# Nemotron 3 Nano 4B vs Qwen3.6-27B

> Qwen3.6-27B scores 67.2 (B) to Nemotron 3 Nano 4B's 56.8 (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-4b-vs-qwen3-6-27b
- Markdown: https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-6-27b.md (~6,250 tokens)
- Slim: https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-6-27b.min.md (~430 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-6-27b.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

Qwen3.6-27B scores 67.2 (B) on agent readiness against Nemotron 3 Nano 4B's 56.8 (C), and leads in 1 of 5 scored categories. Nemotron 3 Nano 4B leads on risk and documentation and upkeep. Both do open-weight models you run yourself.

- 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
- Qwen3.6-27B: grade B, 67.2/100, rank #11 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-6-27b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-6-27b.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 4B (C)

Good for: Agents on laptops, edge boards and small GPUs that need a cheap local tool-calling helper in English

Ahead on:
- Risk, 65 against 55
- Documentation and upkeep, 55 against 36

Watch for: The card's Transformers and vLLM commands pass trust_remote_code and load a parser plugin from the repository

### Qwen3.6-27B (B)

Good for: Agents that want an open coding and tool-calling model with vision on one 24 to 32 GB GPU or Mac, with plain licence terms.

Ahead on:
- Capability, 66 against 25

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


## Score by category

| Category | Weight | Nemotron 3 Nano 4B | Qwen3.6-27B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 25 | 66 | Qwen3.6-27B +41 |
| Reach | 25% (25 this run) | 90 | 90 | even |
| Risk | 20% (20 this run) | 65 | 55 | Nemotron 3 Nano 4B +10 |
| Agent interface | 10% (10 this run) | 70 | 70 | even |
| Documentation and upkeep | 10% (10 this run) | 55 | 36 | Nemotron 3 Nano 4B +19 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **56.8 · C** | **67.2 · B** | |

## Facts side by side

| Fact | Nemotron 3 Nano 4B | Qwen3.6-27B |
| --- | --- | --- |
| 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 | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-03-16 | 2026-04-22 |
| Terms last updated | no document linked | 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 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.

**Qwen3.6-27B.** A capable dense cousin for agentic coding and tool calls with images and video in, running in every major runtime. Ollama's 4-bit build needs about 22 GB, and three providers host it. The main risk is silence, with no safety results, no agent guidance, no answers in its repository and Qwen3.8-27B now ahead of it.

## Before you call either

### Nemotron 3 Nano 4B

1. Quickest start is `ollama run nemotron-3-nano:4b`, a 2.8 GB build
2. With llama.cpp use `llama-server -hf nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF --jinja`, NVIDIA's own Q4_K_M file
3. 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`
4. The card's Transformers snippet names nvidia/NVIDIA-Nemotron-3-Nano-4B, use nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16 instead
5. Thinking is on by default. Pass `enable_thinking=False` to the chat template for short tasks
6. 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
7. It's tuned for English only, and validate JSON yourself, since structured output isn't documented

### Qwen3.6-27B

1. Quickest start is `ollama run qwen3.6:27b`, about 18 GB of files with the vision projector
2. Alibaba's FP8 build with the card's parsers is `vllm serve Qwen/Qwen3.6-27B-FP8 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, vllm 0.19.0 or later
3. Use the qwen3_coder tool parser, the template writes calls as function and parameter blocks inside tool_call
4. Thinking is on by default, pass `"chat_template_kwargs": {"enable_thinking": false}` for short tasks, and `/think` and `/nothink` aren't supported
5. `"preserve_thinking": true` in chat_template_kwargs keeps earlier reasoning in context, which helps long agent sessions but grows the prompt
6. Sampling per the card is temperature 1.0, top_p 0.95, top_k 20 when thinking, and temperature 0.7, top_p 0.8, presence_penalty 1.5 without
7. Keep at least 128K of context if you rely on thinking, and turn on YaRN only beyond 262,144 tokens
8. Validate any JSON it returns or use the runtime's guided decoding, since structured output isn't documented
9. Qwen3.8-27B (listed as qwen3-8-27b) is the newer model at this size on the same runtimes

## Questions

### Which is better for AI agents, Nemotron 3 Nano 4B or Qwen3.6-27B?

Qwen3.6-27B scores 67.2 (B) on agent readiness against Nemotron 3 Nano 4B's 56.8 (C), and leads in 1 of 5 scored categories. Nemotron 3 Nano 4B leads on risk and documentation and upkeep.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-6-27b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-6-27b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "nemotron-3-nano-4b", "b": "qwen3-6-27b"}`. From a terminal: `anchor compare nemotron-3-nano-4b qwen3-6-27b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-4b.json and https://www.anchorterminal.com/api/v1/tools/qwen3-6-27b.json

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- [Nemotron 3 Nano 4B vs SmolLM3-3B](https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-smollm3-3b.md)
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- [Ornith-1.5-35B-A3B vs Qwen3.6-27B](https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-6-27b.md)
- [Ornith-1.5-397B vs Qwen3.6-27B](https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-6-27b.md)
- [Ornith-1.5-9B vs Qwen3.6-27B](https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-6-27b.md)
- [Qwen3-30B-A3B-Instruct-2507 vs Qwen3.6-27B](https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-qwen3-6-27b.md)
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- [Qwen3.6-27B vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-swift-1-5-qwen3-8-flash-next.md)
- [Qwen3.6-27B vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-thinkingcap-qwen3-8-27b.md)

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