Cloudflare’s new model answers in one pass, charges nothing for output

Cloudflare released Clairic, an extraction model available in preview on Workers AI as @cf/cloudflare/clairic on October 19, which returns typed fields such as dates, dollar amounts, probability distributions and forecasts in a single forward pass without generating output tokens, priced at $0.10 per million input tokens with output billed at nothing. Cloudflare positions Clairic as the third of its System One models, after Clef and Clef-flash launched October 1, and says open weights will follow. The approach targets the output-token cost and latency that large language models incur, a category TypeSafe opened with Jev on September 15 at $0.042 per million input tokens.

Clairic prices input tokens at $0.10 per million and bills nothing for output, because it produces none. It is a single forward pass through a neural network: feed it text and a list of typed fields, and it returns a date, a dollar amount, a probability distribution, or a forecast, without generating a single word of explanation. Cloudflare put the model into preview on Workers AI as @cf/cloudflare/clairic on October 19, with open weights promised to follow, as described in Cloudflare’s launch post https://blog.cloudflare.com/clairic-extraction-model/ . That distinction, no output tokens, matters because output tokens are what large language models spend most of their latency and most of a customer’s bill generating. An LLM asked to pull an invoice total out of an email reasons its way to an answer one token at a time, at anywhere from $0.20 to $10 per million input tokens with output priced roughly five times higher, according to figures TypeSafe published when it opened this category a month earlier. Clairic instead answers every requested field in one parallel pass across the whole input. Cloudflare calls this an extraction model, a new branch of what it and TypeSafe both label System One models, a name borrowed from Daniel Kahneman’s distinction between fast, intuitive judgment System One and slow, deliberate reasoning System Two, where the chatbot-style LLM sits . Jev, Clef and now Clairic TypeSafe, a San Francisco lab founded by Diogo Almeida, a co-author of the InstructGPT paper that underpins ChatGPT’s training method, launched the first such model, Jev, on September 15. Jev answers typed questions, a choice among up to 255 options, a score on a described scale, a yes/no probability, with a probability attached to each possible answer, in 70 to 500 milliseconds against 3 to 329 seconds for a frontier LLM doing comparable reasoning, priced at $0.042 per million input tokens, according to TypeSafe’s launch post https://typesafe.ai/blog/introducing-system-one-models-and-jev . Demand was high enough that TypeSafe briefly lost the ability to serve its own API, TechCrunch reported https://techcrunch.com/2026/09/18/a-new-kind-of-ai-model-from-a-chatgpt-inventor-is-thrilling-developers/ , with Vercel citing results five to 18 times faster than the LLM it replaced on a safety-classification task. Cloudflare answered on October 1 with Clef and Clef-flash, its own decision models 27 billion and 9 billion parameters, built on Qwen3.8-27B and Qwen3.5-9B , open-weighted under Apache 2.0 and priced at roughly $0.24 and $0.09 per million input tokens with output unbilled. On the BANKING77 intent-classification benchmark, Cloudflare reports Clef scoring a macro-F1 of 94.20 against Jev’s 79.74, and Clef-flash answering in a median 38.8 milliseconds against Jev’s 524.1 milliseconds, in Cloudflare’s own announcement https://blog.cloudflare.com/clef-decision-models/ . Those are Cloudflare’s numbers, on Cloudflare’s chosen benchmark, testing a rival’s model; TypeSafe has not published a competing BANKING77 score for Jev. Clairic is the third leg Cloudflare has now built: Clef picks among a fixed set of options, Clairic pulls out or computes numbers and typed values, and an LLM still writes the prose a human reads. How a request works A Clairic call sends a state, text plus up to four images, and a map of named fields, each with a type text, number, date, time, duration, list, estimate or forecast and an optional plain-language instruction. Every scalar answer returns with p null, the model’s estimated probability that the field isn’t actually present in the input, and a confidence score. Estimates come back as a median plus an 80% interval p10 to p90 , with all 99 percentiles available on request; forecasts run up to 64 steps ahead, each with its own interval. There is no cap on the number of fields: a request too large for one pass gets split across parallel calls that each see the entire input, then merged, according to Cloudflare’s developer changelog https://developers.cloudflare.com/changelog/post/2026-10-19-clairic-workers-ai/ . Cloudflare’s own worked example is a customer email: an order arrived damaged, the customer paid $649.00 plus $52.10 tax, and wrote eleven days after delivery. Clairic correctly computed the combined total $701.10 and the day count 11 as derived values, not just copied text, and returned the order number with p null of 3.4 × 10⁻⁶ and confidence 0.999957. Asked for a tracking number that the email never mentions, the model still internally flagged the tax figure, $52.10, as a tentative guess, but assigned it only 0.416 confidence and correctly returned null rather than a wrong but well-formatted answer. That is the point of the confidence score: it flags its own best wrong guess rather than silently emitting one. The extract-decide-write pipeline How Cloudflare's claims-processing demo chains three model types | From | To | How | |---|---|---| | Customer claim text | Clairic extracts typed values, 0.46s | text + fields | | Clairic extracts typed values, 0.46s | Clef picks a decision, 0.22s | typed values | | Clef picks a decision, 0.22s | GLM-5.3 writes the reply, 18.6s | decision | | GLM-5.3 writes the reply, 18.6s | Reply to customer | | Based on Cloudflare https://blog.cloudflare.com/clairic-extraction-model/ The diagram shows where the time actually goes in Cloudflare’s benchmark: extraction and decision are fast regardless of which model does them, but the final step, turning a decision into a written reply, still needs an LLM and still takes seconds. What the benchmark shows, and what it doesn’t Cloudflare ran a cascade, Clairic extracts, Clef decides, GLM-5.3 writes the reply, against a baseline doing all three steps with GLM-5.3 alone, on a claims-processing demo. The cascade finished in 19.3 seconds against 61.7 seconds for the all-LLM version, 69% less time, about 3.2 times faster, at a total cost of $6,032 per million claims against $11,740, a 49% reduction, per Cloudflare’s post https://blog.cloudflare.com/clairic-extraction-model/ . Broken down by step: extraction took Clairic 0.46 seconds against GLM-5.3’s 16.1 seconds; the decision step took Clef 0.22 seconds against GLM-5.3’s 21.9 seconds. Where the speed gain comes from Time per pipeline step, seconds | | System One model s | GLM-5.3 all-LLM s | |---|---|---| | Extraction | 0.46 | 16.1 | | Decision | 0.22 | 21.9 | Source: Cloudflare https://blog.cloudflare.com/clairic-extraction-model/ Notice what the chart leaves out: the final write-the-reply step, done by the same GLM-5.3 in both versions, actually took longer in the cascade 18.6 seconds than in the all-LLM run 16.4 seconds . All of the pipeline’s speed gain comes from skipping two LLM calls, not from any faster writing. Cloudflare also reports that Clairic returned the identical repair-cost estimate across repeated runs on the same input, where GLM-5.3 returned $250, $250 and $220, with a different interval each time, a reproducibility gap that matters for anything an auditor might later check. Separately, Cloudflare tested Clairic forecasting 21 days of daily TSA checkpoint traveler counts: the model’s median forecasts were off by 3.3% on average, and 17 of the 21 actual counts, 81%, fell inside its stated 80% interval, close to what a properly calibrated model should produce. The four misses clustered around Columbus Day weekend, a holiday effect the model apparently hadn’t learned. Cloudflare itself flags weak multi-step arithmetic, miscalibrated intervals on unfamiliar data and weak long-range forecasting, and calls the current release “an early checkpoint.” None of these benchmark numbers come from an outside evaluator; they are Cloudflare measuring its own product against a baseline, GLM-5.3, that Cloudflare chose. The money behind the model class TypeSafe’s bet on this category has already been repriced once, hard. The company raised $40 million in seed funding led by DCVC when it launched Jev on September 15, at a reported $200 million valuation. Twenty-four days later, on October 9, it closed an $870 million Series A led by Andreessen Horowitz, with Sequoia Capital and DCVC also participating and a16z partner Martin Casado joining the board, a $7.5 billion valuation, according to TypeSafe’s own announcement https://typesafe.ai/blog/series-ai and a16z’s investment post https://a16z.com/announcement/investing-in-typesafe-ai/ . That is a roughly 37-fold step-up in valuation in under four weeks. The adoption numbers behind that step-up don’t quite agree with each other. TypeSafe’s own blog says “a third of the Fortune 500” use Jev; a16z’s post, investing in the same company, puts the figure at 25%. a16z also calls Jev the fastest-growing model it has tracked, citing 1 trillion tokens generated in the three days after launch; separately, Almeida has described the business running at roughly 1 trillion tokens per day, a different and much larger quantity than a one-time three-day total, and coverage of his remarks has not reconciled the two figures. Taking a16z’s lower, three-day number as a steady daily rate and applying Jev’s own list price of $0.042 per million input tokens treating input and total volume as the same, since output tokens aren’t billed works out to about $42,000 a day, or $15.3 million a year, against a $7.5 billion valuation: roughly 489 times that run rate. None of this makes Jev’s underlying technology slower or less accurate than claimed. It does mean the valuation is pricing in a great deal of future growth that a single self-reported token count, measured by the company being valued, cannot itself confirm. Cloudflare has not disclosed revenue or usage figures for Clef or Clairic, and neither model has attracted a funding round because Cloudflare is a public company bankrolling the research itself. The open question is the same one that applies to every number in Cloudflare’s own launch post: whether the 3.2-times speedup and the 49% cost cut survive contact with a workload nobody at Cloudflare picked, and whether the promised open weights for Clairic actually ship, as opposed to staying, like TypeSafe’s reconciled token counts, a figure quoted once and never checked again.