Julia 1: a decision model trained for about $104

More detail has emerged on Julia 1, the 144.3-million-parameter open decision model released on Hugging Face under Apache 2.0 by Supersonic Labs, a small Brazilian lab.

It never generates free text. It selects an answer from 2 to 20 options the caller supplies and returns probability scores. It runs on a plain CPU (benchmarked on an Apple M4, an Intel Core i5 laptop chip and a Samsung tablet), with an optional GPU path and a browser build. It is built on Johns Hopkins' mmBERT-small encoder with a new decision head; the lab says it is not a fine-tuned Qwen.

The numbers

• disclosed cloud GPU spend for training and experiments: about R$540, roughly US$104
• beat the published reference values of the hosted-only Jev model on several pilot tests
• did notably worse on the 72-label Banking77 test: 64% against 87%
• a planned hosted API was not yet open at launch

Only one detailed English write-up covers it so far.

Why it matters

A lot of real work is picking, not writing. A model this small and this cheap is a direct counter to the idea that everything needs a giant generative model behind it.