Compared on repository metadata · updated 2 November 2026
decider-2b vs Jev
Both are decision models. What separates them is licence, architecture, size and whether anyone has checked the numbers. Only one of those is a matter of opinion.
Side by side
Rows marked with a dot are where the two genuinely differ. Everything here comes from each repository’s own metadata, read on 2 November 2026.
| Dimension | decider-2b | Jev |
|---|---|---|
| Published by | Mapika | TypeSafe |
| Weights | Open | Hosted only |
| Licence | apache-2.0 | Proprietary |
| Base model | Qwen/Qwen3.5-2B-Base | Not disclosed |
| Architecture | Decoder | Not disclosed |
| Parameters | 1.9B | Not disclosed |
| Local formats | Original weights only | Original weights only |
| Measured by us | No | Yes |
| Attention | 54,359 downloads, 113 likes | Not on Hugging Face |
What actually separates them
The licence decides this one before any benchmark does
Jev is licensed Proprietary, which is not a commercial licence. If this is going into a product, decider-2b is the only one of the two you can ship, and no accuracy figure changes that.
One you run, one you call
Jev is a hosted API: no weights to operate, but a key, a network round trip and a vendor version number in the path of every decision. decider-2b is yours to run, which removes all three and hands you the operational cost instead.
One has been measured and one has not
We ran Jev on a frozen 2,000-decision split and published accuracy, calibration and a risk and coverage curve. Nobody outside its own authors has published anything comparable for decider-2b. That is a gap in the evidence, not a verdict about the model.
Settle it on your traffic
This page narrows the choice to the rows that matter. A private benchmark runs decider-2b and Jev on a frozen set of your own historical decisions and returns accuracy at every coverage level, so you can see which one clears your error budget.
Find out what your data actually supports
We turn your historical decisions into a frozen test set, then tell you which model, which threshold, and how much of it you can safely automate.