Recent discussions on Kimi K3 and Qwen 3.8 highlight a narrowing gap between open and closed weights. Experts analyze how distillation techniques transfer capabilities from frontier models to smaller, efficient versions. This trend reduces the cost of high-performance inference. Practitioners should prioritize these distilled models to optimize latency without sacrificing reasoning quality.