Head to head · Open-weight models you run yourself · October 2026 research run
Nemotron 3 Nano 4B vs Qwen3.5-4B
Qwen3.5-4B scores 61.2 (C) 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.
Best open-weight models you download and run yourself for AI agents · All 2870 open weights comparisons
Nemotron 3 Nano 4BNVIDIA · Open-weight model
C56.8/100#73 of 100 open-weight models
Qwen3.5-4BAlibaba (Qwen team) · Open-weight model
C61.2/100#33 of 100 open-weight models
Which one, for what
Best 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 34
Watch for
The card's Transformers and vLLM commands pass trust_remote_code and load a parser plugin from the repository
Best for Agents that want a cheap local helper for tool calls, short reasoning and reading images on a laptop or small GPU
Ahead on
- Capability, 53 against 25
Watch for
No quantised build from Alibaba for this size
Score by category
| Category | Weight this run | Nemotron 3 Nano 4B | Qwen3.5-4B | Edge |
|---|---|---|---|---|
| Capability | 35%35 | 25 | 53 | Qwen3.5-4B +28 |
| Reach | 25%25 | 90 | 85 | Nemotron 3 Nano 4B +5 |
| Risk | 20%20 | 65 | 55 | Nemotron 3 Nano 4B +10 |
| Agent interface | 10%10 | 70 | 70 | even |
| Documentation and upkeep | 10%10 | 55 | 34 | Nemotron 3 Nano 4B +21 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 56.8/100 | C 61.2/100 |
Facts side by side
| Fact | Nemotron 3 Nano 4B | Qwen3.5-4B |
|---|---|---|
| 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 | 2026-03-02 |
| 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.
Qwen3.5-4B
A small vision-language cousin for tool calls, light agent steps and image or video reading on almost any machine, with switchable thinking. Ollama's 3.3 GB build or the 9.3 GB BF16 files run in every major runtime under plain Apache 2.0. The main risks are no safety results, no structured-output guarantee and no answers from Alibaba since March.
Before you call either
Nemotron 3 Nano 4B
- Quickest start is
ollama run nemotron-3-nano:4b, a 2.8 GB build - With llama.cpp use
llama-server -hf nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF --jinja, NVIDIA's own Q4_K_M file - 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 - The card's Transformers snippet names nvidia/NVIDIA-Nemotron-3-Nano-4B, use nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16 instead
- Thinking is on by default. Pass
enable_thinking=Falseto the chat template for short tasks - 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
- It's tuned for English only, and validate JSON yourself, since structured output isn't documented
Qwen3.5-4B
- Smallest start is
ollama run qwen3.5:4b(Q4_K_M, about 4 GB with context) - Full precision
vllm serve Qwen/Qwen3.5-4B --port 8000 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder - Turn thinking off with
"chat_template_kwargs": {"enable_thinking": false}in the request body, since it's on by default and/thinkand/nothinkaren't supported - There's no generation_config.json, so pass the card's sampling yourself, for example
temperature=0.7, top_p=0.8, top_k=20, presence_penalty=1.5without thinking - Keep the context at 128K or more if you rely on thinking, and turn on YaRN only past 262,144 tokens
- Validate any JSON it returns, since structured output isn't documented
Questions
Which is better for AI agents, Nemotron 3 Nano 4B or Qwen3.5-4B?
Qwen3.5-4B scores 61.2 (C) 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.
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Machine-readable
- This page as Markdown
/compare/nemotron-3-nano-4b-vs-qwen3-5-4b.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/nemotron-3-nano-4b.json·/api/v1/tools/qwen3-5-4b.json - From a terminal
anchor compare nemotron-3-nano-4b qwen3-5-4b(the CLI) - Over MCP
compare_tools {"a": "nemotron-3-nano-4b", "b": "qwen3-5-4b"}at/mcp, no key