Large language models are homogeneously creative

Abstract Numerous large language models (LLMs) are marketed for use as creativity support tools, despite several studies showing that using an LLM as a creative partner narrows creative outputs. However, these studies only consider the effects of interacting with a single LLM on specific creativity tasks, begging the question of whether narrowed creativity stems from using a particular LLM—with an arguably limited range of outputs—or from using LLMs in general. To test this, we elicit creative responses from many humans and LLMs using standardized creativity tasks and compare population-level response diversity. We find that LLM responses mirror other LLM responses far more than humans do other humans, even after controlling for key confounding variables. This finding adds a new dimension to the ongoing discussion about creativity and LLMs. If today’s LLMs behave similarly, using them as creative partners—regardless of the model used—may drive users toward similar “creative” outputs.