# Jev vs LLM classification

> Compare Jev and chat classifiers for moderation, fraud, and routing. Fixed options and scores you can threshold beat parsing YES/NO from chat text.

## Answer

Use Jev when you need thresholdable probabilities over a fixed option set; use LLMs when you need open-ended language generation.

## Probabilities you can threshold

Jev returns explicit probability mass over options. Instead of parsing YES/NO from chat output, you set if (confidence > 0.9) in code. That matters for moderation, fraud, and safety pipelines.

## Parallel questions

Ask many typed questions in one request over the same state. LLM classification often serializes prompts or pays for redundant context tokens.

## When LLMs still win

Open-ended drafting, long reasoning chains, and novel text generation remain LLM territory. Many production stacks use Jev for discrete decisions and LLMs for language-heavy steps. Browser Use Ultrafast is a well-known hybrid.

## Cost and token math

Generative classifiers often resend long instructions and examples on every call. System One calls keep prompts compact because questions are typed fields, not paragraphs of rubric text. For high-volume gates that difference shows up in both latency and spend.

## Evaluation and testing

Fixed option sets make golden tests easier: feed recorded state, expect option B above 0.8. LLM label parsing tests brittle string contains checks. Teams migrating often keep LLM baselines while they calibrate Jev thresholds on production logs.

## Directory examples

Browse moderation and routing listings in this directory for repos that publish benchmarks against chat baselines. Treat them as patterns, not endorsements. Always read the linked source for maintenance status.

## FAQ

### When do I pick Jev instead of a chat label?

Use Jev when you need thresholdable probabilities over a fixed option set. Use an LLM when you need open-ended language generation.

### Can I just parse YES or NO?

Jev returns explicit probability mass over options. You set if (confidence > 0.9) in code instead of parsing YES/NO from chat output.

### When does the chat model still win?

Open-ended drafting, long reasoning chains, and novel text generation remain LLM territory. Many production stacks use Jev for discrete decisions and LLMs for language-heavy steps.

Directory copy checked 2026-09-30. This directory is independent of TypeSafe AI unless a listing says it is official.
Canonical: https://jev.aitools.fyi/learn/jev-vs-llm-classification
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