Turning a complaint email into structured data usually means asking an LLM to spit out JSON. Cloudflare has a different answer: on October 19 it shipped Clairic as a preview on Workers AI, a model that generates no tokens at all — one forward pass, and the answer comes out as a typed value instead of text you have to parse again.
The third piece of the System One lineup
The concept traces back to System One models, which TypeSafe introduced a month ago, borrowing Daniel Kahneman's fast-and-slow framing: let a model handle the fast, precise part and leave deliberate reasoning to LLMs. Cloudflare's earlier decision model Clef picks among given options; Clairic opens a new class — extraction models — and Cloudflare calls it the first of that class. Its target is concrete: the flood of structured extraction calls that today go to frontier LLMs — invoice totals, delivery dates, repair cost estimates.
One forward pass, all fields in parallel
The API is declarative: pass a state (text, plus up to four images) and a map of typed fields — text, number, date, duration, list, estimate, forecast. Each field returns a value of its type: text answers carry character-offset spans, numbers follow type rules (two decimals for cents), estimates return a median and an 80% interval, forecasts return one interval per step ahead. Every answer ships with p_null (the probability the value isn't in the input) and a confidence score; when the value isn't there, the model returns null rather than a plausible guess. The context window is 65,536 tokens, there is no limit on field count, and fields that don't fit beside the state in one 8,192-token pass are split into parallel passes and merged. In the official email example, a $649.00 price plus $52.10 tax was summed to $701.10 in the same pass, the 11 days between delivery and the email was worked out from two dates, and a tracking number that never appeared came back null.
Cascade test: 3.2x faster, 49% cheaper
Cloudflare benchmarked a damaged-product claim pipeline: Clairic extracts, Clef decides, GLM-5.3 writes the reply, versus GLM-5.3 doing everything. The cascade finished in 19.3 seconds against 61.7; cost per million claims fell from $11,740 to $6,032. Two details stand out: GLM-5.3's repair-cost estimate drifted on every run ($250, $250, $220) while Clairic's stayed stable, and in the all-LLM pipeline both Gemma 4 and Qwen failed outright — they couldn't deduce the repair cost.
Forecasting has real-data backing too: over 21 days of rolling forecasts on TSA checkpoint counts, average error was 3.3%, actuals landed inside the 80% interval on 17 of 21 days, and the model picked up the weekly rhythm — quiet Tuesdays and Saturdays, busy Thursdays, Fridays and Sundays — from raw numbers alone.
Clear limits, restrained posture
Cloudflare is candid about weaknesses: multi-step arithmetic isn't there yet (let code do the math), estimates need evidence in the input, forecasting is basic and not a replacement for dedicated time-series models, and calibration is measured, not guaranteed. Pricing is $0.10 per million input tokens, with no output-token charge — because there are no output tokens. Open weights are promised "soon".
My take: the trend Clairic represents matters more than the model itself. When a class of calls is stable enough — fixed fields, fixed types, high volume — it pays to peel it off the general LLM into a purpose-built one-pass model and drive cost and latency to the floor. Jev, Clef and Clairic appearing in quick succession suggests that "small non-LLM models" are turning from concept into a product line. The question worth asking: of the calls you send to an LLM today, how many only ever wanted a number?
Source: https://blog.cloudflare.com/clairic-extraction-model/