# Nanbeige4.3-33B vs Nemotron 3 Nano 30B A3B > Nemotron 3 Nano 30B A3B scores 67.1 (B) to Nanbeige4.3-33B'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/nanbeige-4-3-33b-vs-nemotron-3-nano-30b-a3b - Markdown: https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-nemotron-3-nano-30b-a3b.md (~7,150 tokens) - Slim: https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-nemotron-3-nano-30b-a3b.min.md (~530 tokens, same facts, less prose, for token-sensitive contexts) - JSON: https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-nemotron-3-nano-30b-a3b.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: 2026-10-25 Nemotron 3 Nano 30B A3B scores 67.1 (B) on agent readiness against Nanbeige4.3-33B's 61 (C), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself. - Nanbeige4.3-33B: grade C, 61/100, rank #56 of 111 open-weight models. Markdown https://www.anchorterminal.com/tools/nanbeige-4-3-33b.md · JSON https://www.anchorterminal.com/api/v1/tools/nanbeige-4-3-33b.json - Nemotron 3 Nano 30B A3B: grade B, 67.1/100, rank #12 of 111 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 - Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md - All 3341 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md ## Which one, for what ### Nanbeige4.3-33B (C) Good for: Agents with about 96 GB of GPU memory, or the 128 GB reference machine, that want an Apache-tagged 33B model for long coding and tool loops and can wait for independent results. Watch for: No independent benchmark result yet; every agentic number on the card is Nanbeige's own run ### 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 55 - Agent interface, 90 against 85 - Documentation and upkeep, 75 against 56 Watch for: The documented vLLM, SGLang and TensorRT-LLM commands pass trust-remote-code and load a parser file from the repository ## Score by category | Category | Weight | Nanbeige4.3-33B | Nemotron 3 Nano 30B A3B | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 55 | 90 | Nemotron 3 Nano 30B A3B +35 | | Risk | 20% (20 this run) | 60 | 60 | even | | Agent interface | 10% (10 this run) | 85 | 90 | Nemotron 3 Nano 30B A3B +5 | | Documentation and upkeep | 10% (10 this run) | 56 | 75 | Nemotron 3 Nano 30B A3B +19 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **61 · C** | **67.1 · B** | | ## Facts side by side | Fact | Nanbeige4.3-33B | Nemotron 3 Nano 30B A3B | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | Nanbeige LLM Lab (BOSS Zhipin) | NVIDIA | | Hosted endpoint | no (local only) | no (local only) | | Transports | | | | Auth | None | None | | Pricing | Free | Free | | x402 | no | no | | Licence | Apache-2.0 (Hub metadata only; the repository has no LICENSE file) | NVIDIA Nemotron Open Model License (custom, Apache-style, commercial use allowed, not an OSI licence) | | Read-only variant documented | no | no | | llms.txt | no | no | | Last release | 2026-10-24 | 2026-08-24 | | Terms last updated | no document linked | | | Privacy policy last updated | no document linked | 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 | none | 3.3/5 (4) | ## Verdicts **Nanbeige4.3-33B.** A dense 33B Llama-architecture cousin for long agentic coding and office tasks, with per-request thinking, XML tool calls that stock vLLM and SGLang parse, and 512K of context. It needs about 96 GB at BF16, as Nanbeige ships no quantised build. Its agentic results so far are Nanbeige's own, with no safety results, training data notes or licence file. **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. ## Before you call either ### Nanbeige4.3-33B 1. Serve with `vllm serve Nanbeige/Nanbeige4.3-33B --enable-auto-tool-choice --reasoning-parser qwen3 --tool-call-parser qwen3_coder`, as the card says, or SGLang with the same two parsers 2. Keep `tool_call_format` at its default `xml`, since qwen3_coder parses that format; the `json` option writes Hermes-style JSON calls, which need a different parser such as vLLM's hermes 3. Pass `chat_template_kwargs` `{"preserve_thinking": true}` for multi-turn tool use and coding, and `{"enable_thinking": false}` to skip reasoning, as the card recommends 4. Use temperature 1.0, top_p 0.95 and up to 65,536 new tokens for agent work, and temperature 0.6 for reasoning and chat, per the card 5. Send your own system prompt, since without one the template injects a Chinese identity line, or a Chinese tool-expert prompt when tools are passed 6. Send one system message at the start only, as the template raises an error for a later one 7. In plain Transformers pass `use_cache=True` to generate, since config.json and generation_config.json set it false; trust_remote_code isn't needed ### 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 ## Questions ### Which is better for AI agents, Nanbeige4.3-33B or Nemotron 3 Nano 30B A3B? Nemotron 3 Nano 30B A3B scores 67.1 (B) on agent readiness against Nanbeige4.3-33B's 61 (C), and leads in 3 of 4 scored categories. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-nemotron-3-nano-30b-a3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-nemotron-3-nano-30b-a3b.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "nanbeige-4-3-33b", "b": "nemotron-3-nano-30b-a3b"}`. 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