Best practices in youth mental health assessment entail collecting reports from multiple informants. However, multi-informant reports commonly yield different estimates of youth mental health (i.e., informant discrepancies), resulting in various clinical decision-making challenges and necessitating strategies for integrating them. Two leading theoretical models exist for interpreting informant discrepancies. Whereas one model posits that informant discrepancies reflect rater biases and thus depress measurement validity (i.e., bias models), the other posits that they reflect meaningful variations in behavior across social contexts (e.g., home, school) and thus enhance measurement validity (i.e., context models). Although greater empirical support exists for context models relative to bias models, measurement models extending from both bias (i.e., Trifactor Model [TFM]) and context (i.e., Trait Score Satellite Model [TSSM]) models have been developed. Across two studies, I rigorously compared the TFM and TSSM. In Study 1, a systematic review of TFM and TSSM research (n = 47) revealed that, relative to TFM studies, TSSM studies were more likely to include (a) informants who varied in where they observe behavior (e.g., parent [home] vs. teacher [school]) and (b) more informants. In Study 2, I subjected these models to validation testing using a sample (n = 134) that included three informants’ reports of adolescent social anxiety and independent ratings of adolescent behavior within peer interactions. I found satisfactory fit for both models when integrating all three informants’ reports. However, when predicting well-established, independent criterion variables (i.e., observed behavior, referral status), the primary score derived from the TSSM outperformed each individual informant’s report, a composite of informants’ reports, and the primary TFM-derived score. Relative to the TFM, the TSSM (a) more closely aligns with best practices in evidence-based assessment of youth mental health, and (b) more effectively integrates multi-informant reports in data conditions where informant discrepancies reflect valid information. When using measurement models designed to integrate multi-informant reports, users of these models must subject them to rigorous validation testing to discern their applicability to the data conditions in which they will be applied. In turn, integrating multi-informant reports requires explicitly linking theory, quantitative methodology, and empirical support observed within relevant data conditions.