Mass spectrometry-based glycoproteomics studies now routinely report thousands of intact glycopeptides. Diverse informatics solutions aiding the challenging glycopeptide identification process have recently appeared, but their strengths and weaknesses have not been established leaving a critical knowledge gap that prevents rapid progress in the field. This interlaboratory study comprising both expert users (13 teams) and developers (9 teams) of glycoproteomics software is the first to evaluate the relative performance of current informatics solutions for accurate and comprehensive N- and O-glycopeptide identification. Two LC-MS/MS datasets of intact glycopeptides from serum proteins were shared with all teams. The relative team performance for large-scale glycopeptide analysis was systematically established through 11 orthogonal performance tests based on the assigned spectra reported by each team. This study has found that a diversity of approaches exists for comprehensive glycopeptide data analysis, has demonstrated the team-to-team (vari)ability of identifying intact glycopeptides from shared datasets, and, importantly, has revealed several high-performance informatics solutions and search strategies that will be useful to improve the challenging glycoproteomics data analysis in the immediate future.