Imagine a team of experts, each brilliant in their own way, but unable to effectively pool their knowledge. This is the challenge with current Large Language Model (LLM) ensembles. While combining multiple LLMs holds the promise of superior performance, existing methods often stumble, struggling to reconcile differences in vocabulary and reasoning styles. They get bogged down in aligning probabilities across vast vocabularies, creating computational bottlenecks. This new research explores why some LLMs work well together while others clash, and introduces a novel approach called "UNITE" (Union Top-k Ensembling). Instead of trying to force complete agreement across every word in a massive vocabulary, UNITE focuses on the most likely next words—the top-k tokens—from each model. Think of it as streamlining communication by concentrating on the most important points. This clever strategy simplifies the ensembling process, dramatically reducing computational overhead while maintaining, and often exceeding, the performance of traditional methods. The research also delves into the tricky problem of model selection. It turns out that simply combining the "best" models doesn't guarantee success. Model compatibility is key. This work introduces a practical framework for determining which LLMs make good teammates, opening up new possibilities for even more powerful and efficient AI ensembles. This targeted approach significantly speeds up processing while boosting accuracy. It's a win-win for AI development, paving the way for more robust and efficient language models capable of tackling even the most complex tasks.