Large Language Models (LLMs) are increasingly deployed in socially complex, interactiondriven tasks, yet their ability to mirror human behavior in emotionally and strategically complex contexts remains underexplored.This study assesses the behavioral alignment of personality-prompted LLMs in adversarial dispute resolution by simulating multi-turn conflict dialogues that incorporate negotiation.Each LLM is guided by a matched Five-Factor personality profile to control for individual variation and enhance realism.We evaluate alignment across three dimensions: linguistic style, emotional expression (e.g., anger dynamics), and strategic behavior.GPT-4.1 achieves the closest alignment with humans in linguistic style and emotional dynamics, while Claude-3.7-Sonnetbest reflects strategic behavior.Nonetheless, substantial alignment gaps persist.Our findings establish a benchmark for alignment between LLMs and humans in socially complex interactions, underscoring both the promise and the limitations of personality conditioning in dialogue modeling.Avg. Within-Human JSD: 0.179 Avg.