Head to head · Open-weight models you run yourself · October 2026 research run
Nemotron 3 Nano 4B vs Qwen3-VL-8B-Instruct
Nemotron 3 Nano 4B scores 56.8 (C) on agent readiness against Qwen3-VL-8B-Instruct's 54.9 (C), and leads in 3 of 5 scored categories. Qwen3-VL-8B-Instruct leads on reach. 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-8B-InstructAlibaba (Qwen team) · Open-weight model
C54.9/100#82 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 31
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
Best for An agent with 8 GB or more to spare that needs a local model to read images, documents and video
Ahead on
- Reach, 100 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-8B-Instruct | Edge |
|---|---|---|---|---|
| Capability | 35%35 | 25 | 28 | Qwen3-VL-8B-Instruct +3 |
| Reach | 25%25 | 90 | 100 | Qwen3-VL-8B-Instruct +10 |
| 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 | 31 | Nemotron 3 Nano 4B +24 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 56.8/100 | C 54.9/100 |
Facts side by side
| Fact | Nemotron 3 Nano 4B | Qwen3-VL-8B-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) | 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-VL-8B-Instruct
A small vision-language cousin for reading screenshots, documents, charts and video, in about 6 GB from Alibaba's Q4_K_M GGUF and every major runtime, under plain Apache 2.0. It has no thinking mode and documents tool calls only in the template. The main risk is no published safety work, so add your own 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-8B-Instruct
- Start it with
llama-server -hf Qwen/Qwen3-VL-8B-Instruct-GGUF:Q4_K_M --jinja -c 32768, orollama run qwen3-vl:8b-instruct - In Ollama the plain
qwen3-vl:8btag is the Thinking edition, so name8b-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 card's sampling,
temperature=0.7, top_p=0.8, top_k=20, presence_penalty=1.5for vision andtemperature=1.0, top_p=1.0, top_k=40, presence_penalty=2.0for text - Validate JSON output yourself, since structured output isn't documented
- vLLM needs 0.11.0 or later and Transformers 4.57.0 or later
Questions
Which is better for AI agents, Nemotron 3 Nano 4B or Qwen3-VL-8B-Instruct?
Nemotron 3 Nano 4B scores 56.8 (C) on agent readiness against Qwen3-VL-8B-Instruct's 54.9 (C), and leads in 3 of 5 scored categories. Qwen3-VL-8B-Instruct leads on reach.
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Machine-readable
- This page as Markdown
/compare/nemotron-3-nano-4b-vs-qwen3-vl-8b-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-8b-instruct.json - From a terminal
anchor compare nemotron-3-nano-4b qwen3-vl-8b-instruct(the CLI) - Over MCP
compare_tools {"a": "nemotron-3-nano-4b", "b": "qwen3-vl-8b-instruct"}at/mcp, no key