Compared on repository metadata · updated 2 November 2026

JEV-CPU vs modernbert-ja-310m-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.

DimensionJEV-CPUmodernbert-ja-310m-jev
Published byMeanblockargos1111
WeightsOpenOpen
Licencemitcc-by-sa-4.0
Base modelQwen/Qwen3-0.6Bsbintuitions/modernbert-ja-310m
ArchitectureDecoderEncoder
ParametersNot disclosed315M
Local formatsOriginal weights onlyOriginal weights only
Measured by usNoNo
Attention64 likes475 downloads, 15 likes

What actually separates them

  1. The licence decides this one before any benchmark does

    modernbert-ja-310m-jev is licensed cc-by-sa-4.0, which is not a commercial licence. If this is going into a product, JEV-CPU is the only one of the two you can ship, and no accuracy figure changes that.

  2. They produce their confidence differently

    modernbert-ja-310m-jev is an encoder and JEV-CPU 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 JEV-CPU and modernbert-ja-310m-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.