Qwen3.5 35B-A3B is a multimodal vision-language model built on an efficient hybrid architecture that combines Gated Delta Networks with a sparse Mixture-of-Experts design. By utilizing 35 billion total parameters with only 3 billion active parameters, the model achieves high-throughput inference while maintaining low latency and cost. It is engineered to serve as a foundation for native multimodal agent applications, offering robust support for text, image, and video inputs. This design intent focuses on delivering exceptional utility across complex reasoning, coding, and visual understanding tasks, positioning it as a powerful tool for developers and enterprises needing scalable performance.

The model benefits from advanced training techniques, including early fusion on multimodal tokens, which allows it to achieve cross-generational parity with previous iterations while outperforming specialized vision-language predecessors. Its development incorporates scalable reinforcement learning to refine its reasoning and agentic capabilities. These post-training advancements ensure the model remains highly effective for interactive chat and complex tool-calling workflows. With its ability to handle large context windows, the model is well-suited for demanding production environments where high-performance multimodal perception and efficient, reliable output generation are required.