We evaluate four representative large language models, namely GPT-4o, Gemini, Llama, and DeepSeek on on a suite of linguistic and cultural tasks in Persian, covering grammar, paraphrasing, inference, translation, factual recall, analogical reasoning, and a Hofstede-based cultural probe under direct and role-based prompts. Our findings reveal consistent performance declines, alongside systematic misalignment with Iranian cultural norms. Role-based prompting yields modest improvements but does not fully restore cultural fidelity. We conclude that advancing truly multilingual models demands richer Persian resources, targeted adaptation, and evaluation frameworks that jointly assess fluency and cultural alignment.