Jev versus text-generating models
Jev does not generate free-form text. A side-by-side video compares parallel decision-making with token-by-token text generation.
Jev Engineering: Turning Agent Chains into Self-Routing Decision Graphs
According to the post, Jev Engineering turns a single agent chain into a decision graph that reroutes itself, removing the expensive model from every decision loop. Tests show up to 193x faster and 444x cheaper.
Jev Engineering turns a single agent chain into a decision graph that reroutes itself, and pulls the expensive model out of every decision loop. In tests: up to 193x faster and 444x cheaper. The key here isn't how many agents you run. It's that execution never follows one
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Pricing, evaluations, and the name Jev
The launch thread explains code-and-AI workflow evaluations, the input price announced at launch, and free output tokens.
Jev Model: Decision-Making Probability Generation, 200× Faster and 400× Cheaper Than LLMs
This model is built for decision-making rather than text generation, outputting probabilities for each option in parallel instead of token-by-token responses. The author shares a simplified comparison of normal LLMs versus this Jev model.
