{ "data": { "a": { "slug": "nemotron-3-nano-30b-a3b", "name": "Nemotron 3 Nano 30B A3B", "vendor": "NVIDIA", "vendorUrl": "https://developer.nvidia.com/nemotron", "kind": "weights", "category": "open-weights", "summary": "Nemotron 3 Nano 30B A3B is NVIDIA's open-weight hybrid Mamba-Transformer mixture of experts for reasoning and agent work, 30B parameters with 3.5B active, published on Hugging Face in BF16, FP8 and NVFP4.", "url": "https://www.anchorterminal.com/tools/nemotron-3-nano-30b-a3b", "markdownUrl": "https://www.anchorterminal.com/tools/nemotron-3-nano-30b-a3b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/nemotron-3-nano-30b-a3b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-30b-a3b.json", "dns": "nemotron-3-nano-30b-a3b.grade.anchorterminal.com", "repo": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16", "license": "NVIDIA Nemotron Open Model License (custom, Apache-style, commercial use allowed, not an OSI licence)", "transports": [], "packages": [ { "registry": "huggingface", "name": "nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16" }, { "registry": "huggingface", "name": "nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-FP8" }, { "registry": "huggingface", "name": "nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4" }, { "registry": "ollama", "name": "nemotron-3-nano:30b" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The BF16, FP8 and NVFP4 repositories are not gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under the NVIDIA Nemotron Open Model License. Running it yourself costs the hardware, 63.2 GB for the BF16 files, 32.7 GB for FP8, 19.4 GB for NVFP4 and 18 GB for Ollama's default build (checked 2026-10-21).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-01" }, "docsUrl": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "mamba", "reasoning", "tool-calling", "commercial-licence", "safetensors", "fp8", "nvfp4", "self-hosted" ], "lastRelease": "2026-08-24", "graded": true, "anchor": { "graded": true, "score": 67.6, "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": 1518, "categoryRank": 9, "methodology": "0.7", "run": "2026-10-01", "scores": { "capability": 46, "interface": 90, "reach": 90, "risk": 60, "upkeep": 80 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-11-01" }, "negative": 0, "verdict": "A fast hybrid Mamba-Transformer cousin with 3.5B active parameters for reasoning and tool calling over very long inputs, in every major local runtime. A 4-bit build needs about 22 to 23 GB. The main risks are that NVIDIA's documented vLLM path runs repository code, no safety results are published and NVIDIA hasn't answered reports since March.", "bestFor": "Agents with a 24 to 32 GB GPU or Mac that want a quick tool-calling and reasoning cousin with very long context.", "strengths": [ "Runs in released llama.cpp, Ollama, vLLM, SGLang, LM Studio and Transformers", "NVIDIA's own FP8 and NVFP4 builds beside BF16, and ungated files in NVIDIA's own organisation with a SHA-256 for every shard", "Tool calling documented end to end with a named parser, several calls to a turn, and thinking that can be turned off or budgeted", "1M tokens of input with no position-encoding scaling", "Full model card and a detailed data card with cut-offs, with much of the training data released" ], "weaknesses": [ "The documented vLLM, SGLang and TensorRT-LLM commands pass trust-remote-code and load a parser file from the repository", "No safety or bias results published, though the subcards say both were run", "No NVIDIA reply on any of the nine questions others opened since 1 March 2026, including a chat-template report", "Licence replaced eight days after release with no dated note, and the subcards still link the old one", "Structured output isn't documented, and the custom licence isn't OSI-approved" ], "agentNotes": [ "Start it with `ollama run nemotron-3-nano:30b` (18 GB MLX on a Mac, 24 GB q4_K_M elsewhere)", "On the reference machine use BF16 with vLLM, after `wget https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16/resolve/main/nano_v3_reasoning_parser.py`, then `vllm serve nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 --max-model-len 262144 --trust-remote-code --enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser-plugin nano_v3_reasoning_parser.py --reasoning-parser nano_v3`", "NVFP4 needs an NVIDIA Blackwell GPU, and FP8 isn't documented on gfx1151", "Without the qwen3_coder tool parser and the nano_v3 reasoning parser, tool calls and reasoning come back as plain text", "Card sampling is temperature 0.6 and top_p 0.95 for tool calling, temperature 1.0 and top_p 1.0 for reasoning", "Thinking is on by default, so pass enable_thinking as false in chat_template_kwargs for short tasks", "Set VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 and raise --max-model-len only when you need more than 262,144 tokens", "In SGLang the main branch names the reasoning parser nemotron_3, not the card's nano_v3", "Filter untrusted tool results before they reach the model, as NVIDIA warns prompt injection can lead to code execution", "Validate JSON output yourself or use the runtime's guided decoding, since none is documented for the model" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3.3, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "B", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 67.6 } ], "editorialScores": { "capability": 46, "interface": 90, "reach": 90, "risk": 60, "upkeep": 80 }, "provenanceScore": 75, "checked": { "categories": { "capability": "2026-11-01", "interface": "2026-11-01", "reach": "2026-11-01", "risk": "2026-11-01", "upkeep": "2026-11-01" }, "last": "2026-11-01", "lastMoved": "2026-11-01", "oldest": "2026-11-01", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "ollama run nemotron-3-nano:30b\n# or, with vLLM per NVIDIA's card (the reasoning parser is a file from the repository):\nwget https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16/resolve/main/nano_v3_reasoning_parser.py\nvllm serve nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 --max-model-len 262144 --trust-remote-code \\\n --enable-auto-tool-choice --tool-call-parser qwen3_coder \\\n --reasoning-parser-plugin nano_v3_reasoning_parser.py --reasoning-parser nano_v3\n# or, with llama.cpp and llama.cpp's own GGUF:\nllama-server -hf ggml-org/NVIDIA-Nemotron-3-Nano-30B-A3B-GGUF:Q4_K_M --jinja", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"nemotron-3-nano:30b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/nemotron-3-nano-30b-a3b" }, "sameCompany": [ "nemotron-3-5-super-vl", "nvidia-nemo-retriever", "nemo-guardrails", "nemotron-3-5-asr-streaming-0-6b", "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-4b", "nemotron-3-nano-omni-30b-a3b-reasoning", "nemotron-3-super-120b-a12b", "nemotron-3-ultra-550b-a55b", "nemotron-3-5-lightning-30b-a3b" ], "weights": { "trainedBy": "NVIDIA", "builtOn": "nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-Base-BF16", "params": "30B", "active": "3.5B", "context": 1048576, "goodFor": "Hybrid Mamba-Transformer mixture of experts for reasoning and agent work, text only, with tool calling and a reasoning budget.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "LM Studio", "Transformers" ], "builds": [ { "name": "NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4", "who": "publisher", "precision": "nvfp4", "sizeGB": 19.3, "memoryGB": 24, "format": "safetensors", "note": "NVFP4 needs NVIDIA Blackwell" }, { "name": "ollama nemotron-3-nano:30b", "url": "https://ollama.com/library/nemotron-3-nano:30b", "who": "ollama", "precision": "q4_k_m", "sizeGB": 24, "memoryGB": 32, "format": "ollama", "runsOn": [ "Ollama" ], "note": "digest 7487f38ecf24. Size as Ollama's tags page shows it, rounded to whole GB above 10 GB; the Linux build is the llama.cpp variant" }, { "name": "ggml-org NVIDIA-Nemotron-3-Nano-30B-A3B-GGUF (Q4_K_M)", "url": "https://huggingface.co/ggml-org/NVIDIA-Nemotron-3-Nano-30B-A3B-GGUF", "who": "llama.cpp", "precision": "q4_k_m", "sizeGB": 22.42, "memoryGB": 32, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "From ggml-org, the llama.cpp project's organisation. 22.42 GB of Q4_K_M weights, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it yet" }, { "name": "NVIDIA-Nemotron-3-Nano-30B-A3B-FP8", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 32.7, "memoryGB": 48, "format": "safetensors" }, { "name": "ggml-org NVIDIA-Nemotron-3-Nano-30B-A3B-GGUF (Q8_0)", "url": "https://huggingface.co/ggml-org/NVIDIA-Nemotron-3-Nano-30B-A3B-GGUF", "who": "llama.cpp", "precision": "q8_0", "sizeGB": 33.59, "memoryGB": 48, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "From ggml-org, the llama.cpp project's organisation. 33.59 GB of Q8_0 weights, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it yet" }, { "name": "NVIDIA-Nemotron-3-Nano-30B-A3B-BF16", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16", "who": "publisher", "precision": "bf16", "sizeGB": 63.2, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] } ], "run": "vllm serve nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4 --max-model-len 262144 --trust-remote-code --enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser-plugin nano_v3_reasoning_parser.py --reasoning-parser nano_v3", "runBuild": "NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4", "checked": "2026-10-21", "minMemoryGB": 24 }, "family": { "lab": "NVIDIA (Nemotron)", "family": "Nemotron 3", "url": "https://www.anchorterminal.com/open-weights/#nvidia", "others": [ { "name": "Nemotron 3 Nano 30B-A3B Base", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-Base-BF16", "params": "30B total, 3B active" }, { "name": "Nemotron 3 Super 120B-A12B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16", "params": "120B total, 12B active", "listing": "https://www.anchorterminal.com/tools/nemotron-3-super-120b-a12b" }, { "name": "Nemotron 3 Super 120B-A12B Base", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-Base-BF16", "params": "120B total, 12B active" }, { "name": "Nemotron 3 Nano 4B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16", "params": "4B", "listing": "https://www.anchorterminal.com/tools/nemotron-3-nano-4b" }, { "name": "Nemotron 3 Nano Omni 30B-A3B Reasoning", "url": "https://huggingface.co/nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16", "params": "30B total, 3B active", "listing": "https://www.anchorterminal.com/tools/nemotron-3-nano-omni-30b-a3b-reasoning" }, { "name": "Nemotron 3 Ultra 550B-A55B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16", "params": "550B total, 55B active", "listing": "https://www.anchorterminal.com/tools/nemotron-3-ultra-550b-a55b" }, { "name": "Nemotron 3 Ultra 550B-A55B Base", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-Base-BF16", "params": "550B total, 55B active" }, { "name": "Nemotron 3 Ultra 550B-A55B GenRM", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-GenRM", "params": "550B total, 55B active" }, { "name": "Nemotron 3.5 Lightning 30B-A3B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16", "params": "30B total, 3B active", "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-lightning-30b-a3b" }, { "name": "Nemotron 3.5 Lightning 30B-A3B Base", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-Base-BF16", "params": "30B total, 3B active" }, { "name": "Nemotron 3.5 Super VL 120B-A12B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16", "params": "120B total, 12B active", "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-super-vl" }, { "name": "Nemotron 3.5 Super VL 120B-A12B Base", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-Base-BF16", "params": "120B total, 12B active" }, { "name": "Nemotron 3.5 Bolt 74B-A9B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Bolt-74B-A9B-BF16", "params": "74B total, 9B active" }, { "name": "Nemotron 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": "https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-nemotron-open-model-license/", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16/commits/main", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The card names NVIDIA Corporation as model developer, and the licence names NVIDIA. RDAP gives nvidia.com a registration date of 20 April 1993.", "Nothing is hosted for this listing, so the Nemotron Open Model License stands in for terms of service and no privacy policy is scored.", "The changelog is the repository's commit history. A few messages say what changed (a reasoning budget example on 19 December 2025, a 1M-context vLLM example on 8 January 2026); most read Update README.md.", "www.nvidia.com/.well-known/security.txt answered 403 on 21 October 2026. Vulnerability reports go to NVIDIA PSIRT, linked from the card, which publishes a disclosure policy and security bulletins." ], "score": 75 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/nemotron-3-nano-30b-a3b.json", "live": { "slug": "nemotron-3-nano-30b-a3b", "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-30B-A3B-BF16/commits/main", "kind": "changelog", "status": 200, "checkedAt": "2026-11-10T16:23:18.236938919Z", "changedAt": "2026-11-10T16:23:18.236938919Z", "fingerprint": "d16f84ee772a" }, { "url": "https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-nemotron-open-model-license/", "kind": "terms", "status": 304, "checkedAt": "2026-11-10T16:43:25.039992347Z", "changedAt": "2026-10-28T04:52:55.000219692Z", "fingerprint": "093132ed6fed" } ], "updatedAt": "2026-11-10T16:43:25.039992347Z" } }, "answer": "Nemotron 3 Nano 30B A3B scores 67.6 (B) on agent readiness against Qwen-AgentWorld-35B-A3B's 52.1 (D), and leads in every scored category.", "b": { "slug": "qwen-agentworld-35b-a3b", "name": "Qwen-AgentWorld-35B-A3B", "vendor": "Alibaba (Qwen team)", "vendorUrl": "https://qwen.ai", "kind": "weights", "category": "open-weights", "summary": "Qwen-AgentWorld-35B-A3B is Alibaba's open-weight language world model, a 35B mixture of experts with 3B active that simulates agent environments by predicting the next observation across seven domains, published in BF16 under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/qwen-agentworld-35b-a3b", "markdownUrl": "https://www.anchorterminal.com/tools/qwen-agentworld-35b-a3b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/qwen-agentworld-35b-a3b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/qwen-agentworld-35b-a3b.json", "dns": "qwen-agentworld-35b-a3b.grade.anchorterminal.com", "repo": "https://huggingface.co/Qwen/Qwen-AgentWorld-35B-A3B", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "Qwen/Qwen-AgentWorld-35B-A3B" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-11-10).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0, with a LICENSE file in the repository. Running it yourself costs the hardware, 69.3 GB for the BF16 files, or 22.1 GB for Unsloth's 4-bit GGUF (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/Qwen/Qwen-AgentWorld-35B-A3B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "world-model", "agent-simulation", "reasoning", "apache-2.0", "safetensors", "self-hosted" ], "lastRelease": "2026-06-25", "graded": true, "anchor": { "graded": true, "score": 52.1, "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": 104, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 50, "reach": 55, "risk": 55, "upkeep": 41 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "A specialist, not a chat model. It plays the environment an agent acts in, across seven domains, so you can train or test an agent without the real thing. Alibaba ships BF16 only, which fits a 128 GB machine, and hasn't answered a discussion on it or changed the files since June.", "bestFor": "Agent builders who want to train or test an agent against simulated tools, terminals, browsers and phones on a 128 GB machine", "strengths": [ "Simulates seven kinds of agent environment in one model, with the system prompts for each published", "Apache 2.0, ungated, safetensors only", "Runs in vLLM, SGLang, Transformers and llama.cpp from their releases", "Fits a 128 GB machine in BF16, and 32 GB from Unsloth's 4-bit GGUF", "A 262,144-token context for long interaction histories" ], "weaknesses": [ "Not a general assistant, and the card doesn't describe using it to call tools", "No quantised build from Alibaba, so the GGUF route depends on a third party", "No reply from Alibaba on any of 10 discussions, and no change to the files since 25 June 2026", "No limitations, safety results or security contact on the card", "Its results are on Alibaba's own benchmark" ], "agentNotes": [ "Use it as the environment, not the agent. Give it the domain's system prompt from Alibaba's GitHub repository, the history and the agent's action, and it returns the predicted observation", "With vLLM pass `--language-model-only`, or startup fails on the missing vision weights", "Use temperature 0.6, top_p 0.95 and top_k 20, and allow up to 32,768 output tokens, as the card recommends", "Keep at least 128K of context, since the simulation depends on the history", "Thinking is on by default and comes back through the qwen3 reasoning parser", "A simulated observation is a prediction. Check anything that matters against the real environment" ], "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": 52.1 } ], "editorialScores": { "interface": 50, "reach": 55, "risk": 55, "upkeep": 41 }, "provenanceScore": 66, "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": "# vLLM, as the card gives it (four GPUs)\nvllm serve Qwen/Qwen-AgentWorld-35B-A3B --port 8000 --tensor-parallel-size 4 \\\n --max-model-len 262144 --reasoning-parser qwen3 --language-model-only --trust-remote-code\n# or, with llama.cpp and Unsloth's GGUF, a third party's conversion:\nllama-server -hf unsloth/Qwen-AgentWorld-35B-A3B-GGUF:UD-Q4_K_M --jinja", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\"model\": \"Qwen/Qwen-AgentWorld-35B-A3B\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}], \"temperature\": 0.6}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/qwen-agentworld-35b-a3b" }, "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", "qwen3-5-9b", "qwen3-6-27b", "qwen3-6-35b-a3b", "qwen3-8-27b", "qwen3-8-flash-next" ], "weights": { "trainedBy": "Alibaba (Qwen)", "builtOn": "Qwen3.5-35B-A3B-Base, named on the card and in its metadata", "params": "34.7B", "active": "3B", "context": 262144, "goodFor": "A simulator of agent environments (MCP tools, search, terminal, software engineering, Android, web and OS) that predicts the next observation, for training and testing agents without the real environment.", "runtimes": [ "vLLM", "SGLang", "Transformers", "llama.cpp" ], "builds": [ { "name": "unsloth Qwen-AgentWorld-35B-A3B-GGUF (UD-Q4_K_M)", "url": "https://huggingface.co/unsloth/Qwen-AgentWorld-35B-A3B-GGUF", "who": "unsloth", "precision": "q4_k_m", "sizeGB": 22.13, "memoryGB": 32, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "From Unsloth, a third party's conversion and not Alibaba's. 22.13 GB of UD-Q4_K_M weights, from the Hub's model information on 10 November 2026. Reach doesn't count it" }, { "name": "unsloth Qwen-AgentWorld-35B-A3B-GGUF (Q8_0)", "url": "https://huggingface.co/unsloth/Qwen-AgentWorld-35B-A3B-GGUF", "who": "unsloth", "precision": "q8_0", "sizeGB": 36.9, "memoryGB": 48, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "From Unsloth, a third party's conversion and not Alibaba's. 36.90 GB of Q8_0 weights, from the Hub's model information on 10 November 2026. Reach doesn't count it" }, { "name": "Qwen-AgentWorld-35B-A3B", "url": "https://huggingface.co/Qwen/Qwen-AgentWorld-35B-A3B", "who": "publisher", "precision": "bf16", "sizeGB": 69.32, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)", "Transformers" ], "note": "The only build Alibaba publishes. Language weights only" } ], "run": "llama-server -hf unsloth/Qwen-AgentWorld-35B-A3B-GGUF:UD-Q4_K_M --jinja", "runBuild": "unsloth Qwen-AgentWorld-35B-A3B-GGUF (UD-Q4_K_M)", "checked": "2026-11-10", "minMemoryGB": 32 }, "family": { "lab": "Alibaba (Qwen)", "family": "Qwen-AgentWorld", "url": "https://www.anchorterminal.com/open-weights/#qwen", "olderCount": 109 }, "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": "https://huggingface.co/Qwen/Qwen-AgentWorld-35B-A3B/commits/main", "securityTxt": "none", "checked": "2026-11-10", "notes": [ "The LICENSE file names Alibaba Cloud, as the Qwen3.6-35B-A3B listing records for the organisation.", "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "The changelog is the repository's commit history, nine commits between 22 and 25 June 2026, the upload and updates to the card.", "The Qwen3.6-35B-A3B listing found no security.txt on qwen.ai. unchecked on 10 November." ], "score": 66 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/qwen-agentworld-35b-a3b.json" }, "edges": [ { "by": 40, "category": "interface", "leads": "nemotron-3-nano-30b-a3b", "name": "Agent interface", "reasons": { "a": "The chat template earns 20 as a Jinja file in the repository. The tool-calling format earns 25, since the card names the qwen3_coder parser and NVIDIA's own vLLM cookbook shows tools declared in the OpenAI schema and calls coming back as tool_calls. Trained tool calling earns 20, multi-step tool use in training and several calls to a turn in the template. Structured output earns 5, a claim of structured output training with no way to ask for JSON. Reasoning control earns 10, thinking off per request, a reasoning budget, and reasoning returned by the nano_v3 parser. Context earns 10 for 1M tokens with no scaling.", "b": "Chat template 20, chat_template.jinja in the repository, the same as the template in tokenizer_config.json. Tool-calling format 10, only the template says how tools are declared and how calls come back. Trained tool calling 10, the template renders several tool_call blocks in one turn, but the card presents the model as a simulator of what tools and environments return, not as a tool caller. Structured output 0, no JSON or JSON Schema output documented for the model. Reasoning control 0, the card says thinking is on by default and names the qwen3 reasoning parser, but doesn't say how to turn thinking off or budget it. Context 10, 262,144 tokens native per the card and the text config. 20 + 10 + 10 + 0 + 0 + 10 = 50." }, "scores": { "a": 90, "b": 50 } }, { "by": 39, "category": "upkeep", "leads": "nemotron-3-nano-30b-a3b", "name": "Documentation and upkeep", "reasons": { "a": "The model card earns 20, with all seven items on the card and its subcards. Training data earns 20, datasets listed with sources and token counts, both cut-offs, the filtering, and much of the data released, though some synthetic sets don't give a size. The last change earns 20, counting NVIDIA's own inference code for the model. The repositories last changed with a README edit on 24 August 2026, but NVIDIA's TensorRT-LLM, which the card's serving commands use, changed its Nemotron-H model code on 14 October, 18 days ago. Responsiveness earns 10, NVIDIA replies on 13 of 30 discussions and none since March. Version policy earns 5 for a first release with no dated notes. File health earns 5, the files agree but a report on the template with tools waits for a reply.", "b": "Model card 11, the card gives intended use (simulating agent environments), architecture, parameters and context length. Training data 5, A general description without a cut-off, the three training stages and a statement that no outputs from external API services were included. Last change 10, the last commit was on 25 June 2026, an update to the card, 138 days before this check, past 90 days and within 180. Responsiveness 0, 10 discussions that aren't pull requests, opened between 24 June and 5 July 2026. Version policy 5, A first release, with nothing replaced. File health 10, Config, both copies of the template and the card agree on the architecture and the context. 11 + 5 + 10 + 0 + 5 + 10 = 41." }, "scores": { "a": 80, "b": 41 } }, { "by": 35, "category": "reach", "leads": "nemotron-3-nano-30b-a3b", "name": "Reach", "reasons": { "a": "Memory earns 25 because the smallest 4-bit build is Ollama's NVFP4 MLX build at 18 GB, 21.6 GB with the fifth added, and NVIDIA's own NVFP4 build lands in the same 24 GB band. Runtimes earn the full 25, with released llama.cpp, Ollama, vLLM, SGLang, LM Studio and Transformers all running it. Builds earn 15 for NVIDIA's own FP8 and NVFP4 builds. Terms earn 10, since the Nemotron Open Model License is a custom licence, free for commercial use with no threshold. Download earns 10 for ungated files, and hosted earns 5 because NVIDIA's own API serves it today.", "b": "Memory 10, Alibaba's only build is the BF16 safetensors, 69.32 GB, and 69.32 GB × 1.2 = 83.2 GB, above 64 GB and inside the reference machine's GPU share. Runtimes 20, Four released runtimes run the architecture, vLLM 0.33.0 (Qwen3_5MoeForConditionalGeneration), SGLang 0.5.23, Transformers 5.22.2 (qwen3_5_moe) and llama.cpp b12242 (its Qwen conversion code). Builds 0, Alibaba publishes the BF16 weights only. Terms 15, Apache 2.0, an OSI licence, in the metadata and as a LICENSE file, with no use policy or other terms added. Download 10, the repository isn't gated, so the files download with no account and no terms to accept. Hosted 0, Hugging Face lists no inference provider for the model (an empty provider mapping on 10 November 2026). 10 + 20 + 0 + 15 + 10 + 0 = 55." }, "scores": { "a": 90, "b": 55 } }, { "by": 5, "category": "risk", "leads": "nemotron-3-nano-30b-a3b", "name": "Risk", "reasons": { "a": "File safety earns 15, safetensors only but with the documented serving path passing trust-remote-code and loading a reasoning parser file from the repository. Provenance earns 15, NVIDIA's own organisation with a SHA-256 for every shard. Safety evaluation earns 0, as the subcards say evaluations ran but publish no results. Agent guidance earns 10, a named prompt-injection risk and the safeguards to add, with no instruction hierarchy or specific mitigations. Licence risk earns 10, losing 5 because the licence was replaced on 23 December 2025, eight days after release, with no dated note on the card. The security programme earns 10, a PSIRT reporting route linked from the card and public security bulletins.", "b": "File safety 25, 21 safetensors shards and no pickle or Python files, and config.json has no auto_map. Provenance 15, files sit in the Qwen organisation, the one Alibaba's Qwen repositories on GitHub link to per the Qwen3.6-35B-A3B listing (10), and the Hub gives a SHA-256 for every large file (5). Safety evaluation 0, no safety results on the card. Agent guidance 0, no prompt-injection guidance, instruction hierarchy, refusal statement or general cautions on the card. Licence risk 15, Apache 2.0 has no use policy, no threshold and no terms the licensor can change or revoke, and the card's licence link points at the LICENSE file in the repository. Security programme 0, no reporting route on the card. 25 + 15 + 0 + 0 + 15 + 0 = 55." }, "scores": { "a": 60, "b": 55 } } ], "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": "NVIDIA Nemotron Open Model License (custom, Apache-style, commercial use allowed, not an OSI licence)", "b": "Apache-2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-08-24", "b": "2026-06-25", "name": "Last release" }, { "a": "2025-12-15", "b": "no document linked", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "not found in the text", "b": "not read yet", "name": "Customer content may train models" }, { "a": "not found in the text", "b": "not read yet", "name": "Terms restrict automated access" }, { "a": "not found in the text", "b": "not read yet", "name": "Terms restrict benchmarking" }, { "a": "not found in the text", "b": "not read yet", "name": "Terms or service can change without notice" }, { "a": "not found in the text", "b": "not read yet", "name": "Arbitration or class-action waiver" }, { "a": "3.3/5 (4)", "b": "none", "name": "Agent reviews" } ], "faq": [ { "answer": "Nemotron 3 Nano 30B A3B scores 67.6 (B) on agent readiness against Qwen-AgentWorld-35B-A3B's 52.1 (D), and leads in every scored category.", "question": "Which is better for AI agents, Nemotron 3 Nano 30B A3B or Qwen-AgentWorld-35B-A3B?" } ], "goodFor": [ { "aheadOn": [ "Reach, 90 against 55", "Risk, 60 against 55", "Agent interface, 90 against 50", "Documentation and upkeep, 80 against 41" ], "also": null, "goodFor": "Agents with a 24 to 32 GB GPU or Mac that want a quick tool-calling and reasoning cousin with very long context.", "slug": "nemotron-3-nano-30b-a3b", "watchFor": "The documented vLLM, SGLang and TensorRT-LLM commands pass trust-remote-code and load a parser file from the repository" }, { "aheadOn": null, "also": null, "goodFor": "Agent builders who want to train or test an agent against simulated tools, terminals, browsers and phones on a 128 GB machine", "slug": "qwen-agentworld-35b-a3b", "watchFor": "Not a general assistant, and the card doesn't describe using it to call tools" } ], "history": { "nemotron-3-nano-30b-a3b": [ { "causes": [ "correction" ], "date": "2026-11-01", "delta": 0.5, "from": 67.1, "fromGrade": "B", "to": 67.6, "toGrade": "B" } ], "qwen-agentworld-35b-a3b": null }, "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": 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"json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwen-agentworld-35b-a3b.json", "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwen-agentworld-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nemotron-3-nano-30b-a3b.json", "title": "Kimi K3.1 Pro vs Nemotron 3 Nano 30B A3B", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nemotron-3-nano-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen-agentworld-35b-a3b.json", "title": "Kimi K3.1 Pro vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen-agentworld-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-nemotron-3-nano-30b-a3b.json", "title": "Kimi K3 vs Nemotron 3 Nano 30B A3B", "url": 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"https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-recursive-flint.json", "title": "Qwen-AgentWorld-35B-A3B vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-ripple-65b-preview.json", "title": "Qwen-AgentWorld-35B-A3B vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-sensenova-loop-9b-preview.json", "title": "Qwen-AgentWorld-35B-A3B vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-smollm3-3b.json", "title": "Qwen-AgentWorld-35B-A3B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-spark-x2-6-4b.json", "title": "Qwen-AgentWorld-35B-A3B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-step-3-7-flash.json", "title": "Qwen-AgentWorld-35B-A3B vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-swift-1-5-qwen3-8-27b.json", "title": "Qwen-AgentWorld-35B-A3B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Qwen-AgentWorld-35B-A3B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen-AgentWorld-35B-A3B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-unlimited-ocr-2.json", "title": "Qwen-AgentWorld-35B-A3B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-wevisone-vl-2b.json", "title": "Qwen-AgentWorld-35B-A3B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-wevisone-vl-4b.json", "title": "Qwen-AgentWorld-35B-A3B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-zaya1-8b.json", "title": "Qwen-AgentWorld-35B-A3B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 35, "edge": "nemotron-3-nano-30b-a3b", "key": "reach", "name": "Reach", "nemotron-3-nano-30b-a3b": 90, "qwen-agentworld-35b-a3b": 55, "weight": 25 }, { "by": 5, "edge": "nemotron-3-nano-30b-a3b", "key": "risk", "name": "Risk", "nemotron-3-nano-30b-a3b": 60, "qwen-agentworld-35b-a3b": 55, "weight": 20 }, { "by": 40, "edge": "nemotron-3-nano-30b-a3b", "key": "interface", "name": "Agent interface", "nemotron-3-nano-30b-a3b": 90, "qwen-agentworld-35b-a3b": 50, "weight": 10 }, { "by": 39, "edge": "nemotron-3-nano-30b-a3b", "key": "upkeep", "name": "Documentation and upkeep", "nemotron-3-nano-30b-a3b": 80, "qwen-agentworld-35b-a3b": 41, "weight": 10 } ], "summary": "Nemotron 3 Nano 30B A3B scores 67.6 (B) on agent readiness against Qwen-AgentWorld-35B-A3B's 52.1 (D), and leads in every scored category. Both do open-weight models you run yourself.", "tasks": null, "tutorials": [], "verdicts": { "nemotron-3-nano-30b-a3b": "A fast hybrid Mamba-Transformer cousin with 3.5B active parameters for reasoning and tool calling over very long inputs, in every major local runtime. A 4-bit build needs about 22 to 23 GB. The main risks are that NVIDIA's documented vLLM path runs repository code, no safety results are published and NVIDIA hasn't answered reports since March.", "qwen-agentworld-35b-a3b": "A specialist, not a chat model. It plays the environment an agent acts in, across seven domains, so you can train or test an agent without the real thing. Alibaba ships BF16 only, which fits a 128 GB machine, and hasn't answered a discussion on it or changed the files since June." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen-agentworld-35b-a3b", "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen-agentworld-35b-a3b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen-agentworld-35b-a3b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen-agentworld-35b-a3b.min.md" }, "markdown": "Nemotron 3 Nano 30B A3B scores 67.6 (B) on agent readiness against Qwen-AgentWorld-35B-A3B's 52.1 (D), and leads in every scored category. Both do open-weight models you run yourself.\n\n- Nemotron 3 Nano 30B A3B: grade B, 67.6/100, rank #12 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-nano-30b-a3b.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-30b-a3b.json\n- Qwen-AgentWorld-35B-A3B: grade D, 52.1/100, rank #141 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen-agentworld-35b-a3b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen-agentworld-35b-a3b.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### Nemotron 3 Nano 30B A3B (B)\n\nGood for: Agents with a 24 to 32 GB GPU or Mac that want a quick tool-calling and reasoning cousin with very long context.\n\nAhead on:\n- Reach, 90 against 55\n- Risk, 60 against 55\n- Agent interface, 90 against 50\n- Documentation and upkeep, 80 against 41\n\nWatch for: The documented vLLM, SGLang and TensorRT-LLM commands pass trust-remote-code and load a parser file from the repository\n\n### Qwen-AgentWorld-35B-A3B (D)\n\nGood for: Agent builders who want to train or test an agent against simulated tools, terminals, browsers and phones on a 128 GB machine\n\nWatch for: Not a general assistant, and the card doesn't describe using it to call tools\n\n\n## Why the edge\n\n### Agent interface: Nemotron 3 Nano 30B A3B +40\n\n- Nemotron 3 Nano 30B A3B (90): The chat template earns 20 as a Jinja file in the repository. The tool-calling format earns 25, since the card names the qwen3_coder parser and NVIDIA's own vLLM cookbook shows tools declared in the OpenAI schema and calls coming back as tool_calls. Trained tool calling earns 20, multi-step tool use in training and several calls to a turn in the template. Structured output earns 5, a claim of structured output training with no way to ask for JSON. Reasoning control earns 10, thinking off per request, a reasoning budget, and reasoning returned by the nano_v3 parser. Context earns 10 for 1M tokens with no scaling.\n- Qwen-AgentWorld-35B-A3B (50): Chat template 20, chat_template.jinja in the repository, the same as the template in tokenizer_config.json. Tool-calling format 10, only the template says how tools are declared and how calls come back. Trained tool calling 10, the template renders several tool_call blocks in one turn, but the card presents the model as a simulator of what tools and environments return, not as a tool caller. Structured output 0, no JSON or JSON Schema output documented for the model. Reasoning control 0, the card says thinking is on by default and names the qwen3 reasoning parser, but doesn't say how to turn thinking off or budget it. Context 10, 262,144 tokens native per the card and the text config. 20 + 10 + 10 + 0 + 0 + 10 = 50.\n\n### Documentation and upkeep: Nemotron 3 Nano 30B A3B +39\n\n- Nemotron 3 Nano 30B A3B (80): The model card earns 20, with all seven items on the card and its subcards. Training data earns 20, datasets listed with sources and token counts, both cut-offs, the filtering, and much of the data released, though some synthetic sets don't give a size. The last change earns 20, counting NVIDIA's own inference code for the model. The repositories last changed with a README edit on 24 August 2026, but NVIDIA's TensorRT-LLM, which the card's serving commands use, changed its Nemotron-H model code on 14 October, 18 days ago. Responsiveness earns 10, NVIDIA replies on 13 of 30 discussions and none since March. Version policy earns 5 for a first release with no dated notes. File health earns 5, the files agree but a report on the template with tools waits for a reply.\n- Qwen-AgentWorld-35B-A3B (41): Model card 11, the card gives intended use (simulating agent environments), architecture, parameters and context length. Training data 5, A general description without a cut-off, the three training stages and a statement that no outputs from external API services were included. Last change 10, the last commit was on 25 June 2026, an update to the card, 138 days before this check, past 90 days and within 180. Responsiveness 0, 10 discussions that aren't pull requests, opened between 24 June and 5 July 2026. Version policy 5, A first release, with nothing replaced. File health 10, Config, both copies of the template and the card agree on the architecture and the context. 11 + 5 + 10 + 0 + 5 + 10 = 41.\n\n### Reach: Nemotron 3 Nano 30B A3B +35\n\n- Nemotron 3 Nano 30B A3B (90): Memory earns 25 because the smallest 4-bit build is Ollama's NVFP4 MLX build at 18 GB, 21.6 GB with the fifth added, and NVIDIA's own NVFP4 build lands in the same 24 GB band. Runtimes earn the full 25, with released llama.cpp, Ollama, vLLM, SGLang, LM Studio and Transformers all running it. Builds earn 15 for NVIDIA's own FP8 and NVFP4 builds. Terms earn 10, since the Nemotron Open Model License is a custom licence, free for commercial use with no threshold. Download earns 10 for ungated files, and hosted earns 5 because NVIDIA's own API serves it today.\n- Qwen-AgentWorld-35B-A3B (55): Memory 10, Alibaba's only build is the BF16 safetensors, 69.32 GB, and 69.32 GB × 1.2 = 83.2 GB, above 64 GB and inside the reference machine's GPU share. Runtimes 20, Four released runtimes run the architecture, vLLM 0.33.0 (Qwen3_5MoeForConditionalGeneration), SGLang 0.5.23, Transformers 5.22.2 (qwen3_5_moe) and llama.cpp b12242 (its Qwen conversion code). Builds 0, Alibaba publishes the BF16 weights only. Terms 15, Apache 2.0, an OSI licence, in the metadata and as a LICENSE file, with no use policy or other terms added. Download 10, the repository isn't gated, so the files download with no account and no terms to accept. Hosted 0, Hugging Face lists no inference provider for the model (an empty provider mapping on 10 November 2026). 10 + 20 + 0 + 15 + 10 + 0 = 55.\n\n### Risk: Nemotron 3 Nano 30B A3B +5\n\n- Nemotron 3 Nano 30B A3B (60): File safety earns 15, safetensors only but with the documented serving path passing trust-remote-code and loading a reasoning parser file from the repository. Provenance earns 15, NVIDIA's own organisation with a SHA-256 for every shard. Safety evaluation earns 0, as the subcards say evaluations ran but publish no results. Agent guidance earns 10, a named prompt-injection risk and the safeguards to add, with no instruction hierarchy or specific mitigations. Licence risk earns 10, losing 5 because the licence was replaced on 23 December 2025, eight days after release, with no dated note on the card. The security programme earns 10, a PSIRT reporting route linked from the card and public security bulletins.\n- Qwen-AgentWorld-35B-A3B (55): File safety 25, 21 safetensors shards and no pickle or Python files, and config.json has no auto_map. Provenance 15, files sit in the Qwen organisation, the one Alibaba's Qwen repositories on GitHub link to per the Qwen3.6-35B-A3B listing (10), and the Hub gives a SHA-256 for every large file (5). Safety evaluation 0, no safety results on the card. Agent guidance 0, no prompt-injection guidance, instruction hierarchy, refusal statement or general cautions on the card. Licence risk 15, Apache 2.0 has no use policy, no threshold and no terms the licensor can change or revoke, and the card's licence link points at the LICENSE file in the repository. Security programme 0, no reporting route on the card. 25 + 15 + 0 + 0 + 15 + 0 = 55.\n\n## Score by category\n\n| Category | Weight | Nemotron 3 Nano 30B A3B | Qwen-AgentWorld-35B-A3B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 90 | 55 | Nemotron 3 Nano 30B A3B +35 |\n| Risk | 20% (20 this run) | 60 | 55 | Nemotron 3 Nano 30B A3B +5 |\n| Agent interface | 10% (10 this run) | 90 | 50 | Nemotron 3 Nano 30B A3B +40 |\n| Documentation and upkeep | 10% (10 this run) | 80 | 41 | Nemotron 3 Nano 30B A3B +39 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **67.6 · B** | **52.1 · D** | |\n\n## Facts side by side\n\n| Fact | Nemotron 3 Nano 30B A3B | Qwen-AgentWorld-35B-A3B |\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 | NVIDIA Nemotron Open Model License (custom, Apache-style, commercial use allowed, not an OSI licence) | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-08-24 | 2026-06-25 |\n| Terms last updated | 2025-12-15 | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models | not found in the text | not read yet |\n| Terms restrict automated access | not found in the text | not read yet |\n| Terms restrict benchmarking | not found in the text | not read yet |\n| Terms or service can change without notice | not found in the text | not read yet |\n| Arbitration or class-action waiver | not found in the text | not read yet |\n| Agent reviews | 3.3/5 (4) | none |\n\n## How the scores have moved\n\n### Nemotron 3 Nano 30B A3B\n\n- +0.5 on 1 November 2026: correction\n\n### Qwen-AgentWorld-35B-A3B\n\nNo score change since it was listed.\n\n## Verdicts\n\n**Nemotron 3 Nano 30B A3B.** A fast hybrid Mamba-Transformer cousin with 3.5B active parameters for reasoning and tool calling over very long inputs, in every major local runtime. A 4-bit build needs about 22 to 23 GB. The main risks are that NVIDIA's documented vLLM path runs repository code, no safety results are published and NVIDIA hasn't answered reports since March.\n\n**Qwen-AgentWorld-35B-A3B.** A specialist, not a chat model. It plays the environment an agent acts in, across seven domains, so you can train or test an agent without the real thing. Alibaba ships BF16 only, which fits a 128 GB machine, and hasn't answered a discussion on it or changed the files since June.\n\n## Before you call either\n\n### Nemotron 3 Nano 30B A3B\n\n1. Start it with `ollama run nemotron-3-nano:30b` (18 GB MLX on a Mac, 24 GB q4_K_M elsewhere)\n2. On the reference machine use BF16 with vLLM, after `wget https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16/resolve/main/nano_v3_reasoning_parser.py`, then `vllm serve nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 --max-model-len 262144 --trust-remote-code --enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser-plugin nano_v3_reasoning_parser.py --reasoning-parser nano_v3`\n3. NVFP4 needs an NVIDIA Blackwell GPU, and FP8 isn't documented on gfx1151\n4. Without the qwen3_coder tool parser and the nano_v3 reasoning parser, tool calls and reasoning come back as plain text\n5. Card sampling is temperature 0.6 and top_p 0.95 for tool calling, temperature 1.0 and top_p 1.0 for reasoning\n6. Thinking is on by default, so pass enable_thinking as false in chat_template_kwargs for short tasks\n7. Set VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 and raise --max-model-len only when you need more than 262,144 tokens\n8. In SGLang the main branch names the reasoning parser nemotron_3, not the card's nano_v3\n9. Filter untrusted tool results before they reach the model, as NVIDIA warns prompt injection can lead to code execution\n10. Validate JSON output yourself or use the runtime's guided decoding, since none is documented for the model\n\n### Qwen-AgentWorld-35B-A3B\n\n1. Use it as the environment, not the agent. Give it the domain's system prompt from Alibaba's GitHub repository, the history and the agent's action, and it returns the predicted observation\n2. With vLLM pass `--language-model-only`, or startup fails on the missing vision weights\n3. Use temperature 0.6, top_p 0.95 and top_k 20, and allow up to 32,768 output tokens, as the card recommends\n4. Keep at least 128K of context, since the simulation depends on the history\n5. Thinking is on by default and comes back through the qwen3 reasoning parser\n6. A simulated observation is a prediction. Check anything that matters against the real environment\n\n## Questions\n\n### Which is better for AI agents, Nemotron 3 Nano 30B A3B or Qwen-AgentWorld-35B-A3B?\n\nNemotron 3 Nano 30B A3B scores 67.6 (B) on agent readiness against Qwen-AgentWorld-35B-A3B's 52.1 (D), and leads in every scored category.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen-agentworld-35b-a3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen-agentworld-35b-a3b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"nemotron-3-nano-30b-a3b\", \"b\": \"qwen-agentworld-35b-a3b\"}`. From a terminal: `anchor compare nemotron-3-nano-30b-a3b qwen-agentworld-35b-a3b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-30b-a3b.json and https://www.anchorterminal.com/api/v1/tools/qwen-agentworld-35b-a3b.json\n\n## Other comparisons with Nemotron 3 Nano 30B A3B or Qwen-AgentWorld-35B-A3B\n\n- [Agents-A1-4B vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-nano-30b-a3b.md)\n- [Agents-A1-4B vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen-agentworld-35b-a3b.md)\n- [Apertus v1.5 8B vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-nano-30b-a3b.md)\n- [Apertus v1.5 8B vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen-agentworld-35b-a3b.md)\n- [Apodex-1.1-mini vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nemotron-3-nano-30b-a3b.md)\n- [Apodex-1.1-mini vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen-agentworld-35b-a3b.md)\n- [Arete-27B vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/arete-27b-vs-nemotron-3-nano-30b-a3b.md)\n- [Arete-27B vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/arete-27b-vs-qwen-agentworld-35b-a3b.md)\n- [BigBang-v1 vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/bigbang-v1-vs-nemotron-3-nano-30b-a3b.md)\n- [BigBang-v1 vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen-agentworld-35b-a3b.md)\n- [Carbon-X vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/carbon-x-vs-nemotron-3-nano-30b-a3b.md)\n- [Carbon-X vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/carbon-x-vs-qwen-agentworld-35b-a3b.md)\n- [Cola2 vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/cola2-vs-nemotron-3-nano-30b-a3b.md)\n- [Cola2 vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/cola2-vs-qwen-agentworld-35b-a3b.md)\n- [Cosmos-Reason2-2B vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nemotron-3-nano-30b-a3b.md)\n- [Cosmos-Reason2-2B vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwen-agentworld-35b-a3b.md)\n- [Darwin-36B-Opus vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-nemotron-3-nano-30b-a3b.md)\n- [Darwin-36B-Opus vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen-agentworld-35b-a3b.md)\n- [DeepSeek-V3.2 vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-nemotron-3-nano-30b-a3b.md)\n- [DeepSeek-V3.2 vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen-agentworld-35b-a3b.md)\n- [DeepSeek V4.1 Flash vs Nemotron 3 Nano 30B 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A3B](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-nemotron-3-nano-30b-a3b.md)\n- [Granite 4.2 8B vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-qwen-agentworld-35b-a3b.md)\n- [Hy-Memory-30B-A3B vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-nemotron-3-nano-30b-a3b.md)\n- [Hy-Memory-30B-A3B vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen-agentworld-35b-a3b.md)\n- [Hy3 vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/hy3-vs-nemotron-3-nano-30b-a3b.md)\n- [Hy3 vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/hy3-vs-qwen-agentworld-35b-a3b.md)\n- [Inkling-Small vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/inkling-small-vs-nemotron-3-nano-30b-a3b.md)\n- [Inkling-Small vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/inkling-small-vs-qwen-agentworld-35b-a3b.md)\n- [Inkling vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/inkling-vs-nemotron-3-nano-30b-a3b.md)\n- [Inkling vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/inkling-vs-qwen-agentworld-35b-a3b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-nemotron-3-nano-30b-a3b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwen-agentworld-35b-a3b.md)\n- [Kimi K3.1 Pro vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nemotron-3-nano-30b-a3b.md)\n- [Kimi K3.1 Pro vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen-agentworld-35b-a3b.md)\n- [Kimi K3 vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/kimi-k3-vs-nemotron-3-nano-30b-a3b.md)\n- [Kimi K3 vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/kimi-k3-vs-qwen-agentworld-35b-a3b.md)\n- [Kolibri-1 vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/kolibri-1-vs-nemotron-3-nano-30b-a3b.md)\n- [Kolibri-1 vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/kolibri-1-vs-qwen-agentworld-35b-a3b.md)\n- [Kronos vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/kronos-vs-nemotron-3-nano-30b-a3b.md)\n- [Kronos vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/kronos-vs-qwen-agentworld-35b-a3b.md)\n- [Laguna M.1 vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/laguna-m-1-vs-nemotron-3-nano-30b-a3b.md)\n- [Laguna M.1 vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen-agentworld-35b-a3b.md)\n- [Laguna S 2.1 vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-nemotron-3-nano-30b-a3b.md)\n- [Laguna S 2.1 vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen-agentworld-35b-a3b.md)\n- [Laguna XS 2.1 vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-nemotron-3-nano-30b-a3b.md)\n- [Laguna XS 2.1 vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen-agentworld-35b-a3b.md)\n- [LBS-27B vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/lbs-27b-vs-nemotron-3-nano-30b-a3b.md)\n- [LBS-27B vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/lbs-27b-vs-qwen-agentworld-35b-a3b.md)\n- [LFM2.5-2.6B vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-nano-30b-a3b.md)\n- [LFM2.5-2.6B vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen-agentworld-35b-a3b.md)\n- [LFM3-1.7B vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-nemotron-3-nano-30b-a3b.md)\n- [LFM3-1.7B vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen-agentworld-35b-a3b.md)\n- [LFM3-Audio-2B vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-nemotron-3-nano-30b-a3b.md)\n- [LFM3-Audio-2B vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen-agentworld-35b-a3b.md)\n- [LFM3-Omni-2B vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-nemotron-3-nano-30b-a3b.md)\n- [LFM3-Omni-2B vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen-agentworld-35b-a3b.md)\n- [LFM3-VL-2B vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-nemotron-3-nano-30b-a3b.md)\n- [LFM3-VL-2B vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen-agentworld-35b-a3b.md)\n- [Ling-3.0-tiny vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-nemotron-3-nano-30b-a3b.md)\n- [Ling-3.0-tiny vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen-agentworld-35b-a3b.md)\n- [Ling-3.1-flash-1019 vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-nemotron-3-nano-30b-a3b.md)\n- [Ling-3.1-flash-1019 vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen-agentworld-35b-a3b.md)\n- [LongCat-2.5 vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/longcat-2-5-vs-nemotron-3-nano-30b-a3b.md)\n- [LongCat-2.5 vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen-agentworld-35b-a3b.md)\n- [MiMo-V2.6 Distill Qwen 9B vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-nemotron-3-nano-30b-a3b.md)\n- [MiMo-V2.6 Distill Qwen 9B vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-qwen-agentworld-35b-a3b.md)\n- [MiMo-V2.6-Flash-RL vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-nemotron-3-nano-30b-a3b.md)\n- [MiMo-V2.6-Flash-RL vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen-agentworld-35b-a3b.md)\n- [MiniCPM-o 4.5 vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-nemotron-3-nano-30b-a3b.md)\n- [MiniCPM-o 4.5 vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen-agentworld-35b-a3b.md)\n- [MiniCPM-V 4.7 1B vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-nemotron-3-nano-30b-a3b.md)\n- [MiniCPM-V 4.7 1B vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen-agentworld-35b-a3b.md)\n- [MiniCPM-V 4.7 35B-A3B vs Nemotron 3 Nano 30B 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[Qwen-AgentWorld-35B-A3B vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-thinkingcap-qwen3-8-27b.md)\n- [Qwen-AgentWorld-35B-A3B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-unlimited-ocr-2.md)\n- [Qwen-AgentWorld-35B-A3B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-wevisone-vl-2b.md)\n- [Qwen-AgentWorld-35B-A3B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-wevisone-vl-4b.md)\n- [Qwen-AgentWorld-35B-A3B vs ZAYA1-8B](https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-zaya1-8b.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from (https://www.anchorterminal.com/terms/#data)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-11-11", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.7", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run", "terms": "https://www.anchorterminal.com/terms/#data" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "Nemotron 3 Nano 30B A3B vs Qwen-AgentWorld-35B-A3B", "url": "" } ], "description": "Nemotron 3 Nano 30B A3B scores 67.6 (B) to Qwen-AgentWorld-35B-A3B's 52.1 (D) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "Nemotron 3 Nano 30B A3B B 67.6", "Qwen-AgentWorld-35B-A3B D 52.1", "scores" ], "h1": "Nemotron 3 Nano 30B A3B vs Qwen-AgentWorld-35B-A3B", "image": "https://www.anchorterminal.com/assets/og/default.png", "path": "/compare/nemotron-3-nano-30b-a3b-vs-qwen-agentworld-35b-a3b", "published": "2026-10-01", "section": "tools", "title": "Nemotron 3 Nano 30B A3B vs Qwen-AgentWorld-35B-A3B (2026)", "toc": null, "updated": "2026-11-10", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen-agentworld-35b-a3b" }, "tokens": { "markdown": 11000, "slim": 930 }, "version": 1 }