Pragmatic reasoning, including inferring intent, manipulation, and hidden meaning in dialogue, is essential for trustworthy language understanding yet remains underexplored in current large language models (LLMs). We introduce HIPO, a new benchmark designed to assess Hierarchical Pragmatic Tactic in multi-turn Conversations. Inspired by linguistic pragmatics, HIPO operationalizes a hierarchical annotation schema for multi-turn conversations and labels each utterance along one dialogue-level goal and three utterance-level dimensions: communicative intention (why), veracity strategy (how), and illocutionary act (what). To ensure reliable supervision, we design a structured generation pipeline to generate high-quality synthetic dialogues. The benchmark comprises over 4,088 benchmarking utterances with 6,350 contextual utterances across 1,131 dialogues drawn from 31 real-world-inspired scenarios. Benchmarking 22 state-of-the-art LLMs reveals a striking gap: while models achieve higher performance at recognizing surface speech acts(83.3% accuracy), they perform poorly on detecting veracity strategies(32.2%) and speaker intentions(45.1%). These results suggest current LLMs remain challenged by the pragmatic inferences operationalized in HIPO, particularly those involving information manipulation and intended effects beyond literal content. We further show that supervised fine-tuning on additional synthetic data generated by our pipeline can improve performance on HIPO, suggesting a practical path for targeted capability improvement within this benchmark setting.