penAI may be reading the tea leaves on enterprises' pivot to efficiency.
On Thursday, the AI lab made Decisions API, its competitor to Typesafe AI's recently popular Jev, generally available to users for the first time. The company is offering Decisions API at 10 cents per million input tokens, one cent per million cached input tokens, and not charging for output tokens—a fraction of what it charges for its frontier large language models. But that's still over twice the cost of Jev.
The company laid out a number of ways it's eating its own dog food, including the team behind its recently released Dots agents using Decisions API to reduce response times and improve interactivity.
OpenAI's Decisions API is functionally quite similar to Jev, creating output that software can act on, rather than focusing on human-readable outputs. The difference is in the models that lie beneath the surface.
- Decisions API is built on top of GPT-6 Luna, the company's most cost-efficient and lightweight model, and is framed by OpenAI as a specialized API. Though the company may work on specific training for these kinds of models, one OpenAI product lead told TechCrunch that it's "just plain old Luna that gets the job done."
- TypeSafe, meanwhile, developed an entirely new class of model for Jev that isn't a language model at all, instead using a tactic called "Reinforcement Learning for Calibrated Decisions," which answers queries with "epistemically honest probabilities," rather than "programmatically verified" outputs.
However, both releases target the same use case: rapid decision-making for software to replace generative responses. And both have seen success in saving time and cutting costs. OpenAI said that, internally, their offering has allowed agents to make near-real time decisions up to 10 times faster than GPT-6 Luna on its own through the Responses API. TechCrunch reported that it cut decision making time from three seconds to as low as 100 milliseconds.
Comparatively, TypeSafe reports that Jev is two orders of magnitude faster than existing models, with an end-to-end response time between 70 and 500 milliseconds, compared to up to 329 seconds for existing LLMs.
TypeSafe's Jev has skyrocketed in recent months as enterprises pivot from shelling out on expensive frontier large language models to more efficient alternatives. Investors, too, are taking an interest: Just weeks after its initial launch, the startup raised $870 million at a $7.5 billion valuation in a round led by Andreessen Horowitz. In their funding announcement, the company said that a third of Fortune 500 companies are "getting their Jev on," and that its product has already saved customers millions of dollars in production costs.
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It only makes sense that OpenAI is leaning into efficiency at a time when enterprises are tightening their purse strings. And seeing the success of Jev (from a former OpenAI researcher and ChatGPT inventor, no less), it's natural for the lab to use the same fundamental tactic of limiting generative responses in favor of software-focused outputs, especially as agents eat up a growing number of tokens. Plus, the fact that all OpenAI had to do to achieve a similar effect was modify its existing Luna model is certainly an added bonus. However, despite Jev's rapid rise to fame, OpenAI may have a few advantages over the startup. For one, the name recognition and market share that OpenAI already has with enterprises could make it easier for companies to trust the tech. Additionally, because OpenAI's models are already integrated within a number of enterprise workflows, it's likely that the Decisions API will be more seamless to add to the stack.