{ "data": { "a": { "slug": "granite-4-2-8b", "name": "Granite 4.2 8B", "vendor": "IBM", "vendorUrl": "https://www.ibm.com/granite", "kind": "weights", "category": "open-weights", "summary": "Granite 4.2 8B is IBM's open-weight dense reasoning model of 8.8B parameters, post-trained from Granite 4.1 8B Base, with switchable thinking, tool calling and a 131,072-token context, published in BF16, FP8, FP4, GGUF and MLX under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/granite-4-2-8b", "markdownUrl": "https://www.anchorterminal.com/tools/granite-4-2-8b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/granite-4-2-8b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/granite-4-2-8b.json", "dns": "granite-4-2-8b.grade.anchorterminal.com", "repo": "https://huggingface.co/ibm-granite/granite-4.2-8b", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "ibm-granite/granite-4.2-8b" }, { "registry": "huggingface", "name": "ibm-granite/granite-4.2-8b-GGUF" }, { "registry": "huggingface", "name": "ibm-granite/granite-4.2-8b-fp8" }, { "registry": "huggingface", "name": "ibm-granite/granite-4.2-8b-mxfp4" }, { "registry": "huggingface", "name": "ibm-granite/granite-4.2-8b-nvfp4" }, { "registry": "huggingface", "name": "ibm-granite/granite-4.2-8b-q4-mlx" }, { "registry": "ollama", "name": "granite4.2:8b" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The BF16 repository and IBM's FP8, MXFP4, NVFP4, GGUF and MLX repositories are not gated (checked 2026-11-10).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Running it yourself costs the hardware, 17.6 GB for the BF16 files, 5.3 GB for IBM's Q4_K_M GGUF or 5.0 GB for its 4-bit MLX build (checked 2026-11-10).", "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 2026-11-10).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-10" }, "docsUrl": "https://huggingface.co/ibm-granite/granite-4.2-8b", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "reasoning", "tool-calling", "multilingual", "apache-2.0", "safetensors", "fp8", "fp4", "gguf", "mlx", "signed", "self-hosted" ], "lastRelease": "2026-10-20", "graded": true, "anchor": { "graded": true, "score": 61, "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": 1518, "categoryRank": 53, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 85, "reach": 95, "risk": 55, "upkeep": 92 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "IBM's mid-size reasoning cousin, with the same strengths as the 3B. It runs in every runtime we count from IBM's own builds on a 16 GB machine, is signed, plain Apache 2.0, and comes with a disclosure of its training data. No safety results are published and structured output isn't documented.", "bestFor": "Agents that want a documented, signed, Apache 2.0 reasoning model with tool calling on a 16 GB machine", "strengths": [ "Runs in llama.cpp, Ollama, vLLM, SGLang and Transformers from IBM's own FP8, FP4, GGUF and MLX builds", "Plain Apache 2.0, ungated, signed with sigstore", "The card documents the tool-calling format, the parser to use and three thinking modes", "IBM publishes a machine-readable disclosure of the training datasets", "IBM answers most questions on the repository" ], "weaknesses": [ "No safety results published for it", "Structured output isn't documented", "The card's vLLM command still loads a Python plugin from the repository, which released vLLM no longer needs", "No knowledge cut-off on the card", "One inference provider, so an agent without the hardware has little choice" ], "agentNotes": [ "Start it with `ollama run granite4.2:8b`, or `llama-server -hf ibm-granite/granite-4.2-8b-GGUF:Q4_K_M --jinja`", "With vLLM 0.32.0 or later leave out `--reasoning-parser-plugin`, since the parser is built in, and use `--tool-call-parser qwen3_coder`", "Thinking is on by default. Pass `enable_thinking` false to turn it off or `low_effort` true for short reasoning", "The context is 131,072 tokens. The card's 512K extension isn't documented", "Validate JSON output yourself, since structured output isn't documented", "IBM recommends Granite Guardian beside it to flag risky inputs and outputs" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61 } ], "editorialScores": { "interface": 85, "reach": 95, "risk": 55, "upkeep": 92 }, "provenanceScore": 88, "checked": { "categories": { "interface": "2026-11-10", "reach": "2026-11-10", "risk": "2026-11-10", "upkeep": "2026-11-10" }, "last": "2026-11-10", "oldest": "2026-11-10", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "ollama run granite4.2:8b\n# or, with llama.cpp and IBM's own GGUF:\nllama-server -hf ibm-granite/granite-4.2-8b-GGUF:Q4_K_M --jinja\n# or, with vLLM 0.32.0 or later:\nvllm serve ibm-granite/granite-4.2-8b --max-model-len 131072 \\\n --reasoning-parser granite_thinking_parser \\\n --tool-call-parser qwen3_coder --enable-auto-tool-choice", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"granite4.2:8b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/granite-4-2-8b" }, "sameCompany": [ "granite-embedding-small-english-r2", "granite-guardian", "granite-speech-5-0-2b", "ibm-watson-stt", "granite-switch", "granite-4-2-30b", "granite-4-2-3b" ], "weights": { "trainedBy": "IBM", "builtOn": "ibm-granite/granite-4.1-8b-base (IBM's own base checkpoint, post-trained; named on the card and in its metadata)", "params": "8.79B", "context": 131072, "goodFor": "Mid-size dense reasoning model for tool calling, coding and multilingual chat on a laptop GPU, with thinking that is on, off or short per request.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "granite-4.2-8b-nvfp4", "url": "https://huggingface.co/ibm-granite/granite-4.2-8b-nvfp4", "who": "publisher", "precision": "nvfp4", "sizeGB": 6.13, "memoryGB": 8, "format": "safetensors", "runsOn": [ "vLLM" ] }, { "name": "granite-4.2-8b-mxfp4", "url": "https://huggingface.co/ibm-granite/granite-4.2-8b-mxfp4", "who": "publisher", "precision": "mxfp4", "sizeGB": 5.88, "memoryGB": 8, "format": "safetensors", "runsOn": [ "vLLM" ] }, { "name": "granite-4.2-8b-Q4_K_M", "url": "https://huggingface.co/ibm-granite/granite-4.2-8b-GGUF", "who": "publisher", "precision": "q4_k_m", "sizeGB": 5.35, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "IBM's GGUF repository also holds Q4_0 (5.06 GB), Q6_K and BF16 files among others" }, { "name": "granite-4.2-8b-q4-mlx", "url": "https://huggingface.co/ibm-granite/granite-4.2-8b-q4-mlx", "who": "publisher", "precision": "q4", "sizeGB": 4.95, "memoryGB": 8, "format": "mlx", "runsOn": [ "mlx-lm" ], "note": "The smallest build at 4-bit or better. IBM also publishes q8-mlx (9.34 GB) and bf16-mlx" }, { "name": "ollama granite4.2:8b", "url": "https://ollama.com/library/granite4.2:8b", "who": "ollama", "precision": "q4_k_m", "sizeGB": 5.3, "memoryGB": 8, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Ollama's library build, size as its tags page shows it on 10 November 2026" }, { "name": "granite-4.2-8b-fp8", "url": "https://huggingface.co/ibm-granite/granite-4.2-8b-fp8", "who": "publisher", "precision": "fp8", "sizeGB": 9.62, "memoryGB": 12, "format": "safetensors", "runsOn": [ "vLLM" ] }, { "name": "granite-4.2-8b-Q8_0", "url": "https://huggingface.co/ibm-granite/granite-4.2-8b-GGUF", "who": "publisher", "precision": "q8_0", "sizeGB": 9.35, "memoryGB": 12, "format": "gguf", "runsOn": [ "llama.cpp" ] }, { "name": "granite-4.2-8b", "url": "https://huggingface.co/ibm-granite/granite-4.2-8b", "who": "publisher", "precision": "bf16", "sizeGB": 17.58, "memoryGB": 24, "format": "safetensors", "runsOn": [ "vLLM", "SGLang", "Transformers" ] } ], "run": "ollama run granite4.2:8b", "runBuild": "ollama granite4.2:8b", "checked": "2026-11-10", "minMemoryGB": 8 }, "family": { "lab": "IBM (Granite)", "family": "Granite 4 (4.0, 4.1, 4.2)", "url": "https://www.anchorterminal.com/open-weights/#ibm", "others": [ { "name": "granite-4.2-3b", "url": "https://huggingface.co/ibm-granite/granite-4.2-3b", "params": "3B (Hub safetensors count 3.66B)", "listing": "https://www.anchorterminal.com/tools/granite-4-2-3b" }, { "name": "granite-4.2-30b", "url": "https://huggingface.co/ibm-granite/granite-4.2-30b", "params": "30B (Hub safetensors count 29.28B)", "listing": "https://www.anchorterminal.com/tools/granite-4-2-30b" }, { "name": "granite-4.1-8b-base", "url": "https://huggingface.co/ibm-granite/granite-4.1-8b-base", "params": "8B (Hub safetensors count 8.38B)" }, { "name": "granite-4.1-8b", "url": "https://huggingface.co/ibm-granite/granite-4.1-8b", "params": "8B (Hub safetensors count 8.79B)" }, { "name": "granite-4.1-3b-base", "url": "https://huggingface.co/ibm-granite/granite-4.1-3b-base", "params": "3B (Hub safetensors count 3.40B)" }, { "name": "granite-4.1-3b", "url": "https://huggingface.co/ibm-granite/granite-4.1-3b", "params": "3B (Hub safetensors count 3.40B)" }, { "name": "granite-4.1-30b-base", "url": "https://huggingface.co/ibm-granite/granite-4.1-30b-base", "params": "30B (Hub safetensors count 28.87B)" }, { "name": "granite-4.1-30b", "url": "https://huggingface.co/ibm-granite/granite-4.1-30b", "params": "30B (Hub safetensors count 28.87B)" }, { "name": "granite-4.0-h-350m-base", "url": "https://huggingface.co/ibm-granite/granite-4.0-h-350m-base", "params": "350M (Hub safetensors count 340M)" }, { "name": "granite-4.0-h-350m", "url": "https://huggingface.co/ibm-granite/granite-4.0-h-350m", "params": "350M (Hub safetensors count 340M)" }, { "name": "granite-4.0-h-1b-base", "url": "https://huggingface.co/ibm-granite/granite-4.0-h-1b-base", "params": "1B (Hub safetensors count 1.46B)" }, { "name": "granite-4.0-h-1b", "url": "https://huggingface.co/ibm-granite/granite-4.0-h-1b", "params": "1B (Hub safetensors count 1.46B)" }, { "name": "granite-4.0-350m-base", "url": "https://huggingface.co/ibm-granite/granite-4.0-350m-base", "params": "350M (Hub safetensors count 352M)" }, { "name": "granite-4.0-350m", "url": "https://huggingface.co/ibm-granite/granite-4.0-350m", "params": "350M (Hub safetensors count 352M)" }, { "name": "granite-4.0-1b-base", "url": "https://huggingface.co/ibm-granite/granite-4.0-1b-base", "params": "1B (Hub safetensors count 1.63B)" }, { "name": "granite-4.0-1b", "url": "https://huggingface.co/ibm-granite/granite-4.0-1b", "params": "1B (Hub safetensors count 1.63B)" }, { "name": "granite-4.0-micro-base", "url": "https://huggingface.co/ibm-granite/granite-4.0-micro-base", "params": "3.40B (Hub safetensors count; the name states none)" }, { "name": "granite-4.0-micro", "url": "https://huggingface.co/ibm-granite/granite-4.0-micro", "params": "3.40B (Hub safetensors count; the name states none)" }, { "name": "granite-4.0-h-tiny-base", "url": "https://huggingface.co/ibm-granite/granite-4.0-h-tiny-base", "params": "6.94B (Hub safetensors count; the name states none)" }, { "name": "granite-4.0-h-tiny", "url": "https://huggingface.co/ibm-granite/granite-4.0-h-tiny", "params": "6.94B (Hub safetensors count; the name states none)" }, { "name": "granite-4.0-h-small-base", "url": "https://huggingface.co/ibm-granite/granite-4.0-h-small-base", "params": "32.21B (Hub safetensors count; the name states none)" }, { "name": "granite-4.0-h-small", "url": "https://huggingface.co/ibm-granite/granite-4.0-h-small", "params": "32.21B (Hub safetensors count; the name states none)" }, { "name": "granite-4.0-h-micro-base", "url": "https://huggingface.co/ibm-granite/granite-4.0-h-micro-base", "params": "3.19B (Hub safetensors count; the name states none)" }, { "name": "granite-4.0-h-micro", "url": "https://huggingface.co/ibm-granite/granite-4.0-h-micro", "params": "3.19B (Hub safetensors count; the name states none)" }, { "name": "granite-4.0-tiny-preview", "url": "https://huggingface.co/ibm-granite/granite-4.0-tiny-preview", "params": "6.67B (Hub safetensors count; the name states none)" }, { "name": "granite-4.0-tiny-base-preview", "url": "https://huggingface.co/ibm-granite/granite-4.0-tiny-base-preview", "params": "6.67B (Hub safetensors count; the name states none)" } ], "olderCount": 64 }, "area": "models", "provenance": { "legalEntity": "International Business Machines Corporation (per IBM's naming guidance, as read for granite-guardian)", "domain": "ibm.com", "domainRegistered": "1986-03-19", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/ibm-granite/granite-4.2-8b/commits/main", "securityTxt": "valid", "checked": "2026-11-10", "notes": [ "IBM's Granite docs page for 4.2 links the ibm-granite collection on Hugging Face and the GitHub repository, and the docs site says it is no longer updated. The domain date is carried from the granite-guardian check.", "The ibm-granite organisation (209 members) isn't verified on the Hub, which the calibration doesn't require.", "Nothing is hosted for this listing, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "The changelog is the Hugging Face commit history, 15 commits from 7 August to 20 October 2026, most titled only \"granite-4.2\".", "www.ibm.com/.well-known/security.txt is present with PSIRT contacts and expires on 21 November 2026.", "Released on 25 August 2026 by the card and IBM's blog, which firstReleased uses. The repository and its weight files date from 7 August 2026, the date BATCH.md and the IBM family page used before 21 October." ], "score": 88 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/granite-4-2-8b.json" }, "answer": "Nemotron 3 Ultra 550B A55B scores 69.3 (B) on agent readiness against Granite 4.2 8B's 61 (C), and leads in 2 of 4 scored categories. Granite 4.2 8B leads on reach and documentation and upkeep.", "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", "dns": "nemotron-3-ultra-550b-a55b.grade.anchorterminal.com", "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": "2026-11-01" }, "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": 69.3, "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": 1518, "categoryRank": 7, "methodology": "0.7", "run": "2026-10-01", "scores": { "capability": 73, "interface": 90, "reach": 55, "risk": 70, "upkeep": 70 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-11-01" }, "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, the newest from 7 July 2026", "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": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 69.3 } ], "editorialScores": { "capability": 73, "interface": 90, "reach": 55, "risk": 70, "upkeep": 70 }, "provenanceScore": 78, "checked": { "categories": { "capability": "2026-11-01", "interface": "2026-11-01", "reach": "2026-11-01", "risk": "2026-11-01", "upkeep": "2026-11-01" }, "last": "2026-11-01", "lastMoved": "2026-11-01", "oldest": "2026-11-01", "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}'\n# or, with llama.cpp and Unsloth's GGUF, a third party's conversion:\nllama-server -hf unsloth/NVIDIA-Nemotron-3-Ultra-550B-A55B-GGUF:UD-Q4_K_M --jinja", "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", "nemotron-3-5-transcribe-30b-a4b", "parakeet-tdt-0-6b-v3", "parakeet-unified-en-0-6b", "nemotron-3-5-parse", "cosmos-reason2-2b", "nemotron-3-5-bolt-e4b-4gb", "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": "unsloth NVIDIA-Nemotron-3-Ultra-550B-A55B-GGUF (UD-Q4_K_M)", "url": "https://huggingface.co/unsloth/NVIDIA-Nemotron-3-Ultra-550B-A55B-GGUF", "who": "unsloth", "precision": "q4_k_m", "sizeGB": 359.23, "memoryGB": 512, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "From Unsloth, a third party's conversion and not NVIDIA's. 359.23 GB of UD-Q4_K_M weights, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it" }, { "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 3.5 Bolt E4B 4GB", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-4GB", "params": "8B total, 4B effective", "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-bolt-e4b-4gb" }, { "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" }, { "name": "Nemotron 3.5 Parse", "url": "https://huggingface.co/nvidia/Nemotron-3.5-Parse", "params": "0.631B (630,800,496 trainable parameters per the card)", "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-parse" }, { "name": "Nemotron 3.5 Transcribe 30B-A4B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Transcribe-30B-A4B", "params": "30B total, about 4.3B active (33,697,781,029 by the Hub's safetensors count)", "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-transcribe-30b-a4b" } ], "olderCount": 47, "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": 78 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/nemotron-3-ultra-550b-a55b.json", "live": { "slug": "nemotron-3-ultra-550b-a55b", "securityTxt": { "url": "https://nvidia.com/.well-known/security.txt", "state": "unknown", "checkedAt": "2026-11-10T16:08:00.271598775Z" }, "domain": { "domain": "nvidia.com", "registered": "1993-04-20", "source": "https://rdap.verisign.com/com/v1/domain/nvidia.com", "checkedAt": "2026-11-08T15:03:11.895084282Z" }, "pages": [ { "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16/commits/main", "kind": "changelog", "status": 200, "checkedAt": "2026-11-10T16:23:22.830688249Z", "changedAt": "2026-11-10T16:23:22.830688249Z", "fingerprint": "0a39093de28f" } ], "updatedAt": "2026-11-10T16:23:22.830688249Z" } }, "edges": [ { "by": 40, "category": "reach", "leads": "granite-4-2-8b", "name": "Reach", "reasons": { "a": "Memory 30, IBM's own 4-bit MLX build granite-4.2-8b-q4-mlx is 4.95 GB, and 4.95 GB × 1.2 = 5.9 GB, within 8 GB. Its Q4_0 GGUF is 5.06 GB and Ollama's library build granite4.2:8b (5.3 GB) fits 8 GB too. Runtimes 25, Released versions run the granite architecture in llama.cpp (granite, b12242), vLLM (GraniteForCausalLM in the v0.33.0 registry), SGLang (granite.py at v0.5.23), Transformers (v5.22.2) and Ollama (its own granite4.2 library entry, 8b tag). Builds 15, IBM publishes FP8, MXFP4 and NVFP4 safetensors builds, a GGUF repository with Q4_0 to Q8_0 and BF16 files, and 4-bit, 8-bit and BF16 MLX builds, so quantised builds at two precisions or more. Terms 15, Apache 2.0, an OSI-approved licence, with no use policy or other terms added on the card. Download 10, the Hub API reports gated false for the BF16, FP8, MXFP4, NVFP4, GGUF and MLX repositories, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps one inference provider (DeepInfra, live), short of the two the line needs. 30 + 25 + 15 + 15 + 10 + 0 = 95.", "b": "Memory earns 0, because the smallest 4-bit build, NVIDIA's 352 GB NVFP4, needs about 423 GB with the fifth added, above the 256 GB band, and no runtime library has a local build. Runtimes earn 20 from released vLLM, SGLang, llama.cpp and Transformers, with Ollama's cloud-only entry not counted. Builds earn 10 for the single NVFP4 build. Terms earn 10, because OpenMDW-1.1 is permissive with no threshold or use policy but isn't an OSI licence. Download earns 10, with no gate or account. Hosted earns 5, since NVIDIA's own API serves it and two providers host it." }, "scores": { "a": 95, "b": 55 } }, { "by": 22, "category": "upkeep", "leads": "granite-4-2-8b", "name": "Documentation and upkeep", "reasons": { "a": "Model card 17, Intended use (best for), limitations (ethical considerations and limitations), architecture, parameters (8B), context length (128K native) and 12 tested languages are on the card. Training data 20, IBM's disclosure file for the 8B lists the datasets used in pre-training and post-training as machine-readable metadata, and the card describes the three training stages. Last change 20, the last commit, on 20 October 2026, replaced model.safetensors.index.json (titled \"granite-4.2 index file fix\"), 21 days before this check. Responsiveness 20, Of the 5 discussions that aren't pull requests (13 September to 5 November 2026), 4 have a reply from an ibm-granite member. Version policy 5, Granite 4.1 8B stays in its own repository and 4.2 came out in new ones. File health 10, Config, template and card agree on the architecture, the 131,072-token context and the thinking switch. 17 + 20 + 20 + 20 + 5 + 10 = 92.", "b": "The model card earns 20, all seven items with limitations in the linked subcard. Training data earns 20, datasets listed with sources and token counts, cut-offs and filtering, and the corpora published. Last change earns 20, counting NVIDIA's own inference code for the model. The repositories last changed on 24 August, but NVIDIA's TensorRT-LLM, the runtime the card's own serving commands use, changed its Nemotron-H model code on 14 October, 18 days ago. Responsiveness earns 0, with no NVIDIA reply to any of nine questions. Version policy earns 5 as a first release. File health earns 5, since there is no LICENSE file, the BF16 card's SGLang command names a parser SGLang doesn't have, and tool-call reports wait without a reply." }, "scores": { "a": 92, "b": 70 } }, { "by": 15, "category": "risk", "leads": "nemotron-3-ultra-550b-a55b", "name": "Risk", "reasons": { "a": "File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the ibm-granite organisation, which IBM's Granite docs and the GitHub README link to (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions. Licence risk 15, Apache 2.0 has no use policy, field-of-use limit, threshold or change clause, and the card's licence link resolves. Security programme 5, www.ibm.com/.well-known/security.txt names IBM PSIRT, read for the 3B listing on 4 November 2026 (5). 15 + 15 + 0 + 5 + 15 + 5 = 55.", "b": "File safety earns 15, safetensors only but with 10 off because the documented commands pass trust-remote-code and the repository ships Python files. Provenance earns 15, files in NVIDIA's linked organisation with a hash for every file. Safety evaluation earns 0, since NVIDIA describes safety training but publishes no results. Agent guidance earns 15, as the technical report documents training the model to ignore instructions injected through tool output, beside the subcard's warnings and safeguards. Licence risk keeps all 15 under OpenMDW-1.1. The security programme earns 10 for the reporting route on the card and NVIDIA's public bulletins." }, "scores": { "a": 55, "b": 70 } }, { "by": 5, "category": "interface", "leads": "nemotron-3-ultra-550b-a55b", "name": "Agent interface", "reasons": { "a": "Chat template 20, chat_template.jinja in the repository, with roles, tools, tool responses, the thinking switch and low effort. Tool-calling format 25, the card has tool-calling sections for vLLM and SGLang, declares tools with OpenAI's function schema and names the parser to use, qwen3_coder, which both runtimes ship in the released versions read. Trained tool calling 20, the template loops over every entry in an assistant turn's tool_calls, so it renders several calls in one turn, and the card names tool calling and agentic workflows as what it's best for. Structured output 0, the card doesn't document JSON or JSON Schema output or how to ask for it. Reasoning control 10, the card documents thinking on by default, non-thinking and low-effort modes per request through the template (enable_thinking and low_effort), with the reasoning returned apart from the answer. Context 10, 131,072 tokens natively per the card and config.json. 20 + 25 + 20 + 0 + 10 + 10 = 85.", "b": "The chat template earns 20, a Jinja file in the repository. The tool-calling format earns 25, since the card names the qwen3_coder parser and shows a tool call declared and read back. Trained tool calling earns 20, with tool-use training and several calls per turn in the template. Structured output earns 5, claimed from training with no instructions. Reasoning control earns 10, thinking off or medium effort per request with a runtime parser that returns the reasoning apart. Context earns 10 for up to 1M tokens." }, "scores": { "a": 85, "b": 90 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "IBM", "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": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-10-20", "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": "none", "b": "2.8/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Nemotron 3 Ultra 550B A55B scores 69.3 (B) on agent readiness against Granite 4.2 8B's 61 (C), and leads in 2 of 4 scored categories. Granite 4.2 8B leads on reach and documentation and upkeep.", "question": "Which is better for AI agents, Granite 4.2 8B or Nemotron 3 Ultra 550B A55B?" } ], "goodFor": [ { "aheadOn": [ "Reach, 95 against 55", "Documentation and upkeep, 92 against 70" ], "also": null, "goodFor": "Agents that want a documented, signed, Apache 2.0 reasoning model with tool calling on a 16 GB machine", "slug": "granite-4-2-8b", "watchFor": "No safety results published for it" }, { "aheadOn": [ "Risk, 70 against 55", "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" } ], "history": { "granite-4-2-8b": null, "nemotron-3-ultra-550b-a55b": [ { "causes": [ "correction" ], "date": "2026-11-01", "delta": 0.5, "from": 68.8, "fromGrade": "B", "to": 69.3, "toGrade": "B" } ] }, "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-granite-4-2-8b.json", "title": "Agents-A1-4B vs Granite 4.2 8B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-granite-4-2-8b" }, { "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-granite-4-2-8b.json", "title": "Apertus v1.5 8B vs Granite 4.2 8B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-granite-4-2-8b" }, { "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": 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}, { "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" }, { 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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-recursive-flint.json", "title": "Nemotron 3 Ultra 550B A55B vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-ripple-65b-preview.json", "title": "Nemotron 3 Ultra 550B A55B vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-sensenova-loop-9b-preview.json", "title": "Nemotron 3 Ultra 550B A55B vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-sensenova-loop-9b-preview" }, { "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-step-3-7-flash.json", "title": "Nemotron 3 Ultra 550B A55B vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-step-3-7-flash" }, { "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" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-unlimited-ocr-2.json", "title": "Nemotron 3 Ultra 550B A55B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-wevisone-vl-2b.json", "title": "Nemotron 3 Ultra 550B A55B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-wevisone-vl-4b.json", "title": "Nemotron 3 Ultra 550B A55B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-zaya1-8b.json", "title": "Nemotron 3 Ultra 550B A55B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 40, "edge": "granite-4-2-8b", "granite-4-2-8b": 95, "key": "reach", "name": "Reach", "nemotron-3-ultra-550b-a55b": 55, "weight": 25 }, { "by": 15, "edge": "nemotron-3-ultra-550b-a55b", "granite-4-2-8b": 55, "key": "risk", "name": "Risk", "nemotron-3-ultra-550b-a55b": 70, "weight": 20 }, { "by": 5, "edge": "nemotron-3-ultra-550b-a55b", "granite-4-2-8b": 85, "key": "interface", "name": "Agent interface", "nemotron-3-ultra-550b-a55b": 90, "weight": 10 }, { "by": 22, "edge": "granite-4-2-8b", "granite-4-2-8b": 92, "key": "upkeep", "name": "Documentation and upkeep", "nemotron-3-ultra-550b-a55b": 70, "weight": 10 } ], "summary": "Nemotron 3 Ultra 550B A55B scores 69.3 (B) on agent readiness against Granite 4.2 8B's 61 (C), and leads in 2 of 4 scored categories. Granite 4.2 8B leads on reach and documentation and upkeep. Both do open-weight models you run yourself.", "tasks": null, "tutorials": [], "verdicts": { "granite-4-2-8b": "IBM's mid-size reasoning cousin, with the same strengths as the 3B. It runs in every runtime we count from IBM's own builds on a 16 GB machine, is signed, plain Apache 2.0, and comes with a disclosure of its training data. No safety results are published and structured output isn't documented.", "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/granite-4-2-8b-vs-nemotron-3-ultra-550b-a55b", "json": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-nemotron-3-ultra-550b-a55b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-nemotron-3-ultra-550b-a55b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-nemotron-3-ultra-550b-a55b.min.md" }, "markdown": "Nemotron 3 Ultra 550B A55B scores 69.3 (B) on agent readiness against Granite 4.2 8B's 61 (C), and leads in 2 of 4 scored categories. Granite 4.2 8B leads on reach and documentation and upkeep. Both do open-weight models you run yourself.\n\n- Granite 4.2 8B: grade C, 61/100, rank #66 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/granite-4-2-8b.md · JSON https://www.anchorterminal.com/api/v1/tools/granite-4-2-8b.json\n- Nemotron 3 Ultra 550B A55B: grade B, 69.3/100, rank #9 of 160 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 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### Granite 4.2 8B (C)\n\nGood for: Agents that want a documented, signed, Apache 2.0 reasoning model with tool calling on a 16 GB machine\n\nAhead on:\n- Reach, 95 against 55\n- Documentation and upkeep, 92 against 70\n\nWatch for: No safety results published for it\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- Risk, 70 against 55\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## Why the edge\n\n### Reach: Granite 4.2 8B +40\n\n- Granite 4.2 8B (95): Memory 30, IBM's own 4-bit MLX build granite-4.2-8b-q4-mlx is 4.95 GB, and 4.95 GB × 1.2 = 5.9 GB, within 8 GB. Its Q4_0 GGUF is 5.06 GB and Ollama's library build granite4.2:8b (5.3 GB) fits 8 GB too. Runtimes 25, Released versions run the granite architecture in llama.cpp (granite, b12242), vLLM (GraniteForCausalLM in the v0.33.0 registry), SGLang (granite.py at v0.5.23), Transformers (v5.22.2) and Ollama (its own granite4.2 library entry, 8b tag). Builds 15, IBM publishes FP8, MXFP4 and NVFP4 safetensors builds, a GGUF repository with Q4_0 to Q8_0 and BF16 files, and 4-bit, 8-bit and BF16 MLX builds, so quantised builds at two precisions or more. Terms 15, Apache 2.0, an OSI-approved licence, with no use policy or other terms added on the card. Download 10, the Hub API reports gated false for the BF16, FP8, MXFP4, NVFP4, GGUF and MLX repositories, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps one inference provider (DeepInfra, live), short of the two the line needs. 30 + 25 + 15 + 15 + 10 + 0 = 95.\n- Nemotron 3 Ultra 550B A55B (55): Memory earns 0, because the smallest 4-bit build, NVIDIA's 352 GB NVFP4, needs about 423 GB with the fifth added, above the 256 GB band, and no runtime library has a local build. Runtimes earn 20 from released vLLM, SGLang, llama.cpp and Transformers, with Ollama's cloud-only entry not counted. Builds earn 10 for the single NVFP4 build. Terms earn 10, because OpenMDW-1.1 is permissive with no threshold or use policy but isn't an OSI licence. Download earns 10, with no gate or account. Hosted earns 5, since NVIDIA's own API serves it and two providers host it.\n\n### Documentation and upkeep: Granite 4.2 8B +22\n\n- Granite 4.2 8B (92): Model card 17, Intended use (best for), limitations (ethical considerations and limitations), architecture, parameters (8B), context length (128K native) and 12 tested languages are on the card. Training data 20, IBM's disclosure file for the 8B lists the datasets used in pre-training and post-training as machine-readable metadata, and the card describes the three training stages. Last change 20, the last commit, on 20 October 2026, replaced model.safetensors.index.json (titled \"granite-4.2 index file fix\"), 21 days before this check. Responsiveness 20, Of the 5 discussions that aren't pull requests (13 September to 5 November 2026), 4 have a reply from an ibm-granite member. Version policy 5, Granite 4.1 8B stays in its own repository and 4.2 came out in new ones. File health 10, Config, template and card agree on the architecture, the 131,072-token context and the thinking switch. 17 + 20 + 20 + 20 + 5 + 10 = 92.\n- Nemotron 3 Ultra 550B A55B (70): The model card earns 20, all seven items with limitations in the linked subcard. Training data earns 20, datasets listed with sources and token counts, cut-offs and filtering, and the corpora published. Last change earns 20, counting NVIDIA's own inference code for the model. The repositories last changed on 24 August, but NVIDIA's TensorRT-LLM, the runtime the card's own serving commands use, changed its Nemotron-H model code on 14 October, 18 days ago. Responsiveness earns 0, with no NVIDIA reply to any of nine questions. Version policy earns 5 as a first release. File health earns 5, since there is no LICENSE file, the BF16 card's SGLang command names a parser SGLang doesn't have, and tool-call reports wait without a reply.\n\n### Risk: Nemotron 3 Ultra 550B A55B +15\n\n- Granite 4.2 8B (55): File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the ibm-granite organisation, which IBM's Granite docs and the GitHub README link to (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions. Licence risk 15, Apache 2.0 has no use policy, field-of-use limit, threshold or change clause, and the card's licence link resolves. Security programme 5, www.ibm.com/.well-known/security.txt names IBM PSIRT, read for the 3B listing on 4 November 2026 (5). 15 + 15 + 0 + 5 + 15 + 5 = 55.\n- Nemotron 3 Ultra 550B A55B (70): File safety earns 15, safetensors only but with 10 off because the documented commands pass trust-remote-code and the repository ships Python files. Provenance earns 15, files in NVIDIA's linked organisation with a hash for every file. Safety evaluation earns 0, since NVIDIA describes safety training but publishes no results. Agent guidance earns 15, as the technical report documents training the model to ignore instructions injected through tool output, beside the subcard's warnings and safeguards. Licence risk keeps all 15 under OpenMDW-1.1. The security programme earns 10 for the reporting route on the card and NVIDIA's public bulletins.\n\n### Agent interface: Nemotron 3 Ultra 550B A55B +5\n\n- Granite 4.2 8B (85): Chat template 20, chat_template.jinja in the repository, with roles, tools, tool responses, the thinking switch and low effort. Tool-calling format 25, the card has tool-calling sections for vLLM and SGLang, declares tools with OpenAI's function schema and names the parser to use, qwen3_coder, which both runtimes ship in the released versions read. Trained tool calling 20, the template loops over every entry in an assistant turn's tool_calls, so it renders several calls in one turn, and the card names tool calling and agentic workflows as what it's best for. Structured output 0, the card doesn't document JSON or JSON Schema output or how to ask for it. Reasoning control 10, the card documents thinking on by default, non-thinking and low-effort modes per request through the template (enable_thinking and low_effort), with the reasoning returned apart from the answer. Context 10, 131,072 tokens natively per the card and config.json. 20 + 25 + 20 + 0 + 10 + 10 = 85.\n- Nemotron 3 Ultra 550B A55B (90): The chat template earns 20, a Jinja file in the repository. The tool-calling format earns 25, since the card names the qwen3_coder parser and shows a tool call declared and read back. Trained tool calling earns 20, with tool-use training and several calls per turn in the template. Structured output earns 5, claimed from training with no instructions. Reasoning control earns 10, thinking off or medium effort per request with a runtime parser that returns the reasoning apart. Context earns 10 for up to 1M tokens.\n\n## Score by category\n\n| Category | Weight | Granite 4.2 8B | Nemotron 3 Ultra 550B A55B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 95 | 55 | Granite 4.2 8B +40 |\n| Risk | 20% (20 this run) | 55 | 70 | Nemotron 3 Ultra 550B A55B +15 |\n| Agent interface | 10% (10 this run) | 85 | 90 | Nemotron 3 Ultra 550B A55B +5 |\n| Documentation and upkeep | 10% (10 this run) | 92 | 70 | Granite 4.2 8B +22 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **69.3 · B** | |\n\n## Facts side by side\n\n| Fact | Granite 4.2 8B | Nemotron 3 Ultra 550B A55B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | IBM | 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 | no | no |\n| Last release | 2026-10-20 | 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| Agent reviews | none | 2.8/5 (4) |\n\n## How the scores have moved\n\n### Granite 4.2 8B\n\nNo score change since it was listed.\n\n### Nemotron 3 Ultra 550B A55B\n\n- +0.5 on 1 November 2026: correction\n\n## Verdicts\n\n**Granite 4.2 8B.** IBM's mid-size reasoning cousin, with the same strengths as the 3B. It runs in every runtime we count from IBM's own builds on a 16 GB machine, is signed, plain Apache 2.0, and comes with a disclosure of its training data. No safety results are published and structured output isn't documented.\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### Granite 4.2 8B\n\n1. Start it with `ollama run granite4.2:8b`, or `llama-server -hf ibm-granite/granite-4.2-8b-GGUF:Q4_K_M --jinja`\n2. With vLLM 0.32.0 or later leave out `--reasoning-parser-plugin`, since the parser is built in, and use `--tool-call-parser qwen3_coder`\n3. Thinking is on by default. Pass `enable_thinking` false to turn it off or `low_effort` true for short reasoning\n4. The context is 131,072 tokens. The card's 512K extension isn't documented\n5. Validate JSON output yourself, since structured output isn't documented\n6. IBM recommends Granite Guardian beside it to flag risky inputs and outputs\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, Granite 4.2 8B or Nemotron 3 Ultra 550B A55B?\n\nNemotron 3 Ultra 550B A55B scores 69.3 (B) on agent readiness against Granite 4.2 8B's 61 (C), and leads in 2 of 4 scored categories. Granite 4.2 8B leads on reach and documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/granite-4-2-8b-vs-nemotron-3-ultra-550b-a55b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/granite-4-2-8b-vs-nemotron-3-ultra-550b-a55b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"granite-4-2-8b\", \"b\": \"nemotron-3-ultra-550b-a55b\"}`. From a terminal: `anchor compare granite-4-2-8b nemotron-3-ultra-550b-a55b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/granite-4-2-8b.json and https://www.anchorterminal.com/api/v1/tools/nemotron-3-ultra-550b-a55b.json\n\n## Other comparisons with Granite 4.2 8B or Nemotron 3 Ultra 550B A55B\n\n- [Agents-A1-4B vs Granite 4.2 8B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-granite-4-2-8b.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 Granite 4.2 8B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-granite-4-2-8b.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 Granite 4.2 8B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-granite-4-2-8b.md)\n- [Apodex-1.1-mini vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nemotron-3-ultra-550b-a55b.md)\n- [Arete-27B vs Granite 4.2 8B](https://www.anchorterminal.com/compare/arete-27b-vs-granite-4-2-8b.md)\n- [Arete-27B vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/arete-27b-vs-nemotron-3-ultra-550b-a55b.md)\n- [BigBang-v1 vs Granite 4.2 8B](https://www.anchorterminal.com/compare/bigbang-v1-vs-granite-4-2-8b.md)\n- [BigBang-v1 vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/bigbang-v1-vs-nemotron-3-ultra-550b-a55b.md)\n- [Carbon-X vs Granite 4.2 8B](https://www.anchorterminal.com/compare/carbon-x-vs-granite-4-2-8b.md)\n- [Carbon-X vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/carbon-x-vs-nemotron-3-ultra-550b-a55b.md)\n- [Cola2 vs Granite 4.2 8B](https://www.anchorterminal.com/compare/cola2-vs-granite-4-2-8b.md)\n- [Cola2 vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/cola2-vs-nemotron-3-ultra-550b-a55b.md)\n- [Cosmos-Reason2-2B vs Granite 4.2 8B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-granite-4-2-8b.md)\n- [Cosmos-Reason2-2B vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nemotron-3-ultra-550b-a55b.md)\n- [Darwin-36B-Opus vs Granite 4.2 8B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-granite-4-2-8b.md)\n- [Darwin-36B-Opus vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-nemotron-3-ultra-550b-a55b.md)\n- [DeepSeek-V3.2 vs Granite 4.2 8B](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-granite-4-2-8b.md)\n- [DeepSeek-V3.2 vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-nemotron-3-ultra-550b-a55b.md)\n- [DeepSeek V4.1 Flash vs Granite 4.2 8B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-granite-4-2-8b.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-Flash-0731 vs Granite 4.2 8B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-granite-4-2-8b.md)\n- [DeepSeek-V4-Flash-0731 vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-nemotron-3-ultra-550b-a55b.md)\n- [Devstral Small 2 vs Granite 4.2 8B](https://www.anchorterminal.com/compare/devstral-small-2-vs-granite-4-2-8b.md)\n- [Devstral Small 2 vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/devstral-small-2-vs-nemotron-3-ultra-550b-a55b.md)\n- [DiffusionGemma 26B A4B IT vs Granite 4.2 8B](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-granite-4-2-8b.md)\n- [DiffusionGemma 26B A4B IT vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-nemotron-3-ultra-550b-a55b.md)\n- [EXAONE 4.5 33B vs Granite 4.2 8B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-granite-4-2-8b.md)\n- [EXAONE 4.5 33B vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-nemotron-3-ultra-550b-a55b.md)\n- [Gemma 4 12B IT vs Granite 4.2 8B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-granite-4-2-8b.md)\n- [Gemma 4 12B IT vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-nemotron-3-ultra-550b-a55b.md)\n- [Gemma 4 26B A4B IT vs Granite 4.2 8B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-granite-4-2-8b.md)\n- [Gemma 4 26B A4B IT vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-nemotron-3-ultra-550b-a55b.md)\n- [Gemma 4 31B IT vs Granite 4.2 8B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-granite-4-2-8b.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 E2B IT vs Granite 4.2 8B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-granite-4-2-8b.md)\n- [Gemma 4 E2B IT vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-nemotron-3-ultra-550b-a55b.md)\n- [Gemma 4 E4B IT vs Granite 4.2 8B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-granite-4-2-8b.md)\n- [Gemma 4 E4B IT vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-nemotron-3-ultra-550b-a55b.md)\n- [GLM-4.5-Air vs Granite 4.2 8B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-granite-4-2-8b.md)\n- [GLM-4.5-Air vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-nemotron-3-ultra-550b-a55b.md)\n- [GLM-4.7-Flash vs Granite 4.2 8B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-granite-4-2-8b.md)\n- [GLM-4.7-Flash vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-nemotron-3-ultra-550b-a55b.md)\n- [GLM-5.3-Flash vs Granite 4.2 8B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-granite-4-2-8b.md)\n- [GLM-5.3-Flash vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-nemotron-3-ultra-550b-a55b.md)\n- [GLM-5.3 vs Granite 4.2 8B](https://www.anchorterminal.com/compare/glm-5-3-vs-granite-4-2-8b.md)\n- [GLM-5.3 vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/glm-5-3-vs-nemotron-3-ultra-550b-a55b.md)\n- [gpt-oss-120b vs Granite 4.2 8B](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-granite-4-2-8b.md)\n- [gpt-oss-120b vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-nemotron-3-ultra-550b-a55b.md)\n- [gpt-oss-20b vs Granite 4.2 8B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-granite-4-2-8b.md)\n- [gpt-oss-20b vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-nemotron-3-ultra-550b-a55b.md)\n- [Granite 4.2 30B vs Granite 4.2 8B](https://www.anchorterminal.com/compare/granite-4-2-30b-vs-granite-4-2-8b.md)\n- [Granite 4.2 30B vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/granite-4-2-30b-vs-nemotron-3-ultra-550b-a55b.md)\n- [Granite 4.2 3B vs Granite 4.2 8B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-granite-4-2-8b.md)\n- [Granite 4.2 3B vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-nemotron-3-ultra-550b-a55b.md)\n- [Granite 4.2 8B vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-hy-memory-30b-a3b.md)\n- [Granite 4.2 8B vs Hy3](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-hy3.md)\n- [Granite 4.2 8B vs Inkling](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-inkling.md)\n- [Granite 4.2 8B vs Inkling-Small](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-inkling-small.md)\n- [Granite 4.2 8B vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [Granite 4.2 8B vs Kimi K3](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-kimi-k3.md)\n- [Granite 4.2 8B vs Kimi K3.1 Pro](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-kimi-k3-1-pro.md)\n- [Granite 4.2 8B vs Kolibri-1](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-kolibri-1.md)\n- [Granite 4.2 8B vs Kronos](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-kronos.md)\n- [Granite 4.2 8B vs Laguna M.1](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-laguna-m-1.md)\n- [Granite 4.2 8B vs Laguna S 2.1](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-laguna-s-2-1.md)\n- [Granite 4.2 8B vs Laguna XS 2.1](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-laguna-xs-2-1.md)\n- [Granite 4.2 8B vs LBS-27B](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-lbs-27b.md)\n- [Granite 4.2 8B vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-lfm2-5-2-6b.md)\n- [Granite 4.2 8B vs LFM3-1.7B](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-lfm3-1-7b.md)\n- [Granite 4.2 8B vs LFM3-Audio-2B](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-lfm3-audio-2b.md)\n- [Granite 4.2 8B vs LFM3-Omni-2B](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-lfm3-omni-2b.md)\n- [Granite 4.2 8B vs LFM3-VL-2B](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-lfm3-vl-2b.md)\n- [Granite 4.2 8B vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-ling-3-0-tiny.md)\n- [Granite 4.2 8B vs Ling-3.1-flash-1019](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-ling-3-1-flash-1019.md)\n- [Granite 4.2 8B vs LongCat-2.5](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-longcat-2-5.md)\n- [Granite 4.2 8B vs MiMo-V2.6 Distill Qwen 9B](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-mimo-v2-6-distill-qwen-9b.md)\n- [Granite 4.2 8B vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-mimo-v2-6-flash-rl.md)\n- [Granite 4.2 8B vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-minicpm-o-4-5.md)\n- [Granite 4.2 8B vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-minicpm-v-4-7-1b.md)\n- [Granite 4.2 8B vs MiniCPM-V 4.7 35B-A3B](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-minicpm-v-4-7-35b-a3b.md)\n- [Granite 4.2 8B vs MiniCPM5-2B](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-minicpm5-2b.md)\n- [Granite 4.2 8B vs MiniMax-M2.5](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-minimax-m2-5.md)\n- [Granite 4.2 8B vs MiniMax-M2.7](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-minimax-m2-7.md)\n- [Granite 4.2 8B vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-minimax-m3-1-flash.md)\n- [Granite 4.2 8B vs Mistral Large 4](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-mistral-large-4.md)\n- [Granite 4.2 8B vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-muse-glimmer-30b.md)\n- [Granite 4.2 8B vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-nanbeige-4-2-3b.md)\n- [Granite 4.2 8B vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-nanbeige-4-3-33b.md)\n- [Granite 4.2 8B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [Granite 4.2 8B vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [Granite 4.2 8B vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-nemotron-3-nano-30b-a3b.md)\n- [Granite 4.2 8B vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-nemotron-3-nano-4b.md)\n- [Granite 4.2 8B vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [Granite 4.2 8B vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-nemotron-3-super-120b-a12b.md)\n- [Granite 4.2 8B vs North Mini Code 1.0](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-north-mini-code-1-0.md)\n- [Granite 4.2 8B vs occamy-1.0](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-occamy-1-0.md)\n- [Granite 4.2 8B vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-olmo-3-7b-think.md)\n- [Granite 4.2 8B vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-ornith-1-5-122b.md)\n- [Granite 4.2 8B vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-ornith-1-5-35b-a3b.md)\n- [Granite 4.2 8B vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-ornith-1-5-397b.md)\n- [Granite 4.2 8B vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-ornith-1-5-9b.md)\n- [Granite 4.2 8B vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-otel-2-0-llm-31b-it.md)\n- [Granite 4.2 8B vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-qwen-agentworld-35b-a3b.md)\n- [Granite 4.2 8B vs Qwen3-30B-A3B-Instruct-2507](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-qwen3-30b-a3b-instruct-2507.md)\n- [Granite 4.2 8B vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-qwen3-4b-instruct-2507.md)\n- [Granite 4.2 8B vs Qwen3.5-0.8B](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-qwen3-5-0-8b.md)\n- [Granite 4.2 8B vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-qwen3-5-122b-a10b.md)\n- [Granite 4.2 8B vs Qwen3.5-2B](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-qwen3-5-2b.md)\n- [Granite 4.2 8B vs 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"meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from (https://www.anchorterminal.com/terms/#data)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-11-11", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.7", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run", "terms": "https://www.anchorterminal.com/terms/#data" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "Granite 4.2 8B vs Nemotron 3 Ultra 550B A55B", "url": "" } ], "description": "Nemotron 3 Ultra 550B A55B scores 69.3 (B) to Granite 4.2 8B's 61 (C) for open-weight models you run yourself. 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