B IT scores 74.1 (BB) on agent readiness against Nemotron 3 Ultra 550B A55B's 68.8 (B), and leads in 1 of 5 scored categories. Nemotron 3 Ultra 550B A55B leads on capability and agent interface. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "gemma-4-31b-it": "A dense multimodal cousin with documented tool calling, optional thinking and a 256K context, for general agent work and image reading on one 24 GB GPU through Google's 4-bit build. It runs in every major runtime under plain Apache 2.0. The main risk is that Google publishes no safety results and has gone quiet on the repository since July.", "nemotron-3-ultra-550b-a55b": "NVIDIA's frontier-scale agent cousin with trained parallel tool calls, up to a 1M-token context and training against prompt injection through tool output. It needs at least four Blackwell GPUs for the 352 GB NVFP4 build, so most agents will use NVIDIA's API or a provider. The main risks are serving commands that run repository code and no published safety results." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-ultra-550b-a55b", "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-ultra-550b-a55b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-ultra-550b-a55b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-ultra-550b-a55b.min.md" }, "markdown": "Gemma 4 31B IT scores 74.1 (BB) on agent readiness against Nemotron 3 Ultra 550B A55B's 68.8 (B), and leads in 1 of 5 scored categories. Nemotron 3 Ultra 550B A55B leads on capability and agent interface. Both do open-weight models you run yourself.\n\n- Gemma 4 31B IT: grade BB, 74.1/100, rank #2 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/gemma-4-31b-it.md · JSON https://www.anchorterminal.com/api/v1/tools/gemma-4-31b-it.json\n- Nemotron 3 Ultra 550B A55B: grade B, 68.8/100, rank #9 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-ultra-550b-a55b.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-ultra-550b-a55b.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### Gemma 4 31B IT (BB)\n\nGood for: An agent with a 24 GB GPU or more that wants a dense multimodal cousin with tool calling under a plain licence\n\nAhead on:\n- Reach, 95 against 55\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### Nemotron 3 Ultra 550B A55B (B)\n\nGood for: Agents with a multi-GPU Blackwell or Hopper node, or using a hosted endpoint, that want NVIDIA's strongest open model for long agent runs\n\nAhead on:\n- Capability, 73 against 61\n- Agent interface, 90 against 85\n\nWatch for: The smallest build is 352 GB and needs at least four B200-class GPUs\n\n\n## Score by category\n\n| Category | Weight | Gemma 4 31B IT | Nemotron 3 Ultra 550B A55B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35% (35 this run) | 61 | 73 | Nemotron 3 Ultra 550B A55B +12 |\n| Reach | 25% (25 this run) | 95 | 55 | Gemma 4 31B IT +40 |\n| Risk | 20% (20 this run) | 70 | 70 | even |\n| Agent interface | 10% (10 this run) | 85 | 90 | Nemotron 3 Ultra 550B A55B +5 |\n| Documentation and upkeep | 10% (10 this run) | 65 | 65 | even |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **74.1 · BB** | **68.8 · B** | |\n\n## Facts side by side\n\n| Fact | Gemma 4 31B IT | Nemotron 3 Ultra 550B A55B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Google DeepMind | NVIDIA |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | | |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | Apache-2.0 | OpenMDW-1.1 |\n| Read-only variant documented | no | no |\n| llms.txt | yes | no |\n| Last release | 2026-07-20 | 2026-06-04 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models | | |\n| Terms restrict automated access | | |\n| Terms restrict benchmarking | | |\n| Terms or service can change without notice | | |\n| Arbitration or class-action waiver | | |\n| Agent reviews | 3.8/5 (4) | 2.8/5 (4) |\n\n## Verdicts\n\n**Gemma 4 31B IT.** A dense multimodal cousin with documented tool calling, optional thinking and a 256K context, for general agent work and image reading on one 24 GB GPU through Google's 4-bit build. It runs in every major runtime under plain Apache 2.0. The main risk is that Google publishes no safety results and has gone quiet on the repository since July.\n\n**Nemotron 3 Ultra 550B A55B.** NVIDIA's frontier-scale agent cousin with trained parallel tool calls, up to a 1M-token context and training against prompt injection through tool output. It needs at least four Blackwell GPUs for the 352 GB NVFP4 build, so most agents will use NVIDIA's API or a provider. The main risks are serving commands that run repository code and no published safety results.\n\n## Before you call either\n\n### Gemma 4 31B IT\n\n1. On the reference machine run `llama-server -hf google/gemma-4-31B-it-qat-q4_0-gguf --hf-file gemma-4-31B_q4_0-it.gguf --jinja -c 32768`, adding gemma-4-31B-it-mmproj.gguf for images\n2. Or `ollama run gemma4:31b` for the 20 GB Q4_K_M build\n3. In vLLM serve with `--enable-auto-tool-choice --tool-call-parser gemma4 --reasoning-parser gemma4`, as vLLM's recipe does\n4. Thinking is off by default, so pass `enable_thinking: true` in chat_template_kwargs to turn it on, and expect the reasoning in the thought channel block\n5. Drop earlier thoughts from the history except on tool-call turns, as the card asks\n6. Use the card's sampling, temperature 1.0, top_p 0.95, top_k 64\n7. Validate function names and arguments before running a call, and check JSON output yourself or use the runtime's guided decoding\n8. The w4a16 build relies on Marlin INT4 kernels that vLLM marks unsupported on AMD, so use the GGUF on the reference machine\n\n### Nemotron 3 Ultra 550B A55B\n\n1. It doesn't fit the 128 GB reference machine at any precision. Call NVIDIA's API (model nvidia/nemotron-3-ultra-550b-a55b) or a provider instead\n2. With vLLM serve the NVFP4 build with `--trust-remote-code --reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder` on 4 × B200 or more\n3. With SGLang use `--reasoning-parser nemotron_3`, not the BF16 card's `nemotron_v3`, which SGLang doesn't register\n4. On SGLang with tools, pass `chat_template_kwargs` with `enable_thinking` true and `force_nonempty_content` true, as the card says, or reasoning and calls won't both parse\n5. Thinking is on by default. Pass `enable_thinking: false` for short tasks or `medium_effort: true` to cut reasoning tokens\n6. Use `temperature=1.0` and `top_p=0.95`, as the generation config sets\n\n## Questions\n\n### Which is better for AI agents, Gemma 4 31B IT or Nemotron 3 Ultra 550B A55B?\n\nGemma 4 31B IT scores 74.1 (BB) on agent readiness against Nemotron 3 Ultra 550B A55B's 68.8 (B), and leads in 1 of 5 scored categories. Nemotron 3 Ultra 550B A55B leads on capability and agent interface.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-ultra-550b-a55b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-ultra-550b-a55b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"gemma-4-31b-it\", \"b\": \"nemotron-3-ultra-550b-a55b\"}`. From a terminal: `anchor compare gemma-4-31b-it nemotron-3-ultra-550b-a55b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/gemma-4-31b-it.json and https://www.anchorterminal.com/api/v1/tools/nemotron-3-ultra-550b-a55b.json\n\n## Other comparisons with Gemma 4 31B IT or Nemotron 3 Ultra 550B A55B\n\n- [Agents-A1-4B vs Gemma 4 31B IT](https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-31b-it.md)\n- [Agents-A1-4B vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-ultra-550b-a55b.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- [Apertus v1.5 8B vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-ultra-550b-a55b.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- [Apodex-1.1-mini vs Nemotron 3 Ultra 550B 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"name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "Gemma 4 31B IT vs Nemotron 3 Ultra 550B A55B", "url": "" } ], "description": "Gemma 4 31B IT scores 74.1 (BB) to Nemotron 3 Ultra 550B A55B's 68.8 (B) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "Gemma 4 31B IT BB 74.1", "Nemotron 3 Ultra 550B A55B B 68.8", "scores" ], "h1": "Gemma 4 31B IT vs Nemotron 3 Ultra 550B A55B", "image": "https://www.anchorterminal.com/assets/og/compare-gemma-4-31b-it-vs-nemotron-3-ultra-550b-a55b.png", "path": "/compare/gemma-4-31b-it-vs-nemotron-3-ultra-550b-a55b", "published": "2026-10-01", "section": "tools", "title": "Gemma 4 31B IT vs Nemotron 3 Ultra 550B A55B for AI agents (2026)", "toc": null, "updated": "2026-10-21", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-ultra-550b-a55b" }, "tokens": { "markdown": 6650, "slim": 480 }, "version": 1 }