Rene-1 is an open-weight 31B parameter classifier model that reads documents and answers multiple typed questions in a single forward pass, returning calibrated probabilities for each option without generating text. The model achieves state-of-the-art results on the Decision Index benchmark, outperforming Jev by 9 points, and runs efficiently on a single GPU using FP8 weight compression.