Large Language Models (LLMs) are increasingly deployed in contexts requiring complex moral reasoning and value trade-offs.However, existing evaluations typically rely on item-level behavioral metrics, which fail to capture how models structurally prioritize competing values as a cohesive system.To address this, we propose a symmetric human-LLM evaluation framework, grounded in Q methodology, to measure value-structure alignment.Under our protocol, humans and models sort an identical 140-item moral statement set into a shared nine-column forced distribution; for LLMs, we elicit strict rankings and deterministically map them to Q-sort buckets.Using a human reference sample (N = 35), we establish a stable three-factor reference geometry specific to this instrument and sample.We evaluate 12 LLMs across four model families via 240 replicated Q-sorts at two temperature settings, quantifying structural alignment via Procrustes similarity (ϕ) and RSA-based Spearman correlation (ρ).Our results reveal significant crossfamily heterogeneity, model-specific sensitivity to generation stochasticity and localized misalignment, which demonstrate that favorable global scores can obscure underlying regional distortions.While rank-and bucket-based analyses remain highly consistent, prompt phrasing introduces notable variance.Ultimately, assessing value-structure alignment provides a crucial structural complement to traditional itemwise moral benchmarks.