Most studies predicting college performance from high-school grade point average (HSGPA) and college admissions test scores use single-level regression models that conflate relationships within and between high schools. Because grading standards vary among high schools, these relationships are likely to differ within and between schools. We used two-level regression models to predict freshman grade point average from HSGPA and scores on both college admissions and state tests. When HSGPA and scores are considered together, HSGPA predicts more strongly within high schools than between, as expected in the light of variations in grading standards. In contrast, test scores, particularly mathematics scores, predict more strongly between schools than within. Within-school variation in mathematics scores has no net predictive value, but between-school variation is substantially predictive. Whereas other studies have shown that adding test scores to HSGPA yields only a minor improvement in aggregate prediction, our findings suggest that a potentially more important effect of admissions tests is statistical moderation, that is, partially offsetting differences in grading standards across high schools.