To develop a magnetic resonance imaging (MRI)-based classification system integrating tear characteristics including tear thickness (partial vs full) and tear retraction (less than or greater than 2 cm) for gluteus medius and/or minimus tears and to determine the inter-rater reliability of this MRI-based classification for gluteus medius and/or minimus tears. Patients who underwent primary endoscopic or open repair of gluteus medius and/or minimus tears between 2012 and 2022 were identified to be included in the review of 1.5-T MRI scans. One hundred MRI scans were randomized for review by 2 orthopaedic surgeons and evaluated for tear thickness (partial vs full), extent of retraction, and degree of fatty infiltration according to an applied Goutallier-Fuchs (G-F) classification. Tears were also graded according to the 3-grade MRI-based classification system as follows: grade 1, partial-thickness tears; grade 2, full-thickness tears with less than 2 cm of retraction; grade 3, full thickness with 2 cm or more retraction. Inter-rater reliability was calculated by absolute and relative agreement using Cohen's kappa (κ). Significance was defined by P value <.05. In total, 221 patients were identified, and after application of exclusion criteria and randomization, 100 scans were evaluated. The 3-grade classification system demonstrated high absolute agreement (88%) comparable to the absolute agreement of the G-F classification (67%). The 3-grade classification system demonstrated substantial inter-rater reliability (κ= 0.753), whereas the G-F classification demonstrated moderate inter-rater reliability (κ= 0.489). The proposed 3-grade MRI-based classification system for gluteus medius and/or minimus tears demonstrated substantial inter-rater reliability, comparable with that of the applied G-F classification. It is important to understand how gluteus medius and/or minimus tear characteristics impact postoperative outcomes. The 3-grade MRI-based classification incorporates tear thickness and amount of retraction that can complement previous classification systems to give the provider and patient more information when considering treatment options.