Supporting 119 text languages as well as 19 languages for speech input and 10 for speech output, Qwen3-Omni excels in multilingual communication scenarios. It leverages a unique MoE-based Thinker–Talker design with AuT pretraining that equips it with strong general representations and includes a multi-codebook design to minimize latency during inference. The model achieves top rankings on numerous audio and video benchmarks, rivaling leading closed-source systems. Its real-time audio and video interaction capability ensures low-latency, natural turn-taking in conversational settings.
Qwen3-Omni offers flexible control through system prompts, allowing customization for specific user needs and applications. It features a highly detailed audio captioner model that produces precise and low-hallucination descriptions of audio inputs, filling gaps in open-source multimodal AI tools. The model ecosystem includes various specialized versions for instructive tasks, thinking and reasoning processes, and downstream fine-tuned captioning applications. Deployment options include Hugging Face Transformers, vLLM inference, Docker images, and a web UI demo for users to explore its rich multimodal capabilities locally or via APIs.
