Benchmark of 104 English items in six categories and five templates, yielding 520 prompts per model across eight models. It combines lexical attribution rate, MiniLM embedding sensitivity, stance density annotated by two LLM judges, cross-phrasing kappa, a 1,000-item bootstrap, and a cohort-relative composite.
Eight models from five providers; each model answers 520 single-turn prompts. There are no human participants or human construct-validity labels. DeepSeek-V3.2 scored .805, Claude Sonnet 4.6 scored .782, and Gemma-3-27B scored .781; the three were statistically tied. Gemini 3.1 Pro scored .399 on the composite. Stance-content density explained most of the contrast. Every model shifted at least one component across conditions.
The composite is cohort-relative and not comparable across evaluations. The implemented PSI contrast does not match the declared conceptual contrast. There are no human gold labels or human-judge agreement. Only eight models, English, and one turn are covered. The bootstrap does not propagate judge or endpoint uncertainty. It does not measure beliefs, subjective experience, or general competence. It does not validate the composite as a human psychometric scale. It does not allow small differences to be interpreted as stable across versions.