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This paper models the competitive pricing strategies of two Large Language Model Providers (LLMPs) with differing intelligence levels, considering user heterogeneity and psychological perception of pay-per-use models. It analyzes four pricing mode combinations (SS, SD, DS, DD) to determine optimal strategies for each provider. The key finding is that the "tick-tock effect" in pay-per-use models reduces prices and profits due to lowered user valuation, and that subscription models are only advantageous when both user usage frequency and perceived psychological cost are high.
LLM providers beware: users' "tick-tock effect" perception of cost in pay-per-use models can significantly reduce service prices and profits, challenging the assumption that subscription models are always superior.
With the rapid growth of user demand for large language models (LLMs) in their work, the application market is driving intense competition among large language model providers (LLMPs). Users have different preferences and psychological perceptions towards the charging models of different LLMPs. LLMPs with different intelligence levels must design pricing strategies based on diverse user characteristics. To investigate the impact of user heterogeneity on the strategic pricing of competing LLMPs, this paper establishes a competitive model with two providers, comprising a highly intelligent initial LLM provider and a follower provider. Both providers can independently decide to adopt either a subscription model or a pay-per-use model, resulting in four pricing mode combinations (dual subscription SS, subscription-pay-per-use SD, pay-per-use-subscription DS, dual pay-per-use DD). The study shows that when the pay-per-use model is adopted, the user’s psychological perception of the “tick-tock effect” reduces the provider’s service price and profit, as the perceived psychological cost lowers the user’s valuation of the product, thereby decreasing demand. Furthermore, we analyze the equilibrium strategies for pricing mode selection by the two providers. The results indicate that the subscription model is not always advantageous for providers. Both providers will only choose to adopt the subscription model when both user usage frequency and perceived psychological cost are high. Conversely, when both user usage frequency and perceived psychological cost are low, the two providers will not simultaneously adopt the subscription model. Interestingly, as the product intelligence levels of the two providers converge, their choices of pricing modes are also more inclined to diverge. These insights guide LLMPs to strategically adjust their pricing models based on user behavioral patterns to maximize profitability in the competitive AI market.
