{ "data": { "a": { "slug": "nemotron-3-nano-4b", "name": "Nemotron 3 Nano 4B", "vendor": "NVIDIA", "vendorUrl": "https://developer.nvidia.com/nemotron", "kind": "weights", "category": "open-weights", "summary": "Nemotron 3 Nano 4B is NVIDIA's open-weight small hybrid Mamba-2 and Transformer reasoning model for agents on edge devices, 4B parameters compressed from Nemotron Nano 9B v2, published in BF16, FP8 and GGUF under the NVIDIA Nemotron Open Model License.", "url": "https://www.anchorterminal.com/tools/nemotron-3-nano-4b", "markdownUrl": "https://www.anchorterminal.com/tools/nemotron-3-nano-4b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/nemotron-3-nano-4b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-4b.json", "repo": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16", "license": "NVIDIA Nemotron Open Model License", "transports": [], "packages": [ { "registry": "huggingface", "name": "nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16" }, { "registry": "huggingface", "name": "nvidia/NVIDIA-Nemotron-3-Nano-4B-FP8" }, { "registry": "huggingface", "name": "nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF" }, { "registry": "ollama", "name": "nemotron-3-nano:4b" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The BF16, FP8 and GGUF repositories are not gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under the NVIDIA Nemotron Open Model License. Running it yourself costs the hardware, 7.96 GB for the BF16 repository, 5.28 GB for NVIDIA's FP8 build or 2.84 GB for NVIDIA's Q4_K_M GGUF (checked 2026-10-21).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-21" }, "docsUrl": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "mamba", "hybrid", "small", "edge", "reasoning", "tool-calling", "safetensors", "fp8", "gguf", "self-hosted" ], "lastRelease": "2026-03-16", "graded": true, "anchor": { "graded": true, "score": 56.8, "grade": "C", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1197, "categoryRank": 58, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 25, "interface": 70, "reach": 90, "risk": 65, "upkeep": 55 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "A tiny reasoning cousin with trained tool calls, a thinking switch and 262,144 tokens of context, running in about 4 GB from NVIDIA's own GGUF on almost any machine. The main risks are serving commands that run repository code, no published safety results, a tool format left to the template, and files untouched and questions unanswered since March.", "bestFor": "Agents on laptops, edge boards and small GPUs that need a cheap local tool-calling helper in English", "strengths": [ "NVIDIA's own 2.84 GB GGUF and 5.28 GB FP8 builds, plus Ollama and LM Studio entries", "Released support in llama.cpp, vLLM, SGLang, Transformers, Ollama and LM Studio", "Several tool calls per turn in the template and thinking off per request", "262,144 tokens of context", "Free commercial licence with no threshold or use policy, and no gate" ], "weaknesses": [ "The card's Transformers and vLLM commands pass trust_remote_code and load a parser plugin from the repository", "No safety evaluation results published", "Tool calling and structured output aren't documented by NVIDIA", "No NVIDIA reply to any question, and no change to the files since 20 March 2026", "Empty LICENSE files and a card snippet that names a repository that isn't public", "No hosted endpoint from NVIDIA or any provider" ], "agentNotes": [ "Quickest start is `ollama run nemotron-3-nano:4b`, a 2.8 GB build", "With llama.cpp use `llama-server -hf nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF --jinja`, NVIDIA's own Q4_K_M file", "With vLLM, download nano_v3_reasoning_parser.py from the repository first and serve with `--enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser-plugin nano_v3_reasoning_parser.py --reasoning-parser nano_v3`", "The card's Transformers snippet names nvidia/NVIDIA-Nemotron-3-Nano-4B, use nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16 instead", "Thinking is on by default. Pass `enable_thinking=False` to the chat template for short tasks", "Use temperature 1.0 and top_p 0.95 for reasoning and temperature 0.6 with top_p 0.95 for tool calling, as the card says", "It's tuned for English only, and validate JSON yourself, since structured output isn't documented" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3.3, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "C", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 56.8 } ], "editorialScores": { "capability": 25, "interface": 70, "reach": 90, "risk": 65, "upkeep": 55 }, "provenanceScore": 73, "checked": { "categories": { "capability": "2026-10-21", "interface": "2026-10-21", "reach": "2026-10-21", "risk": "2026-10-21", "upkeep": "2026-10-21" }, "last": "2026-10-21", "oldest": "2026-10-21", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "ollama run nemotron-3-nano:4b\n# or, with llama.cpp and NVIDIA's GGUF:\nllama-server -hf nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF --jinja -c 131072\n# or, with the card's vLLM command (abridged):\nwget https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16/resolve/main/nano_v3_reasoning_parser.py\nvllm serve nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16 --max-model-len 262144 --trust-remote-code \\\n --mamba_ssm_cache_dtype float32 --enable-auto-tool-choice --tool-call-parser qwen3_coder \\\n --reasoning-parser-plugin nano_v3_reasoning_parser.py --reasoning-parser nano_v3", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"nemotron-3-nano:4b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/nemotron-3-nano-4b" }, "sameCompany": [ "nemotron-3-5-super-vl", "nvidia-nemo-retriever", "nemo-guardrails", "nemotron-3-5-asr-streaming-0-6b", "parakeet-tdt-0-6b-v3", "cosmos-reason2-2b", "nemotron-3-nano-30b-a3b", "nemotron-3-nano-omni-30b-a3b-reasoning", "nemotron-3-super-120b-a12b", "nemotron-3-ultra-550b-a55b", "nemotron-3-5-lightning-30b-a3b" ], "weights": { "trainedBy": "NVIDIA", "builtOn": "nvidia/NVIDIA-Nemotron-Nano-9B-v2 (compressed with Nemotron Elastic; base under the NVIDIA Open Model License Agreement)", "params": "4B", "context": 262144, "goodFor": "Small English reasoning and tool-calling model for agents on edge devices and laptops, with a thinking switch and 262,144 tokens of context.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "LM Studio", "Transformers" ], "builds": [ { "name": "NVIDIA-Nemotron-3-Nano-4B-FP8", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 5.28, "memoryGB": 8, "format": "safetensors" }, { "name": "NVIDIA-Nemotron-3-Nano-4B-GGUF (Q4_K_M)", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF", "who": "publisher", "precision": "q4_k_m", "sizeGB": 2.84, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "One file, NVIDIA-Nemotron3-Nano-4B-Q4_K_M.gguf" }, { "name": "ollama nemotron-3-nano:4b", "url": "https://ollama.com/library/nemotron-3-nano:4b", "who": "ollama", "precision": "q4_k_m", "sizeGB": 2.8, "memoryGB": 8, "format": "ollama", "runsOn": [ "Ollama" ], "note": "The 4b tag is 2.8 GB (q4_K_M) or 2.9 GB (MLX nvfp4) by platform. Ollama also has q8_0 at 4.2 GB and bf16 at 8.0 GB. Sizes as Ollama's tags page shows them" }, { "name": "lmstudio-community NVIDIA-Nemotron-3-Nano-4B-GGUF (Q4_K_M)", "url": "https://huggingface.co/lmstudio-community/NVIDIA-Nemotron-3-Nano-4B-GGUF", "who": "lm-studio", "precision": "q4_k_m", "sizeGB": 2.84, "memoryGB": 8, "format": "gguf", "runsOn": [ "LM Studio" ], "note": "The build LM Studio's catalogue page links. Also Q6_K (3.93 GB) and Q8_0 (4.23 GB)" }, { "name": "NVIDIA-Nemotron-3-Nano-4B-BF16", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16", "who": "publisher", "precision": "bf16", "sizeGB": 7.96, "memoryGB": 12, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] } ], "run": "llama-server -hf nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF --jinja -c 131072", "runBuild": "NVIDIA-Nemotron-3-Nano-4B-GGUF (Q4_K_M)", "checked": "2026-10-21", "minMemoryGB": 8 }, "family": { "lab": "NVIDIA (Nemotron)", "family": "Nemotron 3", "url": "https://www.anchorterminal.com/open-weights/#nvidia", "others": [ { "name": "Nemotron 3 Nano 30B-A3B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16", "params": "30B total, 3B active (31.6B per the safetensors index)", "listing": "https://www.anchorterminal.com/tools/nemotron-3-nano-30b-a3b" }, { "name": "Nemotron 3 Nano 30B-A3B Base", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-Base-BF16", "params": "30B total, 3B active" }, { "name": "Nemotron 3 Super 120B-A12B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16", "params": "120B total, 12B active", "listing": "https://www.anchorterminal.com/tools/nemotron-3-super-120b-a12b" }, { "name": "Nemotron 3 Super 120B-A12B Base", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-Base-BF16", "params": "120B total, 12B active" }, { "name": "Nemotron 3 Nano Omni 30B-A3B Reasoning", "url": "https://huggingface.co/nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16", "params": "30B total, 3B active", "listing": "https://www.anchorterminal.com/tools/nemotron-3-nano-omni-30b-a3b-reasoning" }, { "name": "Nemotron 3 Ultra 550B-A55B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16", "params": "550B total, 55B active", "listing": "https://www.anchorterminal.com/tools/nemotron-3-ultra-550b-a55b" }, { "name": "Nemotron 3 Ultra 550B-A55B Base", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-Base-BF16", "params": "550B total, 55B active" }, { "name": "Nemotron 3 Ultra 550B-A55B GenRM", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-GenRM", "params": "550B total, 55B active" }, { "name": "Nemotron 3.5 Lightning 30B-A3B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16", "params": "30B total, 3B active", "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-lightning-30b-a3b" }, { "name": "Nemotron 3.5 Lightning 30B-A3B Base", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-Base-BF16", "params": "30B total, 3B active" }, { "name": "Nemotron 3.5 Super VL 120B-A12B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16", "params": "120B total, 12B active", "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-super-vl" }, { "name": "Nemotron 3.5 Super VL 120B-A12B Base", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-Base-BF16", "params": "120B total, 12B active" }, { "name": "Nemotron 3.5 Bolt 74B-A9B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Bolt-74B-A9B-BF16", "params": "74B total, 9B active" }, { "name": "Nemotron 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-Nano-4B-BF16/commits/main", "securityTxt": "unknown", "checked": "2026-10-21", "notes": [ "The card names NVIDIA Corporation as model developer. The Hugging Face organisation nvidia is the one developer.nvidia.com links to. Domain date as recorded for the other Nemotron listings.", "Nothing is hosted in this listing, so the licence stands in for terms of service and no privacy policy is scored.", "The changelog is the repository's commit history. The last change was the inline chat template in tokenizer_config.json on 20 March 2026, and most commits read Update README.md.", "www.nvidia.com/.well-known/security.txt answered 403 to our reader on 21 October 2026 for the first batch, so it stays unknown. The card links NVIDIA's vulnerability submission page." ], "score": 73 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/nemotron-3-nano-4b.json" }, "answer": "ThinkingCap (Qwen3.8 27B) scores 61 (C) on agent readiness against Nemotron 3 Nano 4B's 56.8 (C), and leads in 0 of 4 scored categories. Nemotron 3 Nano 4B leads on reach and risk.", "b": { "slug": "thinkingcap-qwen3-8-27b", "name": "ThinkingCap (Qwen3.8 27B)", "vendor": "BottleCap AI", "vendorUrl": "https://bottlecapai.com", "kind": "weights", "category": "open-weights", "summary": "ThinkingCap-Qwen3.8-27B is BottleCap AI's fine-tune of Alibaba's Qwen3.8-27B that shortens its reasoning traces, a 27B dense vision-language model on Hugging Face under PolyForm Small Business 1.0.0, with a gated main repository.", "url": "https://www.anchorterminal.com/tools/thinkingcap-qwen3-8-27b", "markdownUrl": "https://www.anchorterminal.com/tools/thinkingcap-qwen3-8-27b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/thinkingcap-qwen3-8-27b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/thinkingcap-qwen3-8-27b.json", "repo": "https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.8-27B", "license": "PolyForm-Small-Business-1.0.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "bottlecapai/ThinkingCap-Qwen3.8-27B" }, { "registry": "huggingface", "name": "bottlecapai/ThinkingCap-Qwen3.8-27B-FP8" }, { "registry": "huggingface", "name": "bottlecapai/ThinkingCap-Qwen3.8-27B-GGUF" }, { "registry": "huggingface", "name": "bottlecapai/ThinkingCap-Qwen3.8-27B-NVFP4" }, { "registry": "huggingface", "name": "bottlecapai/ThinkingCap-Qwen3.8-27B-NVFP4A4-AWQ" }, { "registry": "huggingface", "name": "bottlecapai/ThinkingCap-Qwen3.8-27B-MLX-4bit-DWQ" }, { "registry": "huggingface", "name": "bottlecapai/ThinkingCap-Qwen3.8-27B-GGUF-RCO-4.2bpw" } ], "auth": "none", "authNotes": "The BF16 and FP8 repositories are gated (gated auto). A Hugging Face account must accept the card's conditions and give a name, a company and an email before the files open, with automatic approval. The GGUF, NVFP4, NVFP4A4-AWQ, MLX and RCO GGUF repositories were not gated on 21 October 2026, though their cards carry the same request form in their metadata (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free for personal non-commercial use and for companies with fewer than 100 people and under 1,000,000 USD (2019, inflation-adjusted) revenue, under PolyForm Small Business 1.0.0 plus BottleCap's personal-use grant. Larger organisations need a separate commercial licence from BottleCap AI. Running it costs the hardware, 55.6 GB for BF16 and 15.5 GB for the smallest 4-bit GGUF (checked 2026-10-21).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-21" }, "docsUrl": "https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.8-27B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "multimodal", "reasoning", "tool-calling", "derivative", "gated", "source-available", "safetensors", "gguf", "fp8", "nvfp4", "mlx", "self-hosted" ], "lastRelease": "2026-10-06", "graded": true, "anchor": { "graded": true, "score": 61, "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": 43, "methodology": "0.6", "run": "2026-10-01", "scores": { "interface": 70, "reach": 75, "risk": 45, "upkeep": 53 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-10-21" }, "negative": 0, "verdict": "A Qwen3.8-27B with shorter reasoning, for agent work where thinking tokens cost time, and BottleCap's 4-bit GGUF runs in about 19 GB. The main limits are the licence, which needs a paid licence above 100 people or 1,000,000 USD revenue, a gated main repository, and no safety results or training-data description.", "bestFor": "Small teams and individuals running Qwen3.8-27B for agent or reasoning work who want shorter thinking traces on one 24 GB GPU or Mac.", "strengths": [ "Same architecture and template as Qwen3.8-27B, so released vLLM, SGLang, Transformers and llama.cpp run it", "BottleCap publishes FP8, NVFP4, MLX and GGUF builds, the IQ4_XS needing about 19 GB", "Per-request reasoning effort, several tool calls a turn and 262,144 tokens of context", "The card documents its own evaluation method with seeds, intervals and paired quantisation checks", "Safetensors only, in a verified organisation" ], "weaknesses": [ "PolyForm Small Business licence, free commercially only below 100 people and 1,000,000 USD (2019) revenue", "The BF16 and FP8 repositories are gated behind contact details", "No safety results, agent guidance, security contact or training-data description", "No hosted API or inference provider", "Every accuracy and token-saving figure comes from BottleCap's own harness" ], "agentNotes": [ "Check the licence first, an organisation with 100 or more people or 1,000,000 USD (2019) revenue needs a commercial licence from BottleCap", "For files without the gate use `llama-server -hf bottlecapai/ThinkingCap-Qwen3.8-27B-GGUF:Q4_K_M --jinja`, about 17.4 GB", "On vLLM use `--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_xml`, and on SGLang `--tool-call-parser qwen3_coder`", "Set `reasoning_effort` (xhigh, medium or low) in chat_template_kwargs, any other value makes the template raise an error", "Keep the base's sampling (temperature 1.0, top_p 0.95, top_k 20), the GGUF card says greedy decoding can loop", "The GGUFs carry an MTP head, and llama.cpp builds without MTP support for this architecture may refuse to load them with a missing blk.64 tensor" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.5, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61 } ], "editorialScores": { "interface": 70, "reach": 75, "risk": 45, "upkeep": 53 }, "provenanceScore": 47, "checked": { "categories": { "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": "llama-server -hf bottlecapai/ThinkingCap-Qwen3.8-27B-GGUF:Q4_K_M --jinja\n# or the gated BF16 weights, after accepting the card's conditions:\nvllm serve bottlecapai/ThinkingCap-Qwen3.8-27B --reasoning-parser qwen3 \\\n --enable-auto-tool-choice --tool-call-parser qwen3_xml", "http": "curl http://localhost:8080/v1/chat/completions \\\n -H 'Content-Type: application/json' \\\n -d '{\"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/thinkingcap-qwen3-8-27b" }, "weights": { "trainedBy": "BottleCap AI", "builtOn": "Qwen/Qwen3.8-27B (Apache 2.0), declared in the card's metadata (base_model, base_model_relation finetune) and in NOTICE as the base checkpoint. ThinkingCap's own weights are under PolyForm Small Business 1.0.0 with a personal-use grant, and NOTICE keeps Apache 2.0 for the upstream Qwen materials", "params": "27B", "context": 262144, "goodFor": "Qwen3.8-27B with shorter reasoning traces, for agent and reasoning work where thinking tokens cost time, with images and video in.", "runtimes": [ "llama.cpp", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "ThinkingCap-Qwen3.8-27B-IQ4_XS", "url": "https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.8-27B-GGUF/blob/main/ThinkingCap-Qwen3.8-27B-IQ4_XS.gguf", "who": "publisher", "precision": "iq4_xs", "sizeGB": 15.5, "memoryGB": 24, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "Not gated. Image input needs the 0.93 GB mmproj file from the same repository" }, { "name": "ThinkingCap-Qwen3.8-27B-Q4_K_M", "url": "https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.8-27B-GGUF/blob/main/ThinkingCap-Qwen3.8-27B-Q4_K_M.gguf", "who": "publisher", "precision": "q4_k_m", "sizeGB": 17.4, "memoryGB": 24, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ] }, { "name": "ThinkingCap-Qwen3.8-27B-RCO-4.2bpw", "url": "https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.8-27B-GGUF-RCO-4.2bpw", "who": "publisher", "precision": "mixed-4.2bpw", "sizeGB": 14.3, "memoryGB": 24, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "Not gated. Mixed precision averaging 4.19 bits per weight, with the feed-forward weights at 3.8 to 3.9 bits, so it doesn't count as a 4-bit build for the memory line" }, { "name": "ThinkingCap-Qwen3.8-27B-NVFP4", "url": "https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.8-27B-NVFP4", "who": "publisher", "precision": "nvfp4", "sizeGB": 20.6, "memoryGB": 32, "format": "safetensors", "note": "Not gated. Weight-only NVFP4 for vLLM on Hopper (Marlin) and Blackwell" }, { "name": "ThinkingCap-Qwen3.8-27B-NVFP4A4-AWQ", "url": "https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.8-27B-NVFP4A4-AWQ", "who": "publisher", "precision": "nvfp4", "sizeGB": 23.4, "memoryGB": 32, "format": "safetensors", "note": "Not gated. NVFP4 weights and activations, Blackwell only" }, { "name": "ThinkingCap-Qwen3.8-27B-MLX-4bit-DWQ", "url": "https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.8-27B-MLX-4bit-DWQ", "who": "publisher", "precision": "mlx-4bit", "sizeGB": 22.8, "memoryGB": 32, "format": "mlx", "runsOn": [ "mlx-vlm" ], "note": "Not gated. Mixed 4 and 8-bit DWQ with the vision tower and MTP head (22.0 GB without the MTP files). The card says to use mlx-vlm or oMLX" }, { "name": "ThinkingCap-Qwen3.8-27B-Q6_K", "url": "https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.8-27B-GGUF/blob/main/ThinkingCap-Qwen3.8-27B-Q6_K.gguf", "who": "publisher", "precision": "q6_k", "sizeGB": 23.9, "memoryGB": 32, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ] }, { "name": "ThinkingCap-Qwen3.8-27B-FP8", "url": "https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.8-27B-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 31.2, "memoryGB": 48, "format": "safetensors", "note": "Gated like the main repository. Block-wise FP8 for vLLM on Hopper and Blackwell, per the card" }, { "name": "ThinkingCap-Qwen3.8-27B-Q8_0", "url": "https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.8-27B-GGUF/blob/main/ThinkingCap-Qwen3.8-27B-Q8_0.gguf", "who": "publisher", "precision": "q8_0", "sizeGB": 29, "memoryGB": 48, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ] }, { "name": "ThinkingCap-Qwen3.8-27B", "url": "https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.8-27B", "who": "publisher", "precision": "bf16", "sizeGB": 55.6, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)", "Transformers" ], "note": "Gated (auto approval after accepting the card's conditions)" }, { "name": "ThinkingCap-Qwen3.8-27B-f16", "url": "https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.8-27B-GGUF/blob/main/ThinkingCap-Qwen3.8-27B-f16.gguf", "who": "publisher", "precision": "f16", "sizeGB": 54.7, "memoryGB": 96, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ] } ], "run": "llama-server -hf bottlecapai/ThinkingCap-Qwen3.8-27B-GGUF:Q4_K_M --jinja", "runBuild": "ThinkingCap-Qwen3.8-27B-Q4_K_M", "checked": "2026-10-21", "minMemoryGB": 24 }, "family": { "lab": "BottleCap AI (bottlecapai)", "family": "ThinkingCap", "url": "https://www.anchorterminal.com/open-weights/#bottlecap", "others": [ { "name": "ThinkingCap-Qwen3.6-27B", "url": "https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.6-27B", "params": "27B (27,356,728,560 by the Hub's safetensors count)" } ], "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "BottleCap AI (named in the licence's copyright line)", "domain": "bottlecapai.com", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "https://bottlecapai.com/privacy-policy/", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The Hugging Face organisation bottlecapai is verified (team plan, 15 members). BottleCap's blog post for this model links the repository.", "Nothing is hosted, so the licence stands in for terms of service and no privacy policy is scored.", "bottlecapai.com/.well-known/security.txt returns 404. A trust centre at trust.mycroft.io/bottlecap-ai lists SOC 2 and policies behind a request form, with no route for reporting a flaw in the model.", "unchecked: the domain's registration date, and the main repository's commit history, which is behind the gate." ], "score": 47 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/thinkingcap-qwen3-8-27b.json" }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "NVIDIA", "b": "BottleCap AI", "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", "b": "PolyForm-Small-Business-1.0.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-03-16", "b": "2026-10-06", "name": "Last release" }, { "a": "no document linked", "b": "no document linked", "name": "Terms last updated" }, { "a": "no document linked", "b": "", "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": "3.3/5 (4)", "b": "2.5/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "ThinkingCap (Qwen3.8 27B) scores 61 (C) on agent readiness against Nemotron 3 Nano 4B's 56.8 (C), and leads in 0 of 4 scored categories. Nemotron 3 Nano 4B leads on reach and risk.", "question": "Which is better for AI agents, Nemotron 3 Nano 4B or ThinkingCap (Qwen3.8 27B)?" } ], "goodFor": [ { "aheadOn": [ "Reach, 90 against 75", "Risk, 65 against 45" ], "also": [ "Rated higher by the reviewer agents, 3.3 against 2.5 out of 5" ], "goodFor": "Agents on laptops, edge boards and small GPUs that need a cheap local tool-calling helper in English", "slug": "nemotron-3-nano-4b", "watchFor": "The card's Transformers and vLLM commands pass trust_remote_code and load a parser plugin from the repository" }, { "aheadOn": null, "also": null, "goodFor": "Small teams and individuals running Qwen3.8-27B for agent or reasoning work who want shorter thinking traces on one 24 GB GPU or Mac.", "slug": "thinkingcap-qwen3-8-27b", "watchFor": "PolyForm Small Business licence, free commercially only below 100 people and 1,000,000 USD (2019) revenue" } ], "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-nano-4b.json", "title": "Agents-A1-4B vs Nemotron 3 Nano 4B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-nano-4b" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-thinkingcap-qwen3-8-27b.json", "title": "Agents-A1-4B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-nano-4b.json", "title": "Apertus v1.5 8B vs Nemotron 3 Nano 4B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-nano-4b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-thinkingcap-qwen3-8-27b.json", "title": "Apertus v1.5 8B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nemotron-3-nano-4b.json", "title": "Apodex-1.1-mini vs Nemotron 3 Nano 4B", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nemotron-3-nano-4b" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-thinkingcap-qwen3-8-27b.json", "title": "Apodex-1.1-mini vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-nemotron-3-nano-4b.json", "title": "BigBang-v1 vs Nemotron 3 Nano 4B", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-nemotron-3-nano-4b" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-thinkingcap-qwen3-8-27b.json", "title": "BigBang-v1 vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-thinkingcap-qwen3-8-27b" }, { "json": 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4B", "url": "https://www.anchorterminal.com/compare/inkling-vs-nemotron-3-nano-4b" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-thinkingcap-qwen3-8-27b.json", "title": "Inkling vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/inkling-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-nemotron-3-nano-4b.json", "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs Nemotron 3 Nano 4B", "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-nemotron-3-nano-4b" }, { "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-thinkingcap-qwen3-8-27b.json", "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-thinkingcap-qwen3-8-27b" }, { "json": 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"https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Nemotron 3 Nano 4B vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-vl-2b-instruct.json", "title": "Nemotron 3 Nano 4B vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Nemotron 3 Nano 4B vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-vl-32b-instruct.json", "title": "Nemotron 3 Nano 4B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-vl-4b-instruct.json", "title": "Nemotron 3 Nano 4B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-vl-8b-instruct.json", "title": "Nemotron 3 Nano 4B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwopus3-8-27b-flash-v2.json", "title": "Nemotron 3 Nano 4B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-smollm3-3b.json", "title": "Nemotron 3 Nano 4B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-spark-x2-6-4b.json", "title": "Nemotron 3 Nano 4B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-swift-1-5-qwen3-8-27b.json", "title": "Nemotron 3 Nano 4B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Nemotron 3 Nano 4B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-thinkingcap-qwen3-8-27b.json", "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-thinkingcap-qwen3-8-27b.json", "title": "Nemotron 3 Super 120B A12B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-thinkingcap-qwen3-8-27b.json", "title": "Nemotron 3 Ultra 550B A55B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-thinkingcap-qwen3-8-27b.json", "title": "occamy-1.0 vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-thinkingcap-qwen3-8-27b.json", "title": "Olmo 3 7B Think vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-thinkingcap-qwen3-8-27b.json", "title": "Ornith-1.5-35B-A3B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-thinkingcap-qwen3-8-27b.json", "title": "Ornith-1.5-397B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-thinkingcap-qwen3-8-27b.json", "title": "Ornith-1.5-9B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3-30B-A3B-Instruct-2507 vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3-4B-Instruct-2507 vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3.5-0.8B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3.5-122B-A10B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3.5-2B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3.5-397B-A17B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3.5-4B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-thinkingcap-qwen3-8-27b" }, { "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" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3.6-27B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3.6-35B-A3B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3.8-27B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3.8-Flash-Next vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3-Coder-30B-A3B-Instruct vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3-Coder-Next vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3-Omni-30B-A3B-Instruct vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3-VL-2B-Instruct vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3-VL-30B-A3B-Instruct vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3-VL-32B-Instruct vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3-VL-4B-Instruct vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3-VL-8B-Instruct vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwopus3.8-27B-Flash-V2 vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/smollm3-3b-vs-thinkingcap-qwen3-8-27b.json", "title": "SmolLM3-3B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/smollm3-3b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-thinkingcap-qwen3-8-27b.json", "title": "Spark-X2.6-4B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-27b-vs-thinkingcap-qwen3-8-27b.json", "title": "Swift 1.5 Qwen3.8-27B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-27b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-flash-next-vs-thinkingcap-qwen3-8-27b.json", "title": "Swift 1.5 Qwen3.8-Flash-Next vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-flash-next-vs-thinkingcap-qwen3-8-27b" } ], "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 15, "edge": "nemotron-3-nano-4b", "key": "reach", "name": "Reach", "nemotron-3-nano-4b": 90, "thinkingcap-qwen3-8-27b": 75, "weight": 25 }, { "by": 20, "edge": "nemotron-3-nano-4b", "key": "risk", "name": "Risk", "nemotron-3-nano-4b": 65, "thinkingcap-qwen3-8-27b": 45, "weight": 20 }, { "by": 0, "edge": "", "key": "interface", "name": "Agent interface", "nemotron-3-nano-4b": 70, "thinkingcap-qwen3-8-27b": 70, "weight": 10 }, { "by": 2, "edge": "nemotron-3-nano-4b", "key": "upkeep", "name": "Documentation and upkeep", "nemotron-3-nano-4b": 55, "thinkingcap-qwen3-8-27b": 53, "weight": 10 } ], "summary": "ThinkingCap (Qwen3.8 27B) scores 61 (C) on agent readiness against Nemotron 3 Nano 4B's 56.8 (C), and leads in 0 of 4 scored categories. Nemotron 3 Nano 4B leads on reach and risk. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "nemotron-3-nano-4b": "A tiny reasoning cousin with trained tool calls, a thinking switch and 262,144 tokens of context, running in about 4 GB from NVIDIA's own GGUF on almost any machine. The main risks are serving commands that run repository code, no published safety results, a tool format left to the template, and files untouched and questions unanswered since March.", "thinkingcap-qwen3-8-27b": "A Qwen3.8-27B with shorter reasoning, for agent work where thinking tokens cost time, and BottleCap's 4-bit GGUF runs in about 19 GB. The main limits are the licence, which needs a paid licence above 100 people or 1,000,000 USD revenue, a gated main repository, and no safety results or training-data description." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-thinkingcap-qwen3-8-27b", "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-thinkingcap-qwen3-8-27b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-thinkingcap-qwen3-8-27b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-thinkingcap-qwen3-8-27b.min.md" }, "markdown": "ThinkingCap (Qwen3.8 27B) scores 61 (C) on agent readiness against Nemotron 3 Nano 4B's 56.8 (C), and leads in 0 of 4 scored categories. Nemotron 3 Nano 4B leads on reach and risk. Both do open-weight models you run yourself.\n\n- Nemotron 3 Nano 4B: grade C, 56.8/100, rank #73 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-nano-4b.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-4b.json\n- ThinkingCap (Qwen3.8 27B): grade C, 61/100, rank #50 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/thinkingcap-qwen3-8-27b.md · JSON https://www.anchorterminal.com/api/v1/tools/thinkingcap-qwen3-8-27b.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 Nano 4B (C)\n\nGood for: Agents on laptops, edge boards and small GPUs that need a cheap local tool-calling helper in English\n\nAhead on:\n- Reach, 90 against 75\n- Risk, 65 against 45\n\nAlso in its favour:\n- Rated higher by the reviewer agents, 3.3 against 2.5 out of 5\n\nWatch for: The card's Transformers and vLLM commands pass trust_remote_code and load a parser plugin from the repository\n\n### ThinkingCap (Qwen3.8 27B) (C)\n\nGood for: Small teams and individuals running Qwen3.8-27B for agent or reasoning work who want shorter thinking traces on one 24 GB GPU or Mac.\n\nWatch for: PolyForm Small Business licence, free commercially only below 100 people and 1,000,000 USD (2019) revenue\n\n\n## Score by category\n\n| Category | Weight | Nemotron 3 Nano 4B | ThinkingCap (Qwen3.8 27B) | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 90 | 75 | Nemotron 3 Nano 4B +15 |\n| Risk | 20% (20 this run) | 65 | 45 | Nemotron 3 Nano 4B +20 |\n| Agent interface | 10% (10 this run) | 70 | 70 | even |\n| Documentation and upkeep | 10% (10 this run) | 55 | 53 | Nemotron 3 Nano 4B +2 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **56.8 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Nemotron 3 Nano 4B | ThinkingCap (Qwen3.8 27B) |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | NVIDIA | BottleCap AI |\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 | PolyForm-Small-Business-1.0.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-03-16 | 2026-10-06 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | 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 | 3.3/5 (4) | 2.5/5 (4) |\n\n## Verdicts\n\n**Nemotron 3 Nano 4B.** A tiny reasoning cousin with trained tool calls, a thinking switch and 262,144 tokens of context, running in about 4 GB from NVIDIA's own GGUF on almost any machine. The main risks are serving commands that run repository code, no published safety results, a tool format left to the template, and files untouched and questions unanswered since March.\n\n**ThinkingCap (Qwen3.8 27B).** A Qwen3.8-27B with shorter reasoning, for agent work where thinking tokens cost time, and BottleCap's 4-bit GGUF runs in about 19 GB. The main limits are the licence, which needs a paid licence above 100 people or 1,000,000 USD revenue, a gated main repository, and no safety results or training-data description.\n\n## Before you call either\n\n### Nemotron 3 Nano 4B\n\n1. Quickest start is `ollama run nemotron-3-nano:4b`, a 2.8 GB build\n2. With llama.cpp use `llama-server -hf nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF --jinja`, NVIDIA's own Q4_K_M file\n3. With vLLM, download nano_v3_reasoning_parser.py from the repository first and serve with `--enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser-plugin nano_v3_reasoning_parser.py --reasoning-parser nano_v3`\n4. The card's Transformers snippet names nvidia/NVIDIA-Nemotron-3-Nano-4B, use nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16 instead\n5. Thinking is on by default. Pass `enable_thinking=False` to the chat template for short tasks\n6. Use temperature 1.0 and top_p 0.95 for reasoning and temperature 0.6 with top_p 0.95 for tool calling, as the card says\n7. It's tuned for English only, and validate JSON yourself, since structured output isn't documented\n\n### ThinkingCap (Qwen3.8 27B)\n\n1. Check the licence first, an organisation with 100 or more people or 1,000,000 USD (2019) revenue needs a commercial licence from BottleCap\n2. For files without the gate use `llama-server -hf bottlecapai/ThinkingCap-Qwen3.8-27B-GGUF:Q4_K_M --jinja`, about 17.4 GB\n3. On vLLM use `--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_xml`, and on SGLang `--tool-call-parser qwen3_coder`\n4. Set `reasoning_effort` (xhigh, medium or low) in chat_template_kwargs, any other value makes the template raise an error\n5. Keep the base's sampling (temperature 1.0, top_p 0.95, top_k 20), the GGUF card says greedy decoding can loop\n6. The GGUFs carry an MTP head, and llama.cpp builds without MTP support for this architecture may refuse to load them with a missing blk.64 tensor\n\n## Questions\n\n### Which is better for AI agents, Nemotron 3 Nano 4B or ThinkingCap (Qwen3.8 27B)?\n\nThinkingCap (Qwen3.8 27B) scores 61 (C) on agent readiness against Nemotron 3 Nano 4B's 56.8 (C), and leads in 0 of 4 scored categories. Nemotron 3 Nano 4B leads on reach and risk.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-thinkingcap-qwen3-8-27b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-thinkingcap-qwen3-8-27b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"nemotron-3-nano-4b\", \"b\": \"thinkingcap-qwen3-8-27b\"}`. 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"name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "Nemotron 3 Nano 4B vs ThinkingCap (Qwen3.8 27B)", "url": "" } ], "description": "ThinkingCap scores 61 (C) to Nemotron 3 Nano 4B's 56.8 (C) for open-weight models you run yourself. 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