We describe the Seals-NLP system for SemEval-2026 Task 9 (POLAR) Subtask 1, binary polarization detection. Our study compares (i) fully fine-tuned encoder-only transformers, (ii) QLoRA-based fine-tuned open-weight LLMs, and (iii) zero-shot prompted LLMs. ModernBERT-large emerges as the most cost-effective option, matching or surpassing larger fine-tuned and zero-shot LLMs in macro-F1 while requiring substantially less memory and lower latency. An error analysis by failure mode and polarization subtype reveals systematic over-triggering on political cue words and under-detection of sarcastic vilification and multifaceted attacks in the POLAR dataset across all models.