基于OpenRouter数据，揭示大模型需求的三类规律。
Demand for LLMs: Descriptive Evidence on Substitution, Market Expansion, and Multihoming
- 通过市场平台数据观察大模型采用趋势
- 新模型上线后数周内用户增长迅速并趋于稳定
- 多模型并行使用普遍，适合关注市场策略的研究者
本文利用OpenRouter这一主流大语言模型（LLM）交易平台的数据，揭示了大模型需求的三个典型事实：第一，新模型在上线初期获得快速采用，并在数周内趋于稳定；第二，不同模型发布时主要吸引新用户或替代现有模型的用户存在显著差异；第三，应用层面普遍存在多模型并行使用（multihoming）现象。这些发现表明大模型市场存在显著的水平与垂直差异化，意味着即使在技术快速迭代的背景下，厂商仍有机会维持用户需求和定价能力。
原文摘要 · Abstract (English)
This paper documents three stylized facts about the demand for Large Language Models (LLMs) using data from OpenRouter, a prominent LLM marketplace. First, new models experience rapid initial adoption that stabilizes within weeks. Second, model releases differ substantially in whether they primarily attract new users or substitute demand from competing models. Third, multihoming, using multiple models simultaneously, is common among apps. These findings suggest significant horizontal and vertical differentiation in the LLM market, implying opportunities for providers to maintain demand and pricing power despite rapid technological advances.
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