Growing numbers of newly-discovered Earth-sized planets in the habitable zone have driven growing interest in understanding their climates, across the field of planetary science. However, the 3D models we use to study those climates have historically been computationally expensive, difficult to learn, and complex to configure, posing a significant barrier to many who would like to build intuition for planetary climate, or quickly explore possible climate states for planet candidates. Furthermore, studies of planetary parameter spaces have been limited to small numbers of models. I will present ExoPlaSim, a GCM that is fast enough to model an Earth-like planet’s climate over your lunch break, flexible enough to handle habitable planets around both Sun-like stars and M dwarfs, and easy enough to learn that you can install it via pip, configure it in a Python Jupyter notebook, and begin modelling climates in under half an hour–even on your laptop. Poster presented at ERES 2021.