Box Decoding is a promising sort-free tree-search MIMO detection algorithm whose complexity is independent of the QAM order, achieved by selecting a fixed set (“box”) of candidates around a reference point at each tree layer. However, its detection complexity scales rapidly with the MIMO order due to the lack of pruning mechanisms. This letter proposes probabilistic tree pruning (PTP) strategy for Box Decoding, termed Box-PTP, which employs a statistically derived threshold to discard unlikely candidates during tree traversal. The pruning threshold combines the minimum distance metric at each layer with a noise-dependent statistical offset. We further derive analytical expressions for the expected number of visited nodes and propose a low-complexity method for computing the minimum distance metric. Simulation results show that Box-PTP offers substantial complexity reduction with negligible performance loss and remains sort-free, making Box Decoding scalable for large MIMO systems.