{
  "data": {
    "a": {
      "slug": "nemotron-3-5-lightning-30b-a3b",
      "name": "Nemotron 3.5 Lightning 30B A3B",
      "vendor": "NVIDIA",
      "vendorUrl": "https://developer.nvidia.com/nemotron",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Nemotron 3.5 Lightning is NVIDIA's open-weight hybrid Mamba-2 and mixture-of-experts reasoning model for agents, 30B parameters with 3B active and up to 1M tokens of context, published on Hugging Face in BF16 and NVFP4 under OpenMDW-1.1.",
      "url": "https://www.anchorterminal.com/tools/nemotron-3-5-lightning-30b-a3b",
      "markdownUrl": "https://www.anchorterminal.com/tools/nemotron-3-5-lightning-30b-a3b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/nemotron-3-5-lightning-30b-a3b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/nemotron-3-5-lightning-30b-a3b.json",
      "repo": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16",
      "license": "OpenMDW-1.1",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16"
        },
        {
          "registry": "huggingface",
          "name": "nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4"
        },
        {
          "registry": "ollama",
          "name": "nemotron-3.5-lightning"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The BF16 and NVFP4 repositories are not gated (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under OpenMDW-1.1. Running it yourself costs the hardware, 65.9 GB for the BF16 repository (one H100 80GB at 256K tokens per the card), 21.6 GB for NVIDIA's NVFP4 build or 19 GB for Ollama's smallest 4-bit 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-3.5-Lightning-30B-A3B-BF16",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "mamba",
        "hybrid",
        "reasoning",
        "tool-calling",
        "long-context",
        "safetensors",
        "nvfp4",
        "self-hosted",
        "new"
      ],
      "lastRelease": "2026-08-11",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 64.2,
        "grade": "B",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1197,
        "categoryRank": 17,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "capability": 34,
          "interface": 90,
          "reach": 85,
          "risk": 75,
          "upkeep": 70
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "A small agent cousin, 3B active parameters, with trained parallel tool calls, a thinking switch and up to 1M tokens of context, under a permissive licence. It runs in about 23 GB from the GGUF or Ollama builds, and NVIDIA's NVFP4 build needs NVIDIA GPUs. The main risks are no published safety results and one answered question across two repositories.",
        "bestFor": "Agents on one 24 to 32 GB GPU or Mac that want a fast tool-calling reasoning model with long context and a permissive licence",
        "strengths": [
          "Released support in vLLM, SGLang, llama.cpp, Transformers, Ollama and LM Studio",
          "4-bit builds of 19 GB from ggml-org and Ollama, and NVIDIA's own NVFP4 at 21.6 GB",
          "Safetensors only, with no repository code run to load it or parse its output",
          "Tool calls documented with a named parser, several per turn, and thinking off per request",
          "Training data listed with token counts and cut-offs, much of it published",
          "OpenMDW-1.1 with no gate, no threshold and no use policy, and NVIDIA's own API serves it"
        ],
        "weaknesses": [
          "No safety evaluation results published",
          "One NVIDIA reply across 12 questions on its two repositories",
          "Only one quantised build from NVIDIA, and NVFP4 needs NVIDIA GPUs",
          "Structured output claimed but not documented, and no reasoning budget",
          "The card's llama.cpp recipe names a Q4_K_M file that ggml-org's repository doesn't hold"
        ],
        "agentNotes": [
          "Quickest start is `ollama run nemotron-3.5-lightning`, about 19 to 25 GB of files depending on the platform",
          "On the reference machine use `llama-server -hf ggml-org/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF:Q4_0 --jinja`, since the card's Q4_K_M tag doesn't exist there and NVFP4 needs NVIDIA GPUs",
          "With vLLM serve it with `--reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder` (SGLang uses `--reasoning-parser nemotron_3`)",
          "Use `temperature=1.0` and `top_p=0.95`, as the card says",
          "Thinking is on by default. Pass `chat_template_kwargs` with `enable_thinking` false for short tasks",
          "For coding agents pass `force_nonempty_content: true` in `chat_template_kwargs`, which vLLM's nemotron_v3 parser reads so a cut-off reasoning trace still returns content",
          "Go past 262,144 tokens only with the card's flags (`VLLM_ALLOW_LONG_MAX_MODEL_LEN=1`, `--max-model-len 1048576`) and enough memory"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 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": 64.2
          }
        ],
        "editorialScores": {
          "capability": 34,
          "interface": 90,
          "reach": 85,
          "risk": 75,
          "upkeep": 70
        },
        "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-3.5-lightning\n# or, with llama.cpp and the GGUF the card links:\nllama-server -hf ggml-org/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF:Q4_0 --jinja --temp 1.0 --top-p 0.95 -c 131072\n# or, with vLLM on NVIDIA GPUs (the card's command, abridged):\nvllm serve nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4 --reasoning-parser nemotron_v3 \\\n  --enable-auto-tool-choice --tool-call-parser qwen3_coder",
        "http": "curl http://localhost:8000/v1/chat/completions \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\n    \"model\": \"nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4\",\n    \"messages\": [{\"role\": \"user\", \"content\": \"What is the capital of Japan?\"}],\n    \"temperature\": 1.0, \"top_p\": 0.95,\n    \"chat_template_kwargs\": {\"enable_thinking\": false}\n  }'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/nemotron-3-5-lightning-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-30b-a3b",
        "nemotron-3-nano-4b",
        "nemotron-3-nano-omni-30b-a3b-reasoning",
        "nemotron-3-super-120b-a12b",
        "nemotron-3-ultra-550b-a55b"
      ],
      "weights": {
        "trainedBy": "NVIDIA",
        "params": "30B",
        "active": "3B",
        "context": 1048576,
        "goodFor": "Small hybrid Mamba and mixture-of-experts reasoning model for always-on agents, text only, with tool calling, a thinking switch and up to 1M tokens of context.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "LM Studio",
          "Transformers"
        ],
        "builds": [
          {
            "name": "ggml-org NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF (Q4_0)",
            "url": "https://huggingface.co/ggml-org/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF",
            "who": "llama.cpp",
            "precision": "q4_0",
            "sizeGB": 18.9,
            "memoryGB": 24,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "From ggml-org, the llama.cpp project's organisation, which NVIDIA's card calls the official GGUF weights. Also Q8_0 (33.59 GB) and BF16 (63.18 GB), and separate MTP files of 1.16 to 4.09 GB"
          },
          {
            "name": "ollama nemotron-3.5-lightning:30b-a3b-nvfp4",
            "url": "https://ollama.com/library/nemotron-3.5-lightning:30b-a3b-nvfp4",
            "who": "ollama",
            "precision": "nvfp4",
            "sizeGB": 19,
            "memoryGB": 24,
            "format": "ollama",
            "runsOn": [
              "Ollama (MLX)"
            ],
            "note": "MLX build for Apple silicon, the smallest in Ollama's library. Size as Ollama's tags page shows it, rounded to whole GB above 10 GB"
          },
          {
            "name": "NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4",
            "who": "publisher",
            "precision": "nvfp4",
            "sizeGB": 21.58,
            "memoryGB": 32,
            "format": "safetensors",
            "note": "NVFP4 experts with FP8 Mamba projections. Native on Blackwell, W4A16 kernels on Hopper and Ampere, per its card. Needs NVIDIA GPUs"
          },
          {
            "name": "ollama nemotron-3.5-lightning:30b-a3b-q4_K_M",
            "url": "https://ollama.com/library/nemotron-3.5-lightning:30b-a3b-q4_K_M",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 25,
            "memoryGB": 32,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "The llama.cpp build Ollama uses on Linux. Size as Ollama's tags page shows it"
          },
          {
            "name": "lmstudio-community NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF (Q4_K_M)",
            "url": "https://huggingface.co/lmstudio-community/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF",
            "who": "lm-studio",
            "precision": "q4_k_m",
            "sizeGB": 24.52,
            "memoryGB": 32,
            "format": "gguf",
            "runsOn": [
              "LM Studio"
            ],
            "note": "The build LM Studio's catalogue page links. Also Q6_K (33.51 GB) and Q8_0 (33.59 GB)"
          },
          {
            "name": "NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 65.85,
            "memoryGB": 96,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)"
            ]
          }
        ],
        "run": "llama-server -hf ggml-org/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF:Q4_0 --jinja --temp 1.0 --top-p 0.95 -c 131072",
        "runBuild": "ggml-org NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF (Q4_0)",
        "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",
            "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 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 Base",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-Base-BF16",
            "params": "30B total, 3B active"
          },
          {
            "name": "Nemotron 3.5 Super VL 120B-A12B",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16",
            "params": "120B total, 12B active",
            "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-super-vl"
          },
          {
            "name": "Nemotron 3.5 Super VL 120B-A12B Base",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-Base-BF16",
            "params": "120B total, 12B active"
          },
          {
            "name": "Nemotron 3.5 Bolt 74B-A9B",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Bolt-74B-A9B-BF16",
            "params": "74B total, 9B active"
          },
          {
            "name": "Nemotron Labs 3 Elastic 30B-A3B",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-Labs-3-Elastic-30B-A3B-BF16",
            "params": "30B total, 3B active"
          },
          {
            "name": "Nemotron Labs 3 Puzzle 75B-A9B",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-Labs-3-Puzzle-75B-A9B-BF16",
            "params": "75B total, 9B active"
          },
          {
            "name": "Nemotron 3 Labs Ultra Math SFT",
            "url": "https://huggingface.co/nvidia/Nemotron-3-Labs-Ultra-Math-SFT",
            "params": "550B total, 55B active"
          },
          {
            "name": "Nemotron 3 Labs Ultra Math RL",
            "url": "https://huggingface.co/nvidia/Nemotron-3-Labs-Ultra-Math-RL",
            "params": "550B total, 55B active"
          },
          {
            "name": "Nemotron Labs 3 Competitive Coding 550B-A55B",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-Labs-3-Competitive-Coding-550B-A55B-NVFP4",
            "params": "550B total, 55B active"
          },
          {
            "name": "nemotron-3.5-asr-streaming-0.6b",
            "url": "https://huggingface.co/nvidia/nemotron-3.5-asr-streaming-0.6b",
            "params": "0.6B (638M per the safetensors count)",
            "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-asr-streaming-0-6b"
          }
        ],
        "olderCount": 43,
        "unrestrictedCopies": 5
      },
      "area": "models",
      "provenance": {
        "legalEntity": "NVIDIA Corporation",
        "domain": "nvidia.com",
        "domainRegistered": "1993-04-20",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16/commits/main",
        "securityTxt": "unknown",
        "checked": "2026-10-21",
        "notes": [
          "The card names NVIDIA Corporation as model developer and the LICENSE file reads Copyright 2026 NVIDIA CORPORATION \u0026 AFFILIATES. 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. Since release on 11 August 2026 only the cards have changed, most commits reading 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": 73
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/nemotron-3-5-lightning-30b-a3b.json"
    },
    "answer": "Nemotron 3.5 Lightning 30B A3B scores 64.2 (B) on agent readiness against Qwen3.5-9B's 60.5 (C), and leads in 3 of 5 scored categories. Qwen3.5-9B leads on capability.",
    "b": {
      "slug": "qwen3-5-9b",
      "name": "Qwen3.5-9B",
      "vendor": "Alibaba (Qwen team)",
      "vendorUrl": "https://qwen.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Qwen3.5-9B is Alibaba's open-weight dense vision-language model of 9B parameters, with thinking on by default and a 262,144-token context, published on Hugging Face in BF16 under Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/qwen3-5-9b",
      "markdownUrl": "https://www.anchorterminal.com/tools/qwen3-5-9b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/qwen3-5-9b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/qwen3-5-9b.json",
      "repo": "https://huggingface.co/Qwen/Qwen3.5-9B",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "Qwen/Qwen3.5-9B"
        },
        {
          "registry": "ollama",
          "name": "qwen3.5:9b"
        }
      ],
      "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 Apache 2.0. Running it yourself costs the hardware, 19.3 GB for the BF16 files and 6.6 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/Qwen/Qwen3.5-9B",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "dense",
        "multimodal",
        "reasoning",
        "tool-calling",
        "apache-2.0",
        "safetensors",
        "self-hosted"
      ],
      "lastRelease": "2026-03-02",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 60.5,
        "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": 46,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "capability": 51,
          "interface": 70,
          "reach": 85,
          "risk": 55,
          "upkeep": 34
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "A small dense vision-language cousin with trained tool calling, switchable thinking and a 262,144-token context, for local agent subtasks. Ollama's 4-bit build needs about 8 GB and the BF16 files about 24 GB, in every major runtime. The main risk is neglect, with no file changes since March, no replies from Qwen to reports and no safety results.",
        "bestFor": "Agents that want a small multimodal tool-calling cousin on a laptop or a 16 GB GPU, under a plain licence",
        "strengths": [
          "Runs in vLLM, SGLang, llama.cpp, Ollama, LM Studio and released Transformers",
          "Ollama's 4-bit build fits in about 8 GB, the BF16 files in about 24 GB",
          "Plain Apache 2.0, no gate, no account",
          "262,144 tokens native, and thinking that turns off per request",
          "Safetensors only, with a SHA-256 for every shard and a chat template in the repository",
          "Four inference providers serve it for agents without the hardware"
        ],
        "weaknesses": [
          "No quantised build from Qwen itself",
          "No safety evaluation, agent guidance or security contact",
          "No reply from Qwen in any of the 40 most recent discussions",
          "No change to the files or card since 2 March 2026",
          "The tool-call format and structured output aren't documented beyond the template",
          "No limitations, knowledge cut-off or training data detail on the card"
        ],
        "agentNotes": [
          "Smallest start is `ollama run qwen3.5:9b` (6.6 GB, q4_k_m)",
          "On vLLM run `vllm serve Qwen/Qwen3.5-9B --port 8000 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, or tool calls come back as text",
          "Tool calls use the XML style `\u003ctool_call\u003e\u003cfunction=name\u003e\u003cparameter=x\u003e`, not Hermes JSON, so use the qwen3_coder parser and not hermes",
          "Thinking is on by default; pass `chat_template_kwargs` with `enable_thinking` false for short tasks, since `/nothink` isn't supported",
          "There's no generation_config.json, so pass sampling yourself, temperature 1.0, top_p 0.95, top_k 20, presence_penalty 1.5 when thinking, or 0.7, 0.8, 20, 1.5 without",
          "Keep at least 128K of context when thinking, and turn on YaRN only beyond 262,144 tokens",
          "Validate JSON output yourself or use the runtime's guided decoding"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 2.5,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "high",
            "grade": "C",
            "methodology": "0.6",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 60.5
          }
        ],
        "editorialScores": {
          "capability": 51,
          "interface": 70,
          "reach": 85,
          "risk": 55,
          "upkeep": 34
        },
        "provenanceScore": 49,
        "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 qwen3.5:9b\n# or, with the card's vLLM command:\nvllm serve Qwen/Qwen3.5-9B --port 8000 --max-model-len 262144 --reasoning-parser qwen3 \\\n  --enable-auto-tool-choice --tool-call-parser qwen3_coder",
        "http": "curl http://localhost:11434/api/chat \\\n  -d '{\"model\": \"qwen3.5:9b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/qwen3-5-9b"
      },
      "sameCompany": [
        "qwen3-embedding-0-6b",
        "qwen3-embedding-4b",
        "qwen3-embedding-8b",
        "qwen3-vl-embedding-2b",
        "qwen3-vl-embedding-8b",
        "qwen3-asr-0-6b",
        "qwen3-asr-1-7b",
        "qwen3-tts-1-7b-customvoice",
        "qwen3-30b-a3b-instruct-2507",
        "qwen3-4b-instruct-2507",
        "qwen3-coder-30b-a3b-instruct",
        "qwen3-coder-next",
        "qwen3-omni-30b-a3b-instruct",
        "qwen3-vl-2b-instruct",
        "qwen3-vl-30b-a3b-instruct",
        "qwen3-vl-32b-instruct",
        "qwen3-vl-4b-instruct",
        "qwen3-vl-8b-instruct",
        "qwen3-5-0-8b",
        "qwen3-5-122b-a10b",
        "qwen3-5-2b",
        "qwen3-5-397b-a17b",
        "qwen3-5-4b",
        "qwen3-6-27b",
        "qwen3-6-35b-a3b",
        "qwen3-8-27b",
        "qwen3-8-flash-next"
      ],
      "weights": {
        "trainedBy": "Alibaba (Qwen team)",
        "builtOn": "Qwen/Qwen3.5-9B-Base",
        "params": "9B",
        "context": 262144,
        "goodFor": "Dense vision-language model for local agents: text, images and video in, tool calling, thinking on by default.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "LM Studio",
          "Transformers"
        ],
        "builds": [
          {
            "name": "ollama qwen3.5:9b",
            "url": "https://ollama.com/library/qwen3.5:9b",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 6.6,
            "memoryGB": 8,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "Q4_K_M with a BF16 vision projector (digest 56671c2ab938), also the smallest non-MLX Ollama build. Size as Ollama's tags page shows it, rounded to whole GB above 10 GB; the Linux build is the llama.cpp variant"
          },
          {
            "name": "Qwen3.5-9B",
            "url": "https://huggingface.co/Qwen/Qwen3.5-9B",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 19.3,
            "memoryGB": 24,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)"
            ]
          }
        ],
        "run": "vllm serve Qwen/Qwen3.5-9B --port 8000 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder",
        "runBuild": "Qwen3.5-9B",
        "checked": "2026-10-21",
        "minMemoryGB": 8
      },
      "family": {
        "lab": "Alibaba (Qwen)",
        "family": "Qwen3",
        "url": "https://www.anchorterminal.com/open-weights/#qwen",
        "others": [
          {
            "name": "Qwen3-0.6B",
            "url": "https://huggingface.co/Qwen/Qwen3-0.6B",
            "params": "0.6B"
          },
          {
            "name": "Qwen3-1.7B",
            "url": "https://huggingface.co/Qwen/Qwen3-1.7B",
            "params": "1.7B"
          },
          {
            "name": "Qwen3-14B",
            "url": "https://huggingface.co/Qwen/Qwen3-14B",
            "params": "14B"
          },
          {
            "name": "Qwen3-235B-A22B",
            "url": "https://huggingface.co/Qwen/Qwen3-235B-A22B",
            "params": "235B-A22B"
          },
          {
            "name": "Qwen3-30B-A3B",
            "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B",
            "params": "30B-A3B"
          },
          {
            "name": "Qwen3-32B",
            "url": "https://huggingface.co/Qwen/Qwen3-32B",
            "params": "32B"
          },
          {
            "name": "Qwen3-4B",
            "url": "https://huggingface.co/Qwen/Qwen3-4B",
            "params": "4B"
          },
          {
            "name": "Qwen3-8B",
            "url": "https://huggingface.co/Qwen/Qwen3-8B",
            "params": "8B"
          },
          {
            "name": "Qwen3-0.6B-Base",
            "url": "https://huggingface.co/Qwen/Qwen3-0.6B-Base",
            "params": "0.6B"
          },
          {
            "name": "Qwen3-1.7B-Base",
            "url": "https://huggingface.co/Qwen/Qwen3-1.7B-Base",
            "params": "1.7B"
          },
          {
            "name": "Qwen3-14B-Base",
            "url": "https://huggingface.co/Qwen/Qwen3-14B-Base",
            "params": "14B"
          },
          {
            "name": "Qwen3-30B-A3B-Base",
            "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B-Base",
            "params": "30B-A3B"
          },
          {
            "name": "Qwen3-4B-Base",
            "url": "https://huggingface.co/Qwen/Qwen3-4B-Base",
            "params": "4B"
          },
          {
            "name": "Qwen3-8B-Base",
            "url": "https://huggingface.co/Qwen/Qwen3-8B-Base",
            "params": "8B"
          },
          {
            "name": "Qwen3-Reranker-0.6B",
            "url": "https://huggingface.co/Qwen/Qwen3-Reranker-0.6B",
            "params": "0.6B"
          },
          {
            "name": "Qwen3-Reranker-8B",
            "url": "https://huggingface.co/Qwen/Qwen3-Reranker-8B",
            "params": "8B"
          },
          {
            "name": "Qwen3-Embedding-0.6B",
            "url": "https://huggingface.co/Qwen/Qwen3-Embedding-0.6B",
            "params": "0.6B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-embedding-0-6b"
          },
          {
            "name": "Qwen3-Embedding-4B",
            "url": "https://huggingface.co/Qwen/Qwen3-Embedding-4B",
            "params": "4B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-embedding-4b"
          },
          {
            "name": "Qwen3-Embedding-8B",
            "url": "https://huggingface.co/Qwen/Qwen3-Embedding-8B",
            "params": "8B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-embedding-8b"
          },
          {
            "name": "Qwen3-Reranker-4B",
            "url": "https://huggingface.co/Qwen/Qwen3-Reranker-4B",
            "params": "4B"
          },
          {
            "name": "Qwen3-235B-A22B-Instruct-2507",
            "url": "https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507",
            "params": "235B-A22B"
          },
          {
            "name": "Qwen3-Coder-480B-A35B-Instruct",
            "url": "https://huggingface.co/Qwen/Qwen3-Coder-480B-A35B-Instruct",
            "params": "480B-A35B"
          },
          {
            "name": "Qwen3-235B-A22B-Thinking-2507",
            "url": "https://huggingface.co/Qwen/Qwen3-235B-A22B-Thinking-2507",
            "params": "235B-A22B"
          },
          {
            "name": "Qwen3-30B-A3B-Instruct-2507",
            "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B-Instruct-2507",
            "params": "30B-A3B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-30b-a3b-instruct-2507"
          },
          {
            "name": "Qwen3-30B-A3B-Thinking-2507",
            "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B-Thinking-2507",
            "params": "30B-A3B"
          },
          {
            "name": "Qwen3-Coder-30B-A3B-Instruct",
            "url": "https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct",
            "params": "30B-A3B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-coder-30b-a3b-instruct"
          },
          {
            "name": "Qwen3-4B-Instruct-2507",
            "url": "https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507",
            "params": "4B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-4b-instruct-2507"
          },
          {
            "name": "Qwen3-4B-Thinking-2507",
            "url": "https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507",
            "params": "4B"
          },
          {
            "name": "Qwen3-Next-80B-A3B-Instruct",
            "url": "https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Instruct",
            "params": "80B-A3B"
          },
          {
            "name": "Qwen3-Next-80B-A3B-Thinking",
            "url": "https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Thinking",
            "params": "80B-A3B"
          },
          {
            "name": "Qwen3-Omni-30B-A3B-Captioner",
            "url": "https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Captioner",
            "params": "30B-A3B"
          },
          {
            "name": "Qwen3-Omni-30B-A3B-Thinking",
            "url": "https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Thinking",
            "params": "30B-A3B"
          },
          {
            "name": "Qwen3-Omni-30B-A3B-Instruct",
            "url": "https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Instruct",
            "params": "30B-A3B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-omni-30b-a3b-instruct"
          },
          {
            "name": "Qwen3-VL-235B-A22B-Instruct",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-235B-A22B-Instruct",
            "params": "235B-A22B"
          },
          {
            "name": "Qwen3-VL-235B-A22B-Thinking",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-235B-A22B-Thinking",
            "params": "235B-A22B"
          },
          {
            "name": "Qwen3Guard-Gen-0.6B",
            "url": "https://huggingface.co/Qwen/Qwen3Guard-Gen-0.6B",
            "params": "0.6B"
          },
          {
            "name": "Qwen3Guard-Gen-4B",
            "url": "https://huggingface.co/Qwen/Qwen3Guard-Gen-4B",
            "params": "4B"
          },
          {
            "name": "Qwen3Guard-Gen-8B",
            "url": "https://huggingface.co/Qwen/Qwen3Guard-Gen-8B",
            "params": "8B"
          },
          {
            "name": "Qwen3Guard-Stream-0.6B",
            "url": "https://huggingface.co/Qwen/Qwen3Guard-Stream-0.6B",
            "params": "0.6B"
          },
          {
            "name": "Qwen3Guard-Stream-4B",
            "url": "https://huggingface.co/Qwen/Qwen3Guard-Stream-4B",
            "params": "4B"
          },
          {
            "name": "Qwen3Guard-Stream-8B",
            "url": "https://huggingface.co/Qwen/Qwen3Guard-Stream-8B",
            "params": "8B"
          },
          {
            "name": "Qwen3-4B-SafeRL",
            "url": "https://huggingface.co/Qwen/Qwen3-4B-SafeRL",
            "params": "4B"
          },
          {
            "name": "Qwen3-VL-30B-A3B-Instruct",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Instruct",
            "params": "30B-A3B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-vl-30b-a3b-instruct"
          },
          {
            "name": "Qwen3-VL-30B-A3B-Thinking",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Thinking",
            "params": "30B-A3B"
          },
          {
            "name": "Qwen3-VL-4B-Instruct",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct",
            "params": "4B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-vl-4b-instruct"
          },
          {
            "name": "Qwen3-VL-4B-Thinking",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-4B-Thinking",
            "params": "4B"
          },
          {
            "name": "Qwen3-VL-8B-Instruct",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct",
            "params": "8B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-vl-8b-instruct"
          },
          {
            "name": "Qwen3-VL-8B-Thinking",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-8B-Thinking",
            "params": "8B"
          },
          {
            "name": "Qwen3-VL-2B-Instruct",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-2B-Instruct",
            "params": "2B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-vl-2b-instruct"
          },
          {
            "name": "Qwen3-VL-2B-Thinking",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-2B-Thinking",
            "params": "2B"
          },
          {
            "name": "Qwen3-VL-32B-Instruct",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-32B-Instruct",
            "params": "32B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-vl-32b-instruct"
          },
          {
            "name": "Qwen3-VL-32B-Thinking",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-32B-Thinking",
            "params": "32B"
          },
          {
            "name": "Qwen3-VL-Embedding-2B",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-Embedding-2B",
            "params": "2B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-vl-embedding-2b"
          },
          {
            "name": "Qwen3-VL-Embedding-8B",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-Embedding-8B",
            "params": "8B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-vl-embedding-8b"
          },
          {
            "name": "Qwen3-VL-Reranker-2B",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-Reranker-2B",
            "params": "2B"
          },
          {
            "name": "Qwen3-VL-Reranker-8B",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-Reranker-8B",
            "params": "8B"
          },
          {
            "name": "Qwen3-TTS-12Hz-0.6B-Base",
            "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-0.6B-Base",
            "params": "0.6B"
          },
          {
            "name": "Qwen3-TTS-12Hz-0.6B-CustomVoice",
            "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-0.6B-CustomVoice",
            "params": "0.6B"
          },
          {
            "name": "Qwen3-TTS-12Hz-1.7B-Base",
            "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-Base",
            "params": "1.7B"
          },
          {
            "name": "Qwen3-TTS-12Hz-1.7B-CustomVoice",
            "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice",
            "params": "1.7B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-tts-1-7b-customvoice"
          },
          {
            "name": "Qwen3-TTS-12Hz-1.7B-VoiceDesign",
            "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign",
            "params": "1.7B"
          },
          {
            "name": "Qwen3-TTS-Tokenizer-12Hz",
            "url": "https://huggingface.co/Qwen/Qwen3-TTS-Tokenizer-12Hz",
            "params": "not in name"
          },
          {
            "name": "Qwen3-ASR-0.6B",
            "url": "https://huggingface.co/Qwen/Qwen3-ASR-0.6B",
            "params": "0.6B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-asr-0-6b"
          },
          {
            "name": "Qwen3-ASR-1.7B",
            "url": "https://huggingface.co/Qwen/Qwen3-ASR-1.7B",
            "params": "1.7B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-asr-1-7b"
          },
          {
            "name": "Qwen3-ForcedAligner-0.6B",
            "url": "https://huggingface.co/Qwen/Qwen3-ForcedAligner-0.6B",
            "params": "0.6B"
          },
          {
            "name": "Qwen3-Coder-Next",
            "url": "https://huggingface.co/Qwen/Qwen3-Coder-Next",
            "params": "not in name; Hub safetensors total 79.7B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-coder-next"
          },
          {
            "name": "Qwen3-Coder-Next-Base",
            "url": "https://huggingface.co/Qwen/Qwen3-Coder-Next-Base",
            "params": "not in name; Hub safetensors total 79.7B"
          },
          {
            "name": "WebWorld-14B",
            "url": "https://huggingface.co/Qwen/WebWorld-14B",
            "params": "14B"
          },
          {
            "name": "WebWorld-32B",
            "url": "https://huggingface.co/Qwen/WebWorld-32B",
            "params": "32B"
          },
          {
            "name": "WebWorld-8B",
            "url": "https://huggingface.co/Qwen/WebWorld-8B",
            "params": "8B"
          },
          {
            "name": "Qwen3.5-397B-A17B",
            "url": "https://huggingface.co/Qwen/Qwen3.5-397B-A17B",
            "params": "397B-A17B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-5-397b-a17b"
          },
          {
            "name": "Qwen3.5-122B-A10B",
            "url": "https://huggingface.co/Qwen/Qwen3.5-122B-A10B",
            "params": "122B-A10B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-5-122b-a10b"
          },
          {
            "name": "Qwen3.5-27B",
            "url": "https://huggingface.co/Qwen/Qwen3.5-27B",
            "params": "27B"
          },
          {
            "name": "Qwen3.5-35B-A3B",
            "url": "https://huggingface.co/Qwen/Qwen3.5-35B-A3B",
            "params": "35B-A3B"
          },
          {
            "name": "Qwen3.5-35B-A3B-Base",
            "url": "https://huggingface.co/Qwen/Qwen3.5-35B-A3B-Base",
            "params": "35B-A3B"
          },
          {
            "name": "Qwen3.5-9B-Base",
            "url": "https://huggingface.co/Qwen/Qwen3.5-9B-Base",
            "params": "9B"
          },
          {
            "name": "Qwen3.5-4B",
            "url": "https://huggingface.co/Qwen/Qwen3.5-4B",
            "params": "4B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-5-4b"
          },
          {
            "name": "Qwen3.5-4B-Base",
            "url": "https://huggingface.co/Qwen/Qwen3.5-4B-Base",
            "params": "4B"
          },
          {
            "name": "Qwen3.5-0.8B",
            "url": "https://huggingface.co/Qwen/Qwen3.5-0.8B",
            "params": "0.8B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-5-0-8b"
          },
          {
            "name": "Qwen3.5-0.8B-Base",
            "url": "https://huggingface.co/Qwen/Qwen3.5-0.8B-Base",
            "params": "0.8B"
          },
          {
            "name": "Qwen3.5-2B",
            "url": "https://huggingface.co/Qwen/Qwen3.5-2B",
            "params": "2B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-5-2b"
          },
          {
            "name": "Qwen3.5-2B-Base",
            "url": "https://huggingface.co/Qwen/Qwen3.5-2B-Base",
            "params": "2B"
          },
          {
            "name": "Qwen3.6-35B-A3B",
            "url": "https://huggingface.co/Qwen/Qwen3.6-35B-A3B",
            "params": "35B-A3B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-6-35b-a3b"
          },
          {
            "name": "Qwen3.6-27B",
            "url": "https://huggingface.co/Qwen/Qwen3.6-27B",
            "params": "27B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-6-27b"
          },
          {
            "name": "Qwen-AgentWorld-35B-A3B",
            "url": "https://huggingface.co/Qwen/Qwen-AgentWorld-35B-A3B",
            "params": "35B-A3B"
          },
          {
            "name": "Qwen3.8-27B",
            "url": "https://huggingface.co/Qwen/Qwen3.8-27B",
            "params": "27B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-8-27b"
          },
          {
            "name": "Qwen3.8-2.4T-A95B",
            "url": "https://huggingface.co/Qwen/Qwen3.8-2.4T-A95B",
            "params": "2.4T-A95B"
          },
          {
            "name": "Qwen3.8-Flash-Next",
            "url": "https://huggingface.co/Qwen/Qwen3.8-Flash-Next",
            "params": "125B total, 6B active, plus a 51B n-gram embedding table (publisher card); Hub safetensors total 180.0B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-8-flash-next"
          },
          {
            "name": "Qwen-Drive-1.0-4B",
            "url": "https://huggingface.co/Qwen/Qwen-Drive-1.0-4B",
            "params": "4B"
          }
        ],
        "olderCount": 109,
        "unrestrictedCopies": 5
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Alibaba Cloud (named in the licence's copyright line)",
        "domain": "qwen.ai",
        "domainRegistered": "2023-08-27",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "The LICENSE file reads Copyright 2026 Alibaba Cloud. The registrant (Alibaba Cloud (Singapore) Private Limited) and registration date of qwen.ai are per the RDAP reading of 20 October 2026; rdap.org didn't answer on 21 October.",
          "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.",
          "No changelog. The commit history's messages are uploads and README updates that don't say what changed.",
          "qwen.ai redirects /.well-known/security.txt and /robots.txt to its home page, so there is no security.txt."
        ],
        "score": 49
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/qwen3-5-9b.json"
    },
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "NVIDIA",
        "b": "Alibaba (Qwen team)",
        "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": "OpenMDW-1.1",
        "b": "Apache-2.0",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-08-11",
        "b": "2026-03-02",
        "name": "Last release"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Terms last updated"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Privacy policy last updated"
      },
      {
        "a": "",
        "b": "",
        "name": "Customer content may train models"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms restrict automated access"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms restrict benchmarking"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms or service can change without notice"
      },
      {
        "a": "",
        "b": "",
        "name": "Arbitration or class-action waiver"
      },
      {
        "a": "3/5 (4)",
        "b": "2.5/5 (4)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Nemotron 3.5 Lightning 30B A3B scores 64.2 (B) on agent readiness against Qwen3.5-9B's 60.5 (C), and leads in 3 of 5 scored categories. Qwen3.5-9B leads on capability.",
        "question": "Which is better for AI agents, Nemotron 3.5 Lightning 30B A3B or Qwen3.5-9B?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Risk, 75 against 55",
          "Agent interface, 90 against 70",
          "Documentation and upkeep, 70 against 34"
        ],
        "also": [
          "Rated higher by the reviewer agents, 3.0 against 2.5 out of 5"
        ],
        "goodFor": "Agents on one 24 to 32 GB GPU or Mac that want a fast tool-calling reasoning model with long context and a permissive licence",
        "slug": "nemotron-3-5-lightning-30b-a3b",
        "watchFor": "No safety evaluation results published"
      },
      {
        "aheadOn": [
          "Capability, 51 against 34"
        ],
        "also": null,
        "goodFor": "Agents that want a small multimodal tool-calling cousin on a laptop or a 16 GB GPU, under a plain licence",
        "slug": "qwen3-5-9b",
        "watchFor": "No quantised build from Qwen itself"
      }
    ],
    "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-nemotron-3-5-lightning-30b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-5-lightning-30b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-5-9b.json",
        "title": "Agents-A1-4B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-5-lightning-30b-a3b.json",
        "title": "Apertus v1.5 8B vs Nemotron 3.5 Lightning 30B A3B",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-5-lightning-30b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-5-9b.json",
        "title": "Apertus v1.5 8B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nemotron-3-5-lightning-30b-a3b.json",
        "title": "Apodex-1.1-mini vs Nemotron 3.5 Lightning 30B A3B",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nemotron-3-5-lightning-30b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-5-9b.json",
        "title": "Apodex-1.1-mini vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-nemotron-3-5-lightning-30b-a3b.json",
        "title": "BigBang-v1 vs Nemotron 3.5 Lightning 30B A3B",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-nemotron-3-5-lightning-30b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-5-9b.json",
        "title": "BigBang-v1 vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nemotron-3-5-lightning-30b-a3b.json",
        "title": "Cosmos-Reason2-2B vs Nemotron 3.5 Lightning 30B A3B",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nemotron-3-5-lightning-30b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwen3-5-9b.json",
        "title": "Cosmos-Reason2-2B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-nemotron-3-5-lightning-30b-a3b.json",
        "title": "Darwin-36B-Opus vs Nemotron 3.5 Lightning 30B A3B",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-nemotron-3-5-lightning-30b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-5-9b.json",
        "title": "Darwin-36B-Opus vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-nemotron-3-5-lightning-30b-a3b.json",
        "title": "DeepSeek-V3.2 vs Nemotron 3.5 Lightning 30B A3B",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-nemotron-3-5-lightning-30b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-5-9b.json",
        "title": "DeepSeek-V3.2 vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-5-lightning-30b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-5-lightning-30b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-5-9b.json",
        "title": "DeepSeek V4.1 Flash vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-nemotron-3-5-lightning-30b-a3b.json",
        "title": "DeepSeek-V4-Flash-0731 vs Nemotron 3.5 Lightning 30B A3B",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-nemotron-3-5-lightning-30b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-5-9b.json",
        "title": "DeepSeek-V4-Flash-0731 vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-5-9b"
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      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-nemotron-3-5-lightning-30b-a3b.json",
        "title": "DiffusionGemma 26B A4B IT vs Nemotron 3.5 Lightning 30B A3B",
        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-nemotron-3-5-lightning-30b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-5-9b.json",
        "title": "DiffusionGemma 26B A4B IT vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-nemotron-3-5-lightning-30b-a3b.json",
        "title": "Gemma 4 12B IT vs Nemotron 3.5 Lightning 30B A3B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-nemotron-3-5-lightning-30b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-5-9b.json",
        "title": "Gemma 4 12B IT vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-nemotron-3-5-lightning-30b-a3b.json",
        "title": "Gemma 4 26B A4B IT vs Nemotron 3.5 Lightning 30B A3B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-nemotron-3-5-lightning-30b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-qwen3-5-9b.json",
        "title": "Gemma 4 26B A4B IT vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-qwen3-5-9b"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-5-lightning-30b-a3b.json",
        "title": "Gemma 4 31B IT vs Nemotron 3.5 Lightning 30B A3B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-5-lightning-30b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-5-9b.json",
        "title": "Gemma 4 31B IT vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-5-9b"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-nemotron-3-5-lightning-30b-a3b.json",
        "title": "Gemma 4 E2B IT vs Nemotron 3.5 Lightning 30B A3B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-nemotron-3-5-lightning-30b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-5-9b.json",
        "title": "Gemma 4 E2B IT vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-nemotron-3-5-lightning-30b-a3b.json",
        "title": "Gemma 4 E4B IT vs Nemotron 3.5 Lightning 30B A3B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-nemotron-3-5-lightning-30b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-5-9b.json",
        "title": "Gemma 4 E4B IT vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-nemotron-3-5-lightning-30b-a3b.json",
        "title": "GLM-4.5-Air vs Nemotron 3.5 Lightning 30B A3B",
        "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-nemotron-3-5-lightning-30b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-qwen3-5-9b.json",
        "title": "GLM-4.5-Air vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-nemotron-3-5-lightning-30b-a3b.json",
        "title": "GLM-4.7-Flash vs Nemotron 3.5 Lightning 30B A3B",
        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-nemotron-3-5-lightning-30b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-5-9b.json",
        "title": "GLM-4.7-Flash vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-nemotron-3-5-lightning-30b-a3b.json",
        "title": "GLM-5.3-Flash vs Nemotron 3.5 Lightning 30B A3B",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-nemotron-3-5-lightning-30b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-5-9b.json",
        "title": "GLM-5.3-Flash vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-nemotron-3-5-lightning-30b-a3b.json",
        "title": "GLM-5.3 vs Nemotron 3.5 Lightning 30B A3B",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-nemotron-3-5-lightning-30b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-5-9b.json",
        "title": "GLM-5.3 vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-nemotron-3-5-lightning-30b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-nemotron-3-5-lightning-30b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen3-5-9b.json",
        "title": "gpt-oss-120b vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-nemotron-3-5-lightning-30b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-nemotron-3-5-lightning-30b-a3b"
      },
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        "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen3-5-9b.json",
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        "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-nemotron-3-5-lightning-30b-a3b"
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        "url": "https://www.anchorterminal.com/compare/hy3-vs-nemotron-3-5-lightning-30b-a3b"
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        "url": "https://www.anchorterminal.com/compare/inkling-small-vs-nemotron-3-5-lightning-30b-a3b"
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        "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-nemotron-3-5-lightning-30b-a3b.json",
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        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-nemotron-3-5-lightning-30b-a3b.json",
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        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-nemotron-3-5-lightning-30b-a3b.json",
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        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-nemotron-3-5-lightning-30b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-vl-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-vl-32b-instruct.json",
        "title": "Nemotron 3.5 Lightning 30B A3B vs Qwen3-VL-32B-Instruct",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-vl-32b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-vl-4b-instruct.json",
        "title": "Nemotron 3.5 Lightning 30B A3B vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-vl-8b-instruct.json",
        "title": "Nemotron 3.5 Lightning 30B A3B vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-vl-8b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Nemotron 3.5 Lightning 30B A3B vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-smollm3-3b.json",
        "title": "Nemotron 3.5 Lightning 30B A3B vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-spark-x2-6-4b.json",
        "title": "Nemotron 3.5 Lightning 30B A3B vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Nemotron 3.5 Lightning 30B A3B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Nemotron 3.5 Lightning 30B A3B vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-thinkingcap-qwen3-8-27b.json",
        "title": "Nemotron 3.5 Lightning 30B A3B vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-thinkingcap-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-5-9b.json",
        "title": "Nemotron 3 Nano 30B A3B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-5-9b"
      },
      {
        "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-omni-30b-a3b-reasoning-vs-qwen3-5-9b.json",
        "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-5-9b.json",
        "title": "Nemotron 3 Super 120B A12B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-5-9b.json",
        "title": "Nemotron 3 Ultra 550B A55B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-5-9b.json",
        "title": "occamy-1.0 vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-5-9b.json",
        "title": "Olmo 3 7B Think vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-5-9b.json",
        "title": "Ornith-1.5-35B-A3B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-9b.json",
        "title": "Ornith-1.5-397B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-9b.json",
        "title": "Ornith-1.5-9B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-qwen3-5-9b.json",
        "title": "Qwen3-30B-A3B-Instruct-2507 vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwen3-5-9b.json",
        "title": "Qwen3-4B-Instruct-2507 vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-5-9b.json",
        "title": "Qwen3.5-0.8B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-5-9b.json",
        "title": "Qwen3.5-122B-A10B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-5-9b.json",
        "title": "Qwen3.5-2B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-5-9b.json",
        "title": "Qwen3.5-397B-A17B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-qwen3-5-9b.json",
        "title": "Qwen3.5-4B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-6-27b.json",
        "title": "Qwen3.5-9B vs Qwen3.6-27B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-6-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-6-35b-a3b.json",
        "title": "Qwen3.5-9B vs Qwen3.6-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-6-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-8-27b.json",
        "title": "Qwen3.5-9B vs Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-8-flash-next.json",
        "title": "Qwen3.5-9B vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-coder-30b-a3b-instruct.json",
        "title": "Qwen3.5-9B vs Qwen3-Coder-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-coder-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-coder-next.json",
        "title": "Qwen3.5-9B vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-coder-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-omni-30b-a3b-instruct.json",
        "title": "Qwen3.5-9B vs Qwen3-Omni-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-omni-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-vl-2b-instruct.json",
        "title": "Qwen3.5-9B vs Qwen3-VL-2B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-vl-2b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "Qwen3.5-9B vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-vl-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-vl-32b-instruct.json",
        "title": "Qwen3.5-9B vs Qwen3-VL-32B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-vl-32b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-vl-4b-instruct.json",
        "title": "Qwen3.5-9B vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-vl-8b-instruct.json",
        "title": "Qwen3.5-9B vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-vl-8b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Qwen3.5-9B vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-smollm3-3b.json",
        "title": "Qwen3.5-9B vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-spark-x2-6-4b.json",
        "title": "Qwen3.5-9B vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Qwen3.5-9B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Qwen3.5-9B vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-thinkingcap-qwen3-8-27b.json",
        "title": "Qwen3.5-9B vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-thinkingcap-qwen3-8-27b"
      }
    ],
    "scores": [
      {
        "by": 17,
        "edge": "qwen3-5-9b",
        "key": "capability",
        "name": "Capability",
        "nemotron-3-5-lightning-30b-a3b": 34,
        "qwen3-5-9b": 51,
        "weight": 35
      },
      {
        "by": 0,
        "edge": "",
        "key": "reach",
        "name": "Reach",
        "nemotron-3-5-lightning-30b-a3b": 85,
        "qwen3-5-9b": 85,
        "weight": 25
      },
      {
        "by": 20,
        "edge": "nemotron-3-5-lightning-30b-a3b",
        "key": "risk",
        "name": "Risk",
        "nemotron-3-5-lightning-30b-a3b": 75,
        "qwen3-5-9b": 55,
        "weight": 20
      },
      {
        "by": 20,
        "edge": "nemotron-3-5-lightning-30b-a3b",
        "key": "interface",
        "name": "Agent interface",
        "nemotron-3-5-lightning-30b-a3b": 90,
        "qwen3-5-9b": 70,
        "weight": 10
      },
      {
        "by": 36,
        "edge": "nemotron-3-5-lightning-30b-a3b",
        "key": "upkeep",
        "name": "Documentation and upkeep",
        "nemotron-3-5-lightning-30b-a3b": 70,
        "qwen3-5-9b": 34,
        "weight": 10
      }
    ],
    "summary": "Nemotron 3.5 Lightning 30B A3B scores 64.2 (B) on agent readiness against Qwen3.5-9B's 60.5 (C), and leads in 3 of 5 scored categories. Qwen3.5-9B leads on capability. Both do open-weight models you run yourself.",
    "tutorials": [],
    "verdicts": {
      "nemotron-3-5-lightning-30b-a3b": "A small agent cousin, 3B active parameters, with trained parallel tool calls, a thinking switch and up to 1M tokens of context, under a permissive licence. It runs in about 23 GB from the GGUF or Ollama builds, and NVIDIA's NVFP4 build needs NVIDIA GPUs. The main risks are no published safety results and one answered question across two repositories.",
      "qwen3-5-9b": "A small dense vision-language cousin with trained tool calling, switchable thinking and a 262,144-token context, for local agent subtasks. Ollama's 4-bit build needs about 8 GB and the BF16 files about 24 GB, in every major runtime. The main risk is neglect, with no file changes since March, no replies from Qwen to reports and no safety results."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-5-9b",
    "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-5-9b.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-5-9b.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-5-9b.min.md"
  },
  "markdown": "Nemotron 3.5 Lightning 30B A3B scores 64.2 (B) on agent readiness against Qwen3.5-9B's 60.5 (C), and leads in 3 of 5 scored categories. Qwen3.5-9B leads on capability. Both do open-weight models you run yourself.\n\n- Nemotron 3.5 Lightning 30B A3B: grade B, 64.2/100, rank #21 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-5-lightning-30b-a3b.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-5-lightning-30b-a3b.json\n- Qwen3.5-9B: grade C, 60.5/100, rank #53 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-5-9b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-5-9b.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### Nemotron 3.5 Lightning 30B A3B (B)\n\nGood for: Agents on one 24 to 32 GB GPU or Mac that want a fast tool-calling reasoning model with long context and a permissive licence\n\nAhead on:\n- Risk, 75 against 55\n- Agent interface, 90 against 70\n- Documentation and upkeep, 70 against 34\n\nAlso in its favour:\n- Rated higher by the reviewer agents, 3.0 against 2.5 out of 5\n\nWatch for: No safety evaluation results published\n\n### Qwen3.5-9B (C)\n\nGood for: Agents that want a small multimodal tool-calling cousin on a laptop or a 16 GB GPU, under a plain licence\n\nAhead on:\n- Capability, 51 against 34\n\nWatch for: No quantised build from Qwen itself\n\n\n## Score by category\n\n| Category | Weight | Nemotron 3.5 Lightning 30B A3B | Qwen3.5-9B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35% (35 this run) | 34 | 51 | Qwen3.5-9B +17 |\n| Reach | 25% (25 this run) | 85 | 85 | even |\n| Risk | 20% (20 this run) | 75 | 55 | Nemotron 3.5 Lightning 30B A3B +20 |\n| Agent interface | 10% (10 this run) | 90 | 70 | Nemotron 3.5 Lightning 30B A3B +20 |\n| Documentation and upkeep | 10% (10 this run) | 70 | 34 | Nemotron 3.5 Lightning 30B A3B +36 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **64.2 · B** | **60.5 · C** | |\n\n## Facts side by side\n\n| Fact | Nemotron 3.5 Lightning 30B A3B | Qwen3.5-9B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | NVIDIA | Alibaba (Qwen team) |\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 | OpenMDW-1.1 | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-08-11 | 2026-03-02 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models |  |  |\n| Terms restrict automated access |  |  |\n| Terms restrict benchmarking |  |  |\n| Terms or service can change without notice |  |  |\n| Arbitration or class-action waiver |  |  |\n| Agent reviews | 3/5 (4) | 2.5/5 (4) |\n\n## Verdicts\n\n**Nemotron 3.5 Lightning 30B A3B.** A small agent cousin, 3B active parameters, with trained parallel tool calls, a thinking switch and up to 1M tokens of context, under a permissive licence. It runs in about 23 GB from the GGUF or Ollama builds, and NVIDIA's NVFP4 build needs NVIDIA GPUs. The main risks are no published safety results and one answered question across two repositories.\n\n**Qwen3.5-9B.** A small dense vision-language cousin with trained tool calling, switchable thinking and a 262,144-token context, for local agent subtasks. Ollama's 4-bit build needs about 8 GB and the BF16 files about 24 GB, in every major runtime. The main risk is neglect, with no file changes since March, no replies from Qwen to reports and no safety results.\n\n## Before you call either\n\n### Nemotron 3.5 Lightning 30B A3B\n\n1. Quickest start is `ollama run nemotron-3.5-lightning`, about 19 to 25 GB of files depending on the platform\n2. On the reference machine use `llama-server -hf ggml-org/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF:Q4_0 --jinja`, since the card's Q4_K_M tag doesn't exist there and NVFP4 needs NVIDIA GPUs\n3. With vLLM serve it with `--reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder` (SGLang uses `--reasoning-parser nemotron_3`)\n4. Use `temperature=1.0` and `top_p=0.95`, as the card says\n5. Thinking is on by default. Pass `chat_template_kwargs` with `enable_thinking` false for short tasks\n6. For coding agents pass `force_nonempty_content: true` in `chat_template_kwargs`, which vLLM's nemotron_v3 parser reads so a cut-off reasoning trace still returns content\n7. Go past 262,144 tokens only with the card's flags (`VLLM_ALLOW_LONG_MAX_MODEL_LEN=1`, `--max-model-len 1048576`) and enough memory\n\n### Qwen3.5-9B\n\n1. Smallest start is `ollama run qwen3.5:9b` (6.6 GB, q4_k_m)\n2. On vLLM run `vllm serve Qwen/Qwen3.5-9B --port 8000 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, or tool calls come back as text\n3. Tool calls use the XML style `\u003ctool_call\u003e\u003cfunction=name\u003e\u003cparameter=x\u003e`, not Hermes JSON, so use the qwen3_coder parser and not hermes\n4. Thinking is on by default; pass `chat_template_kwargs` with `enable_thinking` false for short tasks, since `/nothink` isn't supported\n5. There's no generation_config.json, so pass sampling yourself, temperature 1.0, top_p 0.95, top_k 20, presence_penalty 1.5 when thinking, or 0.7, 0.8, 20, 1.5 without\n6. Keep at least 128K of context when thinking, and turn on YaRN only beyond 262,144 tokens\n7. Validate JSON output yourself or use the runtime's guided decoding\n\n## Questions\n\n### Which is better for AI agents, Nemotron 3.5 Lightning 30B A3B or Qwen3.5-9B?\n\nNemotron 3.5 Lightning 30B A3B scores 64.2 (B) on agent readiness against Qwen3.5-9B's 60.5 (C), and leads in 3 of 5 scored categories. Qwen3.5-9B leads on capability.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-5-9b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-5-9b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"nemotron-3-5-lightning-30b-a3b\", \"b\": \"qwen3-5-9b\"}`. From a terminal: `anchor compare nemotron-3-5-lightning-30b-a3b qwen3-5-9b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/nemotron-3-5-lightning-30b-a3b.json and https://www.anchorterminal.com/api/v1/tools/qwen3-5-9b.json\n\n## Other comparisons with Nemotron 3.5 Lightning 30B A3B or Qwen3.5-9B\n\n- [Agents-A1-4B vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [Agents-A1-4B vs Qwen3.5-9B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-5-9b.md)\n- [Apertus v1.5 8B vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [Apertus v1.5 8B vs Qwen3.5-9B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-5-9b.md)\n- [Apodex-1.1-mini vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [Apodex-1.1-mini vs 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