Qwen3.5-397B-A17B is a large-scale, native vision-language model engineered to handle complex reasoning, coding, and agent-based tasks. Its design centers on a hybrid architecture that combines Gated Delta Networks for linear attention with a sparse mixture-of-experts framework. This approach allows the model to dynamically activate only 17 billion of its 397 billion total parameters during any single forward pass, providing the intelligence of a massive model while maintaining the practical inference speed and cost efficiency required for real-world deployment.

The model benefits from advancements in reinforcement learning at scale and early fusion training on multimodal tokens, which enables it to process text, images, and video with high proficiency. By expanding language and dialect support to 201, it offers broad global accessibility for developers and enterprises. Its architecture is optimized for high-throughput environments, making it a versatile choice for building sophisticated AI systems that require deep visual understanding, GUI interaction, and robust code generation capabilities.