e de nition of a concise and e ective testbed for Genetic Programming (GP) is a recurrent ma er in the research community.is paper takes a new step in this direction, proposing a di erent approach to measure the quality of the symbolic regression benchmarks quantitatively. e proposed approach is based on meta-learning and uses a set of dataset meta-features-such as the number of examples or output skewness-to describe the datasets. Our idea is to correlate these meta-features with the errors obtained by a GP method. ese meta-features de ne a space of benchmarks that should, ideally, have datasets (points) covering di erent regions of the space. An initial analysis of 63 datasets showed that current benchmarks are concentrated in a small region of this benchmark space. We also found out that number of instances and output skewness are the most relevant meta-features to GP output error. Both conclusions can help de ne which datasets should compose an e ective testbed for symbolic regression methods.