{
  "data": {
    "a": {
      "slug": "longcat-2-5",
      "name": "LongCat-2.5",
      "vendor": "Meituan (LongCat)",
      "vendorUrl": "https://longcat.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "LongCat-2.5 is Meituan's open-weight flagship, a multimodal mixture-of-experts model of 1.6T parameters with about 48B active per token, built for long agentic coding work. MIT licence. The weights are 3,562 GB, and the card documents deployment on NPU hardware only.",
      "url": "https://www.anchorterminal.com/tools/longcat-2-5",
      "markdownUrl": "https://www.anchorterminal.com/tools/longcat-2-5.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/longcat-2-5.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/longcat-2-5.json",
      "dns": "longcat-2-5.grade.anchorterminal.com",
      "repo": "https://huggingface.co/meituan-longcat/LongCat-2.5",
      "license": "MIT",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "meituan-longcat/LongCat-2.5"
        },
        {
          "registry": "huggingface",
          "name": "meituan-longcat/LongCat-2.5-W4A4"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Neither repository is gated (checked 2026-11-10).",
      "pricing": "free",
      "pricingNotes": "Free to download and use under the MIT licence. Running it yourself is a data-centre job: 3,562 GB in BF16, or 1,336 GB for Meituan's 4-bit build, which the card says is for INT4 computation on NPU accelerators (checked 2026-11-10).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-11-10).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-11-10"
      },
      "docsUrl": "https://huggingface.co/meituan-longcat/LongCat-2.5",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "multimodal",
        "reasoning",
        "tool-calling",
        "agentic-coding",
        "mit",
        "safetensors",
        "int4",
        "npu",
        "self-hosted"
      ],
      "lastRelease": "2026-11-10",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 50.5,
        "grade": "D",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1518,
        "categoryRank": 107,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 75,
          "reach": 35,
          "risk": 60,
          "upkeep": 46
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-10"
        },
        "negative": 0,
        "verdict": "Meituan's flagship is MIT-licensed and documented for tool calling and switchable reasoning, with strong numbers of its own on agentic coding. Almost nobody can run it: 3,562 GB of weights, a 1,336 GB 4-bit build for NPU accelerators, and no released runtime with its architecture. A model to watch until a runtime or a provider picks it up.",
        "bestFor": "Operators with NPU clusters, and anyone tracking what the open flagships can do",
        "strengths": [
          "MIT licence, ungated",
          "Tool calling documented with a worked example",
          "Reasoning can be switched per request and comes back as reasoning_content",
          "Meituan publishes its own 4-bit build",
          "Updated on the day we checked"
        ],
        "weaknesses": [
          "3,562 GB in BF16 and 1,336 GB at 4 bits",
          "No released runtime has its architecture",
          "The only documented deployment is on NPU hardware through Meituan's fork",
          "config.json names model code that isn't in the repository",
          "No stated context length, languages, safety results or data sources",
          "No inference provider serves it"
        ],
        "agentNotes": [
          "You can't run this on a workstation or a single GPU server. Plan for a provider, and none serves it yet",
          "If an operator serves it, call it as an OpenAI-compatible chat API with tools in the function format",
          "Pass `chat_template_kwargs` with `enable_thinking` true or false",
          "With thinking on, keep earlier reasoning in the history (`save_history_reasoning_content` true), as the card recommends",
          "The card's results use temperature 1.0 and top_p 0.95",
          "Its benchmark table is Meituan's own. The unmarked scores are in-house"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 0,
        "avgRating": 0,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "D",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 50.5
          }
        ],
        "editorialScores": {
          "interface": 75,
          "reach": 35,
          "risk": 60,
          "upkeep": 46
        },
        "provenanceScore": 49,
        "checked": {
          "categories": {
            "interface": "2026-11-10",
            "reach": "2026-11-10",
            "risk": "2026-11-10",
            "upkeep": "2026-11-10"
          },
          "last": "2026-11-10",
          "oldest": "2026-11-10",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "# No released runtime loads it. Meituan's NPU instructions are in its SGLang fork:\n# github.com/meituan-longcat/SGLang-FluentLLM, branch longcat-2.5-npu, npu_test/ReadMe.md",
        "http": "# The card's example calls an OpenAI-compatible chat completions API with tools, and passes\n# extra_body={\"chat_template_kwargs\": {\"enable_thinking\": true, \"save_history_reasoning_content\": true}}"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/longcat-2-5"
      },
      "weights": {
        "trainedBy": "Meituan (LongCat team)",
        "builtOn": "LongCat-2.0 and LongCat-Next, per the card",
        "params": "1.6T",
        "active": "48B",
        "context": 524288,
        "goodFor": "Long agentic coding and research runs with images, for an operator with NPU clusters. Nobody else can run it yet.",
        "runtimes": [],
        "builds": [
          {
            "name": "LongCat-2.5-W4A4",
            "url": "https://huggingface.co/meituan-longcat/LongCat-2.5-W4A4",
            "who": "publisher",
            "precision": "int4",
            "sizeGB": 1336,
            "memoryGB": 2048,
            "format": "safetensors",
            "note": "Meituan's 4-bit build of the experts' weights and activations, for INT4 computation on NPU accelerators"
          },
          {
            "name": "LongCat-2.5 (BF16 safetensors)",
            "url": "https://huggingface.co/meituan-longcat/LongCat-2.5",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 3562,
            "memoryGB": 8192,
            "format": "safetensors",
            "note": "210 files"
          }
        ],
        "run": "See github.com/meituan-longcat/SGLang-FluentLLM, branch longcat-2.5-npu",
        "runBuild": "LongCat-2.5-W4A4",
        "checked": "2026-11-10",
        "minMemoryGB": 2048
      },
      "family": {
        "lab": "Meituan (LongCat)",
        "family": "LongCat-2",
        "url": "https://www.anchorterminal.com/open-weights/#meituan-longcat",
        "others": [
          {
            "name": "LongCat-2.5-W4A4",
            "url": "https://huggingface.co/meituan-longcat/LongCat-2.5-W4A4"
          },
          {
            "name": "LongCat-2.0",
            "url": "https://huggingface.co/meituan-longcat/LongCat-2.0"
          }
        ]
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Meituan, named in the card's licence section. The legal entity wasn't looked up",
        "domain": "longcat.ai",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "https://huggingface.co/meituan-longcat/LongCat-2.5/blob/main/LICENSE",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/meituan-longcat/LongCat-2.5/commits/main",
        "securityTxt": "unknown",
        "checked": "2026-11-10",
        "notes": [
          "longcat.ai has no robots.txt (the request returns a not-found message) and its pages are JavaScript applications that gave no text on 10 November 2026, so the site's own statements weren't read.",
          "The domain's registration date wasn't looked up.",
          "Nothing is hosted under this listing, so the MIT licence stands in for terms of service and no privacy policy is scored.",
          "The changelog is the commit history, 50 commits or more since 4 November 2026."
        ],
        "score": 49
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/longcat-2-5.json"
    },
    "answer": "Nemotron 3 Nano 4B scores 56.8 (C) on agent readiness against LongCat-2.5's 50.5 (D), and leads in 3 of 4 scored categories. LongCat-2.5 leads on agent interface.",
    "b": {
      "slug": "nemotron-3-nano-4b",
      "name": "Nemotron 3 Nano 4B",
      "vendor": "NVIDIA",
      "vendorUrl": "https://developer.nvidia.com/nemotron",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Nemotron 3 Nano 4B is NVIDIA's open-weight small hybrid Mamba-2 and Transformer reasoning model for agents on edge devices, 4B parameters compressed from Nemotron Nano 9B v2, published in BF16, FP8 and GGUF under the NVIDIA Nemotron Open Model License.",
      "url": "https://www.anchorterminal.com/tools/nemotron-3-nano-4b",
      "markdownUrl": "https://www.anchorterminal.com/tools/nemotron-3-nano-4b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/nemotron-3-nano-4b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-4b.json",
      "dns": "nemotron-3-nano-4b.grade.anchorterminal.com",
      "repo": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16",
      "license": "NVIDIA Nemotron Open Model License",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16"
        },
        {
          "registry": "huggingface",
          "name": "nvidia/NVIDIA-Nemotron-3-Nano-4B-FP8"
        },
        {
          "registry": "huggingface",
          "name": "nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF"
        },
        {
          "registry": "ollama",
          "name": "nemotron-3-nano:4b"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The BF16, FP8 and GGUF repositories are not gated (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under the NVIDIA Nemotron Open Model License. Running it yourself costs the hardware, 7.96 GB for the BF16 repository, 5.28 GB for NVIDIA's FP8 build or 2.84 GB for NVIDIA's Q4_K_M GGUF (checked 2026-10-21).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-10-21"
      },
      "docsUrl": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "dense",
        "mamba",
        "hybrid",
        "small",
        "edge",
        "reasoning",
        "tool-calling",
        "safetensors",
        "fp8",
        "gguf",
        "self-hosted"
      ],
      "lastRelease": "2026-03-16",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 56.8,
        "grade": "C",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1518,
        "categoryRank": 82,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "capability": 25,
          "interface": 70,
          "reach": 90,
          "risk": 65,
          "upkeep": 55
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-11-04"
        },
        "negative": 0,
        "verdict": "A 3.97-billion-parameter reasoning model with trained tool calls, a thinking switch and 262,144 tokens of context, which runs in about 4 GB from NVIDIA's own GGUF. The main risks are serving commands that run repository code, no published safety results, a tool format left to the template, and files unchanged and questions unanswered since March 2026.",
        "bestFor": "Agents on laptops, edge boards and small GPUs that need a cheap local tool-calling helper in English",
        "strengths": [
          "NVIDIA's own 2.84 GB GGUF and 5.28 GB FP8 builds, plus Ollama and LM Studio entries",
          "Released support in llama.cpp, vLLM, SGLang, Transformers, Ollama and LM Studio",
          "Several tool calls per turn in the template and thinking off per request",
          "262,144 tokens of context",
          "Free commercial licence with no threshold or use policy, and no gate"
        ],
        "weaknesses": [
          "The card's Transformers and vLLM commands pass trust_remote_code and load a parser plugin from the repository",
          "No safety evaluation results published",
          "Tool calling and structured output aren't documented by NVIDIA",
          "No NVIDIA reply to any question, and no change to the files since 20 March 2026",
          "Empty LICENSE files and a card snippet that names a repository that isn't public",
          "No hosted endpoint from NVIDIA or any provider"
        ],
        "agentNotes": [
          "Quickest start is `ollama run nemotron-3-nano:4b`, a 2.8 GB build",
          "With llama.cpp use `llama-server -hf nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF --jinja`, NVIDIA's own Q4_K_M file",
          "With vLLM, download nano_v3_reasoning_parser.py from the repository first and serve with `--enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser-plugin nano_v3_reasoning_parser.py --reasoning-parser nano_v3`",
          "The card's Transformers snippet names nvidia/NVIDIA-Nemotron-3-Nano-4B, use nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16 instead",
          "Thinking is on by default. Pass `enable_thinking=False` to the chat template for short tasks",
          "Use temperature 1.0 and top_p 0.95 for reasoning and temperature 0.6 with top_p 0.95 for tool calling, as the card says",
          "It's tuned for English only, and validate JSON yourself, since structured output isn't documented"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 3.3,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "high",
            "grade": "C",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 56.8
          }
        ],
        "editorialScores": {
          "capability": 25,
          "interface": 70,
          "reach": 90,
          "risk": 65,
          "upkeep": 55
        },
        "provenanceScore": 76,
        "checked": {
          "categories": {
            "capability": "2026-11-04",
            "interface": "2026-11-04",
            "reach": "2026-11-04",
            "risk": "2026-11-04",
            "upkeep": "2026-11-04"
          },
          "last": "2026-11-04",
          "oldest": "2026-11-04",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "ollama run nemotron-3-nano:4b\n# or, with llama.cpp and NVIDIA's GGUF:\nllama-server -hf nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF --jinja -c 131072\n# or, with the card's vLLM command (abridged):\nwget https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16/resolve/main/nano_v3_reasoning_parser.py\nvllm serve nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16 --max-model-len 262144 --trust-remote-code \\\n  --mamba_ssm_cache_dtype float32 --enable-auto-tool-choice --tool-call-parser qwen3_coder \\\n  --reasoning-parser-plugin nano_v3_reasoning_parser.py --reasoning-parser nano_v3",
        "http": "curl http://localhost:11434/api/chat \\\n  -d '{\"model\": \"nemotron-3-nano:4b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/nemotron-3-nano-4b"
      },
      "sameCompany": [
        "nemotron-3-5-super-vl",
        "nvidia-nemo-retriever",
        "nemo-guardrails",
        "nemotron-3-5-asr-streaming-0-6b",
        "nemotron-3-5-transcribe-30b-a4b",
        "parakeet-tdt-0-6b-v3",
        "parakeet-unified-en-0-6b",
        "nemotron-3-5-parse",
        "cosmos-reason2-2b",
        "nemotron-3-5-bolt-e4b-4gb",
        "nemotron-3-nano-30b-a3b",
        "nemotron-3-nano-omni-30b-a3b-reasoning",
        "nemotron-3-super-120b-a12b",
        "nemotron-3-ultra-550b-a55b",
        "nemotron-3-5-lightning-30b-a3b"
      ],
      "weights": {
        "trainedBy": "NVIDIA",
        "builtOn": "nvidia/NVIDIA-Nemotron-Nano-9B-v2 (compressed with Nemotron Elastic; base under the NVIDIA Open Model License Agreement)",
        "params": "4B",
        "context": 262144,
        "goodFor": "Small English reasoning and tool-calling model for agents on edge devices and laptops, with a thinking switch and 262,144 tokens of context.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "LM Studio",
          "Transformers"
        ],
        "builds": [
          {
            "name": "NVIDIA-Nemotron-3-Nano-4B-FP8",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-FP8",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 5.28,
            "memoryGB": 8,
            "format": "safetensors"
          },
          {
            "name": "NVIDIA-Nemotron-3-Nano-4B-GGUF (Q4_K_M)",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 2.84,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "One file, NVIDIA-Nemotron3-Nano-4B-Q4_K_M.gguf"
          },
          {
            "name": "ollama nemotron-3-nano:4b",
            "url": "https://ollama.com/library/nemotron-3-nano:4b",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 2.8,
            "memoryGB": 8,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "The 4b tag is 2.8 GB (q4_K_M) or 2.9 GB (MLX nvfp4) by platform. Ollama also has q8_0 at 4.2 GB and bf16 at 8.0 GB. Sizes as Ollama's tags page shows them"
          },
          {
            "name": "lmstudio-community NVIDIA-Nemotron-3-Nano-4B-GGUF (Q4_K_M)",
            "url": "https://huggingface.co/lmstudio-community/NVIDIA-Nemotron-3-Nano-4B-GGUF",
            "who": "lm-studio",
            "precision": "q4_k_m",
            "sizeGB": 2.84,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "LM Studio"
            ],
            "note": "The build LM Studio's catalogue page links. Also Q6_K (3.93 GB) and Q8_0 (4.23 GB)"
          },
          {
            "name": "NVIDIA-Nemotron-3-Nano-4B-BF16",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 7.96,
            "memoryGB": 12,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)"
            ]
          }
        ],
        "run": "llama-server -hf nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF --jinja -c 131072",
        "runBuild": "NVIDIA-Nemotron-3-Nano-4B-GGUF (Q4_K_M)",
        "checked": "2026-10-21",
        "minMemoryGB": 8
      },
      "family": {
        "lab": "NVIDIA (Nemotron)",
        "family": "Nemotron 3",
        "url": "https://www.anchorterminal.com/open-weights/#nvidia",
        "others": [
          {
            "name": "Nemotron 3 Nano 30B-A3B",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16",
            "params": "30B total, 3B active (31.6B per the safetensors index)",
            "listing": "https://www.anchorterminal.com/tools/nemotron-3-nano-30b-a3b"
          },
          {
            "name": "Nemotron 3 Nano 30B-A3B Base",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-Base-BF16",
            "params": "30B total, 3B active"
          },
          {
            "name": "Nemotron 3 Super 120B-A12B",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16",
            "params": "120B total, 12B active",
            "listing": "https://www.anchorterminal.com/tools/nemotron-3-super-120b-a12b"
          },
          {
            "name": "Nemotron 3 Super 120B-A12B Base",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-Base-BF16",
            "params": "120B total, 12B active"
          },
          {
            "name": "Nemotron 3 Nano Omni 30B-A3B Reasoning",
            "url": "https://huggingface.co/nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16",
            "params": "30B total, 3B active",
            "listing": "https://www.anchorterminal.com/tools/nemotron-3-nano-omni-30b-a3b-reasoning"
          },
          {
            "name": "Nemotron 3 Ultra 550B-A55B",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16",
            "params": "550B total, 55B active",
            "listing": "https://www.anchorterminal.com/tools/nemotron-3-ultra-550b-a55b"
          },
          {
            "name": "Nemotron 3 Ultra 550B-A55B Base",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-Base-BF16",
            "params": "550B total, 55B active"
          },
          {
            "name": "Nemotron 3 Ultra 550B-A55B GenRM",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-GenRM",
            "params": "550B total, 55B active"
          },
          {
            "name": "Nemotron 3.5 Lightning 30B-A3B",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16",
            "params": "30B total, 3B active",
            "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-lightning-30b-a3b"
          },
          {
            "name": "Nemotron 3.5 Lightning 30B-A3B Base",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-Base-BF16",
            "params": "30B total, 3B active"
          },
          {
            "name": "Nemotron 3.5 Super VL 120B-A12B",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16",
            "params": "120B total, 12B active",
            "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-super-vl"
          },
          {
            "name": "Nemotron 3.5 Super VL 120B-A12B Base",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-Base-BF16",
            "params": "120B total, 12B active"
          },
          {
            "name": "Nemotron 3.5 Bolt 74B-A9B",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Bolt-74B-A9B-BF16",
            "params": "74B total, 9B active"
          },
          {
            "name": "Nemotron 3.5 Bolt E4B 4GB",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-4GB",
            "params": "8B total, 4B effective",
            "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-bolt-e4b-4gb"
          },
          {
            "name": "Nemotron Labs 3 Elastic 30B-A3B",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-Labs-3-Elastic-30B-A3B-BF16",
            "params": "30B total, 3B active"
          },
          {
            "name": "Nemotron Labs 3 Puzzle 75B-A9B",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-Labs-3-Puzzle-75B-A9B-BF16",
            "params": "75B total, 9B active"
          },
          {
            "name": "Nemotron 3 Labs Ultra Math SFT",
            "url": "https://huggingface.co/nvidia/Nemotron-3-Labs-Ultra-Math-SFT",
            "params": "550B total, 55B active"
          },
          {
            "name": "Nemotron 3 Labs Ultra Math RL",
            "url": "https://huggingface.co/nvidia/Nemotron-3-Labs-Ultra-Math-RL",
            "params": "550B total, 55B active"
          },
          {
            "name": "Nemotron Labs 3 Competitive Coding 550B-A55B",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-Labs-3-Competitive-Coding-550B-A55B-NVFP4",
            "params": "550B total, 55B active"
          },
          {
            "name": "nemotron-3.5-asr-streaming-0.6b",
            "url": "https://huggingface.co/nvidia/nemotron-3.5-asr-streaming-0.6b",
            "params": "0.6B (638M per the safetensors count)",
            "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-asr-streaming-0-6b"
          },
          {
            "name": "Nemotron 3.5 Parse",
            "url": "https://huggingface.co/nvidia/Nemotron-3.5-Parse",
            "params": "0.631B (630,800,496 trainable parameters per the card)",
            "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-parse"
          },
          {
            "name": "Nemotron 3.5 Transcribe 30B-A4B",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Transcribe-30B-A4B",
            "params": "30B total, about 4.3B active (33,697,781,029 by the Hub's safetensors count)",
            "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-transcribe-30b-a4b"
          }
        ],
        "olderCount": 47,
        "unrestrictedCopies": 5
      },
      "area": "models",
      "provenance": {
        "legalEntity": "NVIDIA Corporation",
        "domain": "nvidia.com",
        "domainRegistered": "1993-04-20",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16/commits/main",
        "securityTxt": "unknown",
        "checked": "2026-10-21",
        "notes": [
          "The card names NVIDIA Corporation as model developer. The Hugging Face organisation nvidia is the one developer.nvidia.com links to. Domain date as recorded for the other Nemotron listings.",
          "Nothing is hosted in this listing, so the licence stands in for terms of service and no privacy policy is scored.",
          "The changelog is the repository's commit history. The last change was the inline chat template in tokenizer_config.json on 20 March 2026, and most commits read Update README.md.",
          "www.nvidia.com/.well-known/security.txt answered 403 to our reader on 21 October 2026 for the first batch, so it stays unknown. The card links NVIDIA's vulnerability submission page."
        ],
        "score": 76
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/nemotron-3-nano-4b.json",
      "live": {
        "slug": "nemotron-3-nano-4b",
        "securityTxt": {
          "url": "https://nvidia.com/.well-known/security.txt",
          "state": "unknown",
          "checkedAt": "2026-11-10T16:08:00.271598775Z"
        },
        "domain": {
          "domain": "nvidia.com",
          "registered": "1993-04-20",
          "source": "https://rdap.verisign.com/com/v1/domain/nvidia.com",
          "checkedAt": "2026-11-08T15:03:11.895084282Z"
        },
        "pages": [
          {
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16/commits/main",
            "kind": "changelog",
            "status": 200,
            "checkedAt": "2026-11-10T16:23:18.735571066Z",
            "changedAt": "2026-11-10T16:23:18.735571066Z",
            "fingerprint": "3b3d07e6bba4"
          }
        ],
        "updatedAt": "2026-11-10T16:23:18.735571066Z"
      }
    },
    "edges": [
      {
        "by": 55,
        "category": "reach",
        "leads": "nemotron-3-nano-4b",
        "name": "Reach",
        "reasons": {
          "a": "Memory 0, the smallest build is Meituan's W4A4 repository, 1,336 GB, and 1,336 GB × 1.2 = 1,603 GB, far above 256 GB. The BF16 files are 3,562 GB. Runtimes 0, none of the six runs it in a released version: vLLM 0.33.0, SGLang 0.5.23, Transformers 5.22.2 and llama.cpp b12242 have no LongcatCausalLM or pro_v code, and the model code config.json names isn't in the repository. Builds 10, one quantised build from Meituan, the W4A4 repository (4-bit experts, 1,336 GB). Terms 15, MIT in the metadata, the LICENSE file and the card, with nothing added. Download 10, the Hub API reports gated false for both repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it. 0 + 0 + 10 + 15 + 10 + 0 = 35.",
          "b": "Memory earns the full 30, since NVIDIA's own 2.84 GB GGUF needs under 4 GB with the fifth added. Runtimes earn the full 25 from released llama.cpp, vLLM, SGLang, Transformers, Ollama and LM Studio. Builds earn 15 for NVIDIA's FP8 and GGUF. Terms earn 10, since the Nemotron licence is custom even though it's free for commercial use with no threshold or use policy. Download earns 10, with no gate or account. Hosted earns 0, as no Hugging Face provider serves it and no NVIDIA hosting was found."
        },
        "scores": {
          "a": 35,
          "b": 90
        }
      },
      {
        "by": 9,
        "category": "upkeep",
        "leads": "nemotron-3-nano-4b",
        "name": "Documentation and upkeep",
        "reasons": {
          "a": "Model card 11, Intended use, limitations (the usage considerations), the architecture and the parameter counts are on the card. Training data 5, A general description of the training stages and where the reinforcement learning tasks came from, with no sources, sizes or cut-off. Last change 20, the last commit, on 10 November 2026, updated the card, on the day of this check. Responsiveness 0, Neither of the 2 discussions that aren't pull requests (9 and 10 November 2026) has a reply from a meituan-longcat member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, LongCat-2.0 stays in its own repositories and 2.5 came out in new ones, with no dated note on the older ones that we read. File health 5, the template is in both places and agrees, but config.json's auto_map names configuration_longcat_oe and modeling_longcat_oe, which aren't in the repository, and the tokenizer config's 131,072-token length is a quarter of the config's 524,288 positions. 11 + 5 + 20 + 0 + 5 + 5 = 46.",
          "b": "The model card earns 20, all seven items with limitations in the linked subcard. Training data earns 20, datasets listed with sources and dates, crawled sets with token counts, a cut-off and published NVIDIA datasets, though most synthetic sizes are undisclosed. Last change earns 5, as the files last changed on 20 March 2026, and TensorRT-LLM's Nemotron-H commits since 14 October touch MoE, NVFP4 and Puzzle paths this dense model doesn't use, in release candidates or no release. Responsiveness earns 0, with no NVIDIA reply to any of six questions, re-counted on 4 November. Version policy earns 5 as a first release. File health earns 5, since the LICENSE file is empty, reports and fixes wait without a reply, and the card's Transformers snippet names a repository that isn't public."
        },
        "scores": {
          "a": 46,
          "b": 55
        }
      },
      {
        "by": 5,
        "category": "risk",
        "leads": "nemotron-3-nano-4b",
        "name": "Risk",
        "reasons": {
          "a": "File safety 25, safetensors only, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, the files sit in the meituan-longcat organisation, the name of Meituan's GitHub organisation that the card links for deployment, and the card's licence names Meituan (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions under Usage Considerations: not evaluated for every use, assess accuracy, safety and fairness before sensitive use, and follow the law. Licence risk 15, MIT with no use policy, no threshold and no right to change the terms. Security programme 0, no way to report a flaw on the card beyond a general contact, and Meituan's own security pages weren't looked up, so this scores as absent and is an open question. 25 + 15 + 0 + 5 + 15 + 0 = 60.",
          "b": "File safety earns 15, safetensors and GGUF only but with 10 off because the documented commands pass trust_remote_code and load a parser plugin from the repository. Provenance earns 15, files in NVIDIA's linked organisation with a hash for every file. Safety evaluation earns 0, no results. Agent guidance earns 10, subcards that name prompt injection and the safeguards to add, without an instruction hierarchy. Licence risk keeps all 15, since the switch to the Nemotron licence happened on release day. The security programme earns 10 for the reporting route on the card and NVIDIA's public bulletins."
        },
        "scores": {
          "a": 60,
          "b": 65
        }
      },
      {
        "by": 5,
        "category": "interface",
        "leads": "longcat-2-5",
        "name": "Agent interface",
        "reasons": {
          "a": "Chat template 20, chat_template.jinja in the repository (11,823 characters), with a copy in tokenizer_config.json. Tool-calling format 25, the card documents how tools are declared (the OpenAI function format) and how calls come back (tool_calls with an id, a name and JSON arguments, answered by a tool message), in a worked example against an OpenAI-compatible server. Trained tool calling 10, the card describes reinforcement learning on long agentic tasks and shows one tool call in a turn. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, enable_thinking on or off per request through chat_template_kwargs, with the reasoning returned as reasoning_content, both shown on the card. Context 10, the card states no context length, but config.json gives 524,288 positions and the card's own evaluations ran at 512K and 1M sequence lengths after what it calls long-context training, so 128K or more is taken as trained. 20 + 25 + 10 + 0 + 10 + 10 = 75.",
          "b": "The chat template earns 20, a Jinja file in the repository. The tool-calling format earns 10 of 25, as only the template shows the format and the parser name appears only in a serving command. Trained tool calling earns 20, with tool-use benchmarks and data and several calls per turn in the template. Structured output earns 0, not claimed or documented. Reasoning control earns 10, thinking off per request with a parser that returns the reasoning apart. Context earns 10 for 262,144 tokens."
        },
        "scores": {
          "a": 75,
          "b": 70
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Meituan (LongCat)",
        "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",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-11-10",
        "b": "2026-03-16",
        "name": "Last release"
      },
      {
        "a": "not read yet",
        "b": "no document linked",
        "name": "Terms last updated"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Privacy policy last updated"
      },
      {
        "a": "none",
        "b": "3.3/5 (4)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Nemotron 3 Nano 4B scores 56.8 (C) on agent readiness against LongCat-2.5's 50.5 (D), and leads in 3 of 4 scored categories. LongCat-2.5 leads on agent interface.",
        "question": "Which is better for AI agents, LongCat-2.5 or Nemotron 3 Nano 4B?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Agent interface, 75 against 70"
        ],
        "also": null,
        "goodFor": "Operators with NPU clusters, and anyone tracking what the open flagships can do",
        "slug": "longcat-2-5",
        "watchFor": "3,562 GB in BF16 and 1,336 GB at 4 bits"
      },
      {
        "aheadOn": [
          "Reach, 90 against 35",
          "Risk, 65 against 60",
          "Documentation and upkeep, 55 against 46"
        ],
        "also": null,
        "goodFor": "Agents on laptops, edge boards and small GPUs that need a cheap local tool-calling helper in English",
        "slug": "nemotron-3-nano-4b",
        "watchFor": "The card's Transformers and vLLM commands pass trust_remote_code and load a parser plugin from the repository"
      }
    ],
    "history": null,
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
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        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-longcat-2-5.json",
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        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-longcat-2-5"
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      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-nano-4b.json",
        "title": "Agents-A1-4B vs Nemotron 3 Nano 4B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-nano-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-longcat-2-5.json",
        "title": "Apertus v1.5 8B vs LongCat-2.5",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-longcat-2-5"
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      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-nano-4b.json",
        "title": "Apertus v1.5 8B vs Nemotron 3 Nano 4B",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-nano-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-longcat-2-5.json",
        "title": "Apodex-1.1-mini vs LongCat-2.5",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-longcat-2-5"
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      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nemotron-3-nano-4b.json",
        "title": "Apodex-1.1-mini vs Nemotron 3 Nano 4B",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nemotron-3-nano-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-longcat-2-5.json",
        "title": "Arete-27B vs LongCat-2.5",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-longcat-2-5"
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        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-nemotron-3-nano-4b.json",
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        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-nemotron-3-nano-4b"
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        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-longcat-2-5.json",
        "title": "BigBang-v1 vs LongCat-2.5",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-longcat-2-5"
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        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-nemotron-3-nano-4b.json",
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        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-nemotron-3-nano-4b"
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        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-longcat-2-5"
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        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nemotron-3-nano-4b"
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        "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-longcat-2-5.json",
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        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-nemotron-3-nano-4b"
      },
      {
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        "title": "Muse Glimmer 30B vs Nemotron 3 Nano 4B",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-nemotron-3-nano-4b"
      },
      {
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        "title": "Nanbeige4.2-3B vs Nemotron 3 Nano 4B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-nemotron-3-nano-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-nemotron-3-nano-4b.json",
        "title": "Nanbeige4.3-33B vs Nemotron 3 Nano 4B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-nemotron-3-nano-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-nemotron-3-nano-4b.json",
        "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Nemotron 3 Nano 4B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-nemotron-3-nano-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-nemotron-3-nano-4b.json",
        "title": "Nemotron 3.5 Lightning 30B A3B vs Nemotron 3 Nano 4B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-nemotron-3-nano-4b"
      },
      {
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        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-nemotron-3-nano-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
        "title": "Nemotron 3 Nano 4B vs Nemotron 3 Nano Omni 30B A3B Reasoning",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-nemotron-3-super-120b-a12b.json",
        "title": "Nemotron 3 Nano 4B vs Nemotron 3 Super 120B A12B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-nemotron-3-super-120b-a12b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-nemotron-3-ultra-550b-a55b.json",
        "title": "Nemotron 3 Nano 4B vs Nemotron 3 Ultra 550B A55B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-nemotron-3-ultra-550b-a55b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-north-mini-code-1-0.json",
        "title": "Nemotron 3 Nano 4B vs North Mini Code 1.0",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-north-mini-code-1-0"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-occamy-1-0.json",
        "title": "Nemotron 3 Nano 4B vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-occamy-1-0"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-olmo-3-7b-think.json",
        "title": "Nemotron 3 Nano 4B vs Olmo 3 7B Think",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-olmo-3-7b-think"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-ornith-1-5-122b.json",
        "title": "Nemotron 3 Nano 4B vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-ornith-1-5-35b-a3b.json",
        "title": "Nemotron 3 Nano 4B vs Ornith-1.5-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-ornith-1-5-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-ornith-1-5-397b.json",
        "title": "Nemotron 3 Nano 4B vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-ornith-1-5-397b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-ornith-1-5-9b.json",
        "title": "Nemotron 3 Nano 4B vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-ornith-1-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-otel-2-0-llm-31b-it.json",
        "title": "Nemotron 3 Nano 4B vs OTel-2.0-LLM-31B-IT",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-otel-2-0-llm-31b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen-agentworld-35b-a3b.json",
        "title": "Nemotron 3 Nano 4B vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen-agentworld-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-30b-a3b-instruct-2507.json",
        "title": "Nemotron 3 Nano 4B vs Qwen3-30B-A3B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-30b-a3b-instruct-2507"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-4b-instruct-2507.json",
        "title": "Nemotron 3 Nano 4B vs Qwen3-4B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-4b-instruct-2507"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-5-0-8b.json",
        "title": "Nemotron 3 Nano 4B vs Qwen3.5-0.8B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-5-0-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-5-122b-a10b.json",
        "title": "Nemotron 3 Nano 4B vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-5-122b-a10b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-5-2b.json",
        "title": "Nemotron 3 Nano 4B vs Qwen3.5-2B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-5-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-5-397b-a17b.json",
        "title": "Nemotron 3 Nano 4B vs Qwen3.5-397B-A17B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-5-397b-a17b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-5-4b.json",
        "title": "Nemotron 3 Nano 4B vs Qwen3.5-4B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-5-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-5-9b.json",
        "title": "Nemotron 3 Nano 4B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-6-27b.json",
        "title": "Nemotron 3 Nano 4B vs Qwen3.6-27B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-6-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-6-35b-a3b.json",
        "title": "Nemotron 3 Nano 4B vs Qwen3.6-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-6-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-8-27b.json",
        "title": "Nemotron 3 Nano 4B vs Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-8-flash-next.json",
        "title": "Nemotron 3 Nano 4B vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-coder-30b-a3b-instruct.json",
        "title": "Nemotron 3 Nano 4B vs Qwen3-Coder-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-coder-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-coder-next.json",
        "title": "Nemotron 3 Nano 4B vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-coder-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-omni-30b-a3b-instruct.json",
        "title": "Nemotron 3 Nano 4B vs Qwen3-Omni-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-omni-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-vl-2b-instruct.json",
        "title": "Nemotron 3 Nano 4B vs Qwen3-VL-2B-Instruct",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-vl-2b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "Nemotron 3 Nano 4B vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-vl-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-vl-32b-instruct.json",
        "title": "Nemotron 3 Nano 4B vs Qwen3-VL-32B-Instruct",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-vl-32b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-vl-4b-instruct.json",
        "title": "Nemotron 3 Nano 4B vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-vl-8b-instruct.json",
        "title": "Nemotron 3 Nano 4B vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-vl-8b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Nemotron 3 Nano 4B vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-recursive-flint.json",
        "title": "Nemotron 3 Nano 4B vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-ripple-65b-preview.json",
        "title": "Nemotron 3 Nano 4B vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-sensenova-loop-9b-preview.json",
        "title": "Nemotron 3 Nano 4B vs SenseNova-Loop-9B-Preview",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-sensenova-loop-9b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-smollm3-3b.json",
        "title": "Nemotron 3 Nano 4B vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-spark-x2-6-4b.json",
        "title": "Nemotron 3 Nano 4B vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-step-3-7-flash.json",
        "title": "Nemotron 3 Nano 4B vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-step-3-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Nemotron 3 Nano 4B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Nemotron 3 Nano 4B vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-thinkingcap-qwen3-8-27b.json",
        "title": "Nemotron 3 Nano 4B vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-thinkingcap-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-unlimited-ocr-2.json",
        "title": "Nemotron 3 Nano 4B vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-wevisone-vl-2b.json",
        "title": "Nemotron 3 Nano 4B vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-wevisone-vl-4b.json",
        "title": "Nemotron 3 Nano 4B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-zaya1-8b.json",
        "title": "Nemotron 3 Nano 4B vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-zaya1-8b"
      }
    ],
    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 55,
        "edge": "nemotron-3-nano-4b",
        "key": "reach",
        "longcat-2-5": 35,
        "name": "Reach",
        "nemotron-3-nano-4b": 90,
        "weight": 25
      },
      {
        "by": 5,
        "edge": "nemotron-3-nano-4b",
        "key": "risk",
        "longcat-2-5": 60,
        "name": "Risk",
        "nemotron-3-nano-4b": 65,
        "weight": 20
      },
      {
        "by": 5,
        "edge": "longcat-2-5",
        "key": "interface",
        "longcat-2-5": 75,
        "name": "Agent interface",
        "nemotron-3-nano-4b": 70,
        "weight": 10
      },
      {
        "by": 9,
        "edge": "nemotron-3-nano-4b",
        "key": "upkeep",
        "longcat-2-5": 46,
        "name": "Documentation and upkeep",
        "nemotron-3-nano-4b": 55,
        "weight": 10
      }
    ],
    "summary": "Nemotron 3 Nano 4B scores 56.8 (C) on agent readiness against LongCat-2.5's 50.5 (D), and leads in 3 of 4 scored categories. LongCat-2.5 leads on agent interface. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "longcat-2-5": "Meituan's flagship is MIT-licensed and documented for tool calling and switchable reasoning, with strong numbers of its own on agentic coding. Almost nobody can run it: 3,562 GB of weights, a 1,336 GB 4-bit build for NPU accelerators, and no released runtime with its architecture. A model to watch until a runtime or a provider picks it up.",
      "nemotron-3-nano-4b": "A 3.97-billion-parameter reasoning model with trained tool calls, a thinking switch and 262,144 tokens of context, which runs in about 4 GB from NVIDIA's own GGUF. The main risks are serving commands that run repository code, no published safety results, a tool format left to the template, and files unchanged and questions unanswered since March 2026."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/longcat-2-5-vs-nemotron-3-nano-4b",
    "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-nemotron-3-nano-4b.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/longcat-2-5-vs-nemotron-3-nano-4b.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/longcat-2-5-vs-nemotron-3-nano-4b.min.md"
  },
  "markdown": "Nemotron 3 Nano 4B scores 56.8 (C) on agent readiness against LongCat-2.5's 50.5 (D), and leads in 3 of 4 scored categories. LongCat-2.5 leads on agent interface. Both do open-weight models you run yourself.\n\n- LongCat-2.5: grade D, 50.5/100, rank #145 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/longcat-2-5.md · JSON https://www.anchorterminal.com/api/v1/tools/longcat-2-5.json\n- Nemotron 3 Nano 4B: grade C, 56.8/100, rank #110 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-nano-4b.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-4b.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### LongCat-2.5 (D)\n\nGood for: Operators with NPU clusters, and anyone tracking what the open flagships can do\n\nAhead on:\n- Agent interface, 75 against 70\n\nWatch for: 3,562 GB in BF16 and 1,336 GB at 4 bits\n\n### Nemotron 3 Nano 4B (C)\n\nGood for: Agents on laptops, edge boards and small GPUs that need a cheap local tool-calling helper in English\n\nAhead on:\n- Reach, 90 against 35\n- Risk, 65 against 60\n- Documentation and upkeep, 55 against 46\n\nWatch for: The card's Transformers and vLLM commands pass trust_remote_code and load a parser plugin from the repository\n\n\n## Why the edge\n\n### Reach: Nemotron 3 Nano 4B +55\n\n- LongCat-2.5 (35): Memory 0, the smallest build is Meituan's W4A4 repository, 1,336 GB, and 1,336 GB × 1.2 = 1,603 GB, far above 256 GB. The BF16 files are 3,562 GB. Runtimes 0, none of the six runs it in a released version: vLLM 0.33.0, SGLang 0.5.23, Transformers 5.22.2 and llama.cpp b12242 have no LongcatCausalLM or pro_v code, and the model code config.json names isn't in the repository. Builds 10, one quantised build from Meituan, the W4A4 repository (4-bit experts, 1,336 GB). Terms 15, MIT in the metadata, the LICENSE file and the card, with nothing added. Download 10, the Hub API reports gated false for both repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it. 0 + 0 + 10 + 15 + 10 + 0 = 35.\n- Nemotron 3 Nano 4B (90): Memory earns the full 30, since NVIDIA's own 2.84 GB GGUF needs under 4 GB with the fifth added. Runtimes earn the full 25 from released llama.cpp, vLLM, SGLang, Transformers, Ollama and LM Studio. Builds earn 15 for NVIDIA's FP8 and GGUF. Terms earn 10, since the Nemotron licence is custom even though it's free for commercial use with no threshold or use policy. Download earns 10, with no gate or account. Hosted earns 0, as no Hugging Face provider serves it and no NVIDIA hosting was found.\n\n### Documentation and upkeep: Nemotron 3 Nano 4B +9\n\n- LongCat-2.5 (46): Model card 11, Intended use, limitations (the usage considerations), the architecture and the parameter counts are on the card. Training data 5, A general description of the training stages and where the reinforcement learning tasks came from, with no sources, sizes or cut-off. Last change 20, the last commit, on 10 November 2026, updated the card, on the day of this check. Responsiveness 0, Neither of the 2 discussions that aren't pull requests (9 and 10 November 2026) has a reply from a meituan-longcat member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, LongCat-2.0 stays in its own repositories and 2.5 came out in new ones, with no dated note on the older ones that we read. File health 5, the template is in both places and agrees, but config.json's auto_map names configuration_longcat_oe and modeling_longcat_oe, which aren't in the repository, and the tokenizer config's 131,072-token length is a quarter of the config's 524,288 positions. 11 + 5 + 20 + 0 + 5 + 5 = 46.\n- Nemotron 3 Nano 4B (55): The model card earns 20, all seven items with limitations in the linked subcard. Training data earns 20, datasets listed with sources and dates, crawled sets with token counts, a cut-off and published NVIDIA datasets, though most synthetic sizes are undisclosed. Last change earns 5, as the files last changed on 20 March 2026, and TensorRT-LLM's Nemotron-H commits since 14 October touch MoE, NVFP4 and Puzzle paths this dense model doesn't use, in release candidates or no release. Responsiveness earns 0, with no NVIDIA reply to any of six questions, re-counted on 4 November. Version policy earns 5 as a first release. File health earns 5, since the LICENSE file is empty, reports and fixes wait without a reply, and the card's Transformers snippet names a repository that isn't public.\n\n### Risk: Nemotron 3 Nano 4B +5\n\n- LongCat-2.5 (60): File safety 25, safetensors only, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, the files sit in the meituan-longcat organisation, the name of Meituan's GitHub organisation that the card links for deployment, and the card's licence names Meituan (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions under Usage Considerations: not evaluated for every use, assess accuracy, safety and fairness before sensitive use, and follow the law. Licence risk 15, MIT with no use policy, no threshold and no right to change the terms. Security programme 0, no way to report a flaw on the card beyond a general contact, and Meituan's own security pages weren't looked up, so this scores as absent and is an open question. 25 + 15 + 0 + 5 + 15 + 0 = 60.\n- Nemotron 3 Nano 4B (65): File safety earns 15, safetensors and GGUF only but with 10 off because the documented commands pass trust_remote_code and load a parser plugin from the repository. Provenance earns 15, files in NVIDIA's linked organisation with a hash for every file. Safety evaluation earns 0, no results. Agent guidance earns 10, subcards that name prompt injection and the safeguards to add, without an instruction hierarchy. Licence risk keeps all 15, since the switch to the Nemotron licence happened on release day. The security programme earns 10 for the reporting route on the card and NVIDIA's public bulletins.\n\n### Agent interface: LongCat-2.5 +5\n\n- LongCat-2.5 (75): Chat template 20, chat_template.jinja in the repository (11,823 characters), with a copy in tokenizer_config.json. Tool-calling format 25, the card documents how tools are declared (the OpenAI function format) and how calls come back (tool_calls with an id, a name and JSON arguments, answered by a tool message), in a worked example against an OpenAI-compatible server. Trained tool calling 10, the card describes reinforcement learning on long agentic tasks and shows one tool call in a turn. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, enable_thinking on or off per request through chat_template_kwargs, with the reasoning returned as reasoning_content, both shown on the card. Context 10, the card states no context length, but config.json gives 524,288 positions and the card's own evaluations ran at 512K and 1M sequence lengths after what it calls long-context training, so 128K or more is taken as trained. 20 + 25 + 10 + 0 + 10 + 10 = 75.\n- Nemotron 3 Nano 4B (70): The chat template earns 20, a Jinja file in the repository. The tool-calling format earns 10 of 25, as only the template shows the format and the parser name appears only in a serving command. Trained tool calling earns 20, with tool-use benchmarks and data and several calls per turn in the template. Structured output earns 0, not claimed or documented. Reasoning control earns 10, thinking off per request with a parser that returns the reasoning apart. Context earns 10 for 262,144 tokens.\n\n## Score by category\n\n| Category | Weight | LongCat-2.5 | Nemotron 3 Nano 4B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 35 | 90 | Nemotron 3 Nano 4B +55 |\n| Risk | 20% (20 this run) | 60 | 65 | Nemotron 3 Nano 4B +5 |\n| Agent interface | 10% (10 this run) | 75 | 70 | LongCat-2.5 +5 |\n| Documentation and upkeep | 10% (10 this run) | 46 | 55 | Nemotron 3 Nano 4B +9 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **50.5 · D** | **56.8 · C** | |\n\n## Facts side by side\n\n| Fact | LongCat-2.5 | Nemotron 3 Nano 4B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Meituan (LongCat) | 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 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-11-10 | 2026-03-16 |\n| Terms last updated | not read yet | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | none | 3.3/5 (4) |\n\n## Verdicts\n\n**LongCat-2.5.** Meituan's flagship is MIT-licensed and documented for tool calling and switchable reasoning, with strong numbers of its own on agentic coding. Almost nobody can run it: 3,562 GB of weights, a 1,336 GB 4-bit build for NPU accelerators, and no released runtime with its architecture. A model to watch until a runtime or a provider picks it up.\n\n**Nemotron 3 Nano 4B.** A 3.97-billion-parameter reasoning model with trained tool calls, a thinking switch and 262,144 tokens of context, which runs in about 4 GB from NVIDIA's own GGUF. The main risks are serving commands that run repository code, no published safety results, a tool format left to the template, and files unchanged and questions unanswered since March 2026.\n\n## Before you call either\n\n### LongCat-2.5\n\n1. You can't run this on a workstation or a single GPU server. Plan for a provider, and none serves it yet\n2. If an operator serves it, call it as an OpenAI-compatible chat API with tools in the function format\n3. Pass `chat_template_kwargs` with `enable_thinking` true or false\n4. With thinking on, keep earlier reasoning in the history (`save_history_reasoning_content` true), as the card recommends\n5. The card's results use temperature 1.0 and top_p 0.95\n6. Its benchmark table is Meituan's own. The unmarked scores are in-house\n\n### Nemotron 3 Nano 4B\n\n1. Quickest start is `ollama run nemotron-3-nano:4b`, a 2.8 GB build\n2. With llama.cpp use `llama-server -hf nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF --jinja`, NVIDIA's own Q4_K_M file\n3. With vLLM, download nano_v3_reasoning_parser.py from the repository first and serve with `--enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser-plugin nano_v3_reasoning_parser.py --reasoning-parser nano_v3`\n4. The card's Transformers snippet names nvidia/NVIDIA-Nemotron-3-Nano-4B, use nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16 instead\n5. Thinking is on by default. Pass `enable_thinking=False` to the chat template for short tasks\n6. Use temperature 1.0 and top_p 0.95 for reasoning and temperature 0.6 with top_p 0.95 for tool calling, as the card says\n7. It's tuned for English only, and validate JSON yourself, since structured output isn't documented\n\n## Questions\n\n### Which is better for AI agents, LongCat-2.5 or Nemotron 3 Nano 4B?\n\nNemotron 3 Nano 4B scores 56.8 (C) on agent readiness against LongCat-2.5's 50.5 (D), and leads in 3 of 4 scored categories. LongCat-2.5 leads on agent interface.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/longcat-2-5-vs-nemotron-3-nano-4b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/longcat-2-5-vs-nemotron-3-nano-4b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"longcat-2-5\", \"b\": \"nemotron-3-nano-4b\"}`. From a terminal: `anchor compare longcat-2-5 nemotron-3-nano-4b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/longcat-2-5.json and https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-4b.json\n\n## Other comparisons with LongCat-2.5 or Nemotron 3 Nano 4B\n\n- [Agents-A1-4B vs LongCat-2.5](https://www.anchorterminal.com/compare/agents-a1-4b-vs-longcat-2-5.md)\n- [Agents-A1-4B vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-nano-4b.md)\n- [Apertus v1.5 8B vs LongCat-2.5](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-longcat-2-5.md)\n- [Apertus v1.5 8B vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-nano-4b.md)\n- [Apodex-1.1-mini vs LongCat-2.5](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-longcat-2-5.md)\n- [Apodex-1.1-mini vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nemotron-3-nano-4b.md)\n- [Arete-27B vs 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