We describe our entry to both tracks of StanceEval-2026, an Arabic stance-detection task built on the Mawqif-XT corpus. Track 1 (Seen Targets) is evaluated on Women Driving, while Track 2 (Unseen Targets) is evaluated on Electric Cars and the Trimester school calendar system. Rather than committing to a single approach, we compare two paradigms: fine-tuned Arabic encoders trained with multi-task learning using Mawqif-XT’s sentiment, sarcasm, and rationale annotations, and native Arabic LLMs used in a few-shot setting without any fine-tuning. For each track, we build the final system around the stronger approach. Across more than forty experimental configurations, few-shot LLMs consistently outperform fine-tuned encoders on both tracks. Our final systems achieve an Favg2 of 0.834 on Track 2 (ranked 9th) and 0.747 on Track 1 (ranked 13th), the latter through a complementary fusion of ALLaM-7B and Fanar-9B. We further observe that model performance may be related to how well pretraining coverage aligns with a target’s cultural and linguistic domain, that fusing two comparably strong models can outperform either model individually, and, perhaps most importantly, that more elaborate methods never surpass a strong LLM with a simple prompt. We release our code and prompts to support reproducibility.