Merging fine-tuned models is a promising alternative to costly multi-task training, but task interference remains a challenge, especially as the number of tasks grows. We present DivMerge, a reference-free method that merges models trained on different tasks by minimizing Jensen-Shannon divergence between their outputs and those of the merged model, automatically balancing task importance. While the method exhibits strong theoretical properties, experiments on classification and generative tasks with autoregressive models show that DivMerge consistently outperforms prior work, and remains robust when scaling to more tasks.