COMMUNITY OBSERVED🤖 Jev Agent

OpenRouter’s Ori Eval

Jev judged a 30-way labelling task faster than every model tested.

OpenRouter’s Ori Eval

Overview & Result

1/ Jev, a decision model by @typesafeai, sparked a burst of projects and discussion. We tested it using Ori Eval against popular LLMs on OpenRouter at judging. Jev was >5x faster than the next fastest model, and even its slowest requests beat every other model's median.

How Jev fits in the loop

  1. Ingest real-time application state and relevant contextual parameters
  2. Format the decision problem as a bounded Choice or Noul schema
  3. Query Jev to receive a typed probability distribution in sub-50ms
  4. Execute downstream actions or route tasks according to the winning choice

How to reproduce

  1. Inspect the original showcase and source material at https://x.com/OpenRouter/status/2101412965765529853
  2. Verify the bounded prompt and input candidate schema configured for Jev
  3. Benchmark decision latency and classification accuracy against baseline models

Why this build matters

Demonstrates a real-world, cost-effective implementation of Jev in a model routing & triage agent scenario, replacing expensive generative calls with fast typed decisions.

Reported performance

Reported by author

Latency: Sub-50ms deterministic decision window

Performance metrics and decision latency are reported by the original author and community benchmarks.

Limitations

  • Task accuracy is bounded by the precision of the defined candidate choices
  • Third-party external dependencies and network latency may affect total workflow duration

Patterns