AIMS: There is growing recognition of the importance of multimorbidity as the population ages. The implications for budgetary management in primary care are unexplored as previous studies have largely focused on secondary care in the United States. We investigate the relationship between multimorbidity and total patient cost in primary care in the UK. METHODS: We used Generalised Linear Models (GLMs) to relate individual cost to age, sex, deprivation and three measures of multimorbidity: (i) QOF chronic disease count, (ii) Charlson Index score and (iii) a count of Expanded Diagnostic Clusters (EDCs) identified by the John Hopkins ACG System. Models were assessed using AIC, BIC and a deviance-based R-squared measure. DATA: We used a stratified sample of 85,946 individuals from 174 practices in the General Practice Research Database (GPRD). We used all historic diagnoses to measure multimorbidity and estimated total cost of primary care resources used over the subsequent 12 months. RESULTS: A model including age, sex, deprivation and practice ID alone explained 16% of observed costs. Inclusion of the count of EDCs, QOF disease count, or Charlson Index score increased this to 31%, 28%, and 23% respectively. AIC and BIC rankings showed similar support. All models suggest a roughly linear relationship between costs and the number of chronic conditions. CONCLUSIONS: Multimorbidity indices improve the prediction of future costs in primary care. These indices can be constructed easily using routinely recorded General Practice data, and therefore use of these indices could help to improve budgetary management in primary care.