The Architecture Divergence: Why Sparse MoE Models Are Winning the Efficiency Race

A deep-dive analysis of the current LLM landscape shows a decisive shift toward sparse mixture-of-experts architectures for frontier models, with every major lab now running MoE variants in production. Sparse routing provides a 4–8x improvement in inference cost per token compared to dense models at equivalent parameter counts. Experts suggest dense models may retain an edge in narrow expert domains requiring deep activation patterns.