Arabic stance detection is a task that identifies whether an Arabic text expresses Favor, Against, or None toward a given target. It is important for understanding public opinion and social media discourse, particularly in Arabic where dialectal variation, sarcasm, and informal expressions make stance interpretation difficult. To address this, we took part in StanceEval-2026 Track 1, where models are trained on provided targets and evaluated on a related target from the same thematic domain. We investigate two complementary approaches: fine-tuning-based systems using Arabic encoders and parameter-efficient LLM adaptation, and prompted GPT-4o without fine-tuning. Our experiments include encoder ensembles, multi-task and contrastive learning, attention aggregation, augmentation, calibration, QLoRA, and prompted LLMs with few-shot demonstrations, target-specific context, structured reasoning, and self-consistency voting. The best fine-tuning-based system achieves 0.701 Favg2, while GPT-4o v4 reaches 0.827 Favg2, 0.630 Favg3, and 0.784 accuracy, ranking 11th on the official leaderboard. The results show that prompted GPT-4o transfers more effectively to the related-target setting, while additional prompt complexity does not always lead to further improvement.