Is Jev more accurate than GPT models?

It depends on the task and the model. For well-defined labels with clear descriptions, decision models are competitive while being much cheaper and faster. Measure on your own labeled data.

Can I switch from an LLM classifier to Jev without relabeling?

Usually yes. Turn your prompt's category definitions into option descriptions and compare results on a sample of past inputs.

Does ClassifierHub only use Jev?

Jev is the current decision model. ClassifierHub is provider-agnostic, so your saved decisions stay the same as more models become available.

Comparison

Jev vs. GPT-style LLMs for classification

Short answer

For classification, a decision model like Jev returns probabilities over labels you define, in a fixed format, with no output tokens to pay for. A general-purpose LLM such as a GPT model can classify too, but returns text you must parse and has no reliable confidence. Use Jev for high-volume decisions and an LLM for generation and complex reasoning.

Chat models have become the default tool for every text task, including classification. That's often convenient, but classification has different requirements from writing: a fixed output shape, consistent answers at volume, low latency and a confidence you can act on.

This page compares the two approaches on those requirements. Always validate with your own labeled examples, because the right choice depends on your task.

Get Early Access to ClassifierHub: 2× credits in your first paid month.

Side by side

Output: Jev returns a probability for each option you define. A chat LLM returns generated text, usually a label or JSON, which you must parse and validate.
Confidence: Jev's probabilities let you set thresholds and route uncertain cases. A chat LLM can state a confidence, but self-reported numbers are not reliable probabilities.
Cost: Jev is billed on input tokens only; output is free. LLMs bill input and output tokens, and output tokens typically cost more.
Latency: Jev answers in one short call (OpenRouter reports hundreds of milliseconds). Larger LLMs often take seconds, more with reasoning enabled.
Flexibility: LLMs can explain, extract, write and use tools. Jev only answers choice, yes/no and score questions.

When to use a decision model

Choose a decision model when the answer is one of a known set and the decision runs often: routing tickets and emails, spam and moderation gates, lead scoring, intent detection, approval checks for AI agents, and bulk categorization of data.

When to use an LLM

Choose an LLM when you need generated output or multi-step reasoning: drafting replies, summarizing, extracting arbitrary fields, answering open questions, or classification that genuinely requires working through a long chain of logic or arithmetic (compute those facts in code first where possible).

Using both

The strongest pattern combines them. The decision model triages, gates and routes; the LLM handles only the requests that need generation. That typically removes a large share of expensive LLM calls and makes the remaining ones more focused.

classify.sh

bash

curl https://classifierhub.com/v1/classify \
  -H "Authorization: Bearer $CLASSIFIERHUB_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "input": "I was charged twice for my subscription this month.",
    "instructions": "Which team should handle this message?",
    "options": {
      "billing": "Payments, invoices, refunds, charges",
      "technical": "Bugs, errors, outages",
      "sales": "Buying, upgrading, pricing questions",
      "other": "Anything else"
    }
  }'

# -> { "label": "billing", "confidence": 0.93,
#      "probabilities": { "billing": 0.93, "technical": 0.03, "sales": 0.02, "other": 0.02 },
#      "usage": { "credits": 1, "latency_ms": 420 } }

The ClassifierHub API opens to Early Access members first.

Frequently asked questions

Related guides

Jev AI

Jev: TypeSafe's decision model, explained