Head to head · Open-weight models you run yourself · October 2026 research run
Qwen3-VL-30B-A3B-Instruct vs SmolLM3-3B
SmolLM3-3B scores 61 (C) on agent readiness against Qwen3-VL-30B-A3B-Instruct's 58 (C), and leads in 3 of 4 scored categories. Qwen3-VL-30B-A3B-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
Qwen3-VL-30B-A3B-InstructAlibaba (Qwen team) · Open-weight model
C58/100#68 of 100 open-weight models
SmolLM3-3BHugging Face · Open-weight model
C61/100#47 of 100 open-weight models
Which one, for what
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
- Reach, 95 against 85
Watch for
No safety evaluation, agent-use guidance or security contact
Best for Agents that want a small, fully transparent tool-calling cousin on a laptop, CPU or edge device
Ahead on
- Risk, 65 against 55
- Agent interface, 80 against 60
- Documentation and upkeep, 54 against 36
Watch for
65,536 tokens trained; 131,072 needs a YaRN edit to config.json
Score by category
| Category | Weight this run | Qwen3-VL-30B-A3B-Instruct | SmolLM3-3B | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 95 | 85 | Qwen3-VL-30B-A3B-Instruct +10 |
| Risk | 20%20 | 55 | 65 | SmolLM3-3B +10 |
| Agent interface | 10%10 | 60 | 80 | SmolLM3-3B +20 |
| Documentation and upkeep | 10%10 | 36 | 54 | SmolLM3-3B +18 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 58/100 | C 61/100 |
Facts side by side
| Fact | Qwen3-VL-30B-A3B-Instruct | SmolLM3-3B |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Alibaba (Qwen team) | Hugging Face |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2025-11-01 | 2025-09-10 |
| 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 | 3/5 (4) | 3.3/5 (4) |
Verdicts
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.
SmolLM3-3B
A tiny, fully open cousin with documented tool calling and switchable thinking, for local subtasks and routing on laptops and CPUs. It needs about 8 GB in BF16, or under 3 GB in Hugging Face's 4-bit ONNX build for ONNX Runtime. The main risks are a 64K native context, no safety results and no repository changes since September 2025.
Before you call either
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
SmolLM3-3B
- Start it with
vllm serve HuggingFaceTB/SmolLM3-3B --enable-auto-tool-choice --tool-call-parser=hermes, or tool calls come back as text - Pass
chat_template_kwargswithenable_thinkingfalse for short tasks, since thinking is on by default; a/thinkor/no_thinkflag in the system prompt overrides the kwarg - Put earlier tool calls in the assistant message content, since the template ignores a
tool_callsfield when it renders history - Add the YaRN
rope_scalingblock (factor 2.0, original 65536) to config.json before sending more than 65,536 tokens - Use temperature 0.6 and top_p 0.95, the card's recommended sampling
- Needs Transformers v4.53.0 or later; for llama.cpp use a GGUF from ggml-org or another converter, since Hugging Face publishes none
- Validate JSON output yourself or use the runtime's guided decoding
Questions
Which is better for AI agents, Qwen3-VL-30B-A3B-Instruct or SmolLM3-3B?
SmolLM3-3B scores 61 (C) on agent readiness against Qwen3-VL-30B-A3B-Instruct's 58 (C), and leads in 3 of 4 scored categories. Qwen3-VL-30B-A3B-Instruct leads on reach.
Other comparisons with Qwen3-VL-30B-A3B-Instruct or SmolLM3-3B
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- Gemma 4 26B A4B IT vs SmolLM3-3B
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- Qwen3-VL-32B-Instruct vs SmolLM3-3B
- Qwen3-VL-4B-Instruct vs SmolLM3-3B
- Qwen3-VL-8B-Instruct vs SmolLM3-3B
- Qwopus3.8-27B-Flash-V2 vs SmolLM3-3B
- SmolLM3-3B vs Spark-X2.6-4B
- SmolLM3-3B vs Swift 1.5 Qwen3.8-27B
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Machine-readable
- This page as Markdown
/compare/qwen3-vl-30b-a3b-instruct-vs-smollm3-3b.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/qwen3-vl-30b-a3b-instruct.json·/api/v1/tools/smollm3-3b.json - From a terminal
anchor compare qwen3-vl-30b-a3b-instruct smollm3-3b(the CLI) - Over MCP
compare_tools {"a": "qwen3-vl-30b-a3b-instruct", "b": "smollm3-3b"}at/mcp, no key