Many benchmarks show that large language models can answer direct questions about culture. We study a different question: do they also change how they speak when culture is only implied by the situation? We introduce a triad-diagnostic framework; applying it to four deployed endpoints to evaluate 60 culturally grounded conversational scenarios. We examine responses across five languages in three conditions: a neutral baseline (Prompt A), an explicit cultural instruction (Prompt B), and implicit situational cueing (Prompt C). We use an LLM-judge ensemble with explicit rubrics to score responses on 12 pragmatic features covering deference to authority, individual-versus-group framing, and uncertainty management. We define Pragmatic Context Sensitivity (PCS) as the fraction of the judge-scored Prompt A→B feature shift that reappears under Prompt A→C. Across four deployed LLM endpoints and five languages (English, German, Hindi, Nepali, and Urdu), the primary stable-only PCS mean is 0.196 (SD = 0.113), indicating that the tested models recover only about one-fifth of the explicitly prompted rubric-aligned shift. Transfer is strongest for authority-related cues (0.299) and weakest for individual-versus-group framing (0.120). We argue that multilingual cultural pragmatics is an explicit-versus-implicit deployment problem, not only a factual knowledge problem.