Clef vs LLM-as-a-judge
A common pattern is to ask a chat model “Is this spam? Answer yes or no” and parse its reply. Clef is built for exactly that job: you define the question and the allowed answers, and it returns a probability for each one.
The difference
| Clef / Clef-Flash | Chat LLM as a judge | |
|---|---|---|
| Output | Probability per allowed answer, in a fixed JSON schema | Free text you prompt into a format and then parse |
| Invalid answers | Only your options can come back | Possible: extra words, a wrong label, broken JSON |
| Thresholds | Built in: act above 0.9, review 0.5–0.9 | Needs log-probs or repeated sampling, if available |
| Many questions | Up to 64 typed questions on one state | One long prompt, one long answer to parse |
| Cost driver | Input tokens; our runs returned 0 output tokens | Input and output tokens, more with reasoning |
| Explanations | None | Yes, it can justify the answer |
When Clef is the better fit
- High-volume decisions with a fixed set of answers: routing, tagging, moderation, spam, intent, lead scoring.
- You want a confidence you can put a threshold on, and send only the unsure cases to a person.
- You need several judgments about the same input at once.
- Cost per decision matters: a short ticket took 186 input tokens in our run, about $0.000017 per decision on Clef-Flash at $0.09 per million input tokens.
When a chat LLM is still better
- You need a written reason, a summary, or rewritten text.
- The answer space is open-ended and can’t be listed as options or levels.
- The judgment needs multi-step reasoning over long material.
Use both
A practical setup: run Clef-Flash on everything, act automatically on confident answers, and send only the low-confidence cases to Clef (27B), a chat model, or a person. See Clef vs Clef-Flash and real examples with thresholds.
FAQ
Is Clef more accurate than an LLM judge?
We haven’t benchmarked it and don’t claim so. Accuracy depends on your data. Run the same 50 examples through both and compare.
Can Clef explain its answer?
No. It returns probabilities, not reasons. If you need a written justification, use a chat model, or run Clef first and ask a chat model to explain only the uncertain cases.
Do I need to parse the output?
No. Answers come back in a fixed JSON shape with one entry per question id, so there is no free text to parse or validate.
Token counts are from our own Clef-Flash runs on 2026-10-02. Prices from Cloudflare’s Workers AI model pages, checked 2026-10-02.