Towards Foundation Decision Models: Drex

Nace AI released Drex, a family of four open-source decision models with 8B (DLM), 9B (v1.5), 35B with 3B active (v2.0), and 27B (Max) parameters that return a probability distribution over user-supplied options without autoregressive decoding. Drex Max set a new state of the art on Decision Index 0.4.1 with 67.52 on the public suite versus 60.70 for Jev 1.14.0, and answered a typical request in 183 ms against about 2.0 s for GPT-6 Sol and 3.5 s for Claude Opus 5.5. On Decision Bench v4, Max was statistically tied with d3-turbo and GPT-6 Sol and trailed Claude Opus 5.5 by 1.6 points, with the highest accuracy among decision models on DecideBench.

arXiv:2611.07854v1 Announce Type: new Abstract: Autoregressive approaches with large language models, such as long chain-of-thought reasoning, have become the default for new AI applications because they reach higher accuracy on a wide range of tasks. In production, however, they give inconsistent answers, require parsing of outputs, and add cost and latency. Decision models address this: given a question and a set of options supplied by the user, they return a probability distribution over the options without autoregressive decoding. We present Drex, a family of four open decision models with 8B DLM , 9B v1.5 , 35B with 3B active v2.0 , and 27B Max parameters. Each request consists of a state a document, record, or agent context, optionally with images and questions about it, each asking for a choice among options, a yes/no judgment, or a score on an ordered scale. Training combines supervised fine-tuning with reinforcement learning from adjusted feedback RLAF , plus vision tuning for Max. Ablations show that RLAF improves accuracy and that consistency losses improve robustness to option order. On Decision Index 0.4.1, Max sets a new state of the art among the decision models we compare 67.52 on the public suite vs. 60.70 for Jev 1.14.0 . On Decision Bench v4, it is statistically tied with d3-turbo and GPT-6 Sol and trails Claude Opus 5.5 by 1.6 points, and it has the highest accuracy among decision models on DecideBench. Max answers a typical request in 183 ms, against about 2.0 s for GPT-6 Sol and 3.5 s for Claude Opus 5.5, and its outputs are stable under option reordering. All four models are open source: https://github.com/nace-ai/drex-decision-models