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.
Why Jev's Structured Output Resists Prompt Injection
The post argues prompt injection works because a model can write the next instruction, whereas Jev's structured evaluation model only outputs a typed probability, label, or point on a scale—hijacking text can move the number but cannot become a command.
Prompt injection works because the model can write the next instruction. A structured evaluation model cannot. Jev's only output is typed a probability, a label, a point on a scale. Text that tries to hijack an agent can move the number. It cannot become a command. That is why
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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.
