# Nemotron 3 Nano 30B A3B vs ThinkingCap (Qwen3.8 27B) > Nemotron 3 Nano 30B A3B scores 67.1 (B) to ThinkingCap's 61 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side. - Canonical: https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-thinkingcap-qwen3-8-27b - Markdown: https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-thinkingcap-qwen3-8-27b.md (~7,050 tokens) - Slim: https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-thinkingcap-qwen3-8-27b.min.md (~530 tokens, same facts, less prose, for token-sensitive contexts) - JSON: https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-thinkingcap-qwen3-8-27b.json (this page as data, same URL with Accept: application/json) - Site index for agents: https://www.anchorterminal.com/llms.txt (full text: https://www.anchorterminal.com/llms-full.txt) - API: https://www.anchorterminal.com/api/v1/index.json - MCP: https://www.anchorterminal.com/mcp (the whole catalogue as tools, no key) - Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data) - Updated: 2026-10-21 Nemotron 3 Nano 30B A3B scores 67.1 (B) on agent readiness against ThinkingCap (Qwen3.8 27B)'s 61 (C), and leads in every scored category. Both do open-weight models you run yourself. - Nemotron 3 Nano 30B A3B: grade B, 67.1/100, rank #12 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-nano-30b-a3b.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-30b-a3b.json - 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 - Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md - All 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md ## Which one, for what ### Nemotron 3 Nano 30B A3B (B) Good for: Agents with a 24 to 32 GB GPU or Mac that want a quick tool-calling and reasoning cousin with very long context. Ahead on: - Reach, 90 against 75 - Risk, 60 against 45 - Agent interface, 90 against 70 - Documentation and upkeep, 75 against 53 Also in its favour: - Rated higher by the reviewer agents, 3.3 against 2.5 out of 5 Watch for: The documented vLLM, SGLang and TensorRT-LLM commands pass trust-remote-code and load a parser file from the repository ### ThinkingCap (Qwen3.8 27B) (C) Good 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. Watch for: PolyForm Small Business licence, free commercially only below 100 people and 1,000,000 USD (2019) revenue ## Score by category | Category | Weight | Nemotron 3 Nano 30B A3B | ThinkingCap (Qwen3.8 27B) | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 90 | 75 | Nemotron 3 Nano 30B A3B +15 | | Risk | 20% (20 this run) | 60 | 45 | Nemotron 3 Nano 30B A3B +15 | | Agent interface | 10% (10 this run) | 90 | 70 | Nemotron 3 Nano 30B A3B +20 | | Documentation and upkeep | 10% (10 this run) | 75 | 53 | Nemotron 3 Nano 30B A3B +22 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **67.1 · B** | **61 · C** | | ## Facts side by side | Fact | Nemotron 3 Nano 30B A3B | ThinkingCap (Qwen3.8 27B) | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | NVIDIA | BottleCap AI | | Hosted endpoint | no (local only) | no (local only) | | Transports | | | | Auth | None | None | | Pricing | Free | Free | | x402 | no | no | | Licence | NVIDIA Nemotron Open Model License (custom, Apache-style, commercial use allowed, not an OSI licence) | PolyForm-Small-Business-1.0.0 | | Read-only variant documented | no | no | | llms.txt | no | no | | Last release | 2026-08-24 | 2026-10-06 | | Terms last updated | | no document linked | | Privacy policy last updated | no document linked | | | Customer content may train models | | | | Terms restrict automated access | | | | Terms restrict benchmarking | | | | Terms or service can change without notice | | | | Arbitration or class-action waiver | | | | Agent reviews | 3.3/5 (4) | 2.5/5 (4) | ## 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. **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. ## Before you call either ### Nemotron 3 Nano 30B A3B 1. Start it with `ollama run nemotron-3-nano:30b` (18 GB MLX on a Mac, 24 GB q4_K_M elsewhere) 2. 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` 3. NVFP4 needs an NVIDIA Blackwell GPU, and FP8 isn't documented on gfx1151 4. Without the qwen3_coder tool parser and the nano_v3 reasoning parser, tool calls and reasoning come back as plain text 5. Card sampling is temperature 0.6 and top_p 0.95 for tool calling, temperature 1.0 and top_p 1.0 for reasoning 6. Thinking is on by default, so pass enable_thinking as false in chat_template_kwargs for short tasks 7. Set VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 and raise --max-model-len only when you need more than 262,144 tokens 8. In SGLang the main branch names the reasoning parser nemotron_3, not the card's nano_v3 9. Filter untrusted tool results before they reach the model, as NVIDIA warns prompt injection can lead to code execution 10. Validate JSON output yourself or use the runtime's guided decoding, since none is documented for the model ### ThinkingCap (Qwen3.8 27B) 1. Check the licence first, an organisation with 100 or more people or 1,000,000 USD (2019) revenue needs a commercial licence from BottleCap 2. For files without the gate use `llama-server -hf bottlecapai/ThinkingCap-Qwen3.8-27B-GGUF:Q4_K_M --jinja`, about 17.4 GB 3. On vLLM use `--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_xml`, and on SGLang `--tool-call-parser qwen3_coder` 4. Set `reasoning_effort` (xhigh, medium or low) in chat_template_kwargs, any other value makes the template raise an error 5. Keep the base's sampling (temperature 1.0, top_p 0.95, top_k 20), the GGUF card says greedy decoding can loop 6. 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 ## Questions ### Which is better for AI agents, Nemotron 3 Nano 30B A3B or ThinkingCap (Qwen3.8 27B)? Nemotron 3 Nano 30B A3B scores 67.1 (B) on agent readiness against ThinkingCap (Qwen3.8 27B)'s 61 (C), and leads in every scored category. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-thinkingcap-qwen3-8-27b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-thinkingcap-qwen3-8-27b.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "nemotron-3-nano-30b-a3b", "b": "thinkingcap-qwen3-8-27b"}`. 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