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.
TypeSafe's Jev Inverts Text-Generator Decision-Making
The post notes three years of using text generators for decisions like classification, routing, and escalation, then parsing tokens into structured output; TypeSafe's Jev inverts this by letting users define the decision logic.
We've spent three years asking text generators to make decisions. Classify this. Route that. Escalate or not. Continue or stop. We parse the tokens back into a decision our software can actually use - and call that structured output. TypeSafe's Jev inverts it. You define the
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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.
