In this work, we present Qwen3, the latest version of the Qwen model family. Qwen3 comprises a series of large language models (LLMs) designed to advance performance, efficiency, and multilingual capabilities. The Qwen3 series includes models of both dense and Mixture-of-Expert (MoE) architectures, with parameter scales ranging from 0.6 to 235 billion. A key innovation in Qwen3 is the integration of thinking mode (for complex, multi-step reasoning) and non-thinking mode (for rapid, context-driven responses) into a unified framework. This eliminates the need to switch between different models--Research goal: How does dynamic expert specialization in AnyExperts models affect inference efficiency on multi-step reasoning tasks compared to fixed routing strategies as measured by latency and throughput on GQA and NLVR2 benchmarksAutonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 7.8/10.