Language Model (LM)-based agents remain challenging to evaluate systematically in mixed-motive settings where agents must leverage short-term cooperation for long-term competitive goals (e.g., multi-party politics). We introduce Cooperate to Compete (C2C), a multi-agent environment where players can engage in private negotiations while competing to be the first to achieve their secret objective. Players have asymmetric objectives and negotiations are non-binding, allowing alliances to form and break as players’ short-term interests align and diverge. We run AI-only games and conduct a user study pitting human players against AI opponents. We find that humans favor lower-complexity deals and talk to more unique partners over the course of the game. We also find that humans are more aggressive negotiators, closing deals only 73.5% of the time compared to 94.0% for LM-based agents. Interventions inspired by these findings shift agent negotiation behavior and can raise win rates from 22.2% to 32.7%, demonstrating that language coordination strategies in C2C substantially alter negotiation dynamics. We run over 1,300 games with over 20,000 private conversations totaling over 180,000 player actions. By combining multi-party, long-horizon play with partial observability, asymmetric objectives, and non-binding negotiations, C2C provides a controlled, gamified testbed for studying strategic coordination in LM-based agents.