This article investigates the technical efficiency in German higher education while accounting for possible heterogeneity in the production technology. We investigate whether a latent class model would identify the different sub-disciplines of life sciences in a sample of biology and agricultural units based on technological differences. We fit a latent class stochastic frontier model to estimate the parameters of an output distance function formulation of the production technology to investigate if a technological separation is meaningful along sub-disciplinary lines. We apply bootstrapping techniques for model validation. Our analysis relies on evaluating a unique dataset that matches information on higher educational institutions provided by the Federal Statistical Office of Germany with the bibliometric information extracted from the ISI Web of Science Database. The estimates indicate that neglecting to account for the possible existence of latent classes leads to a biased perception of efficiency. A classification into a research-focused and teaching-focused decision-making unit improves model fit compared to the pooled stochastic frontier model. Additionally, research-focused units have a higher median technical efficiency than teaching-focused units. As the research focus is more prevalent in the biology subsample an analysis not considering the potential existence of latent classes might misleadingly give the appearance of a higher mean efficiency of biology. In fact, we find no evidence of a difference in the mean technical efficiencies for German agricultural sciences and biology using the latent class model.