Large Language Models have become central to Automated Essay Scoring (AES), typically through fine-tuned transformer encoders or prompt-based applications of decoder models. However, the representational capacity of decoder models as frozen embedding extractors remains largely unexplored. In this paper, we present a controlled comparison between encoder and decoder transformer embeddings for prompt-agnostic AES. Using regression models, we evaluate frozen representations across two English datasets. We analyzed scaling effects and the impact of integrating explicit linguistic features in hybrid configurations. Our results show that decoder embeddings consistently outperform encoder embeddings in embedding-only settings, with gains generalizing across holistic essay scoring and proficiency prediction. Scaling effects are modest, and hybrid models that combine contextual embeddings with linguistic features yield further improvements. Notably, frozen decoder embeddings achieve performance competitive with a fine-tuned BERT. These findings highlight the importance of representation-level properties in essay scoring.