The development of assays for detecting recent HIV infections has become crucial for analyzing trends in infection in different populations, both for surveillance and prevention activities. The anti-HIV avidity index (AI), measured with third-generation immunoassays (which detect anti-HIV antibody), has been shown to be an accurate tool for discriminating recent HIV infections (<6 months) from established infections (≥ 6 months). We compared a third-generation immunoassay (AxSYM HIV 1/2 gO; Abbott Diagnostics) to a fourth-generation immunoassay (Architect HIV Ag/Ab Combo; Abbott Diagnostics; which detects anti-HIV antibody and p24 antigen) in terms of AI performance in distinguishing between recent and established HIV infections. A total of 142 samples from 75 HIV-infected individuals with an estimated date of seroconversion were assayed. The two assays showed the same accuracy in identifying a recent infection (91.5%), using an AI cutoff of 0.80, although Architect HIV Ag/Ab Combo was slightly more sensitive (89.4% versus 84.8%; P > 0.05) and yet less specific (93.4% versus 97.4%; P > 0.05). The correlation between assays was high (r = 0.87). When 20 specimens falling in the gray zone around the cutoff point (0.75 ≤ AI ≤ 0.84) were excluded, the accuracy of AI with Architect HIV Ag/Ab Combo was 94.7%, and the concordance between the two assays was 99.2%. The anti-HIV AI is a serological marker that accurately discriminates recent from established HIV infections. It can be successfully applied on fully automated fourth-generation HIV Ab/Ag immunoassays, which have several advantages, including increased throughput, high reproducibility, no need for specific technical skills, and easy comparability of results obtained in different settings.