Today, we're announcing a preview of a new class of System One-style models: extraction models. Our first one, Clairic, is available hosted on Workers AI as @cf/cloudflare/clairic. We intend to release the model open-weights soon.
Clairic is an extraction model that is able to extract, compute, estimate, and forecast values from unstructured inputs — all at a significantly higher accuracy, faster latency, and cheaper cost than traditional LLMs. Context When System One models were announced by TypeSafe a month ago, it took the industry by storm. As referenced by TypeSafe in their blog and defined by Daniel Kahneman’s book Thinking, Fast and Slow , a System One model should be fast and precise, which contrasts a System Two model that is capable of deliberate reasoning (like LLMs).
With this, TypeSafe introduced Jev — a model that is purpose-fit for a specific use case, and priced accessibly given its niche domain. It excelled where LLMs failed, helping users make decisions based on probabilities and well-typed outputs. The introduction of Jev prompted people to start thinking about a new class of models that were not LLMs and how to integrate them into their workflows.
We were inspired by the concept of System One models and started thinking about other tasks where LLMs are inefficient. There are many structured-output calls going to frontier LLMs today with the same shapes again and again: What's the invoice total? When was this delivered?
How many days late is it? What will this repair cost? What will traffic look like over the next 24 hours?
None of these have answers you can list out in advance, which is needed for a decision model to give probabilities against. That led us to the class of models we're introducing today: System One extraction models. Introducing System One extraction models Until now, every System One model has been a decision model: Jev and Clef take an input state and a list of questions and give probabilities against the inputs and your listed questions.
Clairic is the first model in a new class: System One extraction models. It is still fast, typed, and cheap, but it answers with values directly extracted from the input, computed from it, or predicted beyond it. Most model tasks ask for one of three kinds of output: a number, a choice from a fixed set, or open-ended text.
Clairic handles the first, returning numbers with an 80% range, and also pulls typed values like names, dates and lists out of the input. Clef handles the second: it picks among options and returns a probability for each. LLMs handle the third.
We call Clairic and Clef System One models because they answer in a single pass, without reasoning or generating tokens. They charge only for input and typically respond in under a second, while an LLM that reasons through the same step takes several seconds. An extraction model can return: Extracted values: Text copied from the input, such as a name, an order ID or a description.
Each one comes back as a span with its character offsets and a confidence score. Including, computed values: Numbers, dates, times, datetimes, durations and lists, read from the input or worked out from it. They follow the rules of their type (for example, two decimals for cents) and come with a confidence score.
Estimated values: Values the input doesn't state but gives evidence for. Each one comes back as a distribution inside the bounds you declare, on the grid you specify, with a median and an 80% interval. Forecasted values: Future values of a series, one estimate per step ahead, each with its own 80% interval.
And because it generates no tokens, it can't produce a mistyped answer or “hallucinate” in the same way as an LLM (though it can, of course, make mistakes). Outputs are a value of the type you asked for, or null when the model fails to find an answer in the input. It also comes with confidence scores against the outputs to help you evaluate if it is appropriate to trust the model or to take a second look.
Here’s how extraction models fit into the landscape of System One and System Two models: System One: Extract System One: Decide System Two Example models Clairic Jev, Clef GPT, Claude, Gemini, DeepSeek Sampling One parallel pass One parallel pass One token at a time Purpose Predict values Make decisions Write and reason ML framing Regression and extraction Classification Generation Thinking mode System 1: fast, one pass System 1: fast, one pass System 2: slow, deliberate Output Typed values: spans, numbers, dates, times, lists, distributions, with calibrated probabilities Typed decisions: options with calibrated probabilities Free-form strings Output space Continuous, but typed and bounded by your schema A finite set of options you list Anything What it answers What's the amount?
When does it happen? How much will it be? Which category is it?
Is it legitimate? Which action should we take? Why did it happen?
How should we approach it? Can you fix it? Output always a valid response?
Yes: every answer is a value of its declared type (or null), inside your bounds Yes: answers are always one of your options No: strings must be parsed and checked Together, these models can work together in unison to help you get closer to parsing through real-world data, making decisions on inputs, and then taking actions. Extraction models turn raw input into typed values, decision models turn values into decisions, and LLMs turn decisions into words.
How a request works Clairic is designed differently than an LLM. Similar to a decision model, it only processes input tokens with a single forward pass, and thus has no charge for output tokens. It is much faster, cheaper, and more accurate than an LLM for certain use cases.
A request is a state (the text to read, plus up to four optional images) and a map of typed fields . You name each field, give it a type and optional plain-language instructions , and get back one typed answer per field, all from one parallel pass.
Originally published at blog.cloudflare.com
