{ "data": { "a": { "slug": "nanbeige-4-2-3b", "name": "Nanbeige4.2-3B", "vendor": "Nanbeige LLM Lab (BOSS Zhipin)", "vendorUrl": "https://huggingface.co/Nanbeige", "kind": "weights", "category": "open-weights", "summary": "Nanbeige4.2-3B is a small agent model from BOSS Zhipin's Nanbeige lab, 4.2B parameters with layers reused in a loop, with tool calling, a thinking switch and a 256K context. Apache 2.0. Released SGLang and llama.cpp run it.", "url": "https://www.anchorterminal.com/tools/nanbeige-4-2-3b", "markdownUrl": "https://www.anchorterminal.com/tools/nanbeige-4-2-3b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/nanbeige-4-2-3b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/nanbeige-4-2-3b.json", "dns": "nanbeige-4-2-3b.grade.anchorterminal.com", "repo": "https://huggingface.co/Nanbeige/Nanbeige4.2-3B", "license": "Apache-2.0 (Hub metadata only; the repository has no LICENSE file)", "transports": [], "packages": [ { "registry": "huggingface", "name": "Nanbeige/Nanbeige4.2-3B" }, { "registry": "huggingface", "name": "Nanbeige/Nanbeige4.2-3B-FP8" }, { "registry": "huggingface", "name": "Nanbeige/Nanbeige4.2-3B-GPTQ-Int8" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. None of the repositories is gated (checked 2026-11-10).", "pricing": "free", "pricingNotes": "Free to download and use under Apache 2.0, per the card's metadata. Running it yourself costs little: 5.2 GB in FP8 or 8-bit from Nanbeige, 8.3 GB in BF16, and 2.7 GB for bartowski's Q4_K_M conversion (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/Nanbeige/Nanbeige4.2-3B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "small", "looped-transformer", "reasoning", "tool-calling", "agentic", "long-context", "apache-2.0", "safetensors", "fp8", "custom-code", "self-hosted" ], "lastRelease": "2026-09-11", "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": 62, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 85, "reach": 85, "risk": 55, "upkeep": 72 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "A small model built for agent work, with tool calling it was trained for, a thinking switch and a 256K context in about 5 GB, under Apache 2.0, from a lab that answers most questions. Its architecture is its own, so check your runtime: SGLang and llama.cpp have it, Transformers needs the repository's code, and Nanbeige ships no GGUF.", "bestFor": "Local agents that need tool calling and a long context on a laptop or a small GPU", "strengths": [ "Trained for multi-step tool use, with parsers in released SGLang", "A 256K context in a model that fits 8 GB", "Apache 2.0, ungated", "FP8 and 8-bit builds from Nanbeige", "Nanbeige replied in most of the recent discussions" ], "weaknesses": [ "Its own architecture: Transformers loads it only with the repository's code", "No GGUF from Nanbeige, so llama.cpp users rely on a third party's conversion", "The card's serve instructions still point at Nanbeige's forks", "No safety results on the card", "Training data described only in general terms", "Not changed since September" ], "agentNotes": [ "Serve it with SGLang 0.5.23 or later and pass `--tool-call-parser nanbeige --reasoning-parser nanbeige`", "Pass `tools` in the request. The card recommends `tool_call_format=\"xml\"`", "Set `preserve_thinking` true for multi-turn tool use and coding, false for plain chat", "Use temperature 1.0 for agent and tool work and 0.6 for reasoning and chat, per the card", "For llama.cpp use b12242 or later. The GGUF files on the Hub are bartowski's, not Nanbeige's", "If you load it with Transformers, read modeling_nanbeige.py first: trust_remote_code runs it" ], "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": 85, "risk": 55, "upkeep": 72 }, "provenanceScore": 58, "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": "pip install \"sglang\u003e=0.5.23\"\npython -m sglang.launch_server --model-path Nanbeige/Nanbeige4.2-3B --reasoning-parser nanbeige --tool-call-parser nanbeige", "http": "# SGLang serves an OpenAI-compatible API at http://localhost:30000/v1. Pass tools in the request, and\n# chat_template_kwargs {\"enable_thinking\": false} for a direct answer" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/nanbeige-4-2-3b" }, "sameCompany": [ "nanbeige-4-3-33b" ], "weights": { "trainedBy": "Nanbeige LLM Lab (BOSS Zhipin)", "builtOn": "Nanbeige4.2-3B-Base, per the card and the metadata", "params": "4.2B", "context": 262144, "goodFor": "A small local agent for tool use, coding and office tasks, with thinking you can turn off and a long context.", "runtimes": [ "llama.cpp", "vLLM", "SGLang" ], "builds": [ { "name": "Nanbeige4.2-3B-FP8", "url": "https://huggingface.co/Nanbeige/Nanbeige4.2-3B-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 5.19, "memoryGB": 8, "format": "safetensors", "runsOn": [ "vLLM", "SGLang" ], "note": "Nanbeige's FP8 build" }, { "name": "Nanbeige4.2-3B-GPTQ-Int8", "url": "https://huggingface.co/Nanbeige/Nanbeige4.2-3B-GPTQ-Int8", "who": "publisher", "precision": "int8", "sizeGB": 5.19, "memoryGB": 8, "format": "safetensors", "runsOn": [ "vLLM", "SGLang" ], "note": "Nanbeige's 8-bit GPTQ build" }, { "name": "bartowski Nanbeige_Nanbeige4.2-3B-GGUF (Q4_K_M)", "url": "https://huggingface.co/bartowski/Nanbeige_Nanbeige4.2-3B-GGUF", "who": "bartowski", "precision": "q4_k_m", "sizeGB": 2.68, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "From bartowski, a third party's conversion and not Nanbeige's. Its base_model tag names Nanbeige/Nanbeige4.2-3B, from the Hub's model information on 10 November 2026" }, { "name": "bartowski Nanbeige_Nanbeige4.2-3B-GGUF (Q8_0)", "url": "https://huggingface.co/bartowski/Nanbeige_Nanbeige4.2-3B-GGUF", "who": "bartowski", "precision": "q8_0", "sizeGB": 4.43, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "From bartowski, a third party's conversion and not Nanbeige's. From the Hub's model information on 10 November 2026" }, { "name": "Nanbeige4.2-3B (BF16 safetensors)", "url": "https://huggingface.co/Nanbeige/Nanbeige4.2-3B", "who": "publisher", "precision": "bf16", "sizeGB": 8.34, "memoryGB": 12, "format": "safetensors", "runsOn": [ "vLLM", "SGLang" ], "note": "Two shards" } ], "run": "python -m sglang.launch_server --model-path Nanbeige/Nanbeige4.2-3B --reasoning-parser nanbeige --tool-call-parser nanbeige", "runBuild": "Nanbeige4.2-3B (BF16 safetensors)", "checked": "2026-11-10", "minMemoryGB": 8 }, "family": { "lab": "Nanbeige LLM Lab (BOSS Zhipin)", "family": "Nanbeige4.2", "url": "https://www.anchorterminal.com/open-weights/#nanbeige", "others": [ { "name": "Nanbeige4.2-3B-Base", "url": "https://huggingface.co/Nanbeige/Nanbeige4.2-3B-Base" } ], "olderCount": 7 }, "area": "models", "provenance": { "legalEntity": "BOSS Zhipin (Kanzhun Limited), unconfirmed", "domain": "zhipin.com", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/Nanbeige/Nanbeige4.2-3B/commits/main", "securityTxt": "none", "checked": "2026-11-10", "notes": [ "The publisher's identity, domain and security facts are carried from our Nanbeige4.3-33B listing: the lab's report for this model names Nanbeige LLM Lab, BOSS Zhipin, and the card's contact address is on kanzhun.com.", "Nothing is hosted under this listing, so Apache 2.0 stands in for terms of service and no privacy policy is scored.", "The changelog is the commit history, 29 commits from 21 July to 11 September 2026." ], "score": 58 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/nanbeige-4-2-3b.json" }, "answer": "Nemotron 3 Super 120B A12B scores 66.7 (B) on agent readiness against Nanbeige4.2-3B's 61 (C), and leads in 2 of 4 scored categories. Nanbeige4.2-3B leads on reach.", "b": { "slug": "nemotron-3-super-120b-a12b", "name": "Nemotron 3 Super 120B A12B", "vendor": "NVIDIA", "vendorUrl": "https://developer.nvidia.com/nemotron", "kind": "weights", "category": "open-weights", "summary": "Nemotron 3 Super is NVIDIA's open-weight hybrid Mamba-2 and mixture-of-experts reasoning model for agents, 120B parameters with 12B active, published on Hugging Face in BF16, FP8 and NVFP4 under the NVIDIA Nemotron Open Model License.", "url": "https://www.anchorterminal.com/tools/nemotron-3-super-120b-a12b", "markdownUrl": "https://www.anchorterminal.com/tools/nemotron-3-super-120b-a12b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/nemotron-3-super-120b-a12b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/nemotron-3-super-120b-a12b.json", "dns": "nemotron-3-super-120b-a12b.grade.anchorterminal.com", "repo": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16", "license": "NVIDIA Nemotron Open Model License", "transports": [], "packages": [ { "registry": "huggingface", "name": "nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16" }, { "registry": "huggingface", "name": "nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-FP8" }, { "registry": "huggingface", "name": "nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4" }, { "registry": "ollama", "name": "nemotron-3-super" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The BF16, FP8 and NVFP4 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, 247.2 GB of BF16 files (8× H100-80GB minimum per the card), 80.3 GB for NVIDIA's NVFP4 build or 87 GB for Ollama's default build (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-Super-120B-A12B-BF16", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "mamba", "hybrid", "reasoning", "tool-calling", "long-context", "safetensors", "fp8", "nvfp4", "self-hosted" ], "lastRelease": "2026-03-11", "graded": true, "anchor": { "graded": true, "score": 66.7, "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": 11, "methodology": "0.7", "run": "2026-10-01", "scores": { "capability": 54, "interface": 90, "reach": 75, "risk": 65, "upkeep": 70 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-11-01" }, "negative": 0, "verdict": "A long-context agent cousin with trained parallel tool calls, a reasoning switch and a full account of its training data, under a free commercial licence. It needs about 100 GB of GPU memory, a 128 GB unified machine or Blackwell hardware for NVIDIA's NVFP4. The main risks are serving commands that run repository code and no published safety results.", "bestFor": "Agents on a 128 GB unified machine or Blackwell GPUs that want a long-context, tool-calling reasoning model with documented training data", "strengths": [ "Runs in released Transformers, vLLM, SGLang, llama.cpp and Ollama, with NVIDIA's own FP8 and NVFP4 builds", "Training data listed by dataset with token counts and cut-offs, much of it published", "Tool calls documented with a named parser, several per turn, and thinking off, low effort or budgeted per request", "Free commercial licence with no threshold or use policy, no gate, and NVIDIA's own API serves it", "Context of 262,144 tokens by default and up to 1M per the card" ], "weaknesses": [ "The card's serving commands pass --trust-remote-code", "No safety evaluation results published", "No NVIDIA reply in any of the repository's nine discussions", "Needs about 96 to 104 GB even at 4-bit, and NVIDIA's NVFP4 build needs Blackwell", "Structured output claimed but not documented" ], "agentNotes": [ "On the reference machine start it with `ollama run nemotron-3-super`, the 87 GB q4_K_M build, the only one that fits (about 104 GB with the margin)", "Raise the Strix Halo GPU memory limit first with `amd-ttm --set 100`, since the default of about 62.8 GiB is far below what it needs", "With vLLM serve it with `--reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder` (SGLang uses `--reasoning-parser nemotron_3`)", "Use `temperature=1.0` and `top_p=0.95` for every task, as the card says", "Thinking is on by default. Pass `enable_thinking: false` or `low_effort: true` in `chat_template_kwargs` for short tasks", "For coding agents pass `force_nonempty_content: true` in `chat_template_kwargs`, so a cut-off reasoning trace still returns content", "Raise context past 262,144 only with the card's flags (`VLLM_ALLOW_LONG_MAX_MODEL_LEN=1`, `--max-model-len 1048576`)" ], "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": 66.7 } ], "editorialScores": { "capability": 54, "interface": 90, "reach": 75, "risk": 65, "upkeep": 70 }, "provenanceScore": 76, "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": "ollama run nemotron-3-super\n# or, with the card's vLLM command (abridged):\npip install vllm==0.18.1\nvllm serve nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16 --served-model-name nvidia/nemotron-3-super \\\n --tensor-parallel-size 8 --max-model-len 262144 --kv-cache-dtype fp8 --mamba-ssm-cache-dtype float32 \\\n --trust-remote-code --reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder\n# or, with llama.cpp and LM Studio's GGUF:\nllama-server -hf lmstudio-community/NVIDIA-Nemotron-3-Super-120B-A12B-GGUF: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-super\",\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-super-120b-a12b" }, "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-ultra-550b-a55b", "nemotron-3-5-lightning-30b-a3b" ], "weights": { "trainedBy": "NVIDIA", "builtOn": "nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-Base-BF16", "params": "120B", "active": "12B", "context": 1048576, "goodFor": "Hybrid Mamba and mixture-of-experts reasoning model for agents, text only, with tool calling, a low-effort mode and a reasoning budget.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "unsloth NVIDIA-Nemotron-3-Super-120B-A12B-GGUF (UD-Q4_K_S)", "url": "https://huggingface.co/unsloth/NVIDIA-Nemotron-3-Super-120B-A12B-GGUF", "who": "unsloth", "precision": "q4_k_s", "sizeGB": 79.02, "memoryGB": 96, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "From Unsloth, a third party's conversion and not NVIDIA's. 79.02 GB of UD-Q4_K_S 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-Super-120B-A12B-NVFP4", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4", "who": "publisher", "precision": "nvfp4", "sizeGB": 80.3, "memoryGB": 128, "format": "safetensors", "note": "Pre-trained in NVFP4; runs on one B200 or DGX Spark per its card. NVFP4 needs NVIDIA Blackwell" }, { "name": "ollama nemotron-3-super:latest", "url": "https://ollama.com/library/nemotron-3-super:latest", "who": "ollama", "precision": "q4_k_m", "sizeGB": 87, "memoryGB": 128, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Also the smallest Ollama build. Size as Ollama's tags page shows it, rounded to whole GB above 10 GB; the Linux build is the llama.cpp variant" }, { "name": "lmstudio-community NVIDIA-Nemotron-3-Super-120B-A12B-GGUF (Q4_K_M)", "url": "https://huggingface.co/lmstudio-community/NVIDIA-Nemotron-3-Super-120B-A12B-GGUF", "who": "lmstudio", "precision": "q4_k_m", "sizeGB": 86.05, "memoryGB": 128, "format": "gguf", "runsOn": [ "LM Studio", "llama.cpp" ], "note": "From lmstudio-community, LM Studio's organisation on the Hub. 86.05 GB of Q4_K_M weights, from the Hub's model information on 10 November 2026. LM Studio's own catalogue page for this model wasn't checked. Recorded after the last grading, so Reach doesn't count it yet" }, { "name": "NVIDIA-Nemotron-3-Super-120B-A12B-FP8", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 128.4, "memoryGB": 192, "format": "safetensors" }, { "name": "lmstudio-community NVIDIA-Nemotron-3-Super-120B-A12B-GGUF (Q6_K)", "url": "https://huggingface.co/lmstudio-community/NVIDIA-Nemotron-3-Super-120B-A12B-GGUF", "who": "lmstudio", "precision": "q6_k", "sizeGB": 112.92, "memoryGB": 192, "format": "gguf", "runsOn": [ "LM Studio", "llama.cpp" ], "note": "From lmstudio-community, LM Studio's organisation on the Hub. 112.92 GB of Q6_K weights, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it yet" }, { "name": "lmstudio-community NVIDIA-Nemotron-3-Super-120B-A12B-GGUF (Q8_0)", "url": "https://huggingface.co/lmstudio-community/NVIDIA-Nemotron-3-Super-120B-A12B-GGUF", "who": "lmstudio", "precision": "q8_0", "sizeGB": 128.47, "memoryGB": 192, "format": "gguf", "runsOn": [ "LM Studio", "llama.cpp" ], "note": "From lmstudio-community, LM Studio's organisation on the Hub. 128.47 GB of Q8_0 weights, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it yet" }, { "name": "NVIDIA-Nemotron-3-Super-120B-A12B-BF16", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16", "who": "publisher", "precision": "bf16", "sizeGB": 247.2, "memoryGB": 512, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] } ], "run": "vllm serve nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4 --served-model-name nvidia/nemotron-3-super --max-model-len 262144 --kv-cache-dtype fp8 --mamba-ssm-cache-dtype float32 --trust-remote-code --reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder", "runBuild": "NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4", "checked": "2026-10-21", "minMemoryGB": 96 }, "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 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", "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-Super-120B-A12B-BF16/commits/main", "securityTxt": "unknown", "checked": "2026-10-21", "notes": [ "The card names NVIDIA Corporation as model developer, and the licence's notice reads Licensed by NVIDIA Corporation. The Hugging Face organisation nvidia is verified and linked from developer.nvidia.com. RDAP gives 20 April 1993 for nvidia.com.", "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 10 March 2026 only the card has changed, with some messages that say what (Update BF16 configs, Update KV Cache/Mamba dtype, Update for MTPv2) and several that read Update README.md.", "www.nvidia.com/.well-known/security.txt answered 403 to our reader on 21 October 2026, so it is counted as unchecked rather than absent. The card links NVIDIA's vulnerability submission page." ], "score": 76 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/nemotron-3-super-120b-a12b.json", "live": { "slug": "nemotron-3-super-120b-a12b", "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-Super-120B-A12B-BF16/commits/main", "kind": "changelog", "status": 200, "checkedAt": "2026-11-10T16:23:20.620593124Z", "changedAt": "2026-11-10T16:23:20.620593124Z", "fingerprint": "b8fce0f674d8" } ], "updatedAt": "2026-11-10T16:23:20.620593124Z" } }, "edges": [ { "by": 10, "category": "reach", "leads": "nanbeige-4-2-3b", "name": "Reach", "reasons": { "a": "Memory 30, the smallest build from Nanbeige is the FP8 repository (the 8-bit GPTQ one is the same size), 5.19 GB, and 5.19 GB × 1.2 = 6.2 GB, within 16 GB. bartowski's GGUF isn't read for this line. Runtimes 15, Released SGLang 0.5.23 has the model (srt/models/nanbeige.py) with its parsers, llama.cpp b12242 has the nanbeige architecture, and vLLM 0.33.0 names NanbeigeForCausalLM in its registry, served through its Transformers backend. Builds 15, Quantised builds from Nanbeige at two precisions, FP8 and 8-bit GPTQ, each in its own repository. Terms 15, Apache 2.0 in the card's metadata, an OSI licence, with no use policy or other terms on the card. Download 10, the Hub API reports gated false for the model and its FP8 and 8-bit repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it, and no Nanbeige or BOSS Zhipin API for it was found. 30 + 15 + 15 + 15 + 10 + 0 = 85.", "b": "Memory earns 10, because the smallest 4-bit build, NVIDIA's 80.3 GB NVFP4, needs about 96 GB with the fifth added, which fits the reference machine but not 64 GB, and Ollama's 87 GB build lands in the same band. Runtimes earn the full 25 from released Transformers, vLLM, SGLang, llama.cpp and Ollama. Builds earn 15 for NVIDIA's own FP8 and NVFP4. 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 5, since NVIDIA's own API serves it." }, "scores": { "a": 85, "b": 75 } }, { "by": 10, "category": "risk", "leads": "nemotron-3-super-120b-a12b", "name": "Risk", "reasons": { "a": "File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the Nanbeige organisation (Nanbeige LLM Lab), which the lab's arXiv report for this model links, so the publisher's own account, although the Hub hasn't verified it (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions: the limitations section says outputs may be inaccurate, biased or harmful. Licence risk 15, Apache 2.0 has no use policy, no threshold and no clause letting the licensor change or revoke it. Security programme 5, BOSS Zhipin runs a security response centre for reporting flaws in its products, as read for the Nanbeige4.3-33B listing (5), though nothing links it to the model. 15 + 15 + 0 + 5 + 15 + 5 = 55.", "b": "File safety earns 15, safetensors only but with 10 off because the documented serving commands pass trust-remote-code and the repository ships model and parser code. Provenance earns 15, files in NVIDIA's linked organisation with a hash for every file. Safety evaluation earns 0, since NVIDIA says it evaluated content safety but publishes no results. Agent guidance earns 10, a subcard that names prompt injection and says what safeguards to add, without an instruction hierarchy or concrete mitigations. Licence risk keeps all 15. The security programme earns 10 for the vulnerability submission route on the card and NVIDIA's public bulletins." }, "scores": { "a": 55, "b": 65 } }, { "by": 5, "category": "interface", "leads": "nemotron-3-super-120b-a12b", "name": "Agent interface", "reasons": { "a": "Chat template 20, A Jinja chat template in tokenizer_config.json (7,961 characters), with enable_thinking, preserve_thinking, tools and tool_call_format switches. Tool-calling format 25, the card says passing tools switches on the tool template and recommends tool_call_format xml, with json for compatibility, and its SGLang and vLLM commands name the nanbeige tool-call parser, which released SGLang 0.5.23 has. Trained tool calling 20, the card describes fine-tuning on tool-use environments and agent scaffolds and reinforcement learning on agent tasks, and the template tells the model it may call one or more functions and renders every call in a turn. Structured output 0, Neither the card nor the files mention JSON or JSON Schema output, beyond json as a tool-call format. Reasoning control 10, the card documents enable_thinking, on by default and False per request, and preserve_thinking for earlier turns, and names the nanbeige reasoning parser, which returns the reasoning apart from the answer. Context 10, 262,144 tokens, which the card says the model supports and config.json gives as max_position_embeddings. 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 NVIDIA documents the format, names the qwen3_coder parser for three runtimes and shows a tool call in its cookbook. Trained tool calling earns 20, with tool-use reinforcement learning and several calls per turn in the template. Structured output earns 5, claimed from training with no instructions. Reasoning control earns 10, thinking off, low effort or a budget per request with the trace returned as reasoning_content. Context earns 10 for 262,144 tokens in the config and up to 1M per the card." }, "scores": { "a": 85, "b": 90 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Nanbeige LLM Lab (BOSS Zhipin)", "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 (Hub metadata only; the repository has no LICENSE file)", "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": "2026-09-11", "b": "2026-03-11", "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 Super 120B A12B scores 66.7 (B) on agent readiness against Nanbeige4.2-3B's 61 (C), and leads in 2 of 4 scored categories. Nanbeige4.2-3B leads on reach.", "question": "Which is better for AI agents, Nanbeige4.2-3B or Nemotron 3 Super 120B A12B?" } ], "goodFor": [ { "aheadOn": [ "Reach, 85 against 75" ], "also": null, "goodFor": "Local agents that need tool calling and a long context on a laptop or a small GPU", "slug": "nanbeige-4-2-3b", "watchFor": "Its own architecture: Transformers loads it only with the repository's code" }, { "aheadOn": [ "Risk, 65 against 55", "Agent interface, 90 against 85" ], "also": null, "goodFor": "Agents on a 128 GB unified machine or Blackwell GPUs that want a long-context, tool-calling reasoning model with documented training data", "slug": "nemotron-3-super-120b-a12b", "watchFor": "The card's serving commands pass --trust-remote-code" } ], "history": { "nanbeige-4-2-3b": null, "nemotron-3-super-120b-a12b": [ { "causes": [ "correction" ], "date": "2026-11-01", "delta": 0.5, "from": 66.2, "fromGrade": "B", "to": 66.7, "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-nanbeige-4-2-3b.json", "title": "Agents-A1-4B vs Nanbeige4.2-3B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nanbeige-4-2-3b" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-super-120b-a12b.json", "title": "Agents-A1-4B vs Nemotron 3 Super 120B A12B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-super-120b-a12b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nanbeige-4-2-3b.json", "title": "Apertus v1.5 8B vs Nanbeige4.2-3B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nanbeige-4-2-3b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-super-120b-a12b.json", "title": "Apertus v1.5 8B vs Nemotron 3 Super 120B A12B", "url": 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"https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-spark-x2-6-4b.json", "title": "Nemotron 3 Super 120B A12B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-step-3-7-flash.json", "title": "Nemotron 3 Super 120B A12B vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-swift-1-5-qwen3-8-27b.json", "title": "Nemotron 3 Super 120B A12B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Nemotron 3 Super 120B A12B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-thinkingcap-qwen3-8-27b.json", "title": "Nemotron 3 Super 120B A12B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-unlimited-ocr-2.json", "title": "Nemotron 3 Super 120B A12B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-wevisone-vl-2b.json", "title": "Nemotron 3 Super 120B A12B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-wevisone-vl-4b.json", "title": "Nemotron 3 Super 120B A12B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-zaya1-8b.json", "title": "Nemotron 3 Super 120B A12B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 10, "edge": "nanbeige-4-2-3b", "key": "reach", "name": "Reach", "nanbeige-4-2-3b": 85, "nemotron-3-super-120b-a12b": 75, "weight": 25 }, { "by": 10, "edge": "nemotron-3-super-120b-a12b", "key": "risk", "name": "Risk", "nanbeige-4-2-3b": 55, "nemotron-3-super-120b-a12b": 65, "weight": 20 }, { "by": 5, "edge": "nemotron-3-super-120b-a12b", "key": "interface", "name": "Agent interface", "nanbeige-4-2-3b": 85, "nemotron-3-super-120b-a12b": 90, "weight": 10 }, { "by": 2, "edge": "nanbeige-4-2-3b", "key": "upkeep", "name": "Documentation and upkeep", "nanbeige-4-2-3b": 72, "nemotron-3-super-120b-a12b": 70, "weight": 10 } ], "summary": "Nemotron 3 Super 120B A12B scores 66.7 (B) on agent readiness against Nanbeige4.2-3B's 61 (C), and leads in 2 of 4 scored categories. Nanbeige4.2-3B leads on reach. Both do open-weight models you run yourself.", "tasks": null, "tutorials": [], "verdicts": { "nanbeige-4-2-3b": "A small model built for agent work, with tool calling it was trained for, a thinking switch and a 256K context in about 5 GB, under Apache 2.0, from a lab that answers most questions. Its architecture is its own, so check your runtime: SGLang and llama.cpp have it, Transformers needs the repository's code, and Nanbeige ships no GGUF.", "nemotron-3-super-120b-a12b": "A long-context agent cousin with trained parallel tool calls, a reasoning switch and a full account of its training data, under a free commercial licence. It needs about 100 GB of GPU memory, a 128 GB unified machine or Blackwell hardware for NVIDIA's NVFP4. 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/nanbeige-4-2-3b-vs-nemotron-3-super-120b-a12b", "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-nemotron-3-super-120b-a12b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-nemotron-3-super-120b-a12b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-nemotron-3-super-120b-a12b.min.md" }, "markdown": "Nemotron 3 Super 120B A12B scores 66.7 (B) on agent readiness against Nanbeige4.2-3B's 61 (C), and leads in 2 of 4 scored categories. Nanbeige4.2-3B leads on reach. Both do open-weight models you run yourself.\n\n- Nanbeige4.2-3B: grade C, 61/100, rank #78 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/nanbeige-4-2-3b.md · JSON https://www.anchorterminal.com/api/v1/tools/nanbeige-4-2-3b.json\n- Nemotron 3 Super 120B A12B: grade B, 66.7/100, rank #15 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-super-120b-a12b.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-super-120b-a12b.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### Nanbeige4.2-3B (C)\n\nGood for: Local agents that need tool calling and a long context on a laptop or a small GPU\n\nAhead on:\n- Reach, 85 against 75\n\nWatch for: Its own architecture: Transformers loads it only with the repository's code\n\n### Nemotron 3 Super 120B A12B (B)\n\nGood for: Agents on a 128 GB unified machine or Blackwell GPUs that want a long-context, tool-calling reasoning model with documented training data\n\nAhead on:\n- Risk, 65 against 55\n- Agent interface, 90 against 85\n\nWatch for: The card's serving commands pass --trust-remote-code\n\n\n## Why the edge\n\n### Reach: Nanbeige4.2-3B +10\n\n- Nanbeige4.2-3B (85): Memory 30, the smallest build from Nanbeige is the FP8 repository (the 8-bit GPTQ one is the same size), 5.19 GB, and 5.19 GB × 1.2 = 6.2 GB, within 16 GB. bartowski's GGUF isn't read for this line. Runtimes 15, Released SGLang 0.5.23 has the model (srt/models/nanbeige.py) with its parsers, llama.cpp b12242 has the nanbeige architecture, and vLLM 0.33.0 names NanbeigeForCausalLM in its registry, served through its Transformers backend. Builds 15, Quantised builds from Nanbeige at two precisions, FP8 and 8-bit GPTQ, each in its own repository. Terms 15, Apache 2.0 in the card's metadata, an OSI licence, with no use policy or other terms on the card. Download 10, the Hub API reports gated false for the model and its FP8 and 8-bit repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it, and no Nanbeige or BOSS Zhipin API for it was found. 30 + 15 + 15 + 15 + 10 + 0 = 85.\n- Nemotron 3 Super 120B A12B (75): Memory earns 10, because the smallest 4-bit build, NVIDIA's 80.3 GB NVFP4, needs about 96 GB with the fifth added, which fits the reference machine but not 64 GB, and Ollama's 87 GB build lands in the same band. Runtimes earn the full 25 from released Transformers, vLLM, SGLang, llama.cpp and Ollama. Builds earn 15 for NVIDIA's own FP8 and NVFP4. 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 5, since NVIDIA's own API serves it.\n\n### Risk: Nemotron 3 Super 120B A12B +10\n\n- Nanbeige4.2-3B (55): File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the Nanbeige organisation (Nanbeige LLM Lab), which the lab's arXiv report for this model links, so the publisher's own account, although the Hub hasn't verified it (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions: the limitations section says outputs may be inaccurate, biased or harmful. Licence risk 15, Apache 2.0 has no use policy, no threshold and no clause letting the licensor change or revoke it. Security programme 5, BOSS Zhipin runs a security response centre for reporting flaws in its products, as read for the Nanbeige4.3-33B listing (5), though nothing links it to the model. 15 + 15 + 0 + 5 + 15 + 5 = 55.\n- Nemotron 3 Super 120B A12B (65): File safety earns 15, safetensors only but with 10 off because the documented serving commands pass trust-remote-code and the repository ships model and parser code. Provenance earns 15, files in NVIDIA's linked organisation with a hash for every file. Safety evaluation earns 0, since NVIDIA says it evaluated content safety but publishes no results. Agent guidance earns 10, a subcard that names prompt injection and says what safeguards to add, without an instruction hierarchy or concrete mitigations. Licence risk keeps all 15. The security programme earns 10 for the vulnerability submission route on the card and NVIDIA's public bulletins.\n\n### Agent interface: Nemotron 3 Super 120B A12B +5\n\n- Nanbeige4.2-3B (85): Chat template 20, A Jinja chat template in tokenizer_config.json (7,961 characters), with enable_thinking, preserve_thinking, tools and tool_call_format switches. Tool-calling format 25, the card says passing tools switches on the tool template and recommends tool_call_format xml, with json for compatibility, and its SGLang and vLLM commands name the nanbeige tool-call parser, which released SGLang 0.5.23 has. Trained tool calling 20, the card describes fine-tuning on tool-use environments and agent scaffolds and reinforcement learning on agent tasks, and the template tells the model it may call one or more functions and renders every call in a turn. Structured output 0, Neither the card nor the files mention JSON or JSON Schema output, beyond json as a tool-call format. Reasoning control 10, the card documents enable_thinking, on by default and False per request, and preserve_thinking for earlier turns, and names the nanbeige reasoning parser, which returns the reasoning apart from the answer. Context 10, 262,144 tokens, which the card says the model supports and config.json gives as max_position_embeddings. 20 + 25 + 20 + 0 + 10 + 10 = 85.\n- Nemotron 3 Super 120B A12B (90): The chat template earns 20, a Jinja file in the repository. The tool-calling format earns 25, since NVIDIA documents the format, names the qwen3_coder parser for three runtimes and shows a tool call in its cookbook. Trained tool calling earns 20, with tool-use reinforcement learning and several calls per turn in the template. Structured output earns 5, claimed from training with no instructions. Reasoning control earns 10, thinking off, low effort or a budget per request with the trace returned as reasoning_content. Context earns 10 for 262,144 tokens in the config and up to 1M per the card.\n\n## Score by category\n\n| Category | Weight | Nanbeige4.2-3B | Nemotron 3 Super 120B A12B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 85 | 75 | Nanbeige4.2-3B +10 |\n| Risk | 20% (20 this run) | 55 | 65 | Nemotron 3 Super 120B A12B +10 |\n| Agent interface | 10% (10 this run) | 85 | 90 | Nemotron 3 Super 120B A12B +5 |\n| Documentation and upkeep | 10% (10 this run) | 72 | 70 | Nanbeige4.2-3B +2 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **66.7 · B** | |\n\n## Facts side by side\n\n| Fact | Nanbeige4.2-3B | Nemotron 3 Super 120B A12B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Nanbeige LLM Lab (BOSS Zhipin) | 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 (Hub metadata only; the repository has no LICENSE file) | NVIDIA Nemotron Open Model License |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-09-11 | 2026-03-11 |\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### Nanbeige4.2-3B\n\nNo score change since it was listed.\n\n### Nemotron 3 Super 120B A12B\n\n- +0.5 on 1 November 2026: correction\n\n## Verdicts\n\n**Nanbeige4.2-3B.** A small model built for agent work, with tool calling it was trained for, a thinking switch and a 256K context in about 5 GB, under Apache 2.0, from a lab that answers most questions. Its architecture is its own, so check your runtime: SGLang and llama.cpp have it, Transformers needs the repository's code, and Nanbeige ships no GGUF.\n\n**Nemotron 3 Super 120B A12B.** A long-context agent cousin with trained parallel tool calls, a reasoning switch and a full account of its training data, under a free commercial licence. It needs about 100 GB of GPU memory, a 128 GB unified machine or Blackwell hardware for NVIDIA's NVFP4. The main risks are serving commands that run repository code and no published safety results.\n\n## Before you call either\n\n### Nanbeige4.2-3B\n\n1. Serve it with SGLang 0.5.23 or later and pass `--tool-call-parser nanbeige --reasoning-parser nanbeige`\n2. Pass `tools` in the request. The card recommends `tool_call_format=\"xml\"`\n3. Set `preserve_thinking` true for multi-turn tool use and coding, false for plain chat\n4. Use temperature 1.0 for agent and tool work and 0.6 for reasoning and chat, per the card\n5. For llama.cpp use b12242 or later. The GGUF files on the Hub are bartowski's, not Nanbeige's\n6. If you load it with Transformers, read modeling_nanbeige.py first: trust_remote_code runs it\n\n### Nemotron 3 Super 120B A12B\n\n1. On the reference machine start it with `ollama run nemotron-3-super`, the 87 GB q4_K_M build, the only one that fits (about 104 GB with the margin)\n2. Raise the Strix Halo GPU memory limit first with `amd-ttm --set 100`, since the default of about 62.8 GiB is far below what it needs\n3. With vLLM serve it with `--reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder` (SGLang uses `--reasoning-parser nemotron_3`)\n4. Use `temperature=1.0` and `top_p=0.95` for every task, as the card says\n5. Thinking is on by default. Pass `enable_thinking: false` or `low_effort: true` in `chat_template_kwargs` for short tasks\n6. For coding agents pass `force_nonempty_content: true` in `chat_template_kwargs`, so a cut-off reasoning trace still returns content\n7. Raise context past 262,144 only with the card's flags (`VLLM_ALLOW_LONG_MAX_MODEL_LEN=1`, `--max-model-len 1048576`)\n\n## Questions\n\n### Which is better for AI agents, Nanbeige4.2-3B or Nemotron 3 Super 120B A12B?\n\nNemotron 3 Super 120B A12B scores 66.7 (B) on agent readiness against Nanbeige4.2-3B's 61 (C), and leads in 2 of 4 scored categories. Nanbeige4.2-3B leads on reach.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-nemotron-3-super-120b-a12b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-nemotron-3-super-120b-a12b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"nanbeige-4-2-3b\", \"b\": \"nemotron-3-super-120b-a12b\"}`. 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