{ "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": "Granite 4.2 8B scores 61 (C) on agent readiness against Nemotron 3 Nano 4B's 56.8 (C), and leads in 3 of 4 scored categories. Nemotron 3 Nano 4B leads on risk.", "b": { "slug": "nemotron-3-nano-4b", "name": "Nemotron 3 Nano 4B", "vendor": "NVIDIA", "vendorUrl": "https://developer.nvidia.com/nemotron", "kind": "weights", "category": "open-weights", "summary": "Nemotron 3 Nano 4B is NVIDIA's open-weight small hybrid Mamba-2 and Transformer reasoning model for agents on edge devices, 4B parameters compressed from Nemotron Nano 9B v2, published in BF16, FP8 and GGUF under the NVIDIA Nemotron Open Model License.", "url": "https://www.anchorterminal.com/tools/nemotron-3-nano-4b", "markdownUrl": "https://www.anchorterminal.com/tools/nemotron-3-nano-4b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/nemotron-3-nano-4b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-4b.json", "dns": "nemotron-3-nano-4b.grade.anchorterminal.com", "repo": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16", "license": "NVIDIA Nemotron Open Model License", "transports": [], "packages": [ { "registry": "huggingface", "name": "nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16" }, { "registry": "huggingface", "name": "nvidia/NVIDIA-Nemotron-3-Nano-4B-FP8" }, { "registry": "huggingface", "name": "nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF" }, { "registry": "ollama", "name": "nemotron-3-nano:4b" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The BF16, FP8 and GGUF repositories are not gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under the NVIDIA Nemotron Open Model License. Running it yourself costs the hardware, 7.96 GB for the BF16 repository, 5.28 GB for NVIDIA's FP8 build or 2.84 GB for NVIDIA's Q4_K_M GGUF (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-Nano-4B-BF16", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "mamba", "hybrid", "small", "edge", "reasoning", "tool-calling", "safetensors", "fp8", "gguf", "self-hosted" ], "lastRelease": "2026-03-16", "graded": true, "anchor": { "graded": true, "score": 56.8, "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": 82, "methodology": "0.7", "run": "2026-10-01", "scores": { "capability": 25, "interface": 70, "reach": 90, "risk": 65, "upkeep": 55 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-11-04" }, "negative": 0, "verdict": "A 3.97-billion-parameter reasoning model with trained tool calls, a thinking switch and 262,144 tokens of context, which runs in about 4 GB from NVIDIA's own GGUF. The main risks are serving commands that run repository code, no published safety results, a tool format left to the template, and files unchanged and questions unanswered since March 2026.", "bestFor": "Agents on laptops, edge boards and small GPUs that need a cheap local tool-calling helper in English", "strengths": [ "NVIDIA's own 2.84 GB GGUF and 5.28 GB FP8 builds, plus Ollama and LM Studio entries", "Released support in llama.cpp, vLLM, SGLang, Transformers, Ollama and LM Studio", "Several tool calls per turn in the template and thinking off per request", "262,144 tokens of context", "Free commercial licence with no threshold or use policy, and no gate" ], "weaknesses": [ "The card's Transformers and vLLM commands pass trust_remote_code and load a parser plugin from the repository", "No safety evaluation results published", "Tool calling and structured output aren't documented by NVIDIA", "No NVIDIA reply to any question, and no change to the files since 20 March 2026", "Empty LICENSE files and a card snippet that names a repository that isn't public", "No hosted endpoint from NVIDIA or any provider" ], "agentNotes": [ "Quickest start is `ollama run nemotron-3-nano:4b`, a 2.8 GB build", "With llama.cpp use `llama-server -hf nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF --jinja`, NVIDIA's own Q4_K_M file", "With vLLM, download nano_v3_reasoning_parser.py from the repository first and serve with `--enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser-plugin nano_v3_reasoning_parser.py --reasoning-parser nano_v3`", "The card's Transformers snippet names nvidia/NVIDIA-Nemotron-3-Nano-4B, use nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16 instead", "Thinking is on by default. Pass `enable_thinking=False` to the chat template for short tasks", "Use temperature 1.0 and top_p 0.95 for reasoning and temperature 0.6 with top_p 0.95 for tool calling, as the card says", "It's tuned for English only, and validate JSON yourself, since structured output isn't documented" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3.3, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 56.8 } ], "editorialScores": { "capability": 25, "interface": 70, "reach": 90, "risk": 65, "upkeep": 55 }, "provenanceScore": 76, "checked": { "categories": { "capability": "2026-11-04", "interface": "2026-11-04", "reach": "2026-11-04", "risk": "2026-11-04", "upkeep": "2026-11-04" }, "last": "2026-11-04", "oldest": "2026-11-04", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "ollama run nemotron-3-nano:4b\n# or, with llama.cpp and NVIDIA's GGUF:\nllama-server -hf nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF --jinja -c 131072\n# or, with the card's vLLM command (abridged):\nwget https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16/resolve/main/nano_v3_reasoning_parser.py\nvllm serve nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16 --max-model-len 262144 --trust-remote-code \\\n --mamba_ssm_cache_dtype float32 --enable-auto-tool-choice --tool-call-parser qwen3_coder \\\n --reasoning-parser-plugin nano_v3_reasoning_parser.py --reasoning-parser nano_v3", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"nemotron-3-nano:4b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/nemotron-3-nano-4b" }, "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-omni-30b-a3b-reasoning", "nemotron-3-super-120b-a12b", "nemotron-3-ultra-550b-a55b", "nemotron-3-5-lightning-30b-a3b" ], "weights": { "trainedBy": "NVIDIA", "builtOn": "nvidia/NVIDIA-Nemotron-Nano-9B-v2 (compressed with Nemotron Elastic; base under the NVIDIA Open Model License Agreement)", "params": "4B", "context": 262144, "goodFor": "Small English reasoning and tool-calling model for agents on edge devices and laptops, with a thinking switch and 262,144 tokens of context.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "LM Studio", "Transformers" ], "builds": [ { "name": "NVIDIA-Nemotron-3-Nano-4B-FP8", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 5.28, "memoryGB": 8, "format": "safetensors" }, { "name": "NVIDIA-Nemotron-3-Nano-4B-GGUF (Q4_K_M)", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF", "who": "publisher", "precision": "q4_k_m", "sizeGB": 2.84, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "One file, NVIDIA-Nemotron3-Nano-4B-Q4_K_M.gguf" }, { "name": "ollama nemotron-3-nano:4b", "url": "https://ollama.com/library/nemotron-3-nano:4b", "who": "ollama", "precision": "q4_k_m", "sizeGB": 2.8, "memoryGB": 8, "format": "ollama", "runsOn": [ "Ollama" ], "note": "The 4b tag is 2.8 GB (q4_K_M) or 2.9 GB (MLX nvfp4) by platform. Ollama also has q8_0 at 4.2 GB and bf16 at 8.0 GB. Sizes as Ollama's tags page shows them" }, { "name": "lmstudio-community NVIDIA-Nemotron-3-Nano-4B-GGUF (Q4_K_M)", "url": "https://huggingface.co/lmstudio-community/NVIDIA-Nemotron-3-Nano-4B-GGUF", "who": "lm-studio", "precision": "q4_k_m", "sizeGB": 2.84, "memoryGB": 8, "format": "gguf", "runsOn": [ "LM Studio" ], "note": "The build LM Studio's catalogue page links. Also Q6_K (3.93 GB) and Q8_0 (4.23 GB)" }, { "name": "NVIDIA-Nemotron-3-Nano-4B-BF16", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16", "who": "publisher", "precision": "bf16", "sizeGB": 7.96, "memoryGB": 12, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] } ], "run": "llama-server -hf nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF --jinja -c 131072", "runBuild": "NVIDIA-Nemotron-3-Nano-4B-GGUF (Q4_K_M)", "checked": "2026-10-21", "minMemoryGB": 8 }, "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 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", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16", "params": "550B total, 55B active", "listing": "https://www.anchorterminal.com/tools/nemotron-3-ultra-550b-a55b" }, { "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-Nano-4B-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. The last change was the inline chat template in tokenizer_config.json on 20 March 2026, and most commits read Update README.md.", "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": 76 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/nemotron-3-nano-4b.json", "live": { "slug": "nemotron-3-nano-4b", "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-Nano-4B-BF16/commits/main", "kind": "changelog", "status": 200, "checkedAt": "2026-11-10T16:23:18.735571066Z", "changedAt": "2026-11-10T16:23:18.735571066Z", "fingerprint": "3b3d07e6bba4" } ], "updatedAt": "2026-11-10T16:23:18.735571066Z" } }, "edges": [ { "by": 37, "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 dates, crawled sets with token counts, a cut-off and published NVIDIA datasets, though most synthetic sizes are undisclosed. Last change earns 5, as the files last changed on 20 March 2026, and TensorRT-LLM's Nemotron-H commits since 14 October touch MoE, NVFP4 and Puzzle paths this dense model doesn't use, in release candidates or no release. Responsiveness earns 0, with no NVIDIA reply to any of six questions, re-counted on 4 November. Version policy earns 5 as a first release. File health earns 5, since the LICENSE file is empty, reports and fixes wait without a reply, and the card's Transformers snippet names a repository that isn't public." }, "scores": { "a": 92, "b": 55 } }, { "by": 15, "category": "interface", "leads": "granite-4-2-8b", "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 10 of 25, as only the template shows the format and the parser name appears only in a serving command. Trained tool calling earns 20, with tool-use benchmarks and data and several calls per turn in the template. Structured output earns 0, not claimed or documented. Reasoning control earns 10, thinking off per request with a parser that returns the reasoning apart. Context earns 10 for 262,144 tokens." }, "scores": { "a": 85, "b": 70 } }, { "by": 10, "category": "risk", "leads": "nemotron-3-nano-4b", "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 and GGUF only but with 10 off because the documented commands pass trust_remote_code and load a parser plugin from the repository. Provenance earns 15, files in NVIDIA's linked organisation with a hash for every file. Safety evaluation earns 0, no results. Agent guidance earns 10, subcards that name prompt injection and the safeguards to add, without an instruction hierarchy. Licence risk keeps all 15, since the switch to the Nemotron licence happened on release day. The security programme earns 10 for the reporting route on the card and NVIDIA's public bulletins." }, "scores": { "a": 55, "b": 65 } }, { "by": 5, "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 the full 30, since NVIDIA's own 2.84 GB GGUF needs under 4 GB with the fifth added. Runtimes earn the full 25 from released llama.cpp, vLLM, SGLang, Transformers, Ollama and LM Studio. Builds earn 15 for NVIDIA's FP8 and GGUF. Terms earn 10, since the Nemotron licence is custom even though it's free for commercial use with no threshold or use policy. Download earns 10, with no gate or account. Hosted earns 0, as no Hugging Face provider serves it and no NVIDIA hosting was found." }, "scores": { "a": 95, "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": "NVIDIA Nemotron Open Model License", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-10-20", "b": "2026-03-16", "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": "3.3/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Granite 4.2 8B scores 61 (C) on agent readiness against Nemotron 3 Nano 4B's 56.8 (C), and leads in 3 of 4 scored categories. Nemotron 3 Nano 4B leads on risk.", "question": "Which is better for AI agents, Granite 4.2 8B or Nemotron 3 Nano 4B?" } ], "goodFor": [ { "aheadOn": [ "Reach, 95 against 90", "Agent interface, 85 against 70", "Documentation and upkeep, 92 against 55" ], "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, 65 against 55" ], "also": null, "goodFor": "Agents on laptops, edge boards and small GPUs that need a cheap local tool-calling helper in English", "slug": "nemotron-3-nano-4b", "watchFor": "The card's Transformers and vLLM commands pass trust_remote_code and load a parser plugin from the repository" } ], "history": null, "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": 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"json": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-spark-x2-6-4b.json", "title": "Granite 4.2 8B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-step-3-7-flash.json", "title": "Granite 4.2 8B vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-swift-1-5-qwen3-8-27b.json", "title": "Granite 4.2 8B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Granite 4.2 8B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": 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}, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-vl-8b-instruct.json", "title": "Nemotron 3 Nano 4B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwopus3-8-27b-flash-v2.json", "title": "Nemotron 3 Nano 4B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-recursive-flint.json", "title": "Nemotron 3 Nano 4B vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-ripple-65b-preview.json", "title": "Nemotron 3 Nano 4B vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-sensenova-loop-9b-preview.json", "title": "Nemotron 3 Nano 4B vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-smollm3-3b.json", "title": "Nemotron 3 Nano 4B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-spark-x2-6-4b.json", "title": "Nemotron 3 Nano 4B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-step-3-7-flash.json", "title": "Nemotron 3 Nano 4B vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-swift-1-5-qwen3-8-27b.json", "title": "Nemotron 3 Nano 4B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Nemotron 3 Nano 4B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-thinkingcap-qwen3-8-27b.json", "title": "Nemotron 3 Nano 4B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-unlimited-ocr-2.json", "title": "Nemotron 3 Nano 4B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-wevisone-vl-2b.json", "title": "Nemotron 3 Nano 4B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-wevisone-vl-4b.json", "title": "Nemotron 3 Nano 4B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-zaya1-8b.json", "title": "Nemotron 3 Nano 4B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 5, "edge": "granite-4-2-8b", "granite-4-2-8b": 95, "key": "reach", "name": "Reach", "nemotron-3-nano-4b": 90, "weight": 25 }, { "by": 10, "edge": "nemotron-3-nano-4b", "granite-4-2-8b": 55, "key": "risk", "name": "Risk", "nemotron-3-nano-4b": 65, "weight": 20 }, { "by": 15, "edge": "granite-4-2-8b", "granite-4-2-8b": 85, "key": "interface", "name": "Agent interface", "nemotron-3-nano-4b": 70, "weight": 10 }, { "by": 37, "edge": "granite-4-2-8b", "granite-4-2-8b": 92, "key": "upkeep", "name": "Documentation and upkeep", "nemotron-3-nano-4b": 55, "weight": 10 } ], "summary": "Granite 4.2 8B scores 61 (C) on agent readiness against Nemotron 3 Nano 4B's 56.8 (C), and leads in 3 of 4 scored categories. Nemotron 3 Nano 4B leads on risk. 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-nano-4b": "A 3.97-billion-parameter reasoning model with trained tool calls, a thinking switch and 262,144 tokens of context, which runs in about 4 GB from NVIDIA's own GGUF. The main risks are serving commands that run repository code, no published safety results, a tool format left to the template, and files unchanged and questions unanswered since March 2026." } }, "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-nano-4b", "json": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-nemotron-3-nano-4b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-nemotron-3-nano-4b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-nemotron-3-nano-4b.min.md" }, "markdown": "Granite 4.2 8B scores 61 (C) on agent readiness against Nemotron 3 Nano 4B's 56.8 (C), and leads in 3 of 4 scored categories. Nemotron 3 Nano 4B leads on risk. 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 Nano 4B: grade C, 56.8/100, rank #110 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-nano-4b.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-4b.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 90\n- Agent interface, 85 against 70\n- Documentation and upkeep, 92 against 55\n\nWatch for: No safety results published for it\n\n### Nemotron 3 Nano 4B (C)\n\nGood for: Agents on laptops, edge boards and small GPUs that need a cheap local tool-calling helper in English\n\nAhead on:\n- Risk, 65 against 55\n\nWatch for: The card's Transformers and vLLM commands pass trust_remote_code and load a parser plugin from the repository\n\n\n## Why the edge\n\n### Documentation and upkeep: Granite 4.2 8B +37\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 Nano 4B (55): The model card earns 20, all seven items with limitations in the linked subcard. Training data earns 20, datasets listed with sources and dates, crawled sets with token counts, a cut-off and published NVIDIA datasets, though most synthetic sizes are undisclosed. Last change earns 5, as the files last changed on 20 March 2026, and TensorRT-LLM's Nemotron-H commits since 14 October touch MoE, NVFP4 and Puzzle paths this dense model doesn't use, in release candidates or no release. Responsiveness earns 0, with no NVIDIA reply to any of six questions, re-counted on 4 November. Version policy earns 5 as a first release. File health earns 5, since the LICENSE file is empty, reports and fixes wait without a reply, and the card's Transformers snippet names a repository that isn't public.\n\n### Agent interface: Granite 4.2 8B +15\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 Nano 4B (70): The chat template earns 20, a Jinja file in the repository. The tool-calling format earns 10 of 25, as only the template shows the format and the parser name appears only in a serving command. Trained tool calling earns 20, with tool-use benchmarks and data and several calls per turn in the template. Structured output earns 0, not claimed or documented. Reasoning control earns 10, thinking off per request with a parser that returns the reasoning apart. Context earns 10 for 262,144 tokens.\n\n### Risk: Nemotron 3 Nano 4B +10\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 Nano 4B (65): File safety earns 15, safetensors and GGUF only but with 10 off because the documented commands pass trust_remote_code and load a parser plugin from the repository. Provenance earns 15, files in NVIDIA's linked organisation with a hash for every file. Safety evaluation earns 0, no results. Agent guidance earns 10, subcards that name prompt injection and the safeguards to add, without an instruction hierarchy. Licence risk keeps all 15, since the switch to the Nemotron licence happened on release day. The security programme earns 10 for the reporting route on the card and NVIDIA's public bulletins.\n\n### Reach: Granite 4.2 8B +5\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 Nano 4B (90): Memory earns the full 30, since NVIDIA's own 2.84 GB GGUF needs under 4 GB with the fifth added. Runtimes earn the full 25 from released llama.cpp, vLLM, SGLang, Transformers, Ollama and LM Studio. Builds earn 15 for NVIDIA's FP8 and GGUF. Terms earn 10, since the Nemotron licence is custom even though it's free for commercial use with no threshold or use policy. Download earns 10, with no gate or account. Hosted earns 0, as no Hugging Face provider serves it and no NVIDIA hosting was found.\n\n## Score by category\n\n| Category | Weight | Granite 4.2 8B | Nemotron 3 Nano 4B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 95 | 90 | Granite 4.2 8B +5 |\n| Risk | 20% (20 this run) | 55 | 65 | Nemotron 3 Nano 4B +10 |\n| Agent interface | 10% (10 this run) | 85 | 70 | Granite 4.2 8B +15 |\n| Documentation and upkeep | 10% (10 this run) | 92 | 55 | Granite 4.2 8B +37 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **56.8 · C** | |\n\n## Facts side by side\n\n| Fact | Granite 4.2 8B | Nemotron 3 Nano 4B |\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 | NVIDIA Nemotron Open Model License |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-20 | 2026-03-16 |\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 | 3.3/5 (4) |\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 Nano 4B.** A 3.97-billion-parameter reasoning model with trained tool calls, a thinking switch and 262,144 tokens of context, which runs in about 4 GB from NVIDIA's own GGUF. The main risks are serving commands that run repository code, no published safety results, a tool format left to the template, and files unchanged and questions unanswered since March 2026.\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 Nano 4B\n\n1. Quickest start is `ollama run nemotron-3-nano:4b`, a 2.8 GB build\n2. With llama.cpp use `llama-server -hf nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF --jinja`, NVIDIA's own Q4_K_M file\n3. With vLLM, download nano_v3_reasoning_parser.py from the repository first and serve with `--enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser-plugin nano_v3_reasoning_parser.py --reasoning-parser nano_v3`\n4. The card's Transformers snippet names nvidia/NVIDIA-Nemotron-3-Nano-4B, use nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16 instead\n5. Thinking is on by default. Pass `enable_thinking=False` to the chat template for short tasks\n6. Use temperature 1.0 and top_p 0.95 for reasoning and temperature 0.6 with top_p 0.95 for tool calling, as the card says\n7. It's tuned for English only, and validate JSON yourself, since structured output isn't documented\n\n## Questions\n\n### Which is better for AI agents, Granite 4.2 8B or Nemotron 3 Nano 4B?\n\nGranite 4.2 8B scores 61 (C) on agent readiness against Nemotron 3 Nano 4B's 56.8 (C), and leads in 3 of 4 scored categories. Nemotron 3 Nano 4B leads on risk.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/granite-4-2-8b-vs-nemotron-3-nano-4b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/granite-4-2-8b-vs-nemotron-3-nano-4b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"granite-4-2-8b\", \"b\": \"nemotron-3-nano-4b\"}`. From a terminal: `anchor compare granite-4-2-8b nemotron-3-nano-4b`\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-nano-4b.json\n\n## Other comparisons with Granite 4.2 8B or Nemotron 3 Nano 4B\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 Nano 4B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-nano-4b.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 Nano 4B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-nano-4b.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 Nano 4B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nemotron-3-nano-4b.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 Nano 4B](https://www.anchorterminal.com/compare/arete-27b-vs-nemotron-3-nano-4b.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 Nano 4B](https://www.anchorterminal.com/compare/bigbang-v1-vs-nemotron-3-nano-4b.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 Nano 4B](https://www.anchorterminal.com/compare/carbon-x-vs-nemotron-3-nano-4b.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 Nano 4B](https://www.anchorterminal.com/compare/cola2-vs-nemotron-3-nano-4b.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 Nano 4B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nemotron-3-nano-4b.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 Nano 4B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-nemotron-3-nano-4b.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 Nano 4B](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-nemotron-3-nano-4b.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 Nano 4B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-nano-4b.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 Nano 4B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-nemotron-3-nano-4b.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 Nano 4B](https://www.anchorterminal.com/compare/devstral-small-2-vs-nemotron-3-nano-4b.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 Nano 4B](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-nemotron-3-nano-4b.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 Nano 4B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-nemotron-3-nano-4b.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 Nano 4B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-nemotron-3-nano-4b.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 Nano 4B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-nemotron-3-nano-4b.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 Nano 4B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-nano-4b.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 Nano 4B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-nemotron-3-nano-4b.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 Nano 4B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-nemotron-3-nano-4b.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 Nano 4B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-nemotron-3-nano-4b.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 Nano 4B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-nemotron-3-nano-4b.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 Nano 4B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-nemotron-3-nano-4b.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 Nano 4B](https://www.anchorterminal.com/compare/glm-5-3-vs-nemotron-3-nano-4b.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 Nano 4B](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-nemotron-3-nano-4b.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 Nano 4B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-nemotron-3-nano-4b.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 Nano 4B](https://www.anchorterminal.com/compare/granite-4-2-30b-vs-nemotron-3-nano-4b.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 Nano 4B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-nemotron-3-nano-4b.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 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 Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-nemotron-3-ultra-550b-a55b.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 Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-qwen3-5-397b-a17b.md)\n- [Granite 4.2 8B vs Qwen3.5-4B](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-qwen3-5-4b.md)\n- [Granite 4.2 8B vs Qwen3.5-9B](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-qwen3-5-9b.md)\n- [Granite 4.2 8B vs Qwen3.6-27B](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-qwen3-6-27b.md)\n- [Granite 4.2 8B vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-qwen3-6-35b-a3b.md)\n- [Granite 4.2 8B vs Qwen3.8-27B](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-qwen3-8-27b.md)\n- [Granite 4.2 8B vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-qwen3-8-flash-next.md)\n- [Granite 4.2 8B vs Qwen3-Coder-30B-A3B-Instruct](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-qwen3-coder-30b-a3b-instruct.md)\n- [Granite 4.2 8B vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-qwen3-coder-next.md)\n- [Granite 4.2 8B vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-qwen3-omni-30b-a3b-instruct.md)\n- [Granite 4.2 8B vs 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"url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "Granite 4.2 8B vs Nemotron 3 Nano 4B", "url": "" } ], "description": "Granite 4.2 8B scores 61 (C) to Nemotron 3 Nano 4B's 56.8 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "Granite 4.2 8B C 61", "Nemotron 3 Nano 4B C 56.8", "scores" ], "h1": "Granite 4.2 8B vs Nemotron 3 Nano 4B", "image": "https://www.anchorterminal.com/assets/og/default.png", "path": "/compare/granite-4-2-8b-vs-nemotron-3-nano-4b", "published": "2026-10-01", "section": "tools", "title": "Granite 4.2 8B vs Nemotron 3 Nano 4B for AI agents (2026)", "toc": null, "updated": "2026-11-10", "url": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-nemotron-3-nano-4b" }, "tokens": { "markdown": 9900, "slim": 730 }, "version": 1 }