The empirical landscape sits between the two extremes. 

- Model similarity is high, yet disagreements let individuals find recourse by switching models. 

- Systemic exclusion is rare, yet more likely than under strong multiplicity. 

- Even in a single model, prompt variations induce multiplicity.

We evaluate 50 LLMs (various sizes & providers) across 6 tasks to assess how well each narrative fits the current LLM landscape, assuming that decision makers will increasingly rely on these models for consequential predictions.

There are two narratives about model ecosystems that grew out of the algorithmic fairness debate:

1. Monoculture: models converge toward homogeneity.

2. Multiplicity: many models solve tasks similarly but disagree on individual predictions, creating outcome variation.