We propose StereoGlue, a method designed for joint feature matching and robust estimation that effectively reduces the combinatorial complexity of these tasks using single-point minimal solvers. StereoGlue is applicable to a range of problems, including but not limited to relative pose and homography estimation, determining absolute pose with 2D-3D correspondences, and estimating 3D rigid transformations between point clouds. StereoGlue starts with a set of one-to-many tentative correspondences, iteratively forms tentative matches, and estimates the minimal sample model. This model then facilitates guided matching, leading to consistent one-to-one matches, whose number serves as the model score. StereoGlue is superior to the state-of-the-art robust estimators on real-world datasets on multiple problems, improving upon a number of recent feature detectors and matchers. Additionally, it shows improvements in point cloud matching and absolute camera pose estimation. The code will be made publicly available.