CODES benchmarks surrogate architectures for coupled ODE systems. In addition to standard metrics such as mean squared error (MSE) and inference time, CODES provides insights into surrogate behavior across multiple dimensions, including interpolation, extrapolation, sparse data, uncertainty quantification, and gradient correlation. The benchmark emphasizes usability through features such as integrated parallel training, a web-based configuration generator, and pre-implemented baseline models and datasets. By offering a fair and multi-faceted comparison, CODES helps researchers select the most suitable surrogate for their specific dataset and application.