Jev AI vs OpenAI Decisions API is the new comparison every AI developer is asking about. Both are AI decision making models: instead of writing text, they read some context, answer a question from a fixed list of options, and hand your code a clean, structured result.
In this guide you'll learn what each model is, how they differ, how they compare to OpenAI Structured Outputs, and which one fits your project.
Why AI Decision Making Models Exist
For years, most AI integrations worked the same way: send a prompt, get a paragraph back, then write code to pull the answer out of the paragraph. That approach has three problems:
- It's slow. The model generates every word, one token at a time.
- It's expensive. You pay for all those output tokens.
- It's fragile. The output format can drift, and your parser breaks.
Many apps don't need a paragraph at all. They need a quick, reliable choice: Is this message spam? Which team owns this ticket? Should the agent call this tool next? AI decision making models are built for exactly that job.
What Is Jev AI?
Jev AI is the first model from TypeSafe AI, a San Francisco startup founded in 2024. Its CEO, Diogo Almeida, spent about four years at OpenAI working on RLHF, InstructGPT and ChatGPT before leaving to start the company. Jev launched in limited early access on September 15, 2026, alongside a $40 million seed round led by DCVC.
TypeSafe calls Jev a "System One model". The name comes from Daniel Kahneman's idea of fast, intuitive "System 1" thinking. Jev handles quick snap judgments and leaves slow, careful reasoning to LLMs or humans.
How Jev AI works
Every Jev request has two parts:
- State: the context, sent as a string, a JSON object or an array of text.
- Questions: one or more typed questions about that state.
Jev answers all of your questions together in a single parallel pass. It supports three question types:
| Question type | What it does | What you get back |
|---|---|---|
| Choice | Picks one option from a list you define | The chosen option, a probability for each option, and a confidence score |
| Score | Rates the state on an ordered scale | The score, a probability for each level, and a confidence score |
| Noul | Judges a yes/no statement | A probability between 0 and 1 |
Here's a simplified example of the idea (not the exact API format):
{
"state": "Customer says: I was charged twice for my order.",
"questions": [
{ "type": "choice", "question": "Which team should handle this?", "options": ["billing", "tech_support", "sales"] },
{ "type": "score", "question": "How urgent is this?", "levels": ["low", "medium", "high"] }
]
}Because you define every possible answer in advance, Jev can't return something outside your list. TypeSafe presents this as removing hallucinations and type errors. A valid answer can still be the wrong answer, though, so you should always test on your own data.
Jev AI speed and cost claims
TypeSafe reports response times of 70 to 500 milliseconds. It claims Jev is roughly 40 to 200 times faster and 40 to 400 times cheaper than frontier LLMs on comparable tasks.
Treat these numbers with care. They come from TypeSafe's own tests, and the company admits its benchmark workflows were built by its own team and that real-world gains will likely be lower.
How to use Jev AI
You can access Jev in three ways:
- TypeSafe's own waitlist at typesafe.ai
- Vercel AI Gateway, as
typesafe-ai/jev - OpenRouter
The Vercel and OpenRouter routes make it easy to try Jev today without waiting for approval. Check the official Jev documentation for current request formats and pricing.
What Is the OpenAI Decisions API?
OpenAI announced the Decisions API at DevDay 2026. It runs on a specialized version of GPT-6 Luna and follows the same basic idea as Jev: you define a question and its possible answers, send in context, and get back a choice your app can act on.
OpenAI's own examples include classifying content, routing support requests to the right team, and choosing an AI agent's next tool call.
Two things make it stand out:
- Image input. You can send images as well as text. Jev currently accepts text only.
- The OpenAI ecosystem. If your app already uses OpenAI, you avoid adding a new vendor, a new API key and a new bill.
Launch coverage reports response times of around 150 milliseconds, about 10 times faster than a normal GPT-6 Luna call. These are early reported figures, not guarantees.
The catch: the Decisions API is currently in limited preview for selected API customers. OpenAI says a broader release is coming soon, but public pricing and full documentation aren't available yet.
Jev AI vs OpenAI Decisions API: Side-by-Side
| Feature | Jev AI (TypeSafe) | OpenAI Decisions API |
|---|---|---|
| Launched | Sept 15, 2026 (early access) | DevDay 2026 (limited preview) |
| Underlying model | Jev, trained specifically for decisions | Specialized version of GPT-6 Luna |
| Input types | Text only (strings, JSON, text arrays) | Text and images |
| Question types | Choice, Score, yes/no (Noul) | Pick from developer-defined answers |
| Confidence output | Per-option probabilities plus confidence | Selected answer (score details still emerging) |
| Reported speed | 70 to 500 ms | About 150 ms |
| Access today | Waitlist, Vercel AI Gateway, OpenRouter | Selected API customers only |
| Pricing | Published on typesafe.ai | Not public yet |
| Generates text | No | No |
The Key Difference: Built for Decisions vs Adapted for Decisions
The biggest difference is under the hood.
Jev was trained from the start to make decisions. TypeSafe uses a method it calls Reinforcement Learning for Calibrated Decisions (RLCD). It trains the model's probabilities against real outcomes rather than against what human raters prefer. The goal is confidence scores you can actually trust when setting thresholds.
OpenAI took a different route. It took its general-purpose GPT-6 Luna model and restricted the output to a fixed set of answers. You get the broad knowledge of a frontier model plus image understanding, but it's not yet clear how well calibrated its confidence scores are.
Neither approach is proven better yet. The right choice depends on your data, so test both.
Decision Models vs OpenAI Structured Outputs
If you already use OpenAI Structured Outputs, you might wonder why you'd need a decision model at all. Structured Outputs forces a regular LLM to return JSON that matches your schema, which already solves the "broken parser" problem.
The difference is in how the answer is produced:
| OpenAI Structured Outputs | Decision models (Jev, Decisions API) | |
|---|---|---|
| How it works | A general LLM generates JSON token by token | The model picks from answers you define upfront |
| Speed | Depends on output length | Built for very fast single decisions |
| Can include free text | Yes, any string field you allow | No, only your predefined answers |
| Per-option probabilities | Not out of the box | Built in (Jev); emerging (Decisions API) |
| Best for | Extracting data, filling forms, mixed text and structure | Classification, routing, yes/no gates |
Rule of thumb: use Structured Outputs when the answer needs real text inside it (like a summary or extracted name). Use a decision model when the answer is always one of a few known options.
LLM Routing and Classification with Decision Models
Two of the most practical jobs for these models are LLM routing and LLM classification.
LLM routing means sending each request to the right model. Simple questions go to a small, cheap model, and hard ones go to a powerful, expensive one. A decision model can make that call in milliseconds, which can cut your AI bill without hurting quality.
LLM classification means sorting inputs into labels: spam or not spam, positive or negative, billing or tech support. Using a full LLM for this is like hiring a novelist to sort mail. A decision model does the same job faster and returns a confidence level with each answer.
AI Decision Making Examples: Real Use Cases
Both models are best at small, repeated decisions your software makes thousands of times a day. Common AI decision making examples include:
- Support ticket routing: send each message to the right team.
- Content moderation: flag spam, abuse or low-quality posts.
- AI agent tool calling: choose which tool an agent should use next, or decide whether a task is finished.
- Safety checks: decide whether an action (sending an email, issuing a refund) needs human approval first.
- Lead scoring: rate incoming leads as hot, warm or cold.
- Real-time apps: early Jev demos include a Minecraft bot choosing its next move and a trading bot re-checking price direction every second.
What Neither Model Can Do
These are not ChatGPT replacements. Neither Jev AI nor the OpenAI Decisions API can:
- write emails, articles or code
- summarize documents
- explain why it made a decision
- brainstorm or plan open-ended tasks
The best setup combines both kinds of model: an LLM for thinking and writing, and a decision model for fast choices. For anything risky, such as payments, deleting data or account access, the model should only recommend. Your own code or a human should make the final call.
Which AI Decision Making Tool Should You Choose?
Choose Jev AI if you:
- want to start building today
- make text-based decisions
- need yes/no probabilities or graded scores, not just a single pick
- want confidence scores designed for setting thresholds
Choose OpenAI Decisions API if you:
- need decisions based on images or screenshots
- already run your stack on OpenAI
- can wait for broader access and public pricing
Still not sure? Pick one real decision in your app, collect 100 to 200 examples with known correct answers, and run both models on them. Accuracy on your data matters more than any benchmark.
Frequently Asked Questions
What is Jev AI?
Jev AI is a decision-only AI model from TypeSafe AI. Instead of generating text, it returns typed answers (a choice, a score or a yes/no probability) along with confidence levels.
Is Jev AI made by OpenAI?
No. Jev is made by TypeSafe AI, an independent startup. Its CEO previously worked at OpenAI on ChatGPT.
Is the OpenAI Decisions API available to everyone?
Not yet. It launched as a limited preview for selected API customers, with a broader release planned.
Is Jev AI better than OpenAI Structured Outputs?
They solve different problems. Structured Outputs is better when your answer needs free text inside it. Jev is better for fast classification and routing where the answer is always one of a few known options.
What is LLM routing?
LLM routing means automatically sending each request to the most suitable AI model, usually to balance cost, speed and quality. Decision models like Jev are well suited to making that routing choice.
Can Jev AI or the Decisions API replace ChatGPT?
No. Both only make decisions. They can't write, summarize or explain, so they work best alongside a regular LLM.
Which is faster, Jev AI or OpenAI Decisions API?
Both report response times in the low hundreds of milliseconds. Real speed depends on your setup, so measure it yourself.
Final Verdict
Jev AI and the OpenAI Decisions API mark a real shift in how AI is built into software: from "write me a paragraph" to "make this one small decision, fast."
Jev has the head start and a model trained specifically for the job. OpenAI has image support and a huge developer base. For most developers, the smart move today is to try Jev now, watch for OpenAI's wider release, and design your app so you can switch between them easily.
Sources: TypeSafe AI: Introducing System One Models and Jev, Jev documentation, OpenAI DevDay 2026, Vercel: What is Jev?. Both products are new and changing quickly, so check the official documentation before building on them.