Nace AI releases Drex Max, a decision model that reads screens and images and picks among set options for computer use agents
Drex Max is here: our multimodal decision model text, images, and video 34 ms/decision, under 1s at 262K tokens $0.06 / 1M input tokens available today at
Context
Computer use agents make many small choices per task, so a fast, cheap model that only picks from given options could cut the cost of running them, if its claims hold up.
The speed, price and benchmark claims are self reported by Nace AI, a startup building small decision models. Its Decision Index benchmark is published by Nace itself, and its docs list Drex Max as released on October 13.
Deep dive
On October 19, Nace AI posted that Drex Max is here, calling it its multimodal decision model for text, images and video. It claimed 34 ms per decision, under one second at 262K tokens, and a price of $0.06 per million input tokens, available on its site.
Unlike a chat model, a decision model writes no text. According to Nace's docs, a developer sends a state and up to 16 named questions, each a yes or no, a choice among given labels, or a score, and gets back a probability for each option in one call. Drex Max is the only Drex model that reads images, up to eight per request, and output tokens are not billed.
Nace's product page claims a score of 67.59 on its own Decision Index, 6.9 points above a rival model called Jev 1.14, across 37 public benchmarks. It also lists frontier models such as GPT 6 Sol and Claude Opus 5.5 for reference, which beat Drex Max on several general tests like HLE and MMLU Pro.
Some figures do not match across Nace's own materials. The post says 34 ms per decision, while the product page gives a median API latency of 183 ms, and the page prices Drex Max at $0.06 per thousand decisions. The post mentions video, but the docs list only image formats. The demo video shows it labeling a webcam feed and choosing clicks in web pages.
What to expect next
Watch for independent tests of Drex Max on computer use benchmarks such as OSWorld, since Nace runs the Decision Index itself. Also watch whether agent builders adopt decision models alongside general models to cut costs, and whether Nace clarifies its latency and price figures. Its open weights Drex 2.0 lets others test the approach on their own hardware, which could show how much of the speed comes from the model itself.