Programming and application development often bring questions about the performance of specific applications. There are many methods for comparing performance, but the two most well-known are energy and execution time measurements. This paper explores the categorization of programming languages based on the specifics of their execution steps. The topic of the paper is the efficiency of code execution in high-level programming languages. Additionally, a detailed description of the execution process for each program in the selected programming languages is provided. A subset of high-level programming languages consisting of popular languages such as C++, Java, Python, and Julia was chosen. Measurements of energy and time consumption were conducted on algorithms of different complexities and various input data sizes within this subset. Finally, a statistical analysis of the measured results was performed, exploring the correlations between energy and time, and ranking the languages based on specific conclusions. The paper attempts to answer some common questions through detailed descriptions and step-by-step executions. Among these questions is the inquiry into whether a faster program is also more energy-efficient and whether time and energy are interconnected concepts. Furthermore, the paper greatly assists in language selection based on specific performance requirements. This final paper was developed within the framework of the ERASMUS+ project Promoting Sustainability as a Fundamental Driver in Software development Training and Education with the code 2020-1-PT01-KA203- 078646.