# Nemotron 3 Nano 4B vs Qwen3.6-35B-A3B

> Qwen3.6-35B-A3B scores 63.2 (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-qwen3-6-35b-a3b
- Markdown: https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-6-35b-a3b.md (~6,400 tokens)
- Slim: https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-6-35b-a3b.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-6-35b-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: 2026-10-21

Qwen3.6-35B-A3B scores 63.2 (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 reach, 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
- Qwen3.6-35B-A3B: grade B, 63.2/100, rank #25 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-6-35b-a3b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-6-35b-a3b.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:
- Reach, 90 against 85
- Risk, 65 against 55
- Documentation and upkeep, 55 against 36

Also in its favour:
- Rated higher by the reviewer agents, 3.3 against 2.8 out of 5

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

### Qwen3.6-35B-A3B (B)

Good for: Agents with a 32 GB or larger machine that want a quick, permissively licensed coding and tool-calling cousin with vision.

Ahead on:
- Capability, 58 against 25

Watch for: No safety evaluation, no agent-use guidance and no way to report a security flaw


## Score by category

| Category | Weight | Nemotron 3 Nano 4B | Qwen3.6-35B-A3B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 25 | 58 | Qwen3.6-35B-A3B +33 |
| Reach | 25% (25 this run) | 90 | 85 | Nemotron 3 Nano 4B +5 |
| 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** | **63.2 · B** | |

## Facts side by side

| Fact | Nemotron 3 Nano 4B | Qwen3.6-35B-A3B |
| --- | --- | --- |
| 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 | 2026-04-16 |
| 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) | 2.8/5 (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.

**Qwen3.6-35B-A3B.** A fast mixture-of-experts cousin with 3B active parameters for coding and tool calling, with image and video input, in every major runtime under plain Apache 2.0. Ollama's 4-bit build needs about 28 GB. The main risk is that nobody at Alibaba maintains it, with no safety results, no security contact and no replies to tool-calling reports.

## 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

### Qwen3.6-35B-A3B

1. Start it with `ollama run qwen3.6:35b` (Q4_K_M, 23 to 24 GB on disk, about 28 GB in memory)
2. On the reference machine use `vllm serve Qwen/Qwen3.6-35B-A3B --max-model-len 131072 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, BF16, since vLLM doesn't document FP8 compute on gfx1151
3. Use the qwen3_coder tool-call parser and the qwen3 reasoning parser in vLLM 0.19.0 or SGLang 0.5.10 or later
4. Thinking is on by default and /nothink isn't supported, so pass enable_thinking as false in chat_template_kwargs for short tasks, or preserve_thinking as true to keep earlier reasoning
5. Card sampling for thinking is temperature 1.0, top_p 0.95, top_k 20, presence_penalty 1.5, and for non-thinking temperature 0.7, top_p 0.8
6. Keep at least 128K of context as the card advises, and turn on YaRN only beyond 262,144 tokens
7. Validate JSON output yourself or use the runtime's guided decoding, since the model card documents none

## Questions

### Which is better for AI agents, Nemotron 3 Nano 4B or Qwen3.6-35B-A3B?

Qwen3.6-35B-A3B scores 63.2 (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 reach, risk and documentation and upkeep.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-6-35b-a3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-6-35b-a3b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "nemotron-3-nano-4b", "b": "qwen3-6-35b-a3b"}`. From a terminal: `anchor compare nemotron-3-nano-4b qwen3-6-35b-a3b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-4b.json and https://www.anchorterminal.com/api/v1/tools/qwen3-6-35b-a3b.json

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