Comparing different video generation models is essential before committing to a platform, because each has unique strengths and weaknesses regarding style, speed, and coherence. I ran a systematic test using the same ten prompts across five different models, evaluating the results on motion smoothness, object consistency, and prompt adherence. The clear winner in terms of temporal stability was Seedance 2.0, which produced fewer flickering artifacts than its competitors and handled occlusions much better, meaning objects could pass in front of each other without causing visual glitches. One test involved a person walking behind a tree and emerging on the other side, which caused most models to either lose the person entirely or change their appearance dramatically. Seedance maintained the person's clothing and facial features correctly, demonstrating a genuine understanding of object permanence that is critical for narrative video. Based on these results, I have made this model my default for all video projects, and I recommend it to anyone who values consistency over flashy but unstable effects.