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How to compare typed-decision models on shared benchmarks with Awesome Jev Gallery

Use the Benchmark and Leaderboard section to run the same public tasks on two candidate models.

  • Research
  • Advanced
  • 180 minutes
  • From the README
  • Any agent

Who it is forAn ML engineer choosing between open decision models for a production classifier or router.

The problemEach project reports its own scores on different test sets, so the numbers cannot be compared directly.

The outcomeA side-by-side table of accuracy on the same public task set for your two shortlisted models.

What you need first

  • A web browser to read the list and the linked repositories
  • Hardware that matches the open model you choose, such as an RTX GPU or Apple Silicon for local runs
  • A decision task with a fixed set of options you want scored

Steps

  1. Open the Benchmark and Leaderboard section and list the benchmark entries.
  2. Note that Open-Jev reports 179 of 231 public JevBench tasks answered correctly by its 9B model, according to its authors.
  3. Find the 13-subset public evaluation suite linked from the Bespoke Nimble entry.
  4. Run the same tasks on each candidate model and record the probability each option receives.
  5. Compare accuracy on the shared tasks and note which model fails on which task type.

Prompt for your agent

Using the Benchmark and Leaderboard section of https://github.com/OmniJev/awesome-jev-gallery, collect the public evaluation suites and their task counts, then produce a table of <model A> and <model B> accuracy on one shared suite. Verify that both models were scored on identical tasks before you report a difference.

Caveat. The list does not publish one verified leaderboard, so every score here is author-reported on that author's own test set.

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