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title: OpenAI&#x27;s Decisions API is a sub-second classifier, and...
description: OpenAI has released a Decisions API in limited preview, powered by a specialized model called Luna (part of GPT-6), designed to pick from a defined set of...
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# OpenAI's Decisions API is a sub-second classifier, and people are already calling it a Jev killer

**[Trends](https://daily.dev/sources/trends)** · 2 min read · 1 upvotes · 0 comments

## Summary

OpenAI has released a Decisions API in limited preview, powered by a specialized model called Luna (part of GPT-6), designed to pick from a defined set of answers in under a few hundred milliseconds, including visual inputs. Positioned for content classification, request routing, and agent decision-making, it's being compared to Jev, an existing constrained decision-making tool, with some already calling it a 'Jev killer.' The comparison and hype are running well ahead of any public benchmarks since access remains limited.

## Content

OpenAI announced a Decisions API at Dev Day, built on its small Luna model. The timing looks like a response to the sudden popularity of Jev, the decision model TypeSafe AI released a few weeks earlier. @iannuttall's reaction was the obvious one: "Jev killer?"

## What it does

You define a question and a set of possible answers, and the API picks one. Instead of chat-style prose, it returns one of your predefined answers along with a confidence score. OpenAI's pitch is classifying content, routing requests, and choosing an agent's next action. @OpenAIDevs describes it as real-time decision-making for apps, powered by GPT-6 Luna.

@thsottiaux says it supports visual inputs and is tuned to decide in a few hundred milliseconds end to end. OpenAI's own numbers put response time at about 150ms, against 1.6 seconds for GPT-6 Luna used the ordinary way.

## What we don't know

Pricing hasn't been disclosed. Neither have the limit on how many candidate answers you can pass or any fine-tuning options. The API is in limited preview, with a broader rollout promised soon.

## The Jev comparison

The similarity to Jev is hard to miss. Jev is a classifier model aimed at the same job: fast, cheap software-automation decisions where a full LLM is overkill. TypeSafe CEO Diogo Almeida joked about "clone wars" but also read OpenAI's move as validation of what he calls the "System One" approach, using cheap, fast classifier-style models for the many software tasks that don't need a frontier model's reasoning.

## The use case I find most interesting

One proposed use is watching AI agents for bad actions. A hackathon demo showed Jev checking agentic actions for $2.94, where doing the same with a frontier LLM would have cost $372. That gap is the whole argument. A cheap gate in front of every agent action could catch something like the recent Hugging Face agent mishap, and at those prices you can afford to check every step rather than sampling.

Whether Luna's Decisions API can match that economics is the open question, and until OpenAI publishes pricing, nobody can answer it.

## Questions this post answers

### What is OpenAI's Decisions API and what is it used for?

It is a narrow, latency-optimized API in limited preview that lets developers define a question and a set of possible answers, with a specialized model called Luna (part of GPT-6) picking one in under a few hundred milliseconds, including from visual inputs. It targets content classification, request routing, and choosing an agent's next action rather than general-purpose reasoning.

_Anyone weighing constrained decision tools for agent routing can follow how this API develops on daily.dev._

## Community take

How the wider developer community reacted, aggregated from 3 discussions and 501 comments across x (as of 2026-09-30).

**TL;DR:** Reception is dominated by comparisons to Jev, with many arguing Decisions API is a fast follow or thin wrapper around Luna rather than something novel, while others note real advantages like multimodal (image) support and OpenAI's platform reach.

**Sentiment:** 20% positive · 35% mixed · 45% skeptical

**The case for**

- Adds image/multimodal input support that Jev reportedly lacks.
- Being on an established platform with broad developer reach could matter even without technical superiority.
- Some testers report Luna as more accurate than Jev, even if slower.
- No fine-tuning or training required to use it, similar to Jev's ease of use.

**The pushback**

- Many see it as just a Luna wrapper doing what structured-output prompting already accomplished since GPT-3.
- Several say Jev is still faster and more accurate in their own testing.
- Pricing is unannounced, so cost competitiveness versus Jev is unknown.
- Some find the image-input use case for yes/no decisions niche or unconvincing.
- Perceived as a rushed, copycat reaction alongside other DevDay announcements mirroring competitors.

**By community**

- x (mixed): Replies swing between dismissing it as a rebranded Luna prompt and acknowledging real speed/multimodal gains, with constant head-to-head comparisons to Jev.

**Hottest debate:** Whether Decisions API offers genuine technical advantages over Jev or is just a marketing repackaging of existing Luna prompting techniques.

**Open questions**

- What will pricing be, and will it undercut or match Jev's cost?
- How does it actually benchmark against Jev in real accuracy/speed tests?
- Will fine-tuning or candidate-answer limits be supported?
- When will general availability open up beyond limited preview?

**Highlights**

> @iannuttall It’s a Luna wrapper! From what I understand so far it is all marketing. You can use Luna the same way without this by giving it a list of decisions and telling it to output a choice and nothing else.  The same thing we have been doing for classifiers and decisions since GPT-3.
> — [BryanMcAnulty on x · 6 points, 2 comments](https://x.com/BryanMcAnulty/status/2104987620904505438)

> @OpenAIDevs Someone needs to benchmark this against jev
> — [realtatendazhou on x · 50 points](https://x.com/realtatendazhou/status/2105021756570481068)

> @PisforPlenty @OpenAIDevs That’s not the point. They were first and used a novel training technique. This is having a fundamentally non-deterministic model doing deterministic outputs. Where’s the unique take or the wow moment. This is dev day lol expecting more
> — [jess\_daniel10 on x · 2 points](https://x.com/jess_daniel10/status/2105024716884828492)

> @VictorTaelin @thsottiaux Sort of but not really in this case. Jev had no moat given that a general classifier was a) already what people were using LLMs for and b) is honestly something you can train and run locally very easily anyway. need to build something they're not going to try and compete with
> — [max\_paperclips on x · 5 points](https://x.com/max_paperclips/status/2105055423715999763)

> @Diego24455685 @merobbins @thsottiaux No public head-to-head yet—Decisions API just launched in limited preview. OpenAI reports ~150 ms latency (vs ~1.6 s for standard Luna chat) and 76/78 correct on an internal computer-task sim. Multimodal (text + images). Jev: typically 70-500 ms, $0.042/MTok input (output
> — [grok on x](https://x.com/grok/status/2105004485101682866)

**Source threads**

- [x](https://x.com/iannuttall/status/2104985820117762426) · 1 points · 59 comments
- [x](https://x.com/OpenAIDevs/status/2105003318917697873) · 0 points · 202 comments
- [x](https://x.com/thsottiaux/status/2104986448269279399) · 0 points · 240 comments

---

Tags: [#openai](https://daily.dev/tags/openai), [#ai-inference](https://daily.dev/tags/ai-inference)

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