Large language models are homogeneously creative

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.