Enhancing the Economic Efficiency of Large Language Models (LLMs)

LLMs demonstrate unprecedented capabilities; however, they come with very high computational and financial costs because of their extensive usage of tokens. This paper presents an intelligent optimization pipeline that reduces the cost of operating LLMs by minimizing prompt size without affecting semantic meaning. Combining summarization, heuristic pruning, token estimation, reinforcement learning-based optimization, and multi-model routing across cloud and local LLMs, this system supports a feedback engine that analyzes responses and perpetually refines optimization strategies. Sample evaluations show that token savings in the order of approximately 30% are achieved while maintaining semantic similarity levels above 90%. Cost comparison results show up to 30% reduction in total expenses for standard LLM operations. This framework will prove that prompt-level optimization combined with dynamic model selection can achieve significant improvements in LLM economic efficiency and make AI systems more affordable and scalable for academic, research, and enterprise applications.