Generative AI models hold substantial potential for improving the accuracy and effectiveness of information transmission, further enhancing platform competitiveness and user satisfaction. Although platforms actively adopt generative AI models in practice, there remains a lack of empirical evidence supporting their effectiveness on e-commerce platforms. This study, grounded in cue utilization theory and employing a Difference-in-Differences (DID) method, systematically examines the impact of generative AI models on consumer purchase behavior under the moderating effect of several extrinsic cues. The findings indicate that generative AI model have significant increment on consumer purchase behavior, particularly for products with high information richness, high review inconsistency. Notably, despite conventional recommendation systems aggravate homogenized market according to the winner-take-all theory, empirical results demonstrate that generative AI model does not meet expectations. Instead, the AI model enhanced sales of the product with low brand awareness, which confirming long tail theory. This research enriches the application of cue utilization theory about the impact of generative AI model with e-commerce environment, elucidates the moderating mechanisms among multiple cues, and provides practical guidance for e-commerce platforms managers in the design of business strategies.