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Powerdrill Bloom on what shipped with Jev 1.13.0: RLCD, the three question types, pricing, and TypeSafe's jaggedness page.
- Format
- Article
- Published by
- Community
- Author
- Powerdrill Bloom
- Last verified
- Sep 2026
Used inJev for Software & DevTools
Highlights
- Published 18 September 2026. Quotes TypeSafe that Jev evaluates typed questions without generating text.
- Names RLCD as reinforcement learning for calibrated decisions.
- Documents jev-1.13.0 at $42 per billion input tokens, output free.
- Summarizes TypeSafe's jaggedness page: literal reading, numbers, dates, and indirection.
Watch out
Vendor documentation restated by Powerdrill. Not an independent benchmark run.
Questions about this entry
Short answers drawn from the linked project or page.
Restated vendor documentation, as the entry copy says. The numbers all trace to TypeSafe's docs: the model version, $42 per billion input tokens with output free, and the published rate and context limits.
Text only, with a size cap. It evaluates strings, JSON objects and arrays of text, the docs it quotes say images, audio and video are not supported yet, and the documented context is 64k per request with 32k for state plus the longest question.
Because the score is a probability-weighted position across the levels you defined, not a snapped bucket. The post's example places a customer between calm and very frustrated and returns 1.4, a shape no text model produces.
You threshold directly. Choice and Score answers carry a separate confidence value from 0 to 1 alongside the distribution, documented as existing so you can threshold on it without doing the arithmetic yourself.
250,000 tokens per second and 1,200 requests per minute, with a 64k-token context per request. Of that context, 32k is available for the state plus the longest question, and the vendor notes the rates may change dynamically.
jev-1.13.0, at $42 per billion tokens or $0.042 per million, charged per input token, with output tokens free. All calls go through POST /v1/systemone, where a model field selects which model handles the request.
Early language models, then pre-trained LLMs, then RLHF chat models, then RLVR reasoning models, and now RLCD, which it expands as reinforcement learning for calibrated decisions.
Reactions & coverage
Posts, threads, and videos about this entry from around the web.
Hacker News: Simon Willison's overview of Noul, Choice, and Score, and where a decision model fits.
benwerd53 pointsJev introduces a new shape of LLMRead the thread on Hacker NewsHacker News: swyx posts the Latent Space interview with Diogo Almeida.
swyx4 pointsJev: System One Models for Prod, Not God – With Diogo Almeida, CEO, TypeSafe AIRead the thread on Hacker NewsX: Diogo Almeida in five minutes
X: LLMs vs. Jev, clearly explained
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