Large Language Models of code have seen significant jumps in performance recently. However, these jumps tend to accompany a notable and perhaps concerning increase in scale and costs. We contribute an evaluation of prediction performance with respect to model size by assessing the layer-wise progression for language and user-defined elements in code, using a new technique of Tuned Lenses. We show that language-defined elements can be predicted more accurately in earlier layers of the PolyCoder model than user-defined elements and contribute an evaluation of the attention mechanism, which shows patterns that explain such aspects of performance and indicate areas of missed potential. These findings encourage research into the internal prediction performance for other characteristic aspects of code and could lead to the introduction of new methods that make use of these characteristics to improve performance without relying on scaling.