Decision model vs LLM: pick by the shape of the answer
The question is never “which is smarter”. It is “is the answer a document or a choice”. That single distinction decides the architecture.
The one-question test
Write down what the step is supposed to produce. If it produces prose a human will read, use a language model. If it produces one of N allowed values, use a decision model. That is the whole test, and it resolves the majority of real cases in under a minute.
- Produces a summary, an email, an explanation → language model.
- Produces a label, a route, a severity, a yes/no with confidence → decision model.
- Produces both → language model for the prose, decision model for the choice.
What you pay for either way
With a language model you pay per token in and out, and you pay again in engineering: prompts that drift, formats that break, retries when the model decides to be creative, and a human to check the ones that fall over.
With a decision model you pay per token in, and the output is bounded by design. Clef-Flash is published at roughly $0.09 per million input tokens with a median latency near 39 ms — fast enough to run synchronously in a request.
The hybrid everyone actually ships
Production systems rarely pick one. The common shape is a language model that handles the open-ended instruction, followed by a decision model for each bounded choice inside it: which tool to invoke, whether the request is safe, which queue it belongs in.
That split also improves debuggability. When a routing mistake happens you can point at a probability distribution and a threshold, instead of at a paragraph of reasoning you have to interpret.
Try the comparison yourself
Paste the same state into a chat model and into the playground on this site. Ask both to pick from a fixed list. The difference in what you can build on top is immediate.
Frequently asked questions
Can an LLM do everything a decision model does?
It can approximate it, by being asked to answer with one of a list. What it cannot give you is a guarantee: a legal decision model cannot return an option outside the schema, while an LLM can.
Which is cheaper?
For bounded choices, decision models are cheaper per call and far cheaper once you count the retries, validation code and human review that free-text output requires.
Is Clef-Flash a small language model?
It shares a base with one, but it is post-trained to return typed answers with probabilities rather than to generate text.
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