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How to shortlist decision models from the JevBench board with Jevbench

Compare open-weights and hosted decision models on accuracy, calibration, speed and cost before you pick one.

  • Top pick
  • Research
  • Beginner
  • 15 minutes
  • From the README
  • Any agent

Who it is forA product or operations lead who needs a decision model for a typed decision workflow and wants published measurements before testing anything.

The problemChoosing a decision model from vendor claims is hard to compare, and a single number hides accuracy, calibration, latency and cost trade-offs.

The outcomeA short list of candidate systems with their Intelligence, Calibration, Speed and Cost values, plus a clear note of where measurements are missing.

What you need first

  • A web browser to read the public boards and release manifest
  • Git and Python for the local repo steps
  • Your decision model reachable from your own machine if you run the option-order test

Steps

  1. Open https://benchmarkheaven.com/jev-models for the open-weights board or https://benchmarkheaven.com/jev-models/api for the hosted API board.
  2. Read the Capability Score and Composite Score definitions, and note the cost cap of USD 0.0646 per 1,000 decisions and the adjusted median-latency cap of 1.23 seconds.
  3. Read the methodology at https://benchmarkheaven.com/jev-models/methodology and the data card at https://benchmarkheaven.com/jev-models/data before trusting a row.
  4. Check the release version and source_sha256 on https://benchmarkheaven.com/api/jevbench/manifest.
  5. Shortlist systems, and treat any null measurement as missing data, never as zero.

Prompt for your agent

Using the JevBench project at github.com/fstandhartinger/jevbench, read the release manifest at https://benchmarkheaven.com/api/jevbench/manifest and the results JSON at https://benchmarkheaven.com/api/decision-results/jevbench/v1.6.1/json. Return a table of the five best open-weights systems for <target workload> with their Intelligence, Calibration, Speed and Cost values, marking any null value as missing. Verify that the release source_sha256 matches 5cd8c1332226ea9c179883d2285c0d8b8cc593ae38f2645197364a7ddd9c83c1 before you report results.

Caveat. A leaderboard score does not establish reliability on your workload, and scores from different method versions should not be compared as the same experiment.

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