In model-driven engineering and beyond, software models are key development artifacts. In practice, they often grow to substantial size and complexity, undergoing thousands of modifications over time due to evolution, refactoring, and maintenance. The rise of AI has sparked interest in how software modeling activities can be automated. Recently, LLM-based approaches for software model completion have been proposed, however, the state of the art supports only single-location model completion by predicting changes at a specific location. Going beyond, we aim to bridge the gap toward handling coordinated changes that span multiple locations across large, complex models. Specifically, we propose a novel global embedding-based next focus predictor, NextFocus, which is capable of multi-location model completion for the first time. The predictor consists of a neural network with an attention mechanism, which is trained on historical software model evolution data from real-world modeling repositories. Starting from an existing change, it predicts further model elements to change, potentially spanning across multiple parts of the model. We evaluate our approach on multi-location model changes that actually have been performed by developers in real-world projects. NextFocus achieves promising results for multi-location model completion even when changes are heavily spread across the model. It archives an average Precision@k score of 0.98 for $k \leq 10$, significantly outperforming the three baseline approaches.