The early prediction of outcome after traumatic brain injury (TBI) is important for several purposes, but no prognostic models have yet been developed with proven generalizability across different settings. The objective of this study was to develop and validate prognostic models that use information available at admission to estimate 6-month outcome after severe or moderate TBI. To this end, this study evaluated mortality and unfavorable outcome, that is, death, and vegetative or severe disability on the Glasgow Outcome Scale (GOS), at 6 months post-injury. Prospectively collected data on 2269 patients from two multi-center clinical trials were used to develop prognostic models for each outcome with logistic regression analysis. We included seven predictive characteristics-age, motor score, pupillary reactivity, hypoxia, hypotension, computed tomography classification, and traumatic subarachnoid hemorrhage. The models were validated internally with bootstrapping techniques. External validity was determined in prospectively collected data from two relatively unselected surveys in Europe (n = 796) and in North America (n = 746). We evaluated the discriminative ability, that is, the ability to distinguish patients with different outcomes, with the area under the receiver operating characteristic curve (AUC). Further, we determined calibration, that is, agreement between predicted and observed outcome, with the Hosmer-Lemeshow goodness-of-fit test. The models discriminated well in the development population (AUC 0.78-0.80). External validity was even better (AUC 0.83-0.89). Calibration was less satisfactory, with poor external validity in the North American survey (p < 0.001). Especially, observed risks were higher than predicted for poor prognosis patients. A score chart was derived from the regression models to facilitate clinical application. Relatively simple prognostic models using baseline characteristics can accurately predict 6-month outcome in patients with severe or moderate TBI. The high discriminative ability indicates the potential of this model for classifying patients according to prognostic risk.