Foundation models build an effective representation of data that can be deployed on diverse downstream tasks. Previous research showed that the OmniLearned, a foundation model for collider physics, could significantly advance discovery potential across collider experiments. In this paper, we show that Foundation Models trained on collider data can transfer to cosmology. Specifically, we improve the prediction of cosmological parameters and halo and galaxy velocities in different datasets from CosmoBench. To the best of our knowledge, this is the first time a collider physics model has been shown to generalize across scientific fields.