Head to head · Open-weight models you run yourself · October 2026 research run
Nemotron 3 Nano 4B vs Qwen3-VL-30B-A3B-Instruct
Qwen3-VL-30B-A3B-Instruct scores 58 (C) on agent readiness against Nemotron 3 Nano 4B's 56.8 (C), and leads in 2 of 5 scored categories. Nemotron 3 Nano 4B leads on risk, agent interface 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-VL-30B-A3B-InstructAlibaba (Qwen team) · Open-weight model
C58/100#68 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
- Risk, 65 against 55
- Agent interface, 70 against 60
- 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
Best for An agent with 24 GB or more to spare that wants a faster, stronger local reader of images, documents and video than the 8B model
Ahead on
- Capability, 39 against 25
- Reach, 95 against 90
Watch for
No safety evaluation, agent-use guidance or security contact
Score by category
| Category | Weight this run | Nemotron 3 Nano 4B | Qwen3-VL-30B-A3B-Instruct | Edge |
|---|---|---|---|---|
| Capability | 35%35 | 25 | 39 | Qwen3-VL-30B-A3B-Instruct +14 |
| Reach | 25%25 | 90 | 95 | Qwen3-VL-30B-A3B-Instruct +5 |
| Risk | 20%20 | 65 | 55 | Nemotron 3 Nano 4B +10 |
| Agent interface | 10%10 | 70 | 60 | Nemotron 3 Nano 4B +10 |
| Documentation and upkeep | 10%10 | 55 | 36 | Nemotron 3 Nano 4B +19 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 56.8/100 | C 58/100 |
Facts side by side
| Fact | Nemotron 3 Nano 4B | Qwen3-VL-30B-A3B-Instruct |
|---|---|---|
| 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 | 2025-11-01 |
| 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-VL-30B-A3B-Instruct
A mid-sized vision-language cousin for screenshots, documents, charts and video that runs fast for its size, with about 3B active parameters. It needs about 23 GB for Alibaba's Q4_K_M GGUF with its vision file, loads in every major runtime under plain Apache 2.0, and three providers host it. With no published safety work, add guardrails before it drives a GUI.
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-VL-30B-A3B-Instruct
- Start it with
llama-server -hf Qwen/Qwen3-VL-30B-A3B-Instruct-GGUF:Q4_K_M --jinja -c 32768, orollama run qwen3-vl:30b-a3b-instruct - In Ollama the plain
qwen3-vl:30btag is the Thinking edition, so name30b-a3b-instruct - With the GGUF, load the mmproj vision file too, or images won't be read
- Parse tool calls as JSON inside
<tool_call>tags, or use SGLang's--tool-call-parser qwen - Use the shipped sampling, temperature 0.7, top_p 0.8 and top_k 20, from generation_config.json
- Validate JSON output yourself, since structured output isn't documented
- vLLM needs 0.11.0 or later, Transformers 4.57.0 or later and llama.cpp b6907 or later
Questions
Which is better for AI agents, Nemotron 3 Nano 4B or Qwen3-VL-30B-A3B-Instruct?
Qwen3-VL-30B-A3B-Instruct scores 58 (C) on agent readiness against Nemotron 3 Nano 4B's 56.8 (C), and leads in 2 of 5 scored categories. Nemotron 3 Nano 4B leads on risk, agent interface and documentation and upkeep.
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Machine-readable
- This page as Markdown
/compare/nemotron-3-nano-4b-vs-qwen3-vl-30b-a3b-instruct.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-vl-30b-a3b-instruct.json - From a terminal
anchor compare nemotron-3-nano-4b qwen3-vl-30b-a3b-instruct(the CLI) - Over MCP
compare_tools {"a": "nemotron-3-nano-4b", "b": "qwen3-vl-30b-a3b-instruct"}at/mcp, no key