Large language models (LLMs) have revolutionized various fields like coding, mathematics, and toxicity detection. However, each model often specializes in one area, sometimes lacking in others. Imagine if we could merge the strengths of different LLMs to create a more versatile and powerful AI. Researchers explored this very idea with an innovative technique called Character-wise Ensemble Decoding, or CharED. Instead of retraining models, which is resource-intensive, CharED works by combining LLMs at the output stage. It breaks down each model's predictions to the character level and then averages those predictions to form a combined response. This method works even when the LLMs being merged use different vocabularies or tokenization methods, which were previously significant barriers to LLM collaboration. Testing this approach with LLMs specialized in coding, math, and toxicity detection, researchers found that the combined models outperformed individual models in many test cases. For example, a combined coding/math model performed as well as each specialized model on their respective strong points while performing better when tested on both topics. The merging process also seemed to self-correct, with one model compensating for the weakness of the other. This work paves the way for more efficient and adaptable AI. Future research will focus on merging more than two models and testing them on complex compositional tasks. More sophisticated merging mechanisms, rather than simple averaging, are also being explored. This fusion of different AI skillsets might be the key to unlocking even greater abilities in large language models.