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AI NewsOpenAI makes its Jev competitor widely available as decision model competition heats up

OpenAI makes its Jev competitor widely available as decision model competition heats up

3:41 AM IST · October 23, 2026

OpenAI makes its Jev competitor widely available as decision model competition heats up

OpenAI made its Decisions API modelwidely availableto users for the first time, entering the market for fast, cheap intelligence that recently made TypeSafe, a startup founded by a former researcher at the frontier lab, a$7.6 billion company. TypeSafe’s Jev modelcreated a categoryfor deep learning models that act as super classifiers: Given a set of options, they can quickly make choices and offer a confidence rating in their decision. Software developers quickly found that these decision or System One models could replace LLMs in a variety of workflows, operating more quickly and a at dramatically lower cost. Nikunj Handa, the product lead for OpenAI’s APIs, told TechCrunch that his colleagues saw Jev and said “wow, we we have like a million use cases for this.” Handa’s team sprinted to offer their own version: Rather than building a specifically calibrated decision model, the team took ChatGPT Luna and reconfigured it to speed up the inference and lower the cost. “The big unlock with Decisions API has been the speed, shrinking things from one to three seconds to 100, 120, 180 milliseconds,” Handa said. “In the future, we may sort of like have specific training to make our models like even better in these ways. But right now, it’s just plain old Luna that gets the job done.” Announced at the company’s developer day on September 29, the Decisions API became available to all customers on Thursday. OpenAI added new voice and visual modalities to the public version, and worked to ensure lower latency in regions around the world by targeting its inference resources geographically. In initial trials with customers like Box, Datadog, Ramp, and Speak, engineers used the API to replace OpenAI’s LLMs with higher throughput. Andrew Hsu, the CTO at Speak, which builds AI language tutors, said the Decisions API allowed the company to cut the time between actions from 1.4 seconds to .13 seconds. OpenAI also used the product to improve their Dots voice assistant’s responsiveness. Still, it’s not clear exactly what makes a good decision model, other than “it works for you” — optimization across intelligence, cost, calibration, and speed seems to differ by workload. TypeSafe CEO Diogo Almeida distrusts benchmarks and urges people to trial the models in their own work, but even TypeSafereleased its own rankingslast week alongside the release of its latest Jev model, which includes new features including support for images and smart-if statements. Public benchmarks like theAIM System One Indexand theArtificial Analysis Decision Indextend to favor D1 Ultra, a new decision model that Liquid released last week, and Base Lab’s Elephant-1. The latest Jev model remains competitive on both indices, in seventh and third, with OpenAI’s Decision Model falling eighth and fourteenth, respectively. However, TypeSafe’s offerings are, for now,the most widely-queriedon OpenRouter. The most fun Jev benchmark, however, isJevMan, a benchmark where decision models play PacMan; the latest Jev model tops that ranking. Felix Wunderlich, the chief growth officer at Opper AI, which built the benchmark, said the classic game “is an interesting test because of its nonstop stream of quick, high-stakes decisions. At every junction the model must decide where to go in a few hundred milliseconds.” Where OpenAI’s arguably more capable model may have an advantage is in multi-modality—for image classification, “Luna is extremely powerful and has been showing better calibrations,” Handa told TechCrunch, adding that OpenAI customers want the Decisions API to be more flexible about using images as outputs as well. Handa also saw further increases in speed enabling new use cases: “Can we get this to something like you know 60 milliseconds, and then you could imagine like an API like call like for every single frame in like a video?” As these offerings get faster and cheaper, we can expect them to live up to Jev’s namesake, an economist whoexplained howusing a resource more efficiently makes it more ubiquitous. Or, as Handa sees it, there will be an expanding “crazy set of use cases” that were hiding in plain sight until the decision models revealed them.

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Microsoft’s Mustafa Suleyman says its AI outperforms doctors on hard cases as he chases ‘medical superintelligence’

Microsoft’s Mustafa Suleyman says its AI outperforms doctors on hard cases as he chases ‘medical superintelligence’

Microsoft’s AI diagnostic system correctly diagnosed complex medical cases in a simulation more than twice as often as generalist physicians, according to astudy published Thursdayin Science. Mustafa Suleyman, Microsoft AI’s CEO, called the result a step toward “medical superintelligence.” The system, dubbed MAI-DxO (DxO for short), coordinates several AI agents that act like a panel of doctors. They question a patient, order tests, and debate a diagnosis. In a simulation using complex cases from the New England Journal of Medicine, it was correct 83% of the time. Primary care doctors and general internists, working individually with reference materials, were correct 36% of the time. One note: Cases this complex are usually handled by specialists or physician teams, who weren’t part of the comparison. “There is something very substantial and exciting here,” Dominic King, vice president of Health at Microsoft AI, told TechCrunch. “The biggest challenge in healthcare delivery at the moment is the growing mismatch between demand and supply of medical expertise.” Inpublishing these results, Microsoft joinsGoogleandOpenAIin claiming that its AI can match or outperform doctors. The study’s own data suggests that much of the improvement comes from more powerful underlying AI models, with Microsoft’s orchestration system adding gains on top. Microsoft tested MAI-DxO with models from several companies, including OpenAI, Google, Anthropic, Meta, and DeepSeek, but its best results came with OpenAI’s GPT-5 and GPT-5.5. Microsoft measured DxO on cost as well as accuracy, meaning the estimated cost of running the AI on each case. On its own with a short, basic prompt, GPT-5 scored 78.9% on complex cases at about $8,400 per case. DxO cut that cost by about $3,000 but raised accuracy by only about four points. On emergency department cases, there was little improvement in either accuracy or cost improvement between GPT-5 on its own and with the assist from DxO. With GPT-5.5, a newer model released in April 2026, the results split. On complex cases, DxO increased GPT-5.5’s accuracy score from 72% to nearly 87% at a slightly cheaper cost. However, the gains once again didn’t show up on emergency department cases. Both (with and without DxO) scored about 85% at a similar cost. DxO helps most on complex cases and with less capable AI models. Gemini 2.5 Flash, for example, scored 46.4% on its own but nearly 69% with DxO. Even so, the gap between the weaker and stronger models was larger than the boost DxO provided. Microsoft’s researchers said that accuracy gains naturally shrink as models approach the theoretical maximum. “The DxO orchestration helps with the broader challenge of optimization both accuracy and cost,” a spokesperson added. Suleyman, who leads Microsoft’s effort to build its own frontier AI, told TechCrunch that the company is working with the Mayo Clinic to train a foundation model for healthcare, with release planned for next year. He believes this model will enable the company to “make really enormous leaps here,” and says DxO is part of the foundational infrastructure Microsoft is building to see real breakthroughs in healthcare. “I really believe that in 2027, we will be on the verge of a coding style breakthrough for medical records,” Suleyman said. “I think we’re going to be able to stream highly accurate predictions about how a medical record might unfold, and obviously that kind of foresight for a clinician is gold dust.” Suleyman has spent his career trying to use AI to solve medical issues. He was previously a co-founder of DeepMind, which was acquired by Google in 2014. The following year, Google DeepMind acquired King’s startup Hark, a clinical task management app, and the two of them launched DeepMind Health together. Suleyman joined Microsoft to lead its AI unit in 2024, and King followed a few months later. “We’re just true believers,” Suleyman said. He said while the use of generative AI in healthcare is still early, he expects healthcare models to get “an order of magnitude better in capability within the next 24 to 36 months.” “As we’ve seen multiple times in the last five or six years, the progress happens very quickly once you have a flywheel,” Suleyman said. “A flywheel means we have a model in production which we are iterating with real-world usage in order to identify gaps in the model and then patch them and improve them. That’s what’s happened in coding. It’s what happened in chat before that. And I think next year it’s going to start to happen for medical records and for clinical decision support.” Through a new partnership with Beth Israel Deaconess Medical Center in Boston, Microsoft will evaluate the clinical reasoning capabilities of DxO across different hospital settings. “This technology is ready for real-world evaluations, and you can imagine the complexity and diversity of issues that come from a Boston emergency department on a Friday evening,” King said. He explained how the study will work: Patients will get standard treatment, including triage, time with a specialist, treatments, investigations, and decision on whether it’s safe to discharge. In parallel, those who consent will also engage with DxO. Microsoft and its partners will compare DxO’s recommendations against the human doctors’. Google publisheda clinical study alongside Beth Israel in The Lancet this month to evaluate Google’s AMIE conversational diagnostic AI in real-world urgent care visits, while human physicians monitored the interactions in real time. “We think this will be the biggest study of its kind,” King said. “It will be across not just emergency settings but urgent care environments, and will give us very compelling evidence by the end of the study period around the ability for these systems to be deployed at greater scale in the future.” Microsoft’s study in Science coincides with a manifesto of sorts around what it will take to build medical superintelligence, defined as “the capacity to surpass the strongest contemporary human-led health systems in medical reasoning and coordination across the full arc of care.” Microsoft hopes to move beyond benchmarking individual tasks — like answering medical exam questions and generating diagnoses — into integrating with clinicians and healthcare systems for better outcomes. “This is the kind of technology that everybody in the world wants, and that’s why we’re committed to doing everything we can to accelerate its introduction to the world,” Suleyman said, adding that this focus could be a differentiator for Microsoft compared to the other frontier labs. Microsoft already sells dictation and radiology tools to clinicians and sees DxO as an expansion to those efforts. “We have to have the absolute number one state-of-the-art medical clinical decision support products over the next few years,” Suleyman said.

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OpenAI makes its Jev competitor widely available as decision model competition heats up

OpenAI makes its Jev competitor widely available as decision model competition heats up

OpenAI made its Decisions API modelwidely availableto users for the first time, entering the market for fast, cheap intelligence that recently made TypeSafe, a startup founded by a former researcher at the frontier lab, a$7.6 billion company. TypeSafe’s Jev modelcreated a categoryfor deep learning models that act as super classifiers: Given a set of options, they can quickly make choices and offer a confidence rating in their decision. Software developers quickly found that these decision or System One models could replace LLMs in a variety of workflows, operating more quickly and a at dramatically lower cost. Nikunj Handa, the product lead for OpenAI’s APIs, told TechCrunch that his colleagues saw Jev and said “wow, we we have like a million use cases for this.” Handa’s team sprinted to offer their own version: Rather than building a specifically calibrated decision model, the team took ChatGPT Luna and reconfigured it to speed up the inference and lower the cost. “The big unlock with Decisions API has been the speed, shrinking things from one to three seconds to 100, 120, 180 milliseconds,” Handa said. “In the future, we may sort of like have specific training to make our models like even better in these ways. But right now, it’s just plain old Luna that gets the job done.” Announced at the company’s developer day on September 29, the Decisions API became available to all customers on Thursday. OpenAI added new voice and visual modalities to the public version, and worked to ensure lower latency in regions around the world by targeting its inference resources geographically. In initial trials with customers like Box, Datadog, Ramp, and Speak, engineers used the API to replace OpenAI’s LLMs with higher throughput. Andrew Hsu, the CTO at Speak, which builds AI language tutors, said the Decisions API allowed the company to cut the time between actions from 1.4 seconds to .13 seconds. OpenAI also used the product to improve their Dots voice assistant’s responsiveness. Still, it’s not clear exactly what makes a good decision model, other than “it works for you” — optimization across intelligence, cost, calibration, and speed seems to differ by workload. TypeSafe CEO Diogo Almeida distrusts benchmarks and urges people to trial the models in their own work, but even TypeSafereleased its own rankingslast week alongside the release of its latest Jev model, which includes new features including support for images and smart-if statements. Public benchmarks like theAIM System One Indexand theArtificial Analysis Decision Indextend to favor D1 Ultra, a new decision model that Liquid released last week, and Base Lab’s Elephant-1. The latest Jev model remains competitive on both indices, in seventh and third, with OpenAI’s Decision Model falling eighth and fourteenth, respectively. However, TypeSafe’s offerings are, for now,the most widely-queriedon OpenRouter. The most fun Jev benchmark, however, isJevMan, a benchmark where decision models play PacMan; the latest Jev model tops that ranking. Felix Wunderlich, the chief growth officer at Opper AI, which built the benchmark, said the classic game “is an interesting test because of its nonstop stream of quick, high-stakes decisions. At every junction the model must decide where to go in a few hundred milliseconds.” Where OpenAI’s arguably more capable model may have an advantage is in multi-modality—for image classification, “Luna is extremely powerful and has been showing better calibrations,” Handa told TechCrunch, adding that OpenAI customers want the Decisions API to be more flexible about using images as outputs as well. Handa also saw further increases in speed enabling new use cases: “Can we get this to something like you know 60 milliseconds, and then you could imagine like an API like call like for every single frame in like a video?” As these offerings get faster and cheaper, we can expect them to live up to Jev’s namesake, an economist whoexplained howusing a resource more efficiently makes it more ubiquitous. Or, as Handa sees it, there will be an expanding “crazy set of use cases” that were hiding in plain sight until the decision models revealed them.

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Canva releases its first image generation model

Canva releases its first image generation model

Design platform Canva released its first image generation model, called Canva Craft, developed by its research team to compete with same-category models from Google, OpenAI, ByteDance, and Meta. The company said its model works in two stages. In the first stage, it interprets users’ prompts and creates a plan with details like spatial relationships of elements, relative scale, visual hierarchy, and information for text generation. In the second stage, its model generates an image of up to 2K resolution in a possible 30 different aspect ratios for various platforms. Canva said that this approach allows it to create images with accurate details of objects and legible text. Plus, users would be able to make precise edits without changing other parts of the image. The company noted that rather than using photographs, it used designs to train this model using human-designed templates from Canva’s library, including posters, social posts, presentations, infographics, menus, and typography-rich layouts. That means images generated with Canva Craft could be converted into layered designs using Canva Magic, the company’s image-to-design transformer. Last year, the platform released itsown model that understands designs and layers. Canva Craft is available on the company’s platform to all users, who can access the model from the Dream Lab tab. The model will also be available through the Canva API. Plus, the company plans to support image generation in third-party chatbots like ChatGPT and Gemini through Canva’s MCP. Canva has used a bunch of image models for generation in its platform, so it’s likely that the new models will help save API costs for the company. It said that besides costs, the new model also allows the platform to have better quality and speed control. In 2024, Canva acquiredmedia generation platform Leonardo.ai. It said that the researchers from Leonardo contributed to the new image model. Canva said that it is developing two cost-efficient versions of the model, called Craftfast and Craft-ultrafast, which will be released soon. The company was on track for$4 billion in revenue last year. However, it cut itsgrowth forecast for 2026, causing its valuation to reportedlyplummet from $42 billion to $35 billion, according to external analysts.

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1Password launches a way for AI agents to log in on your behalf without seeing your password

1Password launches a way for AI agents to log in on your behalf without seeing your password

Concerns have swirled over the access that consumers are increasingly entrusting to AI agents. Now, security services are trying to catch up. Password manager 1Password on Thursday launched a new tool by which users can allow agents to log into their personal accounts on their behalf, without the agent ever having access to a user’s credentials. The new platform, dubbed1Password for Agents, accomplishes this by creating a system of communication between the agent, the user, and the password manager. When an agent attempts to log in on a user’s behalf, a unique digital key is sent to 1Password to confirm the agent’s digital identity. The user is then sent an alert about the login attempt and can personally confirm the request. 1Password then signs in the agent, filling in the credentials on the digital assistant’s behalf, which allows it to log in and complete whatever task is at hand. 1Password for Agents allows users to choose their agent of choice — whether that’s Claude Duet from Anthropic, OpenAI’s Dots or ChatGPT Work, Grok Bot, Meta’s Muse, or agents from Town, which is an AI assistant startup. The company says it is working with other companies to expand the service. “Handing over a password, secret, or credential is a common shortcut to an agent, but can leave people with sensitive information in places they can no longer control as a copy gets saved for the next time,” the company saidin a blog post. “1Password for Agents gives people a trusted way to delegate tasks to personal AI agents while keeping control of the passwords, secrets, and credentials those tasks require.” Online agentic activity has increasingly unsettled major institutions. Indeed, 1Password’s new platform is launching on the same day that Axiosreportsthat U.S. banks have begun to detect agentic activity inside user accounts. This isn’t necessarily a case of rogue agents pilfering accounts but, rather, an example of how consumers are increasingly allowing automated services to act on their behalf, including on financial matters. However, the potential for agent-related problems is still large. The big issue appears to be that most banks cannot tell if the agentic activity is tied to user requests or not. Bankers interviewed by the outlet described the situation as “horrifying,” and a Chase spokesperson interviewed by Axios additionally told the outlet that customers “should not give their usernames and passwords to anyone.”

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