These May and September 2025 technical reports introduce and evaluate two distinct but related large language models: the **Qwen3 family** and the **Qwen3-Omni** multimodal system. The first source focuses on the text-based Qwen3 models, highlighting their development process, which includes a sophisticated **multilingual data annotation system** and a multi-stage training pipeline incorporating **Strong-to-Weak Distillation** and "Thinking Mode" for complex tasks like coding and mathematics. The second, more comprehensive source describes Qwen3-Omni, a single model designed to excel across **text, image, audio, and video** modalities without performance degradation, utilizing a novel **Thinker–Talker Mixture-of-Experts (MoE) architecture** for real-time speech and reasoning. Crucially, Qwen3-Omni achieves **state-of-the-art performance** in audio tasks and boasts extremely low **first-packet latency** for interactive applications, supporting a wide range of **multilingual capabilities** in both speech and text.
Sources:
https://arxiv.org/pdf/2505.09388
https://arxiv.org/pdf/2509.17765
