Visual explainer · 24 Sept 2026

Jev vs LLM

How to choose in 60 seconds

Jev

Bounded

Answers questions you define, with a probability for each option

Input / context
Answers all your questions at once
Typed answers + probabilities
Your code branches on them

Support ticket: “The app crashes every time I log in since this morning. We have a demo in an hour.”

Urgent?

Yes0.96
No0.04

Which team?

Engineering0.91
Billing0.06
Support0.03

Risk

LowMediumHigh

confidence 0.88

Safe to auto-close?

Yes0.07
No0.93

Typed answers with probabilities. Nothing to parse. (Illustrative numbers.)

$0.042 per 1M input tokens · output free

Same inputTwo different contracts: one returns choices with odds, the other returns text.

LLM

Open-ended

Writes text, code or JSON, one token at a time

Input / context
Generates token by token
Text / code / JSON
Your code parses it, then decides

Support ticket: “The app crashes every time I log in since this morning. We have a demo in an hour.”

{
  "owner": "engineering",
  "priority": "high",
  "risk": "high",
  "summary": "App crashes on login"
}

Text you parse, then decide on in code.

Input + every generated token (varies by model)

Jev's three question types

Define the questions. Get typed answers with confidence.

Choice

Pick one option from a list you define

Route to

Engineering0.91
Billing0.06
Sales0.02

Routing · model selection · next tool

Score

Place the input on an ordered scale

Severity

LowMediumHigh

confidence 0.88

Risk · quality · severity

Boolean

Probability that a statement is true

Is this command dangerous?

Yes0.96
No0.04

Guardrails · verification · escalation

Real agent use cases

Thousands of small judgments, every minute.

RouteWhich team should handle this?
GateDoes this meet our policy?
RerankWhich result is most relevant?
VerifyDoes this output meet the criteria?
Stop or retryShould the agent keep going?

Use Jev when

You can list the possible answers up front (max 255 per question)
You need a typed result, not text to parse
Confidence matters: automate sure cases, send the rest to a human
Thousands of small decisions, fast (70–500 ms)

Can't write text, code or summaries · Text and structured input only, no images

Use LLM when

You need writing, code, summaries or explanations
The answer can't be listed in advance
Several reasoning steps depend on each other
Images, audio or documents as input

You pay for every output token · Its stated confidence is a sentence, not a number

up to 193.6xfaster (TypeSafe's own tests)

194 msvs 1,106 ms for GPT Luna (OpenRouter test)

$0.042per 1M input tokens

LLM writes. Jev decides. Your code acts.