# Nemotron 3 Nano 30B A3B vs Qwen3.8-27B

> Qwen3.8-27B scores 68.6 (B) to Nemotron 3 Nano 30B A3B's 67.1 (B) 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-qwen3-8-27b
- Markdown: https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-8-27b.md (~6,600 tokens)
- Slim: https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-8-27b.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-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

Qwen3.8-27B scores 68.6 (B) on agent readiness against Nemotron 3 Nano 30B A3B's 67.1 (B), and leads in 1 of 5 scored categories. Nemotron 3 Nano 30B A3B leads on risk, agent interface and documentation and upkeep. 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
- Qwen3.8-27B: grade B, 68.6/100, rank #10 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-8-27b.md · JSON https://www.anchorterminal.com/api/v1/tools/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:
- Risk, 60 against 55
- Agent interface, 90 against 70
- Documentation and upkeep, 75 against 36

Watch for: The documented vLLM, SGLang and TensorRT-LLM commands pass trust-remote-code and load a parser file from the repository

### Qwen3.8-27B (B)

Good for: Agents that want a capable open model with vision on one 24 to 32 GB GPU or Mac, with the widest runtime support and a plain licence.

Ahead on:
- Capability, 70 against 46

Watch for: No safety results, prompt-injection guidance or security contact


## Score by category

| Category | Weight | Nemotron 3 Nano 30B A3B | Qwen3.8-27B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 46 | 70 | Qwen3.8-27B +24 |
| Reach | 25% (25 this run) | 90 | 90 | even |
| Risk | 20% (20 this run) | 60 | 55 | Nemotron 3 Nano 30B A3B +5 |
| Agent interface | 10% (10 this run) | 90 | 70 | Nemotron 3 Nano 30B A3B +20 |
| Documentation and upkeep | 10% (10 this run) | 75 | 36 | Nemotron 3 Nano 30B A3B +39 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **67.1 · B** | **68.6 · B** | |

## Facts side by side

| Fact | Nemotron 3 Nano 30B A3B | Qwen3.8-27B |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | NVIDIA | Alibaba (Qwen team) |
| 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) | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-08-24 | 2026-08-14 |
| 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 | 3.3/5 (4) | 3/5 (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.

**Qwen3.8-27B.** A dense cousin for coding and agent work with image and video input that runs almost anywhere. Ollama's 4-bit build needs about 22 GB, and five providers host it for agents without the hardware. The main risk is that nobody at Alibaba answers, there are no safety results or agent guidance, and the tool format is only in the template.

## 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

### Qwen3.8-27B

1. Quickest start is `ollama run qwen3.8`, about 18 GB of files
2. On our reference machine use BF16 with `vllm serve Qwen/Qwen3.8-27B --max-model-len 131072 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, since FP8 on gfx1151 isn't documented
3. Use the qwen3_coder tool parser, SGLang's cookbook says the Hermes parser leaves its calls unparsed
4. Thinking is on by default, pass `chat_template_kwargs` with `enable_thinking` false for short tasks, and read the reasoning from `reasoning_content`
5. `reasoning_effort` takes xhigh (default), medium or low, and any other value makes the template raise an error
6. Sampling per the card is temperature 1.0 and top_p 0.95 when thinking, temperature 0.7, top_p 0.8 and presence_penalty 1.5 without
7. Preserved thinking keeps earlier reasoning in the context unless `preserve_thinking` is false, which grows long agent sessions
8. Turn on YaRN only beyond 262,144 tokens, the card warns static YaRN can cost quality on short texts
9. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented

## Questions

### Which is better for AI agents, Nemotron 3 Nano 30B A3B or Qwen3.8-27B?

Qwen3.8-27B scores 68.6 (B) on agent readiness against Nemotron 3 Nano 30B A3B's 67.1 (B), and leads in 1 of 5 scored categories. Nemotron 3 Nano 30B A3B leads on risk, agent interface and documentation and upkeep.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-8-27b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-8-27b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "nemotron-3-nano-30b-a3b", "b": "qwen3-8-27b"}`. From a terminal: `anchor compare nemotron-3-nano-30b-a3b qwen3-8-27b`
- 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/qwen3-8-27b.json

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