ML-based program cost models have been shown to yield fairly accurate program cost predictions. They can replace heavily engineered analytical program cost models in mainstream compiler workflows, but their black-box nature discourages their adoption. In this work, we develop the first framework, COMET, for generating faithful, generalizable, and intuitive explanations for x86 cost models, such as the ML cost model Ithemal. We generate and compare COMET’s explanations for Ithemal against those for an accurate analytical cost model, uiCA. Our empirical findings show an inverse correlation between the prediction error of a cost model and the semantic richness of COMET’s explanations for the cost model, thus indicating potential sources of higher error of Ithemal with respect to uiCA.