Just created this with Jev by @typesafeai. A live viral post analyzer. As soon as you stop typing for .5 seconds it analyzes the viral potential. Going to try and actually make this good, will need to scrape a lot of twitter data... Notice how it also categorizes the tweet Show more
API & Models
Output tokens
The tokens Jev emits for answers; they are reported in usage and are free of charge.
- Category
- API & Models
- Also known as
- —
- Related terms
- 3
- Directory entries
- 1
- Docs
- docs.typesafe.ai
- Added
- 2026-09-24
Definition
Answers are small compared to the state that produced them, and TypeSafe does not bill for them. The response still reports output_tokens, which is useful for monitoring request shapes even though the cost is zero.
Free output is one half of the pricing argument: Jev bills input at $42 per billion tokens and gives the structured answers away.
Tagsapipricing
Related terms
Definitions that connect to this one.
Input tokensThe tokens billed on every request: state plus questions, including speculative questions your code ignores.API & ModelsToken pricingJev's published rate: $42 per billion input tokens, with output tokens free.API & ModelsUsageThe response field reporting input and output token counts for a request, used for cost tracking.API & Models
From the directory
The model card for jev-1.13.0: $42 per billion input tokens with free output, a 64k context, 250k tokens per second, and how to list the models your account can call.
Sites & GuidesDocs#official#docs#models
Official
From the community
Posts from builders shipping with Jev right now.
Live viral post analyzer
Jev plays Tetris: 134 lines in two minutes
Puse a Jev, la nueva IA de @typesafeai, a jugar al Tetris en modo súper difícil. Decidió cada jugada en unos 0,3 segundos. Acomodó 357 piezas e hizo 134 líneas en solo 2 minutos. Increíble.
Support answers in a Mac app
First @typesafeai use case, live in our Mac app: setup and troubleshooting help when no model is loaded. Model downloading, load failed, API returning 503, phone won't pair: the user asks, Jev reads the question with the whole built-in manual as state and decides, with Show more
A local Jev build with room to get faster
ローカルTypesafe Jev、できたんやないか？ もうちょい速度は改善余地ありそう
typesafeのJev、これローカルLLMで割といけるんじゃないか？ ていうかそういう使い方するのもローカルLLMの醍醐味だったわけで！
jev-review: a local-first score loop for coding agents
built `jev-review` @typesafeai it's an experimental, local-first MCP plugin that gives coding agents a score quality feedback loop across different metrics. agents call jev while they work, get scored, make improvements, and repeat the loop try below 👇
@CompleteSkeptic
After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI? I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev • 20-200x faster • 40-400x
The X algorithm, rebuilt with Jev
rebuilt the X algorithm with Jev - uses real weights - simulates virality of your post - has a global feed (you see everyone) it's insanely accurate