OpenAI’s Jev Replica Could Help the Frontier Lab Contain Swarming Agents

Meta description: OpenAI's Decisions API preview mirrors TypeSafe AI's Jev, promising fast LLM choices for agents and monitoring while raising calibration and cost questions.

Tags: OpenAI Decisions API, Jev, TypeSafe AI, AI agents, LLM costs

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OpenAI Decisions API Mirrors Jev For Faster AI Choices

An aside from CEO Sam Altman at OpenAI's Dev Day event on Tuesday revealed the new OpenAI Decisions API, making it one of the more intriguing announcements of the day. The product matters because it places OpenAI alongside a newer category of fast decision models for software. The API appears to offer functionality comparable to Jev, a model released by TypeSafe AI earlier this month and explicitly designed for software automation. Jev works like a super-powered classifier built on an LLM, or large language model, letting developers supply a set of choices and receive probability estimates cheaply and at high speeds.

Background: A Need for Fast, Narrow Decisions

The broader context is that LLMs, as commonly used, are not always the right tool for every software task. They can be comparatively slow and expensive when a program only needs to pick among a limited set of options. TypeSafe AI positioned Jev as a way to address that gap. Developers have used it to augment LLMs and, in doing so, have found that they are faster and cheaper.

The company's framing is also conceptual. TypeSafe AI uses "System One" to describe fast, intuitive thinking, and "System 2" to describe deliberate reasoning. In that language, a decision model is meant to do the quick classification work, while a larger reasoning model handles slower tasks.

Development: OpenAI Adds a Decisions API at Dev Day

Altman described OpenAI's Decisions API as a way to give the lab's Luna model a predefined set of options to choose between, such as categories in which to classify an image or different agent behaviors. "By focusing the model on that choice, we can make it extremely fast while keeping capabilities like image understanding, broad language support, and safety protections," Altman said.

The resemblance to Jev was not lost on the startup's founder. TypeSafe did not respond to TechCrunch's questions about the new product, but CEO Diogo Almeida, a former OpenAI engineer who co-invented reinforcement learning, joked on X about the beginning of the clone wars. He added that OpenAI's interest could be "a sign…that building in a System One compatible way is the future."

As of the event, OpenAI released the Decisions API as a limited preview. It is not clear how similar the Decisions API will be to Jev, and TechCrunch has not spotted developers running it through its paces. However, conversations on X show clear interest. It is also not the only Jev-like API on the internet: other startups are rolling out similar models, and OpenAI will not be the last tech giant to produce one.

Latest Progress: Calibration, Cost, and Agent Safety

The central question for these products is not speed alone. A key issue is how well calibrated each decision model's outputs will be to real life. Almeida says his company's moat is the synthetic data it creates to generate statistically useful outputs.

"Fast and cheap is very easy, you know," Almeida told TechCrunch last week. "If you want it really fast and cheap, use dice, right? Intelligence is the hard part, and my North Star is always pushing the intelligence-per-dollar Pareto curve."

After just weeks, the category appears to have a future ahead of it, and one likely application is monitoring and securing AI agents. Following a series of incidents where OpenAI's agents misbehaved on the open internet, the company introduced one of its new security measures: using a separate model to watch for bad actions at "significant compute cost."

Shapor Naghibzadeh, a long-time cybersecurity professional who leads the start-up QueryStory, thinks a model like Jev could make that possible far more cheaply. He built a demo for a hackathon held last weekend that uses Jev to check each agentic action against the task it was given, blocking actions it had high confidence were bad, flagging others for review, and permitting the rest.

The demo's cost comparison is specific: in theory, such monitoring could have stopped the Hugging Face incident, and monitoring of that kind costs $2.94 with Jev, versus $372 with a frontier LLM. A key observation is that Jev is arguably cheap enough to run on every agentic action, which offers a layer of review that could improve the reliability of agents more broadly.

Current Stage: Limited Preview, Broader Pattern

The current stage is still early: OpenAI's Decisions API is a limited preview, and TechCrunch has not spotted developers running it through its paces. What has shifted is the signal that OpenAI has entered a category TypeSafe AI had already begun to build, and one where the next test is whether fast decision models can be accurate enough to sit inside high-volume agent workflows. It is the kind of outcome TypeSafe was hoping to achieve, and the report frames OpenAI's move as evidence that it has seen the value too.