Assessing the Influence of End-to-End Request Latency and Response Length on Waiting Time Satisfaction of LLM Users

The increasing deployment and resource consumption of Large Language Model (LLM) systems necessitate understanding the influencing factors of the perceived quality of these systems. A deep understanding of factors influencing the Quality of Experience (QoE) for LLM users enables novel approaches for resource optimization while maintaining high user experience quality. Existing research on the QoE for LLM users lacks controlled experiments isolating timing effects from other potential influencing factors. To address this gap, this paper presents a controlled experimental setup for assessing how system parameters—specifically end-to-end request latency and response length—influence user waiting time satisfaction as a key component of QoE. A purpose-built application emulated a realistic interface experience, allowing us to collect 2189 ratings from 178 crowdsourced workers. The statistical analysis reveals significant non-linear relationships between system-controlled End-to-End Request Latency, response length, and user-perceived waiting time satisfaction. The methodology presented provides an empirical foundation for future investigations, including animated response delivery and Time-to-First-Token-driven optimization strategies for LLM systems.