Jev compared with GPT-5.6 and Sonnet 4.6 in agent evaluation
A new evaluation says Jev matched the human oracle on 500/500 binary decisions with 913x lower variance than GPT-5.6 Terra, at 0.44 seconds and $0.00035 per call.
Are you guys even paying attention? 913× lower score variance. That's what Jev recorded versus GPT-5.6 Terra in a new agent evaluation test. Jev was tested against GPT-5.6 Luna, Terra and Claude Sonnet 4.6 and delivered: → 500/500 binary decisions matched the human oracle → 92–913× lower variance → 0.44s average latency → $0.00035 per call Why it matters: Traditional code evals are rigid. LLM judges can be expensive, slow and inconsistent. Jev takes a different approach: structured decisions instead of generated text. That could make continuous, large-scale agent evaluation dramatically cheaper. The bigger unlock isn't just better agents. It's being able to test them constantly.
This post quotes another post: “LangChain tests Jev against LLM judges for agent evaluation”