YouTube keeps describing Jev as "a cheaper ChatGPT". That misses what is different about it, so I made a video that walks through each step on screen.
The short version:
- An LLM predicts the next token, writes it, and loops. Its output is a sentence.
- Jev looks at the state and answers a question you already defined. Its output is a probability. No sentence.
Both end in the same softmax. The job is what differs. One spreads its probability over roughly 100,000 tokens. The other spreads it over 3 labels.
Try it. Drag the slider to change the input and watch both distributions.
LLM · next token · ~100,000 options
Then it writes one token and does this again.
Jev · decision · 3 labels
One step. The probability is the answer.
On the left, even a confident model leaves a long tail of other tokens it might have written, and it has to do this again for every token in the reply. On the right there is one step and three numbers, and you can read the confidence straight off the bars.
That is the fundamental split: generate vs decide. If the question is fixed and the answer is a choice, you do not need a model that can write.