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API & Models

Usage

The response field reporting input and output token counts for a request, used for cost tracking.

Category
API & Models
Also known as
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Related terms
4
Directory entries
2
Docs
docs.typesafe.ai
Added
2026-09-24

Definition

Every response includes usage with input_tokens and output_tokens. Since only input is billed, input_tokens is the number that maps to cost at the published per-billion rate.

The playground also surfaces usage next to probabilities, confidence, and latency, which is how thresholds get tuned against real cost per call.

Tagsapipricing

From the directory

The raw HTTP contract behind every SDK: POST a state plus typed noul, choice, and score questions to /v1/systemone, and get one answer per question, with error codes and retry guidance.
Sites & GuidesDocs#official#docs#api
Official
Browser playground in the TypeSafe console for trying System One: paste state, define typed questions, and inspect answers with probabilities, confidence, latency, and usage.
Cookbooks & DemosPlayground#official#playground#demos
Official

From the community

Posts from builders shipping with Jev right now.

Follow @typesafeai

700 live ads in 40 seconds

1,891 ads in 19 seconds

ESTA HERRAMIENTA ACABA DE ROMPER TODO EL MERCADO DEL AD SPY Maxfusion ha cogido JEV, el modelo nuevo de TypeSafe, y le ha metido la ad library entera de una marca → 1.891 anuncios clasificados → 19 segundos → 0,12 $ Y no es un resumen: cada anuncio etiquetado por etapa del Show more

Ori Silver
Ori Silver
@OriSilver

JEV makes competitor research feel like a cheat code We fed it Resilia’s ad library and it classified 1,891 ads in 19 seconds for $0.12 Customer journey stage, ad style, and a full account deep dive Coming soon to the maxfusion MCP

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Game levels generated in real time

900 images in 40 seconds

Intent-based search in Gmail

Jev is really good at intent-based search! How it looks in Gmail: (for a huge inbox you'd prob let semantic search / embeddings pull first but still much better experience)

nader dabit
nader dabit
Cognition
@dabit3

Another crazy @typesafeai Jev example: Predictive spreadsheets Spreadsheets recalculate numbers, not meaning. Jev reads intent. Type "Urgency" at the top of a column and, as you type, it figures out you want each row rated from "no follow-up needed" to "urgent" in ~100 ms.

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End-to-end tests run by agents