Cloudflare previews Clairic, a System One extraction model on Workers AI that returns typed values, estimates and forecasts in one pass without generating tokens; open weights planned

Why it matters

【Key points】

Cloudflare released a preview of Clairic on Oct 19 (17:48 UTC), calling it a new class of System One model: an extraction model. It is hosted on Workers AI as @cf/cloudflare/clairic, and Cloudflare says open weights are coming "soon".

Earlier System One models, like TypeSafe's Jev and Cloudflare's Clef, are decision models that score options; Clairic does regression and extraction. You send text (plus up to four images) and a map of typed fields, and one forward pass returns a value for each field. It generates no tokens, bills only input, and usually answers in under a second. It can copy spans (names, order IDs) with offsets; read or compute numbers, dates and lists; estimate implied values with a median and 80% interval; and forecast a series. Every answer carries a confidence score and a null probability, and missing values come back null.

In Cloudflare's tests, a Clairic → Clef → GLM-5.3 pipeline for damage claims took 19.3s end to end versus 61.7s using GLM-5.3 for every step, at 49% lower cost. Forecasting daily TSA checkpoint counts for 21 days, it averaged 3.3% error, with 17 of 21 actuals inside its 80% interval. Cloudflare says multi-step arithmetic and aggregating across many rows are not reliable yet, and forecasting is basic.

【Verifiable】

Cloudflare blog: https://blog.cloudflare.com/clairic-extraction-model/

Model docs: https://developers.cloudflare.com/workers-ai/models/clairic/

【Our read】

1. System One models now go beyond scoring options to extracting and estimating values, the structured-output calls that today burn LLM tokens.

2. Input-only billing, no token generation and always-typed outputs remove the worst pains of LLM extraction: JSON parsing, made-up numbers and retries.

3. This is an early checkpoint tested on Cloudflare's own setups. Watch when the weights ship, and whether Liquid, Perplexity and OpenAI, all building decision models, follow with extraction models.