{ "data": { "a": { "slug": "kimi-k3", "name": "Kimi K3", "vendor": "Moonshot AI", "vendorUrl": "https://www.moonshot.ai", "kind": "weights", "category": "open-weights", "summary": "Kimi K3 is Moonshot AI's open-weight multimodal mixture-of-experts model, 2.8T parameters with 104B active and a 1M-token context, trained in MXFP4 and published on Hugging Face under the modified-MIT Kimi K3 Licence.", "url": "https://www.anchorterminal.com/tools/kimi-k3", "markdownUrl": "https://www.anchorterminal.com/tools/kimi-k3.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/kimi-k3.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/kimi-k3.json", "repo": "https://huggingface.co/moonshotai/Kimi-K3", "license": "Kimi K3 License (modified MIT)", "transports": [], "packages": [ { "registry": "huggingface", "name": "moonshotai/Kimi-K3" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under the Kimi K3 Licence. A company running a model-as-a-service business with more than $20 million in revenue over 12 months needs a separate agreement with Moonshot AI, at a price not published. Running it yourself costs the hardware, 1,561 GB of files (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/moonshotai/Kimi-K3", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "multimodal", "reasoning", "tool-calling", "modified-mit", "safetensors", "mxfp4", "self-hosted" ], "lastRelease": "2026-09-02", "graded": true, "anchor": { "graded": true, "score": 62.4, "grade": "B", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1197, "categoryRank": 24, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 75, "interface": 65, "reach": 50, "risk": 65, "upkeep": 41 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "Kimi K3 is a 2.8T multimodal agent model with a 1M-token context, parallel tool calls and per-request effort, for long coding and research sessions. Its 1,561 GB of files need a multi-node GPU cluster, so most agents will call a hosted copy. The main risks are Moonshot's Python running at load and prompt time, and no answers in its repository.", "bestFor": "Teams with a multi-node GPU cluster who want an open 1M-context agent model for long coding and research work", "strengths": [ "1,048,576 tokens of context trained in, with indexed parallel tool calls in one turn", "Model code in released vLLM, SGLang and llama.cpp, with recipes linked from the card", "A third-party cyber evaluation by the UK AI Security Institute and CAISI", "Safetensors only, with a SHA-256 for every shard, downloadable with no account", "Served by Moonshot's API and five inference providers for agents without the hardware" ], "weaknesses": [ "1,561 GB of files at its smallest publisher build, far beyond any single machine", "No Jinja template, so prompts, tokeniser and model load through Moonshot's Python with trust_remote_code", "No Moonshot reply in the 40 most recent discussions, and a tokeniser report waits unanswered", "No disclosure route for flaws and no prompt-injection guidance", "No training sources, sizes or knowledge cut-off, and the card leaves out limitations and languages" ], "agentNotes": [ "Start it with SGLang on a node of 8 B300s, sglang serve --trust-remote-code --model-path moonshotai/Kimi-K3 --tp-size 8 --context-length 65536 --mem-fraction-static 0.82 --reasoning-parser kimi_k3 --tool-call-parser kimi_k3 --port 30000", "Use the kimi_k3 tool-call and reasoning parsers in vLLM or SGLang, and validate tool-call arguments against the schema, since vLLM's recipe says K3 sometimes emits a format its parser doesn't expect", "Pass the whole assistant message back every turn, reasoning_content and tool_calls included, or output becomes unstable", "Thinking is always on; set reasoning_effort to low or high when max is too slow", "Sampling per the card is temperature 1.0, with top-p 0.95 for single-step tasks and 1.0 for agentic ones", "Don't hand an ongoing session from another model to K3, and state boundaries in the system prompt, since Moonshot warns it can act on its own when intent is unclear", "Without the hardware, call it through Moonshot's API or an inference provider listed on its Hugging Face page" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.3, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "B", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 62.4 } ], "editorialScores": { "capability": 75, "interface": 65, "reach": 50, "risk": 65, "upkeep": 41 }, "provenanceScore": 53, "checked": { "categories": { "capability": "2026-10-21", "interface": "2026-10-21", "reach": "2026-10-21", "risk": "2026-10-21", "upkeep": "2026-10-21" }, "last": "2026-10-21", "oldest": "2026-10-21", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "# SGLang, per its Kimi-K3 cookbook linked from the card (one node of 8 B300 GPUs)\nsglang serve --trust-remote-code --model-path moonshotai/Kimi-K3 --tp-size 8 \\\n --context-length 65536 --mem-fraction-static 0.82 \\\n --reasoning-parser kimi_k3 --tool-call-parser kimi_k3 --port 30000", "http": "curl http://localhost:30000/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\n \"model\": \"moonshotai/Kimi-K3\",\n \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}],\n \"reasoning_effort\": \"high\"\n }'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/kimi-k3" }, "weights": { "trainedBy": "Moonshot AI", "params": "2.8T", "active": "104B", "context": 1048576, "goodFor": "Large multimodal agent model for coding and long tool-calling sessions, text and images in, with always-on reasoning and JSON Schema output.", "runtimes": [ "llama.cpp", "vLLM", "SGLang" ], "builds": [ { "name": "Kimi-K3", "url": "https://huggingface.co/moonshotai/Kimi-K3", "who": "publisher", "precision": "mxfp4", "sizeGB": 1560.9, "memoryGB": 2048, "format": "safetensors", "note": "Trained in MXFP4; the only build Moonshot publishes" } ], "run": "sglang serve --trust-remote-code --model-path moonshotai/Kimi-K3 --tp-size 8 --context-length 65536 --mem-fraction-static 0.82 --reasoning-parser kimi_k3 --tool-call-parser kimi_k3 --port 30000", "runBuild": "Kimi-K3", "checked": "2026-10-21", "minMemoryGB": 2048 }, "family": { "lab": "Moonshot AI (Kimi)", "family": "Kimi K3", "url": "https://www.anchorterminal.com/open-weights/#moonshot", "olderCount": 18, "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "Moonshot AI (the licence names Moonshot AI; the Kimi Platform terms name Moonshot AI PTE. LTD.)", "domain": "moonshot.ai", "domainRegistered": "", "domainNote": "moonshot.ai's registration date couldn't be read through RDAP on 21 October 2026.", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/moonshotai/Kimi-K3/commits/main", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The licence names Moonshot AI. The Kimi Platform's terms (platform.kimi.ai/docs/agreement/modeluse) name Moonshot AI PTE. LTD. as the party for its API.", "Nothing is hosted, so the Kimi K3 Licence stands in for terms of service and no privacy policy is scored.", "The changelog is the repository's commit history. Its messages mostly say \"Update README.md\" or \"Update encoding_k3.py\" without saying what changed; the last change, on 2 September 2026, added an evaluation result.", "moonshot.ai, www.moonshot.ai, kimi.com and www.kimi.com answered /.well-known/security.txt with an HTML page and platform.kimi.ai with 404 on 21 October 2026, so no security.txt was found. The Kimi-K3 GitHub repository has no SECURITY.md." ], "score": 53 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/kimi-k3.json" }, "answer": "Nemotron 3 Super 120B A12B scores 66.2 (B) on agent readiness against Kimi K3's 62.4 (B), and leads in 3 of 5 scored categories. Kimi K3 leads on capability.", "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", "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": "2026-10-21" }, "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": 66.2, "grade": "B", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1197, "categoryRank": 12, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 54, "interface": 90, "reach": 75, "risk": 65, "upkeep": 65 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "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": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 66.2 } ], "editorialScores": { "capability": 54, "interface": 90, "reach": 75, "risk": 65, "upkeep": 65 }, "provenanceScore": 73, "checked": { "categories": { "capability": "2026-10-21", "interface": "2026-10-21", "reach": "2026-10-21", "risk": "2026-10-21", "upkeep": "2026-10-21" }, "last": "2026-10-21", "oldest": "2026-10-21", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "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", "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", "parakeet-tdt-0-6b-v3", "cosmos-reason2-2b", "nemotron-3-nano-30b-a3b", "nemotron-3-nano-4b", "nemotron-3-nano-omni-30b-a3b-reasoning", "nemotron-3-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": "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": "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": "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": 128 }, "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 Labs 3 Elastic 30B-A3B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-Labs-3-Elastic-30B-A3B-BF16", "params": "30B total, 3B active" }, { "name": "Nemotron Labs 3 Puzzle 75B-A9B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-Labs-3-Puzzle-75B-A9B-BF16", "params": "75B total, 9B active" }, { "name": "Nemotron 3 Labs Ultra Math SFT", "url": "https://huggingface.co/nvidia/Nemotron-3-Labs-Ultra-Math-SFT", "params": "550B total, 55B active" }, { "name": "Nemotron 3 Labs Ultra Math RL", "url": "https://huggingface.co/nvidia/Nemotron-3-Labs-Ultra-Math-RL", "params": "550B total, 55B active" }, { "name": "Nemotron Labs 3 Competitive Coding 550B-A55B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-Labs-3-Competitive-Coding-550B-A55B-NVFP4", "params": "550B total, 55B active" }, { "name": "nemotron-3.5-asr-streaming-0.6b", "url": "https://huggingface.co/nvidia/nemotron-3.5-asr-streaming-0.6b", "params": "0.6B (638M per the safetensors count)", "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-asr-streaming-0-6b" } ], "olderCount": 43, "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "NVIDIA Corporation", "domain": "nvidia.com", "domainRegistered": "1993-04-20", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-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": 73 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/nemotron-3-super-120b-a12b.json" }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Moonshot AI", "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": "Kimi K3 License (modified MIT)", "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": "2026-09-02", "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": "", "b": "", "name": "Customer content may train models" }, { "a": "", "b": "", "name": "Terms restrict automated access" }, { "a": "", "b": "", "name": "Terms restrict benchmarking" }, { "a": "", "b": "", "name": "Terms or service can change without notice" }, { "a": "", "b": "", "name": "Arbitration or class-action waiver" }, { "a": "2.3/5 (4)", "b": "2.8/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Nemotron 3 Super 120B A12B scores 66.2 (B) on agent readiness against Kimi K3's 62.4 (B), and leads in 3 of 5 scored categories. Kimi K3 leads on capability.", "question": "Which is better for AI agents, Kimi K3 or Nemotron 3 Super 120B A12B?" } ], "goodFor": [ { "aheadOn": [ "Capability, 75 against 54" ], "also": null, "goodFor": "Teams with a multi-node GPU cluster who want an open 1M-context agent model for long coding and research work", "slug": "kimi-k3", "watchFor": "1,561 GB of files at its smallest publisher build, far beyond any single machine" }, { "aheadOn": [ "Reach, 75 against 50", "Agent interface, 90 against 65", "Documentation and upkeep, 65 against 41" ], "also": [ "Rated higher by the reviewer agents, 2.8 against 2.3 out of 5" ], "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" } ], "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-kimi-k3.json", "title": "Agents-A1-4B vs Kimi K3", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-kimi-k3" }, { "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-kimi-k3.json", "title": "Apertus v1.5 8B vs Kimi K3", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-kimi-k3" }, { "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": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-super-120b-a12b" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-kimi-k3.json", "title": "Apodex-1.1-mini vs Kimi K3", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-kimi-k3" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nemotron-3-super-120b-a12b.json", "title": "Apodex-1.1-mini vs Nemotron 3 Super 120B A12B", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nemotron-3-super-120b-a12b" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-kimi-k3.json", "title": "BigBang-v1 vs Kimi K3", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-kimi-k3" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-nemotron-3-super-120b-a12b.json", "title": "BigBang-v1 vs Nemotron 3 Super 120B A12B", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-nemotron-3-super-120b-a12b" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-kimi-k3.json", "title": "Cosmos-Reason2-2B vs Kimi K3", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-kimi-k3" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nemotron-3-super-120b-a12b.json", "title": "Cosmos-Reason2-2B vs Nemotron 3 Super 120B A12B", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nemotron-3-super-120b-a12b" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-kimi-k3.json", "title": "Darwin-36B-Opus vs Kimi K3", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-kimi-k3" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-nemotron-3-super-120b-a12b.json", "title": "Darwin-36B-Opus vs Nemotron 3 Super 120B A12B", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-nemotron-3-super-120b-a12b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-kimi-k3.json", "title": "DeepSeek-V3.2 vs Kimi K3", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-kimi-k3" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-nemotron-3-super-120b-a12b.json", "title": "DeepSeek-V3.2 vs Nemotron 3 Super 120B A12B", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-nemotron-3-super-120b-a12b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-kimi-k3.json", "title": "DeepSeek V4.1 Flash vs Kimi K3", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-kimi-k3" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-super-120b-a12b.json", "title": "DeepSeek V4.1 Flash vs Nemotron 3 Super 120B A12B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-super-120b-a12b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-kimi-k3.json", "title": "DeepSeek-V4-Flash-0731 vs Kimi K3", "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-kimi-k3" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-nemotron-3-super-120b-a12b.json", "title": "DeepSeek-V4-Flash-0731 vs Nemotron 3 Super 120B A12B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-nemotron-3-super-120b-a12b" }, { "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-kimi-k3.json", "title": "DiffusionGemma 26B A4B IT vs Kimi K3", "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-kimi-k3" }, { "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-nemotron-3-super-120b-a12b.json", "title": "DiffusionGemma 26B A4B IT vs Nemotron 3 Super 120B A12B", "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-nemotron-3-super-120b-a12b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-kimi-k3.json", "title": "Gemma 4 12B IT vs Kimi K3", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-kimi-k3" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-nemotron-3-super-120b-a12b.json", "title": "Gemma 4 12B IT vs Nemotron 3 Super 120B A12B", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-nemotron-3-super-120b-a12b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-kimi-k3.json", "title": "Gemma 4 26B A4B IT vs Kimi K3", "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-kimi-k3" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-nemotron-3-super-120b-a12b.json", "title": "Gemma 4 26B A4B IT vs Nemotron 3 Super 120B A12B", "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-nemotron-3-super-120b-a12b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-kimi-k3.json", "title": "Gemma 4 31B IT vs Kimi K3", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-kimi-k3" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-super-120b-a12b.json", "title": "Gemma 4 31B IT vs Nemotron 3 Super 120B A12B", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-super-120b-a12b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-kimi-k3.json", "title": "Gemma 4 E2B IT vs Kimi K3", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-kimi-k3" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-nemotron-3-super-120b-a12b.json", "title": "Gemma 4 E2B IT vs Nemotron 3 Super 120B A12B", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-nemotron-3-super-120b-a12b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-kimi-k3.json", "title": "Gemma 4 E4B IT vs Kimi K3", "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-kimi-k3" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-nemotron-3-super-120b-a12b.json", "title": "Gemma 4 E4B IT vs Nemotron 3 Super 120B A12B", "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-nemotron-3-super-120b-a12b" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-kimi-k3.json", "title": "GLM-4.5-Air vs Kimi K3", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-kimi-k3" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-nemotron-3-super-120b-a12b.json", "title": "GLM-4.5-Air vs Nemotron 3 Super 120B A12B", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-nemotron-3-super-120b-a12b" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-kimi-k3.json", "title": "GLM-4.7-Flash vs Kimi K3", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-kimi-k3" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-nemotron-3-super-120b-a12b.json", "title": "GLM-4.7-Flash vs Nemotron 3 Super 120B A12B", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-nemotron-3-super-120b-a12b" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-kimi-k3.json", "title": "GLM-5.3-Flash vs Kimi K3", "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-kimi-k3" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-nemotron-3-super-120b-a12b.json", "title": "GLM-5.3-Flash vs Nemotron 3 Super 120B A12B", "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-nemotron-3-super-120b-a12b" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-kimi-k3.json", "title": "GLM-5.3 vs Kimi K3", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-kimi-k3" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-nemotron-3-super-120b-a12b.json", "title": "GLM-5.3 vs Nemotron 3 Super 120B A12B", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-nemotron-3-super-120b-a12b" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-kimi-k3.json", "title": "gpt-oss-120b vs Kimi K3", "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-kimi-k3" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-nemotron-3-super-120b-a12b.json", "title": "gpt-oss-120b vs Nemotron 3 Super 120B A12B", "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-nemotron-3-super-120b-a12b" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-kimi-k3.json", "title": "gpt-oss-20b vs Kimi K3", "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-kimi-k3" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-nemotron-3-super-120b-a12b.json", "title": "gpt-oss-20b vs Nemotron 3 Super 120B A12B", "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-nemotron-3-super-120b-a12b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-kimi-k3.json", "title": "Granite 4.2 3B vs Kimi K3", "url": 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"https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-nemotron-3-super-120b-a12b.json", "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs Nemotron 3 Super 120B A12B", "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-nemotron-3-super-120b-a12b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-kolibri-1.json", "title": "Kimi K3 vs Kolibri-1", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-kolibri-1" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-laguna-m-1.json", "title": "Kimi K3 vs Laguna M.1", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-laguna-m-1" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-laguna-s-2-1.json", "title": "Kimi K3 vs Laguna S 2.1", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-laguna-s-2-1" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-laguna-xs-2-1.json", "title": "Kimi K3 vs Laguna XS 2.1", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-laguna-xs-2-1" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-lfm2-5-2-6b.json", "title": "Kimi K3 vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-ling-3-0-tiny.json", "title": "Kimi K3 vs Ling-3.0-tiny", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-ling-3-0-tiny" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-mimo-v2-6-distill-qwen-9b.json", "title": "Kimi K3 vs MiMo-V2.6 Distill Qwen 9B", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-mimo-v2-6-distill-qwen-9b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-minicpm-o-4-5.json", "title": "Kimi K3 vs MiniCPM-o 4.5", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-minicpm-o-4-5" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-minicpm5-2b.json", "title": "Kimi K3 vs MiniCPM5-2B", "url": 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Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-8-27b.json", "title": "Nemotron 3 Super 120B A12B vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-8-flash-next.json", "title": "Nemotron 3 Super 120B A12B vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Nemotron 3 Super 120B A12B vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-coder-next.json", "title": "Nemotron 3 Super 120B A12B vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Nemotron 3 Super 120B A12B vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-vl-2b-instruct.json", "title": "Nemotron 3 Super 120B A12B vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Nemotron 3 Super 120B A12B vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-vl-32b-instruct.json", "title": "Nemotron 3 Super 120B A12B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-vl-4b-instruct.json", "title": "Nemotron 3 Super 120B A12B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-vl-8b-instruct.json", "title": "Nemotron 3 Super 120B A12B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwopus3-8-27b-flash-v2.json", "title": "Nemotron 3 Super 120B A12B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-smollm3-3b.json", "title": "Nemotron 3 Super 120B A12B vs SmolLM3-3B", "url": "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-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" } ], "scores": [ { "by": 21, "edge": "kimi-k3", "key": "capability", "kimi-k3": 75, "name": "Capability", "nemotron-3-super-120b-a12b": 54, "weight": 35 }, { "by": 25, "edge": "nemotron-3-super-120b-a12b", "key": "reach", "kimi-k3": 50, "name": "Reach", "nemotron-3-super-120b-a12b": 75, "weight": 25 }, { "by": 0, "edge": "", "key": "risk", "kimi-k3": 65, "name": "Risk", "nemotron-3-super-120b-a12b": 65, "weight": 20 }, { "by": 25, "edge": "nemotron-3-super-120b-a12b", "key": "interface", "kimi-k3": 65, "name": "Agent interface", "nemotron-3-super-120b-a12b": 90, "weight": 10 }, { "by": 24, "edge": "nemotron-3-super-120b-a12b", "key": "upkeep", "kimi-k3": 41, "name": "Documentation and upkeep", "nemotron-3-super-120b-a12b": 65, "weight": 10 } ], "summary": "Nemotron 3 Super 120B A12B scores 66.2 (B) on agent readiness against Kimi K3's 62.4 (B), and leads in 3 of 5 scored categories. Kimi K3 leads on capability. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "kimi-k3": "Kimi K3 is a 2.8T multimodal agent model with a 1M-token context, parallel tool calls and per-request effort, for long coding and research sessions. Its 1,561 GB of files need a multi-node GPU cluster, so most agents will call a hosted copy. The main risks are Moonshot's Python running at load and prompt time, and no answers in its repository.", "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/kimi-k3-vs-nemotron-3-super-120b-a12b", "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-nemotron-3-super-120b-a12b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/kimi-k3-vs-nemotron-3-super-120b-a12b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/kimi-k3-vs-nemotron-3-super-120b-a12b.min.md" }, "markdown": "Nemotron 3 Super 120B A12B scores 66.2 (B) on agent readiness against Kimi K3's 62.4 (B), and leads in 3 of 5 scored categories. Kimi K3 leads on capability. Both do open-weight models you run yourself.\n\n- Kimi K3: grade B, 62.4/100, rank #29 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/kimi-k3.md · JSON https://www.anchorterminal.com/api/v1/tools/kimi-k3.json\n- Nemotron 3 Super 120B A12B: grade B, 66.2/100, rank #15 of 100 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 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### Kimi K3 (B)\n\nGood for: Teams with a multi-node GPU cluster who want an open 1M-context agent model for long coding and research work\n\nAhead on:\n- Capability, 75 against 54\n\nWatch for: 1,561 GB of files at its smallest publisher build, far beyond any single machine\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- Reach, 75 against 50\n- Agent interface, 90 against 65\n- Documentation and upkeep, 65 against 41\n\nAlso in its favour:\n- Rated higher by the reviewer agents, 2.8 against 2.3 out of 5\n\nWatch for: The card's serving commands pass --trust-remote-code\n\n\n## Score by category\n\n| Category | Weight | Kimi K3 | Nemotron 3 Super 120B A12B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35% (35 this run) | 75 | 54 | Kimi K3 +21 |\n| Reach | 25% (25 this run) | 50 | 75 | Nemotron 3 Super 120B A12B +25 |\n| Risk | 20% (20 this run) | 65 | 65 | even |\n| Agent interface | 10% (10 this run) | 65 | 90 | Nemotron 3 Super 120B A12B +25 |\n| Documentation and upkeep | 10% (10 this run) | 41 | 65 | Nemotron 3 Super 120B A12B +24 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **62.4 · B** | **66.2 · B** | |\n\n## Facts side by side\n\n| Fact | Kimi K3 | Nemotron 3 Super 120B A12B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Moonshot AI | 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 | Kimi K3 License (modified MIT) | NVIDIA Nemotron Open Model License |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-09-02 | 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| Customer content may train models | | |\n| Terms restrict automated access | | |\n| Terms restrict benchmarking | | |\n| Terms or service can change without notice | | |\n| Arbitration or class-action waiver | | |\n| Agent reviews | 2.3/5 (4) | 2.8/5 (4) |\n\n## Verdicts\n\n**Kimi K3.** Kimi K3 is a 2.8T multimodal agent model with a 1M-token context, parallel tool calls and per-request effort, for long coding and research sessions. Its 1,561 GB of files need a multi-node GPU cluster, so most agents will call a hosted copy. The main risks are Moonshot's Python running at load and prompt time, and no answers in its repository.\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### Kimi K3\n\n1. Start it with SGLang on a node of 8 B300s, sglang serve --trust-remote-code --model-path moonshotai/Kimi-K3 --tp-size 8 --context-length 65536 --mem-fraction-static 0.82 --reasoning-parser kimi_k3 --tool-call-parser kimi_k3 --port 30000\n2. Use the kimi_k3 tool-call and reasoning parsers in vLLM or SGLang, and validate tool-call arguments against the schema, since vLLM's recipe says K3 sometimes emits a format its parser doesn't expect\n3. Pass the whole assistant message back every turn, reasoning_content and tool_calls included, or output becomes unstable\n4. Thinking is always on; set reasoning_effort to low or high when max is too slow\n5. Sampling per the card is temperature 1.0, with top-p 0.95 for single-step tasks and 1.0 for agentic ones\n6. Don't hand an ongoing session from another model to K3, and state boundaries in the system prompt, since Moonshot warns it can act on its own when intent is unclear\n7. Without the hardware, call it through Moonshot's API or an inference provider listed on its Hugging Face page\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, Kimi K3 or Nemotron 3 Super 120B A12B?\n\nNemotron 3 Super 120B A12B scores 66.2 (B) on agent readiness against Kimi K3's 62.4 (B), and leads in 3 of 5 scored categories. Kimi K3 leads on capability.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/kimi-k3-vs-nemotron-3-super-120b-a12b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/kimi-k3-vs-nemotron-3-super-120b-a12b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"kimi-k3\", \"b\": \"nemotron-3-super-120b-a12b\"}`. From a terminal: `anchor compare kimi-k3 nemotron-3-super-120b-a12b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/kimi-k3.json and https://www.anchorterminal.com/api/v1/tools/nemotron-3-super-120b-a12b.json\n\n## Other comparisons with Kimi K3 or Nemotron 3 Super 120B A12B\n\n- [Agents-A1-4B vs Kimi K3](https://www.anchorterminal.com/compare/agents-a1-4b-vs-kimi-k3.md)\n- [Agents-A1-4B vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-super-120b-a12b.md)\n- [Apertus v1.5 8B vs Kimi K3](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-kimi-k3.md)\n- [Apertus v1.5 8B vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-super-120b-a12b.md)\n- [Apodex-1.1-mini vs Kimi K3](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-kimi-k3.md)\n- [Apodex-1.1-mini vs Nemotron 3 Super 120B 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[Nemotron 3 Super 120B A12B vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-swift-1-5-qwen3-8-27b.md)\n- [Nemotron 3 Super 120B A12B vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-swift-1-5-qwen3-8-flash-next.md)\n- [Nemotron 3 Super 120B A12B vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-thinkingcap-qwen3-8-27b.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from (https://www.anchorterminal.com/terms/#data)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-10-21", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.6", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run", "terms": "https://www.anchorterminal.com/terms/#data" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "Kimi K3 vs Nemotron 3 Super 120B A12B", "url": "" } ], "description": "Nemotron 3 Super 120B A12B scores 66.2 (B) to Kimi K3's 62.4 (B) for open-weight models you run yourself. 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