Models of the determinants of individuals’ primary care costs can be used to set capitation payments toproviders and to test for horizontal equity. We compare the ability of eight measures of patient morbidityand multimorbidity to predict future primary care costs and examine capitation payments based onthem. The measures were derived from four morbidity descriptive systems: 17 chronic diseases in theQuality and Outcomes Framework (QOF); 17 chronic diseases in the Charlson scheme; 114 ExpandedDiagnosis Clusters (EDCs); and 68 Adjusted Clinical Groups (ACGs). These were applied to patient recordsof 86,100 individuals in 174 English practices. For a given disease description system, counts of diseasesand sets of disease dummy variables had similar explanatory power. The EDC measures performed bestfollowed by the QOF and ACG measures. The Charlson measures had the worst performance but stillimproved markedly on models containing only age, gender, deprivation and practice effects. Comparisonsof predictive power for different morbidity measures were similar for linear and exponential models, butthe relative predictive power of the models varied with the morbidity measure. Capitation payments foran individual patient vary considerably with the different morbidity measures included in the cost model.Even for the best fitting model large differences between expected cost and capitation for some types ofpatient suggest incentives for patient selection. Models with any of the morbidity measures show highercost for more deprived patients but the positive effect of deprivation on cost was smaller in better fittingmodels.