Are All Large Language Models Created Equal? Evidence of Task-Level Differences in Generation X E-Commerce Marketing Analytics

With GPT-4, Claude, and Gemini each commanding premium enterprise pricing and distinct vendor claims, the question of which large language model (LLM) best serves marketing analytics practitioners is increasingly consequential. This article reports findings from a quantitative comparative study evaluating these three models on five analytical tasks applied to Generation X e-commerce customer engagement data (n = 385). Across precision, recall, F1-score, and AUC, no statistically significant aggregate differences emerged among the three models, supporting retention of the null hypothesis. Yet task-level distinctions are real and actionable: GPT-4 delivers the highest output stability, Gemini excels in processing speed and clustering quality, and Claude produces the strongest psychographic segmentation signal. Cost analysis reveals meaningful API pricing differences of $10–$120 per million output tokens across these platforms, making model-task alignment a budget decision as well as a performance one. The findings offer marketing teams an evidence-based framework for choosing LLMs by task priority rather than brand reputation.