Compared on repository metadata · updated 2 November 2026

laya-vision-smolvlm-256m vs open-jev-deberta-v3-large

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.

Dimensionlaya-vision-smolvlm-256mopen-jev-deberta-v3-large
Published bythaiteacom-kotobalabs
WeightsOpenOpen
Licencecc-by-nc-sa-4.0apache-2.0
Base modelHuggingFaceTB/SmolVLM-256M-Instructmicrosoft/deberta-v3-large
ArchitectureDecoderEncoder
Parameters237M434M
Local formatsOriginal weights onlyOriginal weights only
Measured by usNoNo
Attention50 likes1,108 downloads, 74 likes

What actually separates them

  1. The licence decides this one before any benchmark does

    laya-vision-smolvlm-256m is licensed cc-by-nc-sa-4.0, which is not a commercial licence. If this is going into a product, open-jev-deberta-v3-large is the only one of the two you can ship, and no accuracy figure changes that.

  2. They produce their confidence differently

    open-jev-deberta-v3-large is an encoder and laya-vision-smolvlm-256m is a decoder. An encoder scores every option in one forward pass, so the probability it returns is defined over the whole option set by construction. A decoder reaches an answer by generating, so a usable probability needs constrained decoding plus a normalisation step over the options. Two decoders can report incomparable confidences while both being honest. That difference is invisible in an accuracy number and decisive if you intend to route on confidence.

  3. Neither has been measured by anyone independent

    Both are days old and every figure in circulation about either was published by the people who trained it. The table above is what can be checked from repository metadata. Everything else about these two is currently unknown.

Settle it on your traffic

This page narrows the choice to the rows that matter. A private benchmark runs laya-vision-smolvlm-256m and open-jev-deberta-v3-large 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.