Object detection in real-time is indispensable in applications like autonomous vehicles, robotics, and surveillance, where both high accuracy and efficiency become necessary. The paper conducts an investigation and benchmarking on state-of-the-art models, including YOLO and RT-DETR, using the Pascal VOC dataset on the most important performance metrics: accuracy-mAP, size of the model, and inference speed-FPS. The results show YOLOv8x with the highest accuracy with an mAP 50:95 of 0.480 and 133 by FPS, while RT-DETRv1 (r50vd), although a little lower in terms of accuracy with an mAP 50:95 of 0.475, achieves 100 FPS by offering a pretty competitive trade-off in terms of model complexity. On the contrary, RT-DETRv2 (r34vd) has the smallest model with an mAP 50:95 of 0.468 and an FPS of 118, making it more adequate for resource-constrained environments. Our results immediately point to the trade-offs between accuracy, model complexity, and speeds providing practical observations that could be used when deploying such models on real-time systems. This research provides a reference that will be useful for model selection, taking into account specific performance requirements while conjugating highly accurate, fast, and resource-efficient models.