Qwen3 Next 80B A3B Instruct

Qwen3 Next 80B A3B Instruct is a foundation model built to prioritize scaling efficiency through a highly sparse Mixture-of-Experts architecture. By activating only 3 billion parameters per inference step, the model achieves a significant reduction in computational cost while maintaining the capacity of an 80-billion parameter system. Its design incorporates Hybrid Attention, which combines Gated DeltaNet and Gated Attention to manage ultra-long context windows effectively. This architecture is specifically engineered to deliver high-throughput performance, making it a robust choice for tasks requiring deep document analysis, complex multi-turn dialogues, and code generation.

The model benefits from advanced stability optimizations, including zero-centered and weight-decayed layer normalization, which support consistent performance during both pre-training and post-training phases. By utilizing Multi-Token Prediction, the model accelerates inference speeds and enhances overall output quality. It is optimized to provide direct, instruction-following responses without visible chain-of-thought traces, positioning it as a reliable tool for agentic workflows, retrieval-augmented generation, and production environments where deterministic results are required. Its ability to match the performance of much larger systems while maintaining superior inference throughput makes it a versatile solution for enterprise-scale applications.