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.
Main Difference Between LLMs and Jev
The video explains that a typical LLM generates text or JSON token by token, with each token depending on the previous one, requiring multiple sequential stages, while Jev works differently.
The video is about the main difference between LLMs and Jev. Summary of this tweet from our friend: - A typical LLM generates text or JSON token by token. - Each token depends on the previous one; therefore, the model has to go through multiple sequential stages to produce a
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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.
