{ "data": { "a": { "slug": "mistral-large-4", "name": "Mistral Large 4", "vendor": "Mistral AI", "vendorUrl": "https://mistral.ai", "kind": "weights", "category": "open-weights", "summary": "Mistral Large 4 (\"Le Chonk\") is Mistral AI's open-weight mixture-of-experts model of about 1T parameters with 52B active, taking text and images, with three reasoning efforts and a 1M-token context, published in FP8 and NVFP4.", "url": "https://www.anchorterminal.com/tools/mistral-large-4", "markdownUrl": "https://www.anchorterminal.com/tools/mistral-large-4.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/mistral-large-4.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/mistral-large-4.json", "repo": "https://huggingface.co/mistralai/Mistral-Large-4-1T-A52B", "license": "Mistral Ecosystem Open-Weight License (modified MIT)", "transports": [], "packages": [ { "registry": "huggingface", "name": "mistralai/Mistral-Large-4-1T-A52B" }, { "registry": "huggingface", "name": "mistralai/Mistral-Large-4-1T-A52B-NVFP4" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Both repositories report gated false (checked 2026-10-30).", "pricing": "free", "pricingNotes": "Free to download. The Mistral Ecosystem Open-Weight License allows commercial use, except that a licensee or affiliate that operates a Model-as-a-Service business needs a separate commercial licence from Mistral before any commercial use; internal use and use through Mistral's own products are exempt. Mistral's API serves it as mistral-large-4 at $0.70 input, $0.07 cached input and $2.10 output per million tokens (checked 2026-10-30).", "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 2026-10-30).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-30" }, "docsUrl": "https://huggingface.co/mistralai/Mistral-Large-4-1T-A52B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "multimodal", "frontier", "reasoning", "tool-calling", "long-context", "multilingual", "safetensors", "fp8", "nvfp4", "self-hosted" ], "lastRelease": "2026-10-29", "graded": true, "anchor": { "graded": true, "score": 54.6, "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": 1239, "categoryRank": 72, "methodology": "0.7", "run": "2026-10-01", "scores": { "capability": 32, "interface": 95, "reach": 50, "risk": 75, "upkeep": 64 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-30" }, "negative": 0, "verdict": "A frontier-sized open cousin for coding agents and defensive security, with strict JSON Schema output, parallel tool calls and three reasoning efforts. It needs a multi-GPU server, about 732 GB for the smallest 4-bit build, and vLLM and SGLang run it only from special images. The main risk is a licence that bars Model-as-a-Service businesses without a separate deal.", "bestFor": "Operators with an 8-GPU Blackwell or H200 node who want a frontier-sized open model for coding agents and defensive security, with a hosted twin on Mistral's API.", "strengths": [ "Safetensors only, loaded by Transformers' own code with a Jinja template, in Mistral's verified organisation with a hash on every file", "Tool calling documented end to end with a named parser, several calls a turn and strict JSON Schema output", "Reasoning effort budgeted per request, low, medium or high, with the reasoning returned apart from the answer", "FP8 and NVFP4 builds from Mistral, and Mistral's own API serves the same model for agents without the hardware", "First among open-weight models on Cognition's FrontierCode 2.0 leaderboard, run in Mistral's own harness" ], "weaknesses": [ "The smallest 4-bit build needs about 732 GB, so nothing fits a single workstation or a 128 GB machine", "No vLLM or SGLang release supports it yet, only Docker images tagged mistral-large-4", "A Model-as-a-Service business needs a separate commercial licence from Mistral before any commercial use", "Safety results are Mistral's own with no settings to rerun them, and no limitations, training data sources or knowledge cut-off are published", "The 1M context comes from YaRN scaling with no stated trained length, and thinking can't be turned off" ], "agentNotes": [ "Serve the NVFP4 build on Blackwell with `vllm/vllm-openai:mistral-large-4 --model mistralai/Mistral-Large-4-1T-A52B-NVFP4 --tensor-parallel-size 8 --kv-cache-dtype fp8_ds_mla --tool-call-parser mistral --reasoning-parser mistral --enable-auto-tool-choice`; released vLLM doesn't load it", "On H200 use the FP8 build with tensor parallelism of 16 or 8 by 2 pipelines, per vLLM's recipe", "Pass `reasoning_effort` low, medium or high (default high); any other value makes the template raise an error, and there is no off switch", "Read the reasoning from `message.reasoning`; `max_completion_tokens` counts reasoning tokens too", "Use temperature 1.0 and top_p 0.95 at every effort, per the card", "Ask for JSON with `response_format` type json_schema and strict true; the template passes the schema to the model", "The template rejects a tool message straight after a user message and a conversation that doesn't start with a user or system message", "Add `--speculative-config '{\"method\": \"dspark\", \"num_speculative_tokens\": 3}'` to use the embedded draft head", "If you'll offer it to others as a hosted model, read the licence first; Model-as-a-Service use needs Mistral's commercial licence" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 54.6 } ], "editorialScores": { "capability": 32, "interface": 95, "reach": 50, "risk": 75, "upkeep": 64 }, "provenanceScore": 84, "checked": { "categories": { "capability": "2026-10-30", "interface": "2026-10-30", "reach": "2026-10-30", "risk": "2026-10-30", "upkeep": "2026-10-30" }, "last": "2026-10-30", "oldest": "2026-10-30", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "# NVFP4 on 4 or 8 Blackwell GPUs, with the card's vLLM image (no vLLM release supports it yet):\ndocker run --runtime nvidia --gpus all -v ~/.cache/huggingface:/root/.cache/huggingface -p 8000:8000 --ipc=host \\\n vllm/vllm-openai:mistral-large-4 --model mistralai/Mistral-Large-4-1T-A52B-NVFP4 \\\n --tensor-parallel-size 8 --kv-cache-dtype fp8_ds_mla --tool-call-parser mistral \\\n --reasoning-parser mistral --enable-auto-tool-choice --gpu-memory-utilization 0.85", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\"model\": \"mistralai/Mistral-Large-4-1T-A52B-NVFP4\", \"reasoning_effort\": \"medium\", \"temperature\": 1.0, \"top_p\": 0.95,\n \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/mistral-large-4" }, "sameCompany": [ "mistral-embeddings", "mistral-moderation", "mistral-voxtral-transcribe", "voxtral-mini-4b-realtime-2602", "mistral-ocr", "mistral-vibe-cli" ], "sameModel": [ "mistral-api" ], "weights": { "trainedBy": "Mistral AI", "params": "~1T total (1.05T per Mistral's docs), 52B active", "context": 1048576, "goodFor": "Frontier-sized multimodal model for coding agents, defensive security work and long documents, with tool calling, strict JSON Schema output and three reasoning efforts, for operators with a multi-GPU server.", "runtimes": [ "llama.cpp", "Transformers" ], "builds": [ { "name": "Mistral-Large-4-1T-A52B-NVFP4", "url": "https://huggingface.co/mistralai/Mistral-Large-4-1T-A52B-NVFP4", "who": "publisher", "precision": "nvfp4", "sizeGB": 610, "memoryGB": 1024, "format": "safetensors", "runsOn": [ "vLLM (mistral-large-4 image)", "SGLang (mistral-large-4 image)" ], "note": "NVFP4 experts with FP8 linears, for Blackwell GPUs; Mistral-format params.json only, no config.json, so not for Transformers" }, { "name": "ggml-org Mistral-Large-4-1T-A52B-GGUF Q4_K_M", "url": "https://huggingface.co/ggml-org/Mistral-Large-4-1T-A52B-GGUF", "who": "ggml-org (llama.cpp)", "precision": "q4_k_m", "sizeGB": 634.8, "memoryGB": 1024, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "llama.cpp's own organisation, linked from Mistral's card. Also BF16, Q8_0, Q2_K (380.7 GB) and IQ2_XXS (297.7 GB), plus an NVFP4 GGUF of 619.4 GB in a second repository; the 2-bit files are below the 4-bit line and aren't counted. Vision projector 3.4 GB extra" }, { "name": "Mistral-Large-4-1T-A52B", "url": "https://huggingface.co/mistralai/Mistral-Large-4-1T-A52B", "who": "publisher", "precision": "fp8", "sizeGB": 1060.6, "memoryGB": 2048, "format": "safetensors", "runsOn": [ "Transformers", "vLLM (mistral-large-4 image)", "SGLang (mistral-large-4 image)" ], "note": "Block-scaled FP8 (128 by 128), the release precision; vision tower, projector, output head and some attention projections kept in BF16. Includes the DSpark draft head" } ], "run": "docker run --runtime nvidia --gpus all -v ~/.cache/huggingface:/root/.cache/huggingface -p 8000:8000 --ipc=host vllm/vllm-openai:mistral-large-4 --model mistralai/Mistral-Large-4-1T-A52B-NVFP4 --tensor-parallel-size 8 --kv-cache-dtype fp8_ds_mla --tool-call-parser mistral --reasoning-parser mistral --enable-auto-tool-choice --gpu-memory-utilization 0.85", "runBuild": "Mistral-Large-4-1T-A52B-NVFP4", "checked": "2026-10-30", "minMemoryGB": 1024 }, "family": { "lab": "Mistral AI", "family": "Mistral Large 4", "url": "https://www.anchorterminal.com/open-weights/#mistral", "olderCount": 49 }, "area": "models", "provenance": { "legalEntity": "Mistral AI (RCS Paris 952 418 325)", "domain": "mistral.ai", "domainRegistered": "2019-05-15", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/mistralai/Mistral-Large-4-1T-A52B/commits/main", "securityTxt": "valid", "checked": "2026-10-30", "notes": [ "The files sit in Mistral's verified mistralai organisation, which the card, Mistral's announcement and its docs page for the model link to. Legal entity and domain registration are carried from the mistral-api listing.", "Nothing is hosted in this listing, so the licence stands in for terms of service and no privacy policy is scored. The hosted model is mistral-api.", "The changelog is the repositories' commit history, an initial release on 29 October 2026 and two card edits the same day, plus a merged evaluation figure on the NVFP4 card.", "mistral.ai/.well-known/security.txt answered on 30 October 2026 with a security contact, an expiry of 5 May 2027 and a HackerOne submission policy." ], "score": 84 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/mistral-large-4.json" }, "answer": "Nemotron 3 Super 120B A12B scores 66.2 (B) on agent readiness against Mistral Large 4's 54.6 (C), and leads in 3 of 5 scored categories. Mistral Large 4 leads on risk and agent interface.", "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": 1239, "categoryRank": 12, "methodology": "0.7", "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": "0.7", "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": 76, "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", "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": "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 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" } ], "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": "2026-10-30T15:37:59.318803626Z" }, "domain": { "domain": "nvidia.com", "registered": "1993-04-20", "source": "https://rdap.verisign.com/com/v1/domain/nvidia.com", "checkedAt": "2026-10-25T13:52:20.255128079Z" }, "pages": [ { "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16/commits/main", "kind": "changelog", "status": 200, "checkedAt": "2026-10-30T05:42:23.94598823Z", "changedAt": "2026-10-30T05:42:23.94598823Z", "fingerprint": "d3d68608c33f" } ], "updatedAt": "2026-10-30T15:37:59.318803626Z" } }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Mistral 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": "Mistral Ecosystem Open-Weight 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": "2026-10-29", "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": "none", "b": "2.8/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Nemotron 3 Super 120B A12B scores 66.2 (B) on agent readiness against Mistral Large 4's 54.6 (C), and leads in 3 of 5 scored categories. 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"https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-mistral-large-4.json", "title": "Apodex-1.1-mini vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-mistral-large-4" }, { "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-mistral-large-4.json", "title": "BigBang-v1 vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-mistral-large-4" }, { "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/carbon-x-vs-mistral-large-4.json", "title": "Carbon-X vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/carbon-x-vs-mistral-large-4" }, { "json": "https://www.anchorterminal.com/compare/carbon-x-vs-nemotron-3-super-120b-a12b.json", "title": "Carbon-X vs Nemotron 3 Super 120B A12B", "url": "https://www.anchorterminal.com/compare/carbon-x-vs-nemotron-3-super-120b-a12b" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-mistral-large-4.json", "title": "Cosmos-Reason2-2B vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-mistral-large-4" }, { "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-mistral-large-4.json", "title": "Darwin-36B-Opus vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-mistral-large-4" }, { "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-mistral-large-4.json", "title": "DeepSeek-V3.2 vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-mistral-large-4" }, { "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-mistral-large-4.json", "title": "DeepSeek V4.1 Flash vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-mistral-large-4" }, { "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-mistral-large-4.json", "title": "DeepSeek-V4-Flash-0731 vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-mistral-large-4" }, { "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-mistral-large-4.json", "title": "DiffusionGemma 26B A4B IT vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-mistral-large-4" }, { "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-mistral-large-4.json", "title": "Gemma 4 12B IT vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-mistral-large-4" }, { "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-mistral-large-4.json", "title": "Gemma 4 26B A4B IT vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-mistral-large-4" }, { "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-mistral-large-4.json", "title": "Gemma 4 31B IT vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-mistral-large-4" }, { "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-mistral-large-4.json", "title": "Gemma 4 E2B IT vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-mistral-large-4" }, { "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-mistral-large-4.json", "title": "Gemma 4 E4B IT vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-mistral-large-4" }, { "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-mistral-large-4.json", "title": "GLM-4.5-Air vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-mistral-large-4" }, { "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-mistral-large-4.json", "title": "GLM-4.7-Flash vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-mistral-large-4" }, { "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-mistral-large-4.json", "title": "GLM-5.3-Flash vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-mistral-large-4" }, { "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-mistral-large-4.json", "title": "GLM-5.3 vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-mistral-large-4" }, { "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-mistral-large-4.json", "title": "gpt-oss-120b vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-mistral-large-4" }, { "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-mistral-large-4.json", "title": "gpt-oss-20b vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-mistral-large-4" }, { "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": 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Mistral Large 4", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-mistral-large-4" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-nemotron-3-super-120b-a12b.json", "title": "Inkling-Small vs Nemotron 3 Super 120B A12B", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-nemotron-3-super-120b-a12b" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-mistral-large-4.json", "title": "Inkling vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/inkling-vs-mistral-large-4" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-nemotron-3-super-120b-a12b.json", "title": "Inkling vs Nemotron 3 Super 120B A12B", "url": "https://www.anchorterminal.com/compare/inkling-vs-nemotron-3-super-120b-a12b" }, { "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-mistral-large-4.json", "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs Mistral Large 4", "url": 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"json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-5-397b-a17b.json", "title": "Mistral Large 4 vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-5-4b.json", "title": "Mistral Large 4 vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-5-9b.json", "title": "Mistral Large 4 vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-6-27b.json", "title": "Mistral Large 4 vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-6-35b-a3b.json", "title": "Mistral Large 4 vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-8-27b.json", "title": "Mistral Large 4 vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-8-flash-next.json", "title": "Mistral Large 4 vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Mistral Large 4 vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-coder-next.json", "title": "Mistral Large 4 vs Qwen3-Coder-Next", "url": 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"url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-nemotron-3-super-120b-a12b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-nemotron-3-super-120b-a12b.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Nemotron 3 Super 120B A12B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-nemotron-3-super-120b-a12b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-nemotron-3-super-120b-a12b.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Nemotron 3 Super 120B A12B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-nemotron-3-super-120b-a12b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-nemotron-3-super-120b-a12b.json", "title": "Nemotron 3 Nano 30B A3B vs Nemotron 3 Super 120B A12B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-nemotron-3-super-120b-a12b" }, { "json": 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120B A12B vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-olmo-3-7b-think.json", "title": "Nemotron 3 Super 120B A12B vs Olmo 3 7B Think", "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-olmo-3-7b-think" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-ornith-1-5-35b-a3b.json", "title": "Nemotron 3 Super 120B A12B vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-ornith-1-5-397b.json", "title": "Nemotron 3 Super 120B A12B vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-ornith-1-5-397b" }, { "json": 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Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-5-9b.json", "title": "Nemotron 3 Super 120B A12B vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-6-27b.json", "title": "Nemotron 3 Super 120B A12B vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-6-35b-a3b.json", "title": "Nemotron 3 Super 120B A12B vs 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-recursive-flint.json", "title": "Nemotron 3 Super 120B A12B vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-recursive-flint" }, { "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" }, { "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" } ], "scores": [ { "by": 22, "edge": "nemotron-3-super-120b-a12b", "key": "capability", "mistral-large-4": 32, "name": "Capability", "nemotron-3-super-120b-a12b": 54, "weight": 35 }, { "by": 25, "edge": "nemotron-3-super-120b-a12b", "key": "reach", "mistral-large-4": 50, "name": "Reach", "nemotron-3-super-120b-a12b": 75, "weight": 25 }, { "by": 10, "edge": "mistral-large-4", "key": "risk", "mistral-large-4": 75, "name": "Risk", "nemotron-3-super-120b-a12b": 65, "weight": 20 }, { "by": 5, "edge": "mistral-large-4", "key": "interface", "mistral-large-4": 95, "name": "Agent interface", "nemotron-3-super-120b-a12b": 90, "weight": 10 }, { "by": 1, "edge": "nemotron-3-super-120b-a12b", "key": "upkeep", "mistral-large-4": 64, "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 Mistral Large 4's 54.6 (C), and leads in 3 of 5 scored categories. Mistral Large 4 leads on risk and agent interface. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "mistral-large-4": "A frontier-sized open cousin for coding agents and defensive security, with strict JSON Schema output, parallel tool calls and three reasoning efforts. It needs a multi-GPU server, about 732 GB for the smallest 4-bit build, and vLLM and SGLang run it only from special images. The main risk is a licence that bars Model-as-a-Service businesses without a separate deal.", "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/mistral-large-4-vs-nemotron-3-super-120b-a12b", "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-nemotron-3-super-120b-a12b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/mistral-large-4-vs-nemotron-3-super-120b-a12b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/mistral-large-4-vs-nemotron-3-super-120b-a12b.min.md" }, "markdown": "Nemotron 3 Super 120B A12B scores 66.2 (B) on agent readiness against Mistral Large 4's 54.6 (C), and leads in 3 of 5 scored categories. Mistral Large 4 leads on risk and agent interface. Both do open-weight models you run yourself.\n\n- Mistral Large 4: grade C, 54.6/100, rank #92 of 117 open-weight models. Markdown https://www.anchorterminal.com/tools/mistral-large-4.md · JSON https://www.anchorterminal.com/api/v1/tools/mistral-large-4.json\n- Nemotron 3 Super 120B A12B: grade B, 66.2/100, rank #17 of 117 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 3588 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### Mistral Large 4 (C)\n\nGood for: Operators with an 8-GPU Blackwell or H200 node who want a frontier-sized open model for coding agents and defensive security, with a hosted twin on Mistral's API.\n\nAhead on:\n- Risk, 75 against 65\n- Agent interface, 95 against 90\n\nWatch for: The smallest 4-bit build needs about 732 GB, so nothing fits a single workstation or a 128 GB 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- Capability, 54 against 32\n- Reach, 75 against 50\n\nWatch for: The card's serving commands pass --trust-remote-code\n\n\n## Score by category\n\n| Category | Weight | Mistral Large 4 | Nemotron 3 Super 120B A12B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35% (35 this run) | 32 | 54 | Nemotron 3 Super 120B A12B +22 |\n| Reach | 25% (25 this run) | 50 | 75 | Nemotron 3 Super 120B A12B +25 |\n| Risk | 20% (20 this run) | 75 | 65 | Mistral Large 4 +10 |\n| Agent interface | 10% (10 this run) | 95 | 90 | Mistral Large 4 +5 |\n| Documentation and upkeep | 10% (10 this run) | 64 | 65 | Nemotron 3 Super 120B A12B +1 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **54.6 · C** | **66.2 · B** | |\n\n## Facts side by side\n\n| Fact | Mistral Large 4 | Nemotron 3 Super 120B A12B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Mistral 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 | Mistral Ecosystem Open-Weight License (modified MIT) | NVIDIA Nemotron Open Model License |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-29 | 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 | none | 2.8/5 (4) |\n\n## Verdicts\n\n**Mistral Large 4.** A frontier-sized open cousin for coding agents and defensive security, with strict JSON Schema output, parallel tool calls and three reasoning efforts. It needs a multi-GPU server, about 732 GB for the smallest 4-bit build, and vLLM and SGLang run it only from special images. The main risk is a licence that bars Model-as-a-Service businesses without a separate deal.\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### Mistral Large 4\n\n1. Serve the NVFP4 build on Blackwell with `vllm/vllm-openai:mistral-large-4 --model mistralai/Mistral-Large-4-1T-A52B-NVFP4 --tensor-parallel-size 8 --kv-cache-dtype fp8_ds_mla --tool-call-parser mistral --reasoning-parser mistral --enable-auto-tool-choice`; released vLLM doesn't load it\n2. On H200 use the FP8 build with tensor parallelism of 16 or 8 by 2 pipelines, per vLLM's recipe\n3. Pass `reasoning_effort` low, medium or high (default high); any other value makes the template raise an error, and there is no off switch\n4. Read the reasoning from `message.reasoning`; `max_completion_tokens` counts reasoning tokens too\n5. Use temperature 1.0 and top_p 0.95 at every effort, per the card\n6. Ask for JSON with `response_format` type json_schema and strict true; the template passes the schema to the model\n7. The template rejects a tool message straight after a user message and a conversation that doesn't start with a user or system message\n8. Add `--speculative-config '{\"method\": \"dspark\", \"num_speculative_tokens\": 3}'` to use the embedded draft head\n9. If you'll offer it to others as a hosted model, read the licence first; Model-as-a-Service use needs Mistral's commercial licence\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, Mistral Large 4 or Nemotron 3 Super 120B A12B?\n\nNemotron 3 Super 120B A12B scores 66.2 (B) on agent readiness against Mistral Large 4's 54.6 (C), and leads in 3 of 5 scored categories. Mistral Large 4 leads on risk and agent interface.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/mistral-large-4-vs-nemotron-3-super-120b-a12b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/mistral-large-4-vs-nemotron-3-super-120b-a12b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"mistral-large-4\", \"b\": \"nemotron-3-super-120b-a12b\"}`. From a terminal: `anchor compare mistral-large-4 nemotron-3-super-120b-a12b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/mistral-large-4.json and https://www.anchorterminal.com/api/v1/tools/nemotron-3-super-120b-a12b.json\n\n## Other comparisons with Mistral Large 4 or Nemotron 3 Super 120B A12B\n\n- [Agents-A1-4B vs Mistral Large 4](https://www.anchorterminal.com/compare/agents-a1-4b-vs-mistral-large-4.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 Mistral Large 4](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-mistral-large-4.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 Mistral Large 4](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-mistral-large-4.md)\n- [Apodex-1.1-mini vs Nemotron 3 Super 120B 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