A recent study investigates which specific tokens are predicted more accurately by hybrid language models compared to standard dense architectures. The research focuses on understanding the distribution of prediction errors across different token types, such as rare words and code snippets. By analyzing the loss landscapes, the authors identify that hybrid models excel at capturing long-range dependencies in sparse data regions. The findings suggest that the mixture of experts mechanism allows for more efficient parameter utilization during inference. This improved accuracy is particularly notable for tokens with low frequency in the training corpus. The paper provides a detailed breakdown of performance metrics across various benchmark datasets. These results highlight the potential of hybrid architectures for handling diverse linguistic structures effectively.