KaKs_Calculator is a software package that calculates nonsynonymous (Ka) and synonymous (Ks) substitution rates using model selection and model averaging. It implements a set of candidate models in a maximum likelihood framework and uses the Akaike information criterion (AIC) to measure model fitness, aiming to include as many features as needed for accurate evolutionary analysis. The software also incorporates existing methods for calculating Ka and Ks and is freely available for academic use. Ka and Ks ratios indicate neutral, negative, or positive selection, providing insights into molecular evolution. Existing methods for estimating these rates fall into two categories: approximate methods and maximum likelihood methods. However, these methods often yield varied results due to different assumptions about sequence evolution. Therefore, model selection and averaging are critical for accurate estimation. KaKs_Calculator uses model selection to choose the best-fit model and model averaging to combine results from multiple models. It employs AIC and AICc (a modified version of AIC) to evaluate model fitness. Model averaging assigns weights to candidate models based on their AIC values, allowing for more reliable estimates. The software is written in C++ and is available on multiple platforms. It integrates various methods for estimating Ka and Ks, including approximate and maximum likelihood approaches. KaKs_Calculator provides comprehensive information, including synonymous and nonsynonymous site and substitution counts, GC content, maximum likelihood scores, and AICc values. KaKs_Calculator is designed to handle diverse datasets and provides more reliable evolutionary information compared to single-model approaches. It is supported by grants from the Chinese Ministry of Science and Technology and the National Natural Science Foundation of China. The software is useful for in-depth studies of phylogeny and molecular evolution.KaKs_Calculator is a software package that calculates nonsynonymous (Ka) and synonymous (Ks) substitution rates using model selection and model averaging. It implements a set of candidate models in a maximum likelihood framework and uses the Akaike information criterion (AIC) to measure model fitness, aiming to include as many features as needed for accurate evolutionary analysis. The software also incorporates existing methods for calculating Ka and Ks and is freely available for academic use. Ka and Ks ratios indicate neutral, negative, or positive selection, providing insights into molecular evolution. Existing methods for estimating these rates fall into two categories: approximate methods and maximum likelihood methods. However, these methods often yield varied results due to different assumptions about sequence evolution. Therefore, model selection and averaging are critical for accurate estimation. KaKs_Calculator uses model selection to choose the best-fit model and model averaging to combine results from multiple models. It employs AIC and AICc (a modified version of AIC) to evaluate model fitness. Model averaging assigns weights to candidate models based on their AIC values, allowing for more reliable estimates. The software is written in C++ and is available on multiple platforms. It integrates various methods for estimating Ka and Ks, including approximate and maximum likelihood approaches. KaKs_Calculator provides comprehensive information, including synonymous and nonsynonymous site and substitution counts, GC content, maximum likelihood scores, and AICc values. KaKs_Calculator is designed to handle diverse datasets and provides more reliable evolutionary information compared to single-model approaches. It is supported by grants from the Chinese Ministry of Science and Technology and the National Natural Science Foundation of China. The software is useful for in-depth studies of phylogeny and molecular evolution.