Polarization detection in low-resource and mid-resource languages remains a significant challenge for social understanding. This paper presents the first comprehensive benchmark to evaluate transformer-based models for detection of polarized language in Persian (also called Farsi) social media. The aim is to evaluate 1) how and if finetuning the pre-trained models have substantial impact; 2) how Persian specific monolingual models compare to multilingual for this task; 3) how and if transfer learning from models trained on other languages such as culturally-distant English, and culturally-close[er] Turkish, and Arabic can be of interest for this task; and 4) how competitive Large Language Models (LLMs) are in a zero-shot setting. Our evaluation of ten transformer-based models and two LLMs on a publicly available Farsi polarization dataset shows promising findings,highlighting both the strengths and limitations of each approach.