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 model assigns probabilities to outcomes
The author mentions he is working on a blog post comparing Jev with Laya (including a fine-tuned version) on probabilistic statements, and emphasizes that an important feature of Jev is assigning probabilities to outcomes.
I figured this out last friday and was working on a blog post where I compare Jev to Laya (as well as a fine-tuned laya) on probabilistic statements and got scooped by this tweet. I think an important takeaway from jev is that it assigns probabilities to outcomes. In that way
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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.
