Negotiation is a core component of social intelligence, requiring agents to balance strategic reasoning, cooperation, and social norms. Recent work shows that LLMs can engage in multi-turn negotiation, yet nearly all evaluations occur exclusively in English. We systematically isolate language effects across English and three Indic framings (Hindi, Punjabi, Gujarati) by holding game rules, model parameters, and incentives constant for multi-agent simulations for Ultimatum, Buy-Sell, and Resource Exchange games. Our results indicate that language choice is correlated to agreement and process dynamics, while final payoffs are statistically unchanged, and that this correlation persists across model architectures. Crucially, effects are task-contingent: Indic languages lower acceptance and longer negotiations in the Ultimatum Game, and higher trade volume in Resource Exchange. Further, they suggest that findings obtained under English-only conditions may not generalize to other linguistic settings. Even within one related language family, we observe task-contingent divergence from English, which cautions against treating English-only evaluation as representative. Whether this extends to typologically distant languages remains open.