Newly launched 4B-param model Athena from @markopolo_ai just beat GPT-5.6 and Claude Opus 4.8 at predicting what a shopper does next！

So Athena watches the shopper's current page plus their interaction history， then predicts the very next browser action. e.g. when a shopper is about to leave the checkout journey， Athena can predict the exit action and the specific page element or navigation step involved even before it happens. That could give a downstream system the opportunity to respond while the shopper is still active.

It scored 24.50% strict exact-match across the full OPeRA test set of 992 actions， the highest of every system entered.

Strict exact match means both the next action and the exact target element must be correct for the prediction to count； even a nearly correct prediction scores zero if the target element is wrong. This is a large， unforgiving output space and Athena still ranked first.

Model available on Huggingface. Size is the surprise， since the winner is a LoRA fine-tune of Qwen3-4B， an open weight you can host yourself .

Until now the way to get a prediction like this was to prompt a frontier model and hope it worked out shopping behavior from general reasoning.

But this release is so significant， because the input can contain detailed browsing sequences， page state， typed text， and signals about what a user is trying to accomplish. Sending all of that to a general external model may be undesirable or too expensive at high event volume.

A compact， self-hostable specialist model offers a much more practical path.