Sorting remains one of the most fundamental operations in computer science, underpinning databases, search engines, analytics pipelines, and machine learning workflows. Among comparison-based sorting methods, Quick Sort is widely adopted due to its average-case O(n log n) performance and in-place execution. However, its vulnerability to unbalanced partitioning particularly on ordered or reversed inputs can degrade performance to O(n²). The QWIMB Sort algorithm was introduced to address these limitations, yet prior evaluations have focused exclusively on primitive data types. This study extends the analysis to derived types String, Double, and Long across three language platforms: C, Java, and Python. Experiments were conducted on datasets up to 500,000 elements, measuring execution time, comparison counts, swap operations, and partition calls. Results confirm O(n log n) growth on random inputs and O(n²) behavior on sorted or reversed datasets. A browser-based dashboard was developed using HTML, CSS, JavaScript, and Chart.js, enabling interactive data generation, CSV import, and real-time sorting visualization. The findings provide valuable insights into datatype-aware sorting performance and the influence of language-specific runtime environments.