Skip to content

Jev next to an LLM

One request, sent to Jev and your pick of 8 OpenAI models at the same moment. Both answers, both latencies, both costs. Each network gets 5 free comparisons a day.

Opens the playground and runs once.

Same question, three models

average latency · drawn to scale
jev-1.13.0111 ms
gpt-5.4-mini1.4 s
gpt-5.5 · reasoning11.1 s

TypeSafe’s consistency cookbook, 2026-09-11, 15 runs: Jev averaged 111 ms; the others are its 12.7× and 100.2× slowdowns applied to that. Theirs, not ours — run your own below.

How we measure

  1. 1. Same moment

    Both calls leave one server function together.
  2. 2. Own stopwatch

    Each side times its own call, body included.
  3. 3. Real model id

    Both sides report the model id that answered. The LLM uses Structured Outputs, no retries, its fastest reasoning setting.
  4. 4. List-price cost

    Tokens × the prices below. Jev’s output is free.

What one run can’t tell you

Accuracy

Agreeing isn’t being right.

Calibration

That takes many runs.

A trend

One run, one region, network included.

LLM confidence

A number it writes isn’t a distribution.
Other people’s numbersshow

Theirs, not ours, and measured under their conditions.

Prices used · per 1M tokens

OpenAI pricing, checked 2026-09-22
List prices used for the cost figures
ModelInputOutput
jev-1.13.0$0.042free
gpt-5.6-sol$4$20
gpt-5.6-terra$2$12
gpt-5.6-luna$0.2$1.2
gpt-5.4-mini$0.75$4.5
gpt-5.4-nano$0.2$1.25
o4-mini$1.1$4.4
gpt-4.1-mini$0.4$1.6
gpt-4o-mini$0.15$0.6

Comparisons run from local.