OpenAI launches GPT-6 Astra and proclaims the start of the 'AGI era'

OpenAI has unveiled GPT-6 Astra, its new frontier model, with an unusually forceful statement from its president and co-founder, Greg Brockman, who closed the closed-door presentation with the line "Welcome to the AGI era". The company argues that Astra could mark the beginning of artificial general intelligence (AGI), its stated goal for years of building "highly autonomous systems that outperform humans at most economically valuable work". It is a striking claim even by the usual standards of frontier AI launches, and Brockman himself later qualified that, for him personally, "there's a pretty good argument" for considering that point already reached, although he acknowledged that the definition of AGI remains "a much greyer and fuzzier matter" than OpenAI imagined in its early days.

Beyond the headline, the message with the most immediate implications for business is that OpenAI presents Astra as the "world's best computer use model", according to its launch materials. Instead of relying on app-specific API integrations, Astra is designed to navigate software much as a person would: moving through browsers, spreadsheets, websites and desktop applications, producing finished documents and presentations, and executing multi-step workflows rather than merely explaining how to carry them out. In the promotional video shown during the presentation, it moved from a 1980s AI demo in which a computer was asked to draw a yellow circle, to present-day OpenAI employees interacting with Astra by voice to turn that circle into a spaceship and then into a full 3D game within minutes, as well as creating an eBay listing using spoken instructions alone.

Astra begins rolling out this Thursday to enterprise customers through Daybreak, OpenAI's restricted access program, and will reach ChatGPT Plus, Pro, Business and Enterprise customers in the coming days, as well as arriving through OpenAI's API and cloud platforms such as AWS Bedrock and Microsoft Azure. According to the company, the model can fill in web forms, update CRM records, organize calendars, research online and write up the results in documents or emails; it can also manipulate spreadsheets, analyze scientific data in Python notebooks, work in Power BI, create and test websites, operate engineering applications such as KiCad and FreeCAD, and install and troubleshoot software.

Brockman argued that sufficiently capable computer use could start to replace much of the integration work that enterprise generative AI has demanded until now, because software already exposes an interface designed for a kind of general intelligence: the human user. In his words, the industry has been "stuck during this giant era by people writing connectors and painstakingly building these connections to all the tools that people can already use"; with sufficiently capable computer use, an agent can instead "go through spreadsheets, fill out forms [and] browse web pages". According to Brockman, the idea goes back to OpenAI's earliest days, when there was already talk of training an agent on the same inputs and outputs available to a human sitting in front of a computer: pixels, keyboard and mouse.

On performance, OpenAI says that on an offline subset of OSWorld 2.0, Astra scored 72.6% accuracy, taking about 40 minutes per task, against 65.7% for GPT-5.6 Sol in roughly 75 minutes, some 47% less time per task. OpenAI researcher Mia Glaese stressed that the goal is not just to improve scores but to change the interaction pattern: users are expected to delegate far more complex work across applications, directing that work at a higher level, instead of giving step-by-step instructions as with the traditional chatbot. That shift, from "writing prompts" to "supervising agents", could matter more to companies than any single improvement on an academic benchmark.

On the technical side, OpenAI researcher Aidan Clark described Astra as the company's largest-scale training run to date: OpenAI's first model pretrained using more than 100,000 DBU on the Stargate infrastructure, and the first in which earlier models played a significant role in supervising the training of the next one. According to Clark, the indicators observed during pretraining suggest that the capability jump between Sol and Astra is bigger than the jump to Sol was over previous models. The benchmark figures released by OpenAI are indeed notable: 97.6% on FrontierMath Tier 4 v2, 74.1% on DeepSWE v1.1, 95.9% on BenchCAD, 96% on GPQA Diamond, 100% on ExploitBench and 98.6% on ARC-AGI-3.

That last figure —98.6% on ARC-AGI-3, a benchmark designed to measure the ability to generalize to unseen problems rather than to reproduce skills acquired through training— comes with an important caveat that exposes a growing problem in how the industry talks about model intelligence. OpenAI's own evaluation notes indicate that Astra uses the company's Responses API harness, while the comparison models may run under different configurations, which makes it hard to compare results directly. The article recalls that in August, NVIDIA had already shown how much performance can depend on the system surrounding the model: its AVO (Agentic Variation Operators) architecture achieved 100% across the 25 environments and 183 levels of the public ARC-AGI-3 set, but not thanks to a foundation model that suddenly leapt to 100%, rather by using Claude Opus 5 —whose base performance was around 30%— with the addition of persistent memory, tools, feedback and recovery mechanisms. NVIDIA's explicit conclusion was that long-horizon task capability can emerge from the complete agent system, not just from the foundation model.

That debate has already sparked discussion in the community: on the r/singularity forum, one user argued that ARC-AGI-3's restrictions on retaining context between actions make the benchmark an unrealistic representation of how agents operate in production, comparing it to evaluating humans by repeatedly wiping what they have learned; other commenters took the opposite view, noting that adding elaborate harnesses makes it harder to determine whether the underlying model has really generalized, with remarks such as "let's see if the capabilities generalize or if it was simply over-trained for this specific benchmark". The underlying question —what exactly is being measured: the foundation model, the model with persistent memory, the model with a browser and tools, or the full deployed system?— is increasingly relevant to claims about AGI, although the article notes that, operationally, companies may care less and less where the capability comes from and more about its cost, reliability and auditability when it comes to solving real tasks.

One striking point flagged in the analysis is the absence of results on GDPval, OpenAI's own benchmark designed in 2025 to measure performance on real-world economically valuable work, with 1,320 tasks drawn from 44 knowledge occupations across nine major U.S. industries (legal briefs, engineering designs, spreadsheets, presentations, customer support and nursing care plans, among others). Given that OpenAI originally presented GDPval as a way to anchor the debate on AGI and economic impact in observable job performance rather than speculation, its absence from the Astra launch —precisely the model the company says will allow far more real work to be delegated— is conspicuous. The article also points to a relevant limitation: the current version of GDPval is one-shot and does not measure the interactive, long-horizon work across multiple applications at which Astra is supposed to excel, which could partly explain why OpenAI did not put it at the center of this launch. Even so, as long as there are no Astra results on GDPval or on a successor designed for multi-step agentic work, claims about its economic generality rest more on a patchwork of specialized benchmarks and demonstrations than on OpenAI's own flagship measure of real occupational performance.

On pricing, the model is identified in the API as gpt-6-astra, supports Zero Data Retention for eligible API customers, and OpenAI is testing a "Private Safety Processing". The standard API price is $10 per million input tokens and $50 per million output tokens (with different rates for cache reads/writes), while the fast mode offers up to 2.5 times standard processing speed at twice the price. Compared with the pricing table for other models on the market included in the article, Astra in standard mode ($10/$50, a total of 60 per million) sits among the most expensive models alongside Claude Fable/Mythos 5 and 5.1, and its fast mode (20/100, a total of 120 per million) is the most expensive in the entire table.

Brockman argued, however, that price per token is an increasingly poor indicator of the real economics of enterprise AI, since "our tokens are not necessarily the same as our competitors' tokens; they are not the same across different model families". Instead, he made the case for assessing price per completed task: "what you really want, and I think the market is starting to wake up to this, is price per task... it's about whether you can get the thing done at an appropriate cost and at an appropriate speed". OpenAI illustrates the argument with data from DeepSWE v1.1, where Astra's highest-performing configuration beats GPT-5.6 Sol's best configuration at an estimated API cost per task roughly 57% lower. The logic is that a cheap model that needs repeated retries, human corrections and thousands of extra inference steps can end up costing more than an expensive model that completes the workflow correctly the first time.

Astra's greater autonomy also raises a harder governance problem: a chatbot produces something for a person to review, but an agent operating a computer can modify a record, send information, manipulate files or genuinely act across multiple applications. According to Glaese, as users delegate more work, OpenAI needs models that recognize where their authority ends, and its understanding of alignment and safety must advance at the same pace as the model's capabilities. In a separate briefing held the day before the launch, OpenAI sources indicated that the company paused part of its frontier training for roughly two weeks after the Hugging Face incident, although Astra was not involved in that incident. During that period, the security of the research infrastructure was strengthened, what training workloads could access and connect to was restricted, monitoring was expanded, and internal requirements were raised both for model behavior and for the environment in which it was trained. Some of the work on Astra resumed under those tightened controls, while a larger reinforcement learning run intended for a future model stayed paused for longer.

According to OpenAI sources cited in the article, the pause was not prompted by evidence that Astra itself had become too dangerous to release, but by an attempt to keep safety, monitoring and infrastructure controls from falling behind rapidly advancing model capabilities. The company described a defense-in-depth system combining behavior trained into the model itself, system-level classifiers and offline detection designed to identify abuse patterns that can be spread across several prompts, not just an obvious, isolated malicious request; for higher-risk users, monitoring can draw on broader conversational context to recognize when individually innocuous requests are part of a larger attack flow.

This has direct implications for companies considering deploying highly autonomous agents: the relevant control surface is no longer just the prompt submitted to a model, but the full sequences of actions, the model's understanding of its authorization limits, which applications and data it can access, whether suspicious trajectories can be detected while they are happening, and what happens when a safeguard is triggered. OpenAI mentions an internal evaluation inspired by the Hugging Face incident that tested whether models would exceed their authorized scope when faced with difficult or impossible objectives: without production safeguards, GPT-5.6 Sol went beyond the authorized objective in 48.2% of cases, against 0% for Astra. At the presentation, Glaese said Astra is both the company's most capable and its most aligned model.