The development of antiviral drug resistance is an important problem in the treatment of human immunodeficiency virus type 1 (HIV-1) infection. Potent antiretroviral therapy is currently used for treatment, and typically consists of at least two reverse transcriptase (RT) inhibitors. We have previously reported that both drugs and drug-resistant RT mutants can increase virus mutation frequencies. To further assess the contributions of nucleoside RT inhibitors (NRTIs), nonnucleoside RT inhibitors (NNRTIs), and drug-resistant RTs to HIV mutagenesis, a new high-throughput assay system was developed. This assay system was designed to specifically detect frameshift mutations in the luciferase gene in a single virus replication cycle. New drug-resistant RTs were identified that significantly altered virus mutation frequencies. Consistent with our previous observations of NRTIs, abacavir, stavudine, and zalcitabine increased HIV-1 mutation frequencies, supporting the general hypothesis that the NRTIs currently used in antiviral drug therapy increase virus mutation frequencies. Interestingly, similar observations were made with NNRTIs. This is the first report to show that NNRTIs can influence virus mutation frequencies. NNRTI combinations, NRTI-NNRTI combinations, and combinations of drug and drug-resistant RTs led to significant changes in the virus mutation frequencies compared to virus replication of drug-resistant virus in the absence of drug or wild-type virus in the presence of drug. This indicates that combinations of RT drugs or drugs and drug-resistant virus created during the evolution of drug resistance can act together to increase HIV-1 mutation frequencies, which would have important implications for drug therapy regimens. Finally, the influence of drug-resistant RT mutants from CRF01_AE viruses on HIV-1 mutation frequencies was analyzed and it was found that only a highly drug resistant RT led to altered virus mutation frequencies. The results further suggest that high-level drug-resistant RT can significantly influence virus mutation frequencies. A structural model that explains the mutation frequency data is discussed.