El modelo Clairic de Cloudflare ofrece una alternativa más rápida y económica a los LLM para extraer datos estructurados de texto e imágenes, reduciendo la latencia y los costos para los desarrolladores.
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Cloudflare has released a preview of Clairic, a new System One extraction model on Workers AI. The Cloudflare Clairic extraction model reads text or images and returns typed values, not generated text. It targets a job developers usually give to LLMs: pulling totals, dates and IDs into JSON. Clairic answers every requested field in 1 forward pass, with no reasoning and no output tokens. TL;DR Size: Parameter count not disclosed. 65,536-token context window, up to 4 images per request. Runs on: Cloudflare-hosted GPUs via Workers AI (@cf/cloudflare/clairic). No local option until weights ship. Performance: Sub-second extraction in Cloudflare’s tests. No standard accuracy benchmark is published yet. Best: Extraction step took 0.46 s vs 16.1 s for GLM-5.3 in Cloudflare’s claims pipeline. Worst: 4 of 21 TSA next-day forecasts fell outside its 80% interval. Multi-step arithmetic is unreliable. What is Clairic? Clairic is a Cloudflare model that extracts, computes, estimates and forecasts typed values from unstructured inputs. It builds on the System One idea from TypeSafe’s Jev, named for Kahneman’s fast, intuitive thinking. Jev and Cloudflare’s own Clef are decision models. They score options you list in advance. Clairic covers answers you cannot list, such as an invoice total, a date or a repair cost. Cloudflare frames a 3-part stack. Extraction models turn raw input into typed values. Decision models turn values into decisions. LLMs turn decisions into words. How does a Clairic request work? A request has a state (text plus up to 4 images) and a map of named, typed fields. Each field can carry plain-language instructions. Supported types are text, number, date, time, datetime, duration, list, estimate and forecast, per the Workers AI changelog. Every scalar answer includes p_null, the probability that the value is absent, plus a confidence score. Text answers return exact spans with character offsets. Estimates and forecasts return a median and an 80% int
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