The "Beyond-2pt" collaboration has organized a community data challenge to test and benchmark novel techniques for analyzing high-precision galaxy survey data, aiming to extend the statistical analysis of galaxy clustering beyond the linear regime and the canonical two-point (2pt) statistics. The challenge dataset consists of high-precision mock galaxy catalogs for real space, redshift space, and light cone clustering. Participants developed end-to-end pipelines to extract unknown cosmological parameters from these catalogs using various methods, including density-split clustering, nearest neighbor statistics, BACCO power spectrum emulator, void statistics, LEFTfield field-level inference, and joint power spectrum and bispectrum analyses. The results demonstrate the robust recovery of cosmological parameters by multiple statistics and modeling frameworks, supporting the credibility of cosmology constraints from these methods. The challenge data set is publicly available, and future submissions from methods not yet represented are welcomed. The paper reviews the results of the challenge, focusing on problems solved, lessons learned, and future research needs. The challenge aims to validate beyond-2pt statistics and their modeling approaches, and to study the information content in beyond-2pt statistics by providing a standard set of simulated mock data. The results show that all participating teams successfully recover the input cosmology within their 1-σ confidence regions, demonstrating the maturity and potential of these novel statistics for near-term data analysis. The paper also discusses the impact of different modeling choices and the importance of understanding the likelihoods and covariances of these statistics.The "Beyond-2pt" collaboration has organized a community data challenge to test and benchmark novel techniques for analyzing high-precision galaxy survey data, aiming to extend the statistical analysis of galaxy clustering beyond the linear regime and the canonical two-point (2pt) statistics. The challenge dataset consists of high-precision mock galaxy catalogs for real space, redshift space, and light cone clustering. Participants developed end-to-end pipelines to extract unknown cosmological parameters from these catalogs using various methods, including density-split clustering, nearest neighbor statistics, BACCO power spectrum emulator, void statistics, LEFTfield field-level inference, and joint power spectrum and bispectrum analyses. The results demonstrate the robust recovery of cosmological parameters by multiple statistics and modeling frameworks, supporting the credibility of cosmology constraints from these methods. The challenge data set is publicly available, and future submissions from methods not yet represented are welcomed. The paper reviews the results of the challenge, focusing on problems solved, lessons learned, and future research needs. The challenge aims to validate beyond-2pt statistics and their modeling approaches, and to study the information content in beyond-2pt statistics by providing a standard set of simulated mock data. The results show that all participating teams successfully recover the input cosmology within their 1-σ confidence regions, demonstrating the maturity and potential of these novel statistics for near-term data analysis. The paper also discusses the impact of different modeling choices and the importance of understanding the likelihoods and covariances of these statistics.