This work investigates sentence-level models (i.e., models that operate at the sentence-level) to study how sentence representations from various encoders influence downstream task performance, and which syntactic, semantic, and discourse-level properties are essential for strong performance.Our experiments encompass encoders with diverse training regimes and pretraining domains, as well as various pooling strategies applied to multi-sentence input tasks (including sentence ordering, sentiment classification, and natural language inference) requiring coarse-to-fine-grained reasoning.We find that "less mature" representations (e.g., mean-pooled representations from BERT's first or last layer, or representations from encoders with limited fine-tuning) exhibit greater generalizability and adaptability to downstream tasks compared to representations from extensively fine-tuned models (e.g.,, SBERT or Sim-CSE).These findings are consistent across different pretraining seed initializations for BERT.Our probing analysis reveals that syntactic and discourse-level properties are stronger indicators of downstream performance than MTEB scores or decodability.Furthermore, the data and time efficiency of sentence-level models, often outperforming token-level models, underscores their potential for future research.