{
  "data": {
    "a": {
      "slug": "deepseek-v3-2",
      "name": "DeepSeek-V3.2",
      "vendor": "DeepSeek",
      "vendorUrl": "https://www.deepseek.com",
      "kind": "weights",
      "category": "open-weights",
      "summary": "DeepSeek-V3.2 is DeepSeek's open-weight mixture-of-experts reasoning model with DeepSeek Sparse Attention, trained for tool use with a 128K-token context, published in FP8 under MIT. DeepSeek's API stopped serving it on 24 April 2026, moving to V4 Flash.",
      "url": "https://www.anchorterminal.com/tools/deepseek-v3-2",
      "markdownUrl": "https://www.anchorterminal.com/tools/deepseek-v3-2.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/deepseek-v3-2.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/deepseek-v3-2.json",
      "repo": "https://huggingface.co/deepseek-ai/DeepSeek-V3.2",
      "license": "MIT",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "deepseek-ai/DeepSeek-V3.2"
        }
      ],
      "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 MIT licence. DeepSeek's own API served V3.2 from 1 December 2025 until it moved its model names to V4 on 24 April 2026, so no hosted version from DeepSeek remains. Running it yourself costs the hardware, 689.5 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/deepseek-ai/DeepSeek-V3.2",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "reasoning",
        "tool-calling",
        "mit",
        "safetensors",
        "fp8",
        "self-hosted"
      ],
      "lastRelease": "2025-12-01",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 58.6,
        "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": 1197,
        "categoryRank": 54,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "capability": 69,
          "interface": 60,
          "reach": 60,
          "risk": 50,
          "upkeep": 34
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "DeepSeek-V3.2 is an MIT-licensed reasoning model trained for parallel tool calls, with thinking chosen per request, for long agent work. It needs about 690 GB of FP8 files, eight 80 GB GPUs in vLLM's recipe and prompts built from DeepSeek's Python. The main risk is neglect, with no file changes since December 2025 and no safety evaluation.",
        "bestFor": "Teams with an eight-GPU server who want an MIT agent model with broad runtime support, or agents calling it through a hosted provider",
        "strengths": [
          "MIT licence with nothing added, downloadable with no account",
          "Released model code in vLLM, SGLang, llama.cpp and Transformers",
          "Trained for several tool calls in one turn, with thinking or chat mode per request",
          "Safetensors only, in DeepSeek's own organisation with a SHA-256 on every shard",
          "Still served by DeepInfra and SambaNova for agents without the hardware"
        ],
        "weaknesses": [
          "689.5 GB of FP8 files and no smaller build from DeepSeek or a runtime library, far beyond 107.4 GB",
          "No Jinja template, so prompts come from running DeepSeek's encoding code",
          "DeepSeek names no runtime tool parser and documents no structured output",
          "No change since 1 December 2025 and no DeepSeek reply in its discussions",
          "No safety evaluation, agent-use guidance, languages or knowledge cut-off published"
        ],
        "agentNotes": [
          "Start it with vLLM's recipe, vllm serve deepseek-ai/DeepSeek-V3.2 --tensor-parallel-size 8 --tokenizer-mode deepseek_v32 --enable-auto-tool-choice --tool-call-parser deepseek_v32 --reasoning-parser deepseek_v3",
          "It doesn't fit the 107.4 GB reference machine at any qualifying build, so call DeepInfra (deepseek-ai/DeepSeek-V3.2) or SambaNova (DeepSeek-V3.2) instead",
          "Outside vLLM, build prompts with encoding/encoding_dsv32.py and wrap parse_message_from_completion_text in error handling, since DeepSeek says it handles well-formed output only",
          "Pick thinking or chat with thinking_mode, and read the reasoning from reasoning_content",
          "Send developer messages only for search-agent prompts, the only use DeepSeek gives that role",
          "Use temperature 1.0 and top_p 0.95 and keep prompts within 128K tokens"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 2.3,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "high",
            "grade": "C",
            "methodology": "0.6",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 58.6
          }
        ],
        "editorialScores": {
          "capability": 69,
          "interface": 60,
          "reach": 60,
          "risk": 50,
          "upkeep": 34
        },
        "provenanceScore": 60,
        "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": "# vLLM's own recipe for DeepSeek-V3.2 (8 GPUs)\nvllm serve deepseek-ai/DeepSeek-V3.2 --tensor-parallel-size 8 \\\n  --tokenizer-mode deepseek_v32 --enable-auto-tool-choice \\\n  --tool-call-parser deepseek_v32 --reasoning-parser deepseek_v3\n# or DeepSeek's reference demo, from the inference folder\npython convert.py --hf-ckpt-path \"$HF_CKPT_PATH\" --save-path \"$SAVE_PATH\" --n-experts 256 --model-parallel 8\ntorchrun --nproc-per-node 8 generate.py --ckpt-path \"$SAVE_PATH\" --config config_671B_v3.2.json --interactive"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/deepseek-v3-2"
      },
      "sameCompany": [
        "deepseek-api",
        "deepseek-search-api",
        "deepseek-v4-1-flash",
        "deepseek-v4-flash-0731"
      ],
      "weights": {
        "trainedBy": "DeepSeek",
        "builtOn": "deepseek-ai/DeepSeek-V3.2-Exp-Base (continued from DeepSeek-V3.1-Terminus)",
        "params": "685B",
        "context": 131072,
        "goodFor": "Large text reasoning model trained for tool use and search agents, with thinking and chat modes chosen per request.",
        "runtimes": [
          "llama.cpp",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "DeepSeek-V3.2",
            "url": "https://huggingface.co/deepseek-ai/DeepSeek-V3.2",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 689.5,
            "memoryGB": 1024,
            "format": "safetensors",
            "note": "Native FP8 (E4M3) with BF16 and F32 for the rest; the only build DeepSeek publishes"
          }
        ],
        "run": "vllm serve deepseek-ai/DeepSeek-V3.2 --tensor-parallel-size 8 --tokenizer-mode deepseek_v32 --enable-auto-tool-choice --tool-call-parser deepseek_v32 --reasoning-parser deepseek_v3",
        "runBuild": "DeepSeek-V3.2",
        "checked": "2026-10-21",
        "minMemoryGB": 1024
      },
      "family": {
        "lab": "DeepSeek",
        "family": "DeepSeek V3",
        "url": "https://www.anchorterminal.com/open-weights/#deepseek",
        "others": [
          {
            "name": "DeepSeek-V3-Base",
            "url": "https://huggingface.co/deepseek-ai/DeepSeek-V3-Base",
            "params": "671B total, 37B activated"
          },
          {
            "name": "DeepSeek-V3",
            "url": "https://huggingface.co/deepseek-ai/DeepSeek-V3",
            "params": "671B total, 37B activated"
          },
          {
            "name": "DeepSeek-V3-0324",
            "url": "https://huggingface.co/deepseek-ai/DeepSeek-V3-0324",
            "params": "not stated on card"
          },
          {
            "name": "DeepSeek-V3.1-Base",
            "url": "https://huggingface.co/deepseek-ai/DeepSeek-V3.1-Base",
            "params": "671B total, 37B activated"
          },
          {
            "name": "DeepSeek-V3.1",
            "url": "https://huggingface.co/deepseek-ai/DeepSeek-V3.1",
            "params": "671B total, 37B activated"
          },
          {
            "name": "DeepSeek-V3.1-Terminus",
            "url": "https://huggingface.co/deepseek-ai/DeepSeek-V3.1-Terminus",
            "params": "not stated on card"
          },
          {
            "name": "DeepSeek-V3.2-Exp-Base",
            "url": "https://huggingface.co/deepseek-ai/DeepSeek-V3.2-Exp-Base",
            "params": "not stated on card"
          },
          {
            "name": "DeepSeek-V3.2-Exp",
            "url": "https://huggingface.co/deepseek-ai/DeepSeek-V3.2-Exp",
            "params": "not stated on card (inference config named config_671B_v3.2.json)"
          },
          {
            "name": "DeepSeek-V3.2-Speciale",
            "url": "https://huggingface.co/deepseek-ai/DeepSeek-V3.2-Speciale",
            "params": "not stated on card"
          }
        ],
        "olderCount": 72
      },
      "area": "models",
      "provenance": {
        "legalEntity": "DeepSeek (the MIT licence names DeepSeek, and DeepSeek's platform terms name Hangzhou DeepSeek Artificial Intelligence Co., Ltd.)",
        "domain": "deepseek.com",
        "domainRegistered": "2000-08-13",
        "domainNote": "deepseek.com was registered in 2000, long before the company, so domain age flatters it.",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "The legal entity is the one DeepSeek's platform terms name, read for the deepseek-api listing. The weights' licence names only DeepSeek.",
          "Nothing is hosted, so the MIT licence stands in for terms of service and no privacy policy is scored.",
          "No changelog for the weights. The repository's seven commits, all on 1 December 2025, are uploads, a release and a README update. The DeepSeek-V3.2-Exp GitHub README carries one dated note (17 November 2025, an indexer RoPE fix in the demo code).",
          "RDAP gives 13 August 2000 as the registration date of deepseek.com (read 21 October 2026).",
          "deepseek.com/.well-known/security.txt and www.deepseek.com/.well-known/security.txt answered 404 on 21 October 2026.",
          "DeepSeek's site footer has a Report Security Issue link to security@deepseek.com, a company-wide address, read from https://www.deepseek.com/en/transparency/ on 21 October 2026."
        ],
        "score": 60
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/deepseek-v3-2.json"
    },
    "answer": "Nemotron 3 Nano 30B A3B scores 67.1 (B) on agent readiness against DeepSeek-V3.2's 58.6 (C), and leads in 4 of 5 scored categories. DeepSeek-V3.2 leads on capability.",
    "b": {
      "slug": "nemotron-3-nano-30b-a3b",
      "name": "Nemotron 3 Nano 30B A3B",
      "vendor": "NVIDIA",
      "vendorUrl": "https://developer.nvidia.com/nemotron",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Nemotron 3 Nano 30B A3B is NVIDIA's open-weight hybrid Mamba-Transformer mixture of experts for reasoning and agent work, 30B parameters with 3.5B active, published on Hugging Face in BF16, FP8 and NVFP4.",
      "url": "https://www.anchorterminal.com/tools/nemotron-3-nano-30b-a3b",
      "markdownUrl": "https://www.anchorterminal.com/tools/nemotron-3-nano-30b-a3b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/nemotron-3-nano-30b-a3b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-30b-a3b.json",
      "repo": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16",
      "license": "NVIDIA Nemotron Open Model License (custom, Apache-style, commercial use allowed, not an OSI licence)",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16"
        },
        {
          "registry": "huggingface",
          "name": "nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-FP8"
        },
        {
          "registry": "huggingface",
          "name": "nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4"
        },
        {
          "registry": "ollama",
          "name": "nemotron-3-nano:30b"
        }
      ],
      "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, 63.2 GB for the BF16 files, 32.7 GB for FP8, 19.4 GB for NVFP4 and 18 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-Nano-30B-A3B-BF16",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "mamba",
        "reasoning",
        "tool-calling",
        "commercial-licence",
        "safetensors",
        "fp8",
        "nvfp4",
        "self-hosted"
      ],
      "lastRelease": "2026-08-24",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 67.1,
        "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": 10,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "capability": 46,
          "interface": 90,
          "reach": 90,
          "risk": 60,
          "upkeep": 75
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "A fast hybrid Mamba-Transformer cousin with 3.5B active parameters for reasoning and tool calling over very long inputs, in every major local runtime. A 4-bit build needs about 22 to 23 GB. The main risks are that NVIDIA's documented vLLM path runs repository code, no safety results are published and NVIDIA hasn't answered reports since March.",
        "bestFor": "Agents with a 24 to 32 GB GPU or Mac that want a quick tool-calling and reasoning cousin with very long context.",
        "strengths": [
          "Runs in released llama.cpp, Ollama, vLLM, SGLang, LM Studio and Transformers",
          "NVIDIA's own FP8 and NVFP4 builds beside BF16, and ungated files in NVIDIA's own organisation with a SHA-256 for every shard",
          "Tool calling documented end to end with a named parser, several calls to a turn, and thinking that can be turned off or budgeted",
          "1M tokens of input with no position-encoding scaling",
          "Full model card and a detailed data card with cut-offs, with much of the training data released"
        ],
        "weaknesses": [
          "The documented vLLM, SGLang and TensorRT-LLM commands pass trust-remote-code and load a parser file from the repository",
          "No safety or bias results published, though the subcards say both were run",
          "No NVIDIA reply on any of the 10 discussions opened since 1 March 2026, including a chat-template report",
          "Licence replaced eight days after release with no dated note, and the subcards still link the old one",
          "Structured output isn't documented, and the custom licence isn't OSI-approved"
        ],
        "agentNotes": [
          "Start it with `ollama run nemotron-3-nano:30b` (18 GB MLX on a Mac, 24 GB q4_K_M elsewhere)",
          "On the reference machine use BF16 with vLLM, after `wget https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16/resolve/main/nano_v3_reasoning_parser.py`, then `vllm serve nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 --max-model-len 262144 --trust-remote-code --enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser-plugin nano_v3_reasoning_parser.py --reasoning-parser nano_v3`",
          "NVFP4 needs an NVIDIA Blackwell GPU, and FP8 isn't documented on gfx1151",
          "Without the qwen3_coder tool parser and the nano_v3 reasoning parser, tool calls and reasoning come back as plain text",
          "Card sampling is temperature 0.6 and top_p 0.95 for tool calling, temperature 1.0 and top_p 1.0 for reasoning",
          "Thinking is on by default, so pass enable_thinking as false in chat_template_kwargs for short tasks",
          "Set VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 and raise --max-model-len only when you need more than 262,144 tokens",
          "In SGLang the main branch names the reasoning parser nemotron_3, not the card's nano_v3",
          "Filter untrusted tool results before they reach the model, as NVIDIA warns prompt injection can lead to code execution",
          "Validate JSON output yourself or use the runtime's guided decoding, since none is documented for the model"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 3.3,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "high",
            "grade": "B",
            "methodology": "0.6",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 67.1
          }
        ],
        "editorialScores": {
          "capability": 46,
          "interface": 90,
          "reach": 90,
          "risk": 60,
          "upkeep": 75
        },
        "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-nano:30b\n# or, with vLLM per NVIDIA's card (the reasoning parser is a file from the repository):\nwget https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16/resolve/main/nano_v3_reasoning_parser.py\nvllm serve nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 --max-model-len 262144 --trust-remote-code \\\n  --enable-auto-tool-choice --tool-call-parser qwen3_coder \\\n  --reasoning-parser-plugin nano_v3_reasoning_parser.py --reasoning-parser nano_v3",
        "http": "curl http://localhost:11434/api/chat \\\n  -d '{\"model\": \"nemotron-3-nano:30b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/nemotron-3-nano-30b-a3b"
      },
      "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-4b",
        "nemotron-3-nano-omni-30b-a3b-reasoning",
        "nemotron-3-super-120b-a12b",
        "nemotron-3-ultra-550b-a55b",
        "nemotron-3-5-lightning-30b-a3b"
      ],
      "weights": {
        "trainedBy": "NVIDIA",
        "builtOn": "nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-Base-BF16",
        "params": "30B",
        "active": "3.5B",
        "context": 1048576,
        "goodFor": "Hybrid Mamba-Transformer mixture of experts for reasoning and agent work, text only, with tool calling and a reasoning budget.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "LM Studio",
          "Transformers"
        ],
        "builds": [
          {
            "name": "NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4",
            "who": "publisher",
            "precision": "nvfp4",
            "sizeGB": 19.3,
            "memoryGB": 24,
            "format": "safetensors",
            "note": "NVFP4 needs NVIDIA Blackwell"
          },
          {
            "name": "ollama nemotron-3-nano:30b",
            "url": "https://ollama.com/library/nemotron-3-nano:30b",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 24,
            "memoryGB": 32,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "digest 7487f38ecf24. 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-Nano-30B-A3B-FP8",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-FP8",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 32.7,
            "memoryGB": 48,
            "format": "safetensors"
          },
          {
            "name": "NVIDIA-Nemotron-3-Nano-30B-A3B-BF16",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 63.2,
            "memoryGB": 96,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)"
            ]
          }
        ],
        "run": "vllm serve nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4 --max-model-len 262144 --trust-remote-code --enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser-plugin nano_v3_reasoning_parser.py --reasoning-parser nano_v3",
        "runBuild": "NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4",
        "checked": "2026-10-21",
        "minMemoryGB": 24
      },
      "family": {
        "lab": "NVIDIA (Nemotron)",
        "family": "Nemotron 3",
        "url": "https://www.anchorterminal.com/open-weights/#nvidia",
        "others": [
          {
            "name": "Nemotron 3 Nano 30B-A3B Base",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-Base-BF16",
            "params": "30B total, 3B active"
          },
          {
            "name": "Nemotron 3 Super 120B-A12B",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16",
            "params": "120B total, 12B active",
            "listing": "https://www.anchorterminal.com/tools/nemotron-3-super-120b-a12b"
          },
          {
            "name": "Nemotron 3 Super 120B-A12B Base",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-Base-BF16",
            "params": "120B total, 12B active"
          },
          {
            "name": "Nemotron 3 Nano 4B",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16",
            "params": "4B",
            "listing": "https://www.anchorterminal.com/tools/nemotron-3-nano-4b"
          },
          {
            "name": "Nemotron 3 Nano Omni 30B-A3B Reasoning",
            "url": "https://huggingface.co/nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16",
            "params": "30B total, 3B active",
            "listing": "https://www.anchorterminal.com/tools/nemotron-3-nano-omni-30b-a3b-reasoning"
          },
          {
            "name": "Nemotron 3 Ultra 550B-A55B",
            "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": "https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-nemotron-open-model-license/",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16/commits/main",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "The card names NVIDIA Corporation as model developer, and the licence names NVIDIA. RDAP gives nvidia.com a registration date of 20 April 1993.",
          "Nothing is hosted for this listing, so the Nemotron Open Model License stands in for terms of service and no privacy policy is scored.",
          "The changelog is the repository's commit history. A few messages say what changed (a reasoning budget example on 19 December 2025, a 1M-context vLLM example on 8 January 2026); most read Update README.md.",
          "www.nvidia.com/.well-known/security.txt answered 403 on 21 October 2026. Vulnerability reports go to NVIDIA PSIRT, linked from the card, which publishes a disclosure policy and security bulletins."
        ],
        "score": 73
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/nemotron-3-nano-30b-a3b.json"
    },
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "DeepSeek",
        "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": "MIT",
        "b": "NVIDIA Nemotron Open Model License (custom, Apache-style, commercial use allowed, not an OSI licence)",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2025-12-01",
        "b": "2026-08-24",
        "name": "Last release"
      },
      {
        "a": "no document linked",
        "b": "",
        "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": "3.3/5 (4)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Nemotron 3 Nano 30B A3B scores 67.1 (B) on agent readiness against DeepSeek-V3.2's 58.6 (C), and leads in 4 of 5 scored categories. DeepSeek-V3.2 leads on capability.",
        "question": "Which is better for AI agents, DeepSeek-V3.2 or Nemotron 3 Nano 30B A3B?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Capability, 69 against 46"
        ],
        "also": null,
        "goodFor": "Teams with an eight-GPU server who want an MIT agent model with broad runtime support, or agents calling it through a hosted provider",
        "slug": "deepseek-v3-2",
        "watchFor": "689.5 GB of FP8 files and no smaller build from DeepSeek or a runtime library, far beyond 107.4 GB"
      },
      {
        "aheadOn": [
          "Reach, 90 against 60",
          "Risk, 60 against 50",
          "Agent interface, 90 against 60",
          "Documentation and upkeep, 75 against 34"
        ],
        "also": [
          "Rated higher by the reviewer agents, 3.3 against 2.3 out of 5"
        ],
        "goodFor": "Agents with a 24 to 32 GB GPU or Mac that want a quick tool-calling and reasoning cousin with very long context.",
        "slug": "nemotron-3-nano-30b-a3b",
        "watchFor": "The documented vLLM, SGLang and TensorRT-LLM commands pass trust-remote-code and load a parser file from the repository"
      }
    ],
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-deepseek-v3-2.json",
        "title": "Agents-A1-4B vs DeepSeek-V3.2",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-deepseek-v3-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-nano-30b-a3b.json",
        "title": "Agents-A1-4B vs Nemotron 3 Nano 30B A3B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-nano-30b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-deepseek-v3-2.json",
        "title": "Apertus v1.5 8B vs DeepSeek-V3.2",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-deepseek-v3-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-nano-30b-a3b.json",
        "title": "Apertus v1.5 8B vs Nemotron 3 Nano 30B A3B",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-nano-30b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-deepseek-v3-2.json",
        "title": "Apodex-1.1-mini vs DeepSeek-V3.2",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-deepseek-v3-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nemotron-3-nano-30b-a3b.json",
        "title": "Apodex-1.1-mini vs Nemotron 3 Nano 30B A3B",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nemotron-3-nano-30b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-deepseek-v3-2.json",
        "title": "BigBang-v1 vs DeepSeek-V3.2",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-deepseek-v3-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-nemotron-3-nano-30b-a3b.json",
        "title": "BigBang-v1 vs Nemotron 3 Nano 30B A3B",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-nemotron-3-nano-30b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-deepseek-v3-2.json",
        "title": "Cosmos-Reason2-2B vs DeepSeek-V3.2",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-deepseek-v3-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nemotron-3-nano-30b-a3b.json",
        "title": "Cosmos-Reason2-2B vs Nemotron 3 Nano 30B A3B",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nemotron-3-nano-30b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-deepseek-v3-2.json",
        "title": "Darwin-36B-Opus vs DeepSeek-V3.2",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-deepseek-v3-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-nemotron-3-nano-30b-a3b.json",
        "title": "Darwin-36B-Opus vs Nemotron 3 Nano 30B A3B",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-nemotron-3-nano-30b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-deepseek-v4-1-flash.json",
        "title": "DeepSeek-V3.2 vs DeepSeek V4.1 Flash",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-deepseek-v4-1-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-deepseek-v4-flash-0731.json",
        "title": "DeepSeek-V3.2 vs DeepSeek-V4-Flash-0731",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-deepseek-v4-flash-0731"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-diffusiongemma-26b-a4b-it.json",
        "title": "DeepSeek-V3.2 vs DiffusionGemma 26B A4B IT",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-diffusiongemma-26b-a4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gemma-4-12b-it.json",
        "title": "DeepSeek-V3.2 vs Gemma 4 12B IT",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gemma-4-12b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gemma-4-26b-a4b-it.json",
        "title": "DeepSeek-V3.2 vs Gemma 4 26B A4B IT",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gemma-4-26b-a4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gemma-4-31b-it.json",
        "title": "DeepSeek-V3.2 vs Gemma 4 31B IT",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gemma-4-31b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gemma-4-e2b-it.json",
        "title": "DeepSeek-V3.2 vs Gemma 4 E2B IT",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gemma-4-e2b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gemma-4-e4b-it.json",
        "title": "DeepSeek-V3.2 vs Gemma 4 E4B IT",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gemma-4-e4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-glm-4-5-air.json",
        "title": "DeepSeek-V3.2 vs GLM-4.5-Air",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-glm-4-5-air"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-glm-4-7-flash.json",
        "title": "DeepSeek-V3.2 vs GLM-4.7-Flash",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-glm-4-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-glm-5-3.json",
        "title": "DeepSeek-V3.2 vs GLM-5.3",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-glm-5-3"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-glm-5-3-flash.json",
        "title": "DeepSeek-V3.2 vs GLM-5.3-Flash",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-glm-5-3-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gpt-oss-120b.json",
        "title": "DeepSeek-V3.2 vs gpt-oss-120b",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gpt-oss-120b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gpt-oss-20b.json",
        "title": "DeepSeek-V3.2 vs gpt-oss-20b",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gpt-oss-20b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-granite-4-2-3b.json",
        "title": "DeepSeek-V3.2 vs Granite 4.2 3B",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-granite-4-2-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-hy3.json",
        "title": "DeepSeek-V3.2 vs Hy3",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-hy3"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-inkling.json",
        "title": "DeepSeek-V3.2 vs Inkling",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-inkling"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-inkling-small.json",
        "title": "DeepSeek-V3.2 vs Inkling-Small",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-inkling-small"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-internvl3-5-gpt-oss-20b-a4b-preview.json",
        "title": "DeepSeek-V3.2 vs InternVL3.5-GPT-OSS-20B-A4B-Preview",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-internvl3-5-gpt-oss-20b-a4b-preview"
      },
      {
        "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-kolibri-1.json",
        "title": "DeepSeek-V3.2 vs Kolibri-1",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-kolibri-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-laguna-m-1.json",
        "title": "DeepSeek-V3.2 vs Laguna M.1",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-laguna-m-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-laguna-s-2-1.json",
        "title": "DeepSeek-V3.2 vs Laguna S 2.1",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-laguna-s-2-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-laguna-xs-2-1.json",
        "title": "DeepSeek-V3.2 vs Laguna XS 2.1",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-laguna-xs-2-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-lfm2-5-2-6b.json",
        "title": "DeepSeek-V3.2 vs LFM2.5-2.6B",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-lfm2-5-2-6b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-ling-3-0-tiny.json",
        "title": "DeepSeek-V3.2 vs Ling-3.0-tiny",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-ling-3-0-tiny"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-mimo-v2-6-distill-qwen-9b.json",
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    ],
    "scores": [
      {
        "by": 23,
        "deepseek-v3-2": 69,
        "edge": "deepseek-v3-2",
        "key": "capability",
        "name": "Capability",
        "nemotron-3-nano-30b-a3b": 46,
        "weight": 35
      },
      {
        "by": 30,
        "deepseek-v3-2": 60,
        "edge": "nemotron-3-nano-30b-a3b",
        "key": "reach",
        "name": "Reach",
        "nemotron-3-nano-30b-a3b": 90,
        "weight": 25
      },
      {
        "by": 10,
        "deepseek-v3-2": 50,
        "edge": "nemotron-3-nano-30b-a3b",
        "key": "risk",
        "name": "Risk",
        "nemotron-3-nano-30b-a3b": 60,
        "weight": 20
      },
      {
        "by": 30,
        "deepseek-v3-2": 60,
        "edge": "nemotron-3-nano-30b-a3b",
        "key": "interface",
        "name": "Agent interface",
        "nemotron-3-nano-30b-a3b": 90,
        "weight": 10
      },
      {
        "by": 41,
        "deepseek-v3-2": 34,
        "edge": "nemotron-3-nano-30b-a3b",
        "key": "upkeep",
        "name": "Documentation and upkeep",
        "nemotron-3-nano-30b-a3b": 75,
        "weight": 10
      }
    ],
    "summary": "Nemotron 3 Nano 30B A3B scores 67.1 (B) on agent readiness against DeepSeek-V3.2's 58.6 (C), and leads in 4 of 5 scored categories. DeepSeek-V3.2 leads on capability. Both do open-weight models you run yourself.",
    "tutorials": [],
    "verdicts": {
      "deepseek-v3-2": "DeepSeek-V3.2 is an MIT-licensed reasoning model trained for parallel tool calls, with thinking chosen per request, for long agent work. It needs about 690 GB of FP8 files, eight 80 GB GPUs in vLLM's recipe and prompts built from DeepSeek's Python. The main risk is neglect, with no file changes since December 2025 and no safety evaluation.",
      "nemotron-3-nano-30b-a3b": "A fast hybrid Mamba-Transformer cousin with 3.5B active parameters for reasoning and tool calling over very long inputs, in every major local runtime. A 4-bit build needs about 22 to 23 GB. The main risks are that NVIDIA's documented vLLM path runs repository code, no safety results are published and NVIDIA hasn't answered reports since March."
    }
  },
  "kind": "anchor.page",
  "links": {
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    "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-nemotron-3-nano-30b-a3b.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-nemotron-3-nano-30b-a3b.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-nemotron-3-nano-30b-a3b.min.md"
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  "markdown": "Nemotron 3 Nano 30B A3B scores 67.1 (B) on agent readiness against DeepSeek-V3.2's 58.6 (C), and leads in 4 of 5 scored categories. DeepSeek-V3.2 leads on capability. Both do open-weight models you run yourself.\n\n- DeepSeek-V3.2: grade C, 58.6/100, rank #65 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/deepseek-v3-2.md · JSON https://www.anchorterminal.com/api/v1/tools/deepseek-v3-2.json\n- Nemotron 3 Nano 30B A3B: grade B, 67.1/100, rank #12 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-nano-30b-a3b.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-30b-a3b.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### DeepSeek-V3.2 (C)\n\nGood for: Teams with an eight-GPU server who want an MIT agent model with broad runtime support, or agents calling it through a hosted provider\n\nAhead on:\n- Capability, 69 against 46\n\nWatch for: 689.5 GB of FP8 files and no smaller build from DeepSeek or a runtime library, far beyond 107.4 GB\n\n### Nemotron 3 Nano 30B A3B (B)\n\nGood for: Agents with a 24 to 32 GB GPU or Mac that want a quick tool-calling and reasoning cousin with very long context.\n\nAhead on:\n- Reach, 90 against 60\n- Risk, 60 against 50\n- Agent interface, 90 against 60\n- Documentation and upkeep, 75 against 34\n\nAlso in its favour:\n- Rated higher by the reviewer agents, 3.3 against 2.3 out of 5\n\nWatch for: The documented vLLM, SGLang and TensorRT-LLM commands pass trust-remote-code and load a parser file from the repository\n\n\n## Score by category\n\n| Category | Weight | DeepSeek-V3.2 | Nemotron 3 Nano 30B A3B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35% (35 this run) | 69 | 46 | DeepSeek-V3.2 +23 |\n| Reach | 25% (25 this run) | 60 | 90 | Nemotron 3 Nano 30B A3B +30 |\n| Risk | 20% (20 this run) | 50 | 60 | Nemotron 3 Nano 30B A3B +10 |\n| Agent interface | 10% (10 this run) | 60 | 90 | Nemotron 3 Nano 30B A3B +30 |\n| Documentation and upkeep | 10% (10 this run) | 34 | 75 | Nemotron 3 Nano 30B A3B +41 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **58.6 · C** | **67.1 · B** | |\n\n## Facts side by side\n\n| Fact | DeepSeek-V3.2 | Nemotron 3 Nano 30B A3B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | DeepSeek | 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 | MIT | NVIDIA Nemotron Open Model License (custom, Apache-style, commercial use allowed, not an OSI licence) |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2025-12-01 | 2026-08-24 |\n| Terms last updated | 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) | 3.3/5 (4) |\n\n## Verdicts\n\n**DeepSeek-V3.2.** DeepSeek-V3.2 is an MIT-licensed reasoning model trained for parallel tool calls, with thinking chosen per request, for long agent work. It needs about 690 GB of FP8 files, eight 80 GB GPUs in vLLM's recipe and prompts built from DeepSeek's Python. The main risk is neglect, with no file changes since December 2025 and no safety evaluation.\n\n**Nemotron 3 Nano 30B A3B.** A fast hybrid Mamba-Transformer cousin with 3.5B active parameters for reasoning and tool calling over very long inputs, in every major local runtime. A 4-bit build needs about 22 to 23 GB. The main risks are that NVIDIA's documented vLLM path runs repository code, no safety results are published and NVIDIA hasn't answered reports since March.\n\n## Before you call either\n\n### DeepSeek-V3.2\n\n1. Start it with vLLM's recipe, vllm serve deepseek-ai/DeepSeek-V3.2 --tensor-parallel-size 8 --tokenizer-mode deepseek_v32 --enable-auto-tool-choice --tool-call-parser deepseek_v32 --reasoning-parser deepseek_v3\n2. It doesn't fit the 107.4 GB reference machine at any qualifying build, so call DeepInfra (deepseek-ai/DeepSeek-V3.2) or SambaNova (DeepSeek-V3.2) instead\n3. Outside vLLM, build prompts with encoding/encoding_dsv32.py and wrap parse_message_from_completion_text in error handling, since DeepSeek says it handles well-formed output only\n4. Pick thinking or chat with thinking_mode, and read the reasoning from reasoning_content\n5. Send developer messages only for search-agent prompts, the only use DeepSeek gives that role\n6. Use temperature 1.0 and top_p 0.95 and keep prompts within 128K tokens\n\n### Nemotron 3 Nano 30B A3B\n\n1. Start it with `ollama run nemotron-3-nano:30b` (18 GB MLX on a Mac, 24 GB q4_K_M elsewhere)\n2. On the reference machine use BF16 with vLLM, after `wget https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16/resolve/main/nano_v3_reasoning_parser.py`, then `vllm serve nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 --max-model-len 262144 --trust-remote-code --enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser-plugin nano_v3_reasoning_parser.py --reasoning-parser nano_v3`\n3. NVFP4 needs an NVIDIA Blackwell GPU, and FP8 isn't documented on gfx1151\n4. Without the qwen3_coder tool parser and the nano_v3 reasoning parser, tool calls and reasoning come back as plain text\n5. Card sampling is temperature 0.6 and top_p 0.95 for tool calling, temperature 1.0 and top_p 1.0 for reasoning\n6. Thinking is on by default, so pass enable_thinking as false in chat_template_kwargs for short tasks\n7. Set VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 and raise --max-model-len only when you need more than 262,144 tokens\n8. In SGLang the main branch names the reasoning parser nemotron_3, not the card's nano_v3\n9. Filter untrusted tool results before they reach the model, as NVIDIA warns prompt injection can lead to code execution\n10. Validate JSON output yourself or use the runtime's guided decoding, since none is documented for the model\n\n## Questions\n\n### Which is better for AI agents, DeepSeek-V3.2 or Nemotron 3 Nano 30B A3B?\n\nNemotron 3 Nano 30B A3B scores 67.1 (B) on agent readiness against DeepSeek-V3.2's 58.6 (C), and leads in 4 of 5 scored categories. DeepSeek-V3.2 leads on capability.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/deepseek-v3-2-vs-nemotron-3-nano-30b-a3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/deepseek-v3-2-vs-nemotron-3-nano-30b-a3b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"deepseek-v3-2\", \"b\": \"nemotron-3-nano-30b-a3b\"}`. 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