If you’ve grown tired of babysitting ChatGPT, the new GPT-5.6 models might be the fix


OpenAI seems to have a new AI model waiting in the wings every few months, and today is no different. The company has officially unveiled the GPT-5.6 family, bringing three new models to ChatGPT, Codex, and its API. The big star of the show is GPT-5.6 Sol, but it’s joined by Terra and Luna, which are designed to deliver strong performance at a lower cost.

The days of endless follow-up prompts may be numbered

If you’ve ever felt like ChatGPT needs too many follow-up prompts to finish a job, OpenAI thinks this update can help. Instead of simply answering one question at a time, GPT-5.6 is designed to handle bigger, multi-step tasks with less hand-holding. For example, imagine you’re planning a weekend trip. Rather than asking ChatGPT where to go, then where to stay, then what to do, and finally asking it to put everything together, GPT-5.6 is supposed to handle much more of that work on its own. The same idea applies to coding projects, research, spreadsheets, or even comparing dozens of products before recommending the best option.

The flagship GPT-5.6 Sol model is built for those heavier workloads, and OpenAI says it delivers better performance while using fewer tokens than previous models. That means it can accomplish more work without driving up computing costs, which is good news for developers and businesses that rely on OpenAI’s models every day. One of the biggest additions is a new Ultra mode. Instead of relying on a single AI process, Ultra can split a complicated task across multiple AI agents working in parallel. You can think of it like assigning research, writing, editing, and fact-checking to four people instead of asking one person to juggle everything. According to OpenAI, this helps solve difficult problems faster while improving the final results.

There’s a GPT-5.6 for every kind of job

For users who don’t need that much power, OpenAI is also introducing GPT-5.6 Terra and GPT-5.6 Luna. These models are designed to be more affordable while still handling common AI tasks well. The company says both outperform competing models in their respective categories while costing significantly less to run, making them attractive options for developers building AI-powered apps. The models are also becoming more independent. Instead of stopping after every instruction, GPT-5.6 can write small programs, use tools, process information, check its own progress, and decide what to do next with fewer prompts from the user. That could make tasks like debugging code, organizing research, or pulling together reports feel much smoother.

If accuracy matters more than speed, OpenAI is also adding a Max mode. It gives GPT-5.6 extra time to think through difficult questions, test different approaches, and double-check its work before responding. Ultra goes even further by using multiple AI agents simultaneously, trading extra computational power for better results on more demanding jobs. OpenAI also says it put GPT-5.6 through its most extensive safety testing yet, combining human red-team exercises with automated evaluations to make the models more resistant to misuse without impeding legitimate use.

The GPT-5.6 family is rolling out starting today across ChatGPT, Codex, and the OpenAI API, with global availability expected to expand over the next 24 hours. For most ChatGPT users, the benchmark numbers probably won’t mean much. What will matter is whether GPT-5.6 can actually save you time by needing fewer prompts, handling larger tasks on its own, and delivering answers that require less back-and-forth. If OpenAI’s claims hold up, that could be the biggest upgrade of all.



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Meta stripped NameTag facial recognition code from its AI app one day after WIRED exposed it on 50 million phones. Meta says no decision has been made.

Meta removed nearly all traces of an unreleased facial recognition system from its smart glasses companion app on Friday, one day after WIRED reported that the software had been quietly embedded in an app installed on more than 50 million phones. The feature, which Meta internally called NameTag, was designed to convert faces captured by the company’s Ray-Ban smart glasses into unique biometric signatures and compare them against a database stored on the user’s device. WIRED also found that faces the system failed to recognise were cropped, indexed, and stored locally for future processing.

Andy Stone, Meta’s vice president of communications, told WIRED on Monday that the feature is “purely exploratory,” adding that no final decision has been made on what to do with it. That characterisation sits uneasily with the evidence WIRED documented. The version of Meta AI published the day of WIRED’s Thursday report contained several code libraries explicitly named for face recognition, a process for running the NameTag recognition pipeline, and a “Person recognised” alert the app would have shown if someone were identified.

Friday’s release stripped all of it out, along with a folder where the app would have stored the cropped images and biometric signatures of unrecognised faces. Meta did not answer WIRED’s questions about why the code was removed or whether the changes were planned before the story was published. A few fragments remain in the latest version, including an internal debug menu label and a dormant link meant to open a recognised person’s profile, pointing to parts of the system that are no longer there.

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The gap between Meta’s public statements and the code WIRED found is the central tension. Before the Thursday report, Stone dismissed the findings by writing that the company could not answer questions about how the system would work because “the feature does not exist.” Andrew Bosworth, Meta’s chief technology officer, called the reporting “incredibly misleading” and “absolutely dishonest.” Yet the code was functional enough to include three AI models, one to detect faces, another to crop them, and a third to encode them as biometric data, all embedded in the companion app for a product already at the centre of a mounting privacy crisis.

Meta declined to answer ten questions WIRED posed before publishing, including whether it had already created the database of face profiles NameTag uses, how long the app retains photographs and biometric data of unrecognised people, and whether that data would ever be sent back to Meta’s servers. The company also did not respond to questions about whether it was building NameTag for blind or low-vision users, or to criticism from privacy advocates who warned the system could let stalkers and abusers identify strangers in public.

NameTag first surfaced in February, when The New York Times, citing internal Meta documents, reported that the company was developing face recognition for its smart glasses and considering a launch as early as this year. One internal memo reportedly described releasing the feature during a “dynamic political environment” when privacy and civil liberties advocates would be distracted by other concerns. WIRED subsequently found that much of NameTag’s machinery had been built into the Meta AI app as early as January, months before any public acknowledgement, adding another layer to the company’s pattern of shipping first and disclosing later when it comes to its smart glasses.

Kade Crockford, director of the technology for liberty programme at the American Civil Liberties Union of Massachusetts, said the removal does not undo the original decision to ship the code and pointed to it as evidence that consumer privacy needs stronger legal protection than Congress has been willing to provide. The Massachusetts House of Representatives last week unanimously passed a consumer privacy bill that, if enacted as written, would impose strong enforcement provisions including a private right of action allowing aggrieved users to sue. “State lawmakers need to do their job and step up to protect consumer privacy,” Crockford said.

Meta’s sneaky tactics in slipping the face-recognition code into its smart glasses show exactly why data privacy bills need the teeth of strong enforcement,” Crockford added. “Companies like Meta prioritise their bottom line, so lawmakers need to speak in the only language its C-suite understands.” Whether a code removal prompted by investigative reporting constitutes a victory or merely a tactical retreat depends on what Meta does next, and on whether the regulatory pressure building on both sides of the Atlantic produces enforceable consequences before the feature quietly returns under a different name.



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