Large language models encode rich semantic information, but how concreteness is represented across layers remains unclear. We examine layer-wise linear separability of concreteness by training linear probes on hidden representations from two opensource model families at multiple scales: Qwen3 and Gemma3-<br/>Instruct. Using human concreteness ratings, we build balanced prompt datasets with four difficulty levels: an extreme abstract–concrete contrast and three finer boundary comparisons at the abstract end, mid-range, and concrete end. Probes achieve high accuracy on the extreme contrast in shallow layers, showing that endpoint differences are strongly linearly separable. For finer distinctions, performance follows a stable hierarchy: midrange concreteness is easiest to separate, abstract-end distinctions are hardest, and concrete-end distinctions are intermediate. Across models, accuracy rises rapidly in early layers, peaks in<br/>middle layers, and declines in later layers. Together, these findings clarify how the linear accessibility of concreteness varies across LLM layers.