This study evaluates whether fine-tuned ModernBERT encoder classifiers can serve as cost-effective alternatives to LLM-based judges for safety evaluation. The researchers benchmarked ModernBERT and Ettin against rule-based prefix matching, fine-tuned LLM classifiers, and various LLM judge methodologies. These LLM judges included strategies from StrongReject, ShieldGemma, JailbreakBench, AILuminate, SorryBench, Claude-as-a-judge, and models like LlamaGuard 3 and 4. The encoder classifiers were trained on judge-labeled data using a majority-voting label strategy and tested on a gold-standard holdout dataset. Performance was measured using F1 score, false negative rate, and precision-recall metrics across open-source adversarial datasets. Results were further analyzed by attack technique, including single-turn prompting, decomposition, escalation, and context manipulation. The findings provide guidance on when encoder classifiers can reliably replace LLM-based judges without substantial performance loss.