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Jev vs ChatGPT: what each does, what it costs and when to use which
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ChatGPT and OpenAI's GPT models write and reason in text. Jev only returns typed decisions with probabilities, so it cannot replace them for writing. For high-volume classification, scoring and routing it costs far less: in one independent test, about five times less than GPT-5.4 nano per 1,000 decisions. That test measured cost, not accuracy.
Side by side
| Feature | Jev (TypeSafe AI) | ChatGPT and OpenAI's GPT models |
|---|---|---|
| What it is | A decision model: it answers typed questions about an input and returns probabilities. It cannot write text. | ChatGPT is OpenAI's chat app; developers use OpenAI's GPT models through its API, which is what the prices below are for. They generate text and handle open-ended work. |
| What comes back | A typed answer: one of your options, a score or a yes/no, with probabilities and a confidence value | Text, which your code then has to read and check |
| Input price (API) | $0.042 per million tokens, output free | Per million tokens, input / output: gpt-6-luna $0.10 / $0.50; gpt-5.4-nano $0.20 / $1.25; gpt-6-sol $2 / $10; gpt-6-astra $10 / $50 |
| One measured task: 8-way routing, cost per 1,000 decisions | About 1.5 cents | About 7 cents (GPT-5.4 nano) |
| Speed | 70 to 500 ms per request (TypeSafe's claim) | OpenAI's pricing page gives no response times |
| Can it write, reason or code? | No | Yes |
Prices are as published on the dates in the sources below and can change. The measured cost comes from one independent test and covers cost only, not accuracy. Speed for Jev is TypeSafe's own claim.
The short answer
These are different kinds of model, so the useful question is not which is better but which job you have. A GPT model generates text one piece at a time, so it can write, explain and plan. Jev does not generate text at all: you define the questions and the allowed answers, and it returns a probability for each. Jev gives up writing in exchange for price, speed and answers that code can use directly.
If the output has to be words, use a GPT model. If the output is a decision (which team should handle this ticket, is this email spam, how well does this lead fit), Jev can do that part for much less, and a GPT model can still do the writing afterwards.
What OpenAI charges
OpenAI's pricing page lists many GPT models. Four that matter for this comparison, per million tokens (input / output): gpt-6-luna $0.10 / $0.50, gpt-5.4-nano $0.20 / $1.25, gpt-6-sol $2 / $10 and gpt-6-astra $10 / $50. Jev lists $0.042 per million input tokens and charges nothing for output.
By our arithmetic from those list prices, Jev is about 2.4 times cheaper per input token than gpt-6-luna and about 4.8 times cheaper than gpt-5.4-nano, and output costs nothing. Per-token prices are not the cost of a whole task, because the models use different numbers of tokens, so the next section matters more.
What an independent test measured
One third party, AY Automate, measured the cost per 1,000 decisions on 791 labelled decisions, run on 19 September 2026. It compared Jev with GPT-5.4 nano and with Gemini 3.5 Flash-Lite. Against GPT-5.4 nano, Jev cost about 1.5 cents against 7 cents for 8-way routing, 4 cents against 20 cents for 77-way routing, and 1.4 cents against 8.1 cents for a prompt-injection check, which is roughly 4.7 to 5.9 times less.
This test compared cost, not accuracy. Jev used more input tokens for the same text, but its lower price and free output still made it cheaper.
What we could not compare
Be careful with accuracy claims in either direction:
- The cost test says nothing about which model gets more decisions right.
- TypeSafe's own workflow evals list models called luna, sol and terra without naming their maker. Their names match OpenAI's gpt-6-luna, gpt-6-sol and gpt-5.6-terra, but TypeSafe does not say so, so we do not use those scores here.
- TypeSafe's speed figures are its own claim, and OpenAI's pricing page gives none.
- Your questions and data will behave differently from any published test. Run both on a labelled sample of your own before you switch.
Which should you pick?
Based on what is documented today:
- Choose a GPT model if the output has to be written text, or the task needs open-ended reasoning, coding or planning.
- Choose Jev if you make the same kind of decision thousands of times, the answers come from a list you define, and price and speed add up. It takes text only.
- Use both if you are unsure: let Jev triage everything and send only the items that need writing to a GPT model. That is the pattern most teams describe.
Keep reading
More comparisons and where Jev fits:
Common questions
- Is Jev better than ChatGPT?
- They do different jobs. ChatGPT and OpenAI's GPT models write and reason in text. Jev only returns typed decisions with probabilities. For classification, scoring and routing it is far cheaper; for anything written you still need a GPT model.
- Is Jev cheaper than GPT?
- On list prices, yes: Jev is $0.042 per million input tokens with free output, against $0.10 to $10 per million input tokens for the GPT models listed above, plus output charges. In one independent test of 791 labelled decisions, Jev cost about five times less than GPT-5.4 nano. That test measured cost, not accuracy.
- Can Jev replace ChatGPT?
- Not for writing. Jev cannot generate text. Many teams keep a GPT model for the writing and use Jev for the high-volume judgments around it.
- What about OpenAI's own decision model?
- OpenAI's Decisions API is the closest match to Jev: it also returns typed decisions with probabilities. We compare the two on a separate page.
- Does Jev make things up?
- Its output always fits the schema you define, so it cannot invent an option or return malformed data. It can still pick the wrong valid option, which is why every answer carries a confidence value.
Sources
Written by Jev Hub from the sources below and checked against them on . Where a figure is a company's own claim, the article says so.
This page is part of Jev vs ChatGPT, Claude and other LLMs, which compares them all in one place. Other models: Jev vs Claude, Jev vs Gemini.