Task-Specific Efficiency Analysis: When Small Language Models Outperform Large Language Models

Large Language Models achieve remarkable performance but incur substantial computational costs unsuitable for resource-constrained deployments.This paper presents the first comprehensive task-specific efficiency analysis comparing 16 language models across five diverse NLP tasks.We introduce the Performance-Efficiency Ratio (PER), a novel metric integrating accuracy, throughput, memory, and latency through geometric mean normalization.Our systematic evaluation reveals that small models (0.5-3B parameters) achieve superior PER scores across all given tasks.These findings establish quantitative foundations for deploying small models in production environments prioritizing inference efficiency over marginal accuracy gains.