{
 "schema_version": 1,
 "generated": "2026-10-20",
 "id": "omnijev-awesome-jev-gallery--compare-typed-decision-models",
 "repo": "OmniJev/awesome-jev-gallery",
 "repo_url": "https://github.com/OmniJev/awesome-jev-gallery",
 "repo_slug": "omnijev-awesome-jev-gallery",
 "title": "Compare typed-decision models on shared benchmarks",
 "page_title": "How to compare typed-decision models on shared benchmarks with Awesome Jev Gallery",
 "summary": "Use the Benchmark and Leaderboard section to run the same public tasks on two candidate models.",
 "who": "An ML engineer choosing between open decision models for a production classifier or router.",
 "problem": "Each project reports its own scores on different test sets, so the numbers cannot be compared directly.",
 "outcome": "A side-by-side table of accuracy on the same public task set for your two shortlisted models.",
 "steps": [
  "Open the Benchmark and Leaderboard section and list the benchmark entries.",
  "Note that Open-Jev reports 179 of 231 public JevBench tasks answered correctly by its 9B model, according to its authors.",
  "Find the 13-subset public evaluation suite linked from the Bespoke Nimble entry.",
  "Run the same tasks on each candidate model and record the probability each option receives.",
  "Compare accuracy on the shared tasks and note which model fails on which task type."
 ],
 "agent_prompt": "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.",
 "agents": [
  "any"
 ],
 "difficulty": "Advanced",
 "time": "180 minutes",
 "basis": "readme",
 "caveat": "The list does not publish one verified leaderboard, so every score here is author-reported on that author's own test set.",
 "category": "Research",
 "tags": [
  "Research"
 ],
 "lang": "python",
 "requirements": [
  "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"
 ],
 "risk": "none",
 "seen_in_the_wild": false,
 "mentioned_publicly": false,
 "risks": [],
 "best": false,
 "score": 0.9,
 "evidence": [],
 "url": "https://dedupedai.com/use-cases/omnijev-awesome-jev-gallery/compare-typed-decision-models/",
 "md": "https://dedupedai.com/use-cases/omnijev-awesome-jev-gallery/compare-typed-decision-models/index.md",
 "disclaimer": "Deduped lists open-source repositories that belong to other people. We do not own, maintain, audit, or vouch for any of them, and a repo appearing here is not an endorsement or a safety check. The use cases, steps, and prompts are written by AI and may be incomplete, outdated, or just wrong. Where a use case is marked \"Seen in the wild\", we link to public posts where someone described something similar, but we have not verified those posts and the use case itself is still our AI's own write-up. Use all of this at your own risk. Only run code, commands, or agents on systems and accounts you are authorized to use, and read the code and every command before you run it, especially anything that touches credentials, files, or the network. Nothing here is professional, security, legal, or financial advice. All trademarks and project names belong to their respective owners, and their use here does not imply affiliation. Content is provided as is, without warranties of any kind. Found a mistake, a safety problem, or something you want removed? Email hello@dedupedai.com and we will look at it promptly.",
 "api": "https://dedupedai.com/api/v1/usecases/omnijev-awesome-jev-gallery--compare-typed-decision-models.json",
 "api_version": "v1"
}