Generative model ecosystems increasingly operate as competitive multi-platform markets, where platforms strategically select models from a shared pool and users with heterogeneous preferences choose among them. Understanding how platforms interact, when market equilibria exist, how outcomes are shaped by model-providers, platforms, and user behavior, and how social welfare is affected is critical for fostering a beneficial market environment. In this paper, we formalize a three-layer model-platform-user market game and identify conditions for the existence of pure Nash equilibrium. Our analysis shows that market structure, whether platforms converge on similar models or differentiate by selecting distinct ones, depends not only on models' global average performance but also on their localized attraction to user groups. We further examine welfare outcomes and show that expanding the model pool does not necessarily increase user welfare or market diversity. Finally, we design novel best-response training schemes that allow model providers to strategically introduce new models into competitive markets.

翻译：生成模型生态系统日益成为竞争性多平台市场，其中平台从共享模型池中策略性地选择模型，而具有异质性偏好的用户则在平台间进行选择。理解平台如何互动、市场均衡何时存在、模型提供者/平台/用户行为如何塑造市场结果、以及社会福利如何受到影响，对于培育有益的市场环境至关重要。本文形式化了一个三层（模型-平台-用户）市场博弈模型，并确定了纯纳什均衡存在的条件。我们的分析表明，市场结构——无论是平台收敛于相似模型还是通过选择不同模型实现差异化——不仅取决于模型的全局平均性能，还取决于其对用户群体的局部吸引力。我们进一步考察了福利结果，证明扩大模型池未必能提升用户福利或市场多样性。最后，我们设计了新颖的最优响应训练方案，使模型提供者能够在竞争市场中策略性地引入新模型。