Hi HN! I built JevBench because Jev kicks ass, and the world deserves to know how the serious open source and fake lookalike projects really perform in comparison.

Jev-class models return bounded choices and probabilities instead of text, and are disruptively faster and cheaper than LLMs, while being similarly intelligent on the text input they operate on.

JevBench allows looking at accuracy, latency and price all at once, in a weighted way - you can even configure the weighting.

A full run asks 534 English decisions. The v1.3 score combines chance-corrected Intelligence, Calibration, Speed and Cost.

Leaderboard right now:

  #1 - Jev            74.4
  #2 - SemIf          73.1
  #3 - djev           73.0
  #4 - Winnow-12B Q8  71.2
  #5 reflex 4B        70.3.

https://github.com/fstandhartinger/jevbench

Two no-signup demos:

https://who-is-right.app.mintapis.com

https://is-it-ai-slop.app.mintapis.com

Limitations: English-only; latency from one German server; local/demo latency gets a disclosed ×2 adjustment (+150 ms on my servers) which is an informed assumption; held-out prompts still reach evaluated services; ~1-point gaps can be noise.

Wdyt?

Good project but this one also exists https://huggingface.co/spaces/multimodalart/jev-decision-ind... and the results do not seem to add up and also model sets are different... still needs time to mature likely

https://is-it-ai-slop.app.mintapis.com/ is a fun tool. Is the source or methodology for that in the github repo? I couldn't find it immediately.

We've been experimenting with Jev for classifying email, some thoughts here: https://housecat.com/blog/classifying-email

Flagging AI written email is a much requested feature too.

jev ceo on why he eschewed benchmarking: https://www.latent.space/i/216783460/privacy-benchmarking-an...