Navigation capability is crucial for the functionality of living machines across various domains. Recently, two types of approaches have been pursued to address navigation tasks: reinforcement learning (RL) based methods and insect-inspired approaches focusing on insect navigation expertise. Recent advancements in insect navigation studies have provided valuable insights, inspiring the development of efficient navigation solutions. In this study, we present a systematic comparison between RL and insect-inspired visual navigation methods in solving identical navigation tasks. Our findings, for the first time, demonstrate that insect-inspired methods exhibit superior cost-efficiency. These methods require significantly less computational resources and storage while achieving performance levels comparable to state-of-the-art RL techniques. This comparative analysis underscores the remarkable potential of insect-inspired models in navigation tasks, highlighting their exceptional cost-effectiveness. Such findings not only advance our understanding of navigation strategies but also emphasize the viability of insect-inspired approaches in engineering living machines.