This article focuses on the multiprocessor task scheduling problem with deterministic task durations and independent tasks. Executing a task on more than one processor can be viewed as a means of improving the system dependability through hardware and software redundancy. The objective of this study is to compare and evaluate several heuristics to solve the multiprocessor task scheduling problem with deterministic task durations. Three models are analyzed: the MC algorithm, a modified version of the Muntz-Coffman algorithm, and Simulated Annealing (SA). The previously proposed model introduced the concept of multiprocessor tasks and aimed to achieve a higher level of system dependability. The alternative models explored in this paper apply other well-known heuristics for this class of problems. Historically, the MC algorithm has been the primary approach used in multiprocessor task scheduling. In this study, we compare the previously introduced model with other adapted heuristics and a metaheuristic (MC and SA). The article extends the author’s previous research by examining the duration of deterministic tasks in different models. Computational experiments were conducted to compare the performance of each model. While the MC algorithm achieves lower resource consumption, it does so at the expense of poorer load balancing and longer task execution times. Although the Muntz-Coffman algorithm maintains an appropriate load balance, it performs less effectively in terms of efficiency. The SA method exhibits the best scalability and efficiency as the number of tasks increases. However, MC may be preferred in embedded systems where resource conservation is critical.