Context Dairy farming in Brazil is characterised by heterogeneity in production techniques, herds and kinds of producers; nonetheless, it has expanded in recent years. The data available in the scientific literature regarding breeds used in the system are inconsistent and may not represent the current genetic, productive and reproductive profile of Brazilian herds. Aim This study was developed to understand differences between animals used in experiments and those in breed control assessments. We aimed to evaluate data of Holstein, Gyr, Guzerat, Jersey, Sindhi, Brown Swiss, Holstein × Gyr and Holstein × Guzerat cattle on the traits 305-day milk yield, fat and protein contents, lactation length, age at first calving, and calving interval by comparing research literature and national official control records. Methods National breeders’ associations (NBAs) for each breed or cross were asked to send their official control from 2019 for comparison with 15 years of published data that we retrieved from the literature. Key results Holsteins showed the closest mean for milk yield between literature and NBA data. In Holstein × Gyr crosses, increasing the proportion of Gyr genes decreased milk yield. Jersey data showed the greatest variance between the literature and NBA. For Brown Swiss milk fat, literature values were 12.7% above NBA values. Holsteins had the longest lactation (305 days), and 5/8 Holstein × Gyr the shortest (262 days). For age at first calving, Jerseys were the youngest (26.3 months) and Guzerats the oldest (45.7 months). Jersey cows showed the shortest (best) mean calving interval (12 months), and Sindhi the longest (17 months). NBA data were not available for comparison for some traits in some breeds/crosses. Conclusions Holstein was the most productive breed. Holstein × Gyr crosses, because of adaptation and management, were able to express their productive and reproductive potentials. There was variance between literature and NBA data. Implications We expected that the literature data would somewhat represent the NBA data; however, for most traits, NBA data do not coincide (or do not exist). Literature data need to be collected to represent more closely what is happening at the field level in the national dairy industry.