jevapi.io

MODEL COMPARISON

Jev vs LLMs:
decide or generate?

Jev and language models solve different parts of an intelligent system. The useful comparison is not which model is universally better, but which interface fits the job in front of your code.

JEV

Bounded intelligence

  • Typed answers
  • Outcomes defined first
  • Probability distribution
  • Parallel questions
  • Designed for branching
VS

LANGUAGE MODELS

Open-ended intelligence

  • Generated strings
  • Flexible output space
  • Reasoning and creation
  • Sequential token generation
  • Designed for language

THE CORE DIFFERENCE

Jev narrows a decision. An LLM expands a possibility.

When software already knows the legal outcomes—choose a queue, score a risk level, decide whether to retry—open-ended text is often extra surface area. Jev returns a typed value directly. When the task requires explanation, planning, code, creative work, or a novel answer, a language model is the right shape.

DimensionJevLLM
Primary outputTyped answer + probabilitiesGenerated text or structured text
Answer spaceDeclared before inferenceOpen-ended
Typical roleClassify, route, score, verifyWrite, reason, code, converse
Application parsingMinimalSchema validation often required
UncertaintyFirst-class distributionUsually indirect
Failure controlThreshold and fallbackValidation, retry, guardrails

SAME TASK, TWO INTERFACES

Routing one support ticket.

Suppose software must send each ticket to billing, technical, or sales. With an LLM, you usually write a prompt, ask for JSON, validate the string that comes back, and handle the cases where the model invents a label or wraps the answer in prose. With Jev, the legal answers are part of the request, so the response is already a typed value with a probability for each option.

StepLLM with structured outputJev
Define outcomesDescribed in the prompt or a schemaDeclared in the criteria map
Get an answerGenerate text token by tokenOne pass over the declared options
Parse and validateParse JSON, check the label, retry on failureRead answers.queue.choice
Judge certaintyAsk for a confidence value, which is self-reportedRead the probability for every option
Billing shapeInput and generated output tokensInput tokens; output is free on the direct API

The billing row is provider-documented for Jev. Speed and cost advantages beyond that come from TypeSafe’s launch materials and are provider-reported. Reproduce them on your own state sizes and traffic before treating them as a plan.

USE THEM TOGETHER

The strongest architecture is usually hybrid.

An LLM can produce a plan or candidate output. Jev can route the plan, score urgency, or verify explicit criteria. Deterministic code owns permissions and side effects. Each component stays in the role its interface supports.

LLM
create→JEV
decide→CODE
execute

A common cascade is code first, Jev second, LLM last: exact rules handle the obvious cases, Jev handles the fuzzy but bounded ones, and only the leftover cases that need language or reasoning reach an LLM or a person. That keeps the expensive, slow interface for the minority of requests that need it.

WHERE EACH ONE FAILS

Know the limits before you pick.

Jev limits

Text only, with English strongest. A 64k-token context, of which 32k covers the state plus the longest question. It cannot write, explain, or invent an answer outside the options you declared.

LLM limits

Output can drift from the requested format, latency grows with output length, and stated confidence is not a calibrated probability. Cost grows with every generated token.

Shared limits

Both can be wrong. A typed answer cannot break its declared shape, but it can still choose the wrong option, so measure accuracy and calibration on your own labeled data.

Code limits

Rules are exact and replayable but brittle against ambiguous language. Use them first where the decision is lexical, numeric, or table-driven.

DECISION GUIDE

Which interface fits the job?

  • Choose Jev when every legal answer is known before the call, the decision repeats often, and your code needs a probability to branch on, as in agent routing or action gating.
  • Choose an LLM when the output must be written, explained, planned, or coded, or when the valid answer cannot be listed in advance.
  • Choose code when the answer is exact: parsing an ID, checking an allowlist, calculating a date.
  • Combine them when a workflow contains all three kinds of step, which most production agents do.

For eighteen concrete patterns, including the cases where rules or an LLM are the better tool, read the Jev use-case guide. For the exact request and response fields, see the API guide, and for where each claim comes from, the sources page.

EARLY ACCESS

Build with Jev when access opens.

The integration is in progress. The API button will connect to the live BeatAPI model page after the first verified request.