Large language models demonstrate significantly superior performance in reasoning tasks compared to traditional models, yet still struggle to meet the demands of complex tasks in terms of computational cost and response quality. Against this backdrop, model interconnection enables the sharing, integration, and complementation of large model capabilities by constructing a collaborative paradigm among models. The cascade architecture represents a typical form of such collaboration, where multiple large models are organized in a chain-like sequence to enhance system performance through step-by-step optimization. Routing in model cascades aims to select appropriate cascade paths and serves as a key factor in improving system capabilities. However, current routing evaluation and selection methods lack systematic consideration of model collaboration relationships. To address this, this study proposes a dynamic routing method based on collaboration relationships. It first builds a model collaboration graph through a mutual evaluation mechanism, and then employs a dynamic collaborative routing algorithm to analyze responses hop by hop and optimize path selection. The mutual evaluation mechanism uses gradient-based mutual assessment to quantify the quality of pairwise model collaboration. Based on the resulting collaboration quality information, the dynamic collaborative routing algorithm adopts a model “consensus rule” to analyze each hop’s response and determine the routing order, thus enabling dynamic path adjustment. Experimental results show that the proposed routing algorithm outperforms both non-preset and non-targeted routing methods in terms of accuracy and response win rate on benchmark task datasets. On the OMGEval dataset, the win rate is improved by up to 45% compared to non-preset routing.