Google Decisions API vs Jev: Which is Better?

Google just launched a direct rival to the fastest model in my zero shot classification test.

Considering Google's track record with AI (not the greatest), I wanted to see how their new "System One" model stacked up.

So in this article, I ran the new Google Decisions API through the same 200 customer messages as my previous article.

TLDR: They're about the same, but Jev wins on speed.

What is the Google Decisions API?

The Google Decisions API is a Gemini API endpoint that answers fixed questions about an input instead of writing text. You send text, images, audio, video, or a PDF with a list of questions, and it returns an answer and a confidence score for each one.

It launched on 11 November 2026 and runs on a new model, Gemini Decision 1 (gemini-decision-1).

It matters to me because of my work building chatbots and agents for online stores. When every shopper message gets sorted before anything else happens, a model that only makes decisions (and makes them fast) is usually the right tool for that step.

How does the Google Decisions API work?

The Google Decisions API takes one shared input and up to 1,000 questions, and scores every question in a single pass. Each question is one of three types:

• noul is a yes or no question. It returns the probability that the answer is yes.
• choice picks one option from a list of 2 to 255. It returns the winner and a probability for every option.
• score rates the input on an ordered scale. It returns a weighted score and a probability for each level.

If those names look familiar, Jev uses exactly the same three question types with the same names.

Zero shot classification is one choice question. This is the call I used in the test:

import { GoogleGenAI } from '@google/genai';

const ai = new GoogleGenAI({ apiVersion: 'v1beta' });
const result = await ai.decisions.create({
  model: 'gemini-decision-1',
  input: message,
  questions: {
    label: {
      type: 'choice',
      instructions: 'Which label best fits this customer message to a bank?',
      criteria: { 'atm support': '', 'request refund': '' }, // one key per label
    },
  },
});
console.log(result.answers.label.choice);

Google Decisions API pricing

Google Decisions API pricing is US$0.10 per million input tokens, with no charge for output tokens. There's also a no-cost tier in Google AI Studio for prototyping.

Here is how Gemini Decision 1 and Jev compare on paper, from Google's Decisions API documentation and TypeSafe's model page:

	Gemini Decision 1	Jev 1.14
Price per million input tokens	US$0.10	US$0.042
Output tokens	Free	Free
Question types	noul, choice, score	noul, choice, score, box
Inputs	Text, images, audio, video, PDFs	Text, images
Context per request	262,144 tokens	64,000 tokens (262,000 with long context on)

Jev is less than half the price per token, but the two APIs count tokens differently. For the same message and the same ten labels, Google billed 152 tokens a request and TypeSafe billed 375 (I have no idea why). Both came to about $0.015 per 1,000 messages.

Testing Google Decisions API vs Jev

I tested the Google Decisions API on the same 200 customer messages as last time: banking support messages from the public Banking77 set, 10 labels, 20 messages each. Every model got the label names and nothing else.

Model	Accuracy	Median time	Cost per 1,000 messages
Gemini Decision 1	92.5%	511 ms	US$0.015
Jev	92.0%	226 ms	US$0.016
Claude Haiku 5.5	93.0%	1,211 ms	US$0.05
Claude Opus 5.5	95.0%	1,926 ms	US$1.96
bart-large-mnli	73.5%	1,264 ms	Free (own machine)

Three things stood out:

• Accuracy was a tie. Gemini Decision 1 got 185 of 200 right and Jev got 184. Twelve of Gemini's 15 misses were the same messages Jev missed
• Jev was more than twice as fast. Jev answered in 226 ms and Gemini Decision 1 in 511 ms. Google's article quotes 150 to 250 ms
• Both left the LLMs behind on speed and cost. Claude Haiku 5.5 took 1.2 seconds, and Claude Opus 5.5 cost over 100 times more per message

I ran every message twice. Gemini Decision 1 gave the same answer both times on all 200, and Jev changed one.

Then I gave both models one tuning round. I wrote a one-line description for each label and tested on 200 fresh messages neither model had seen.

Both models finished on 99%. Gemini Decision 1 went from 93.5% to 99%, and Jev went from 93% to 99%.

Limitations of the Google Decisions API

The main limitations of the Google Decisions API are that it is literally brand new, and that a choice question always picks something. Google's documentation lists these as well:

• It is in beta. The Decisions API is only on the Gemini Developer API (v1beta), with Google Cloud support still to come
• A choice question never says "none of these". Probability is always spread across your options, so you need a confidence threshold or an "other" option
• No batch mode or context caching. Repeated input is billed in full on every request
• Questions are text only. You can send media as the input, but not inside a question

In my test, its misses came from overlapping label names. It filed "Which ATM's will accept my card?" under card acceptance when the dataset says atm support.

The confidence score caught most of them. Twelve of its 15 misses had a confidence below 0.8, and it was right on 145 of the 148 answers at 0.8 or higher.

Should you use the Google Decisions API or Jev?

Use the Google Decisions API or Jev based on speed and input type, because accuracy and cost were level in this test.

• Use the Google Decisions API when you already build on Gemini, when you need to classify audio, video, or PDFs, or when you want the no-cost tier.
• Use Jev when response time matters most. It was more than twice as fast from Australia.
• Use an LLM when you need the highest accuracy from label names alone and can wait a second or two.

Whichever you choose, write a description for every label. That one change took both decision models from 93% to 99%.

Comments

[Matt Duffin]

One thing I forgot to mention: gemini decision takes images, audio, video, and PDFs, while Jev only takes text and images so far. If you need to classify call recordings or scanned documents Google wins I guess.