Qwen3-30B-A3B is a mixture-of-experts model from Alibaba that activates only 3 billion of its 30 billion parameters per inference, outperforming QwQ-32B while running at a fraction of the compute cost.
import { streamText } from 'ai'
const result = streamText({ model: 'alibaba/qwen-3-30b', prompt: 'Why is the sky blue?'})Qwen3-30B-A3B occupies a distinctive position in the Qwen3 lineup: it's the smaller of two MoE models in the family, but its efficiency story is the more striking one. Inference activates only 3 billion parameters, comparable to serving a small model, yet the full 30 billion parameter capacity gives it a much larger representational space than a genuinely 3B model would have.
Alibaba's benchmarks position this model above QwQ-32B, which was previously one of the stronger open reasoning models. QwQ-32B is a dense model that activates all 32 billion of its parameters on every token, meaning Qwen3-30B-A3B achieves superior results at roughly one-tenth the active parameter count. For teams running inference at volume, this ratio has direct cost implications.
Like the rest of the Qwen3 family, the model supports hybrid thinking modes. The enable_thinking parameter switches between step-by-step chain-of-thought reasoning and direct-response mode. The thinking budget can be configured per request, so applications can use extended reasoning for genuinely complex queries while defaulting to fast responses for routine ones.
The 30B-A3B supports 119 languages and dialects, the same multilingual coverage as the rest of the Qwen3 family, and includes tool calling, agentic workflow, and MCP support, giving the model strong instruction-following and coding capabilities relative to its inference cost.