《OpenRouter&a16z：2025人工智能状况报告：基于OpenRouter平台的100万亿Token实证研究（英文版）（36页）.pdf》由会员分享，可在线阅读，更多相关《OpenRouter&a16z：2025人工智能状况报告：基于OpenRouter平台的100万亿Token实证研究（英文版）（36页）.pdf（36页珍藏版）》请在三个皮匠报告上搜索。

1、State of AI:An Empirical 100 Trillion Token Study with OpenRouterMalika Aubakirova,Alex Atallah,Chris Clark,Justin Summerville,and Anjney MidhaOpenRouter Inc.a16z(Andreessen Horowitz)December,2025AbstractThe past year has marked a turning point in the evolution and real-world use of large language m

2、odels(LLMs).With the release of the first widely adopted reasoning model,o1,on December 5th,2024,the fieldshifted from single-pass pattern generation to multi-step deliberation inference,accelerating deployment,experimentation,and new classes of applications.As this shift unfolded at a rapid pace,ou

3、r empiricalunderstanding of how these models have actually been used in practice has lagged behind.In this work,we leverage the OpenRouter platform,which is an AI inference provider across a wide variety of LLMs,to analyze over 100 trillion tokens of real-world LLM interactions across tasks,geograph

4、ies,and time.In our empirical study,we observe substantial adoption of open-weight models,the outsized popularityof creative roleplay(beyond just the productivity tasks many assume dominate)and coding assistancecategories,plus the rise of agentic inference.Furthermore,our retention analysis identifi

5、es foundationalcohorts:early users whose engagement persists far longer than later cohorts.We term this phenomenonthe Cinderella“Glass Slipper”effect.These findings underscore that the way developers and end-usersengage with LLMs“in the wild”is complex and multifaceted.We discuss implications for mo

6、del builders,AI developers,and infrastructure providers,and outline how a data-driven understanding of usage caninform better design and deployment of LLM systems.1IntroductionJust a year ago,the landscape of large language models looked fundamentally different.Prior to late2024,state-of-the-art sys