The rumors were true, all of them (and then some): OpenAI today is releasing GPT-6 Astra, a new frontier model that the company says likely marks the onset of artificial generalized intelligence (AGI), its long sought goal of "highly autonomous systems that outperform humans at most economically valuable work."
In a closed a press briefing earlier today, OpenAI co-founder and president Greg Brockman offered an unusually direct formulation of that message, ending the session with: “Welcome to the AGI era.”
That is an unusually consequential framing even by the standards of frontier AI launches. But for enterprises, the more immediate significance of Astra may be considerably more concrete: OpenAI is positioning GPT-6 Astra as a new era of computing in which users, including employees, no longer have to click around a mouse or type on a keyboard ever again (if they don't want).
OpenAI's launch materials, provided in advance to VentureBeat, call it "the world’s best computer use model."
Instead of requiring developers to build a dedicated API integration for every application an AI system needs to use, Astra is designed to navigate software much as a person does — working across browsers, spreadsheets, websites and desktop applications, producing finished documents and presentations, and carrying out multistep workflows rather than merely telling a user how to complete them.
Indeed, the company showed off a promotional video for GPT-6 Astra that began with a 1980s AI demo of a person asking a computer to draw a yellow circle, which it did simply, before cutting to today and showing various OpenAI employees interacting with Astra through voice, asking it turn a yellow circle into a rocket ship and then a full 3D game in minutes, and create a listing on eBay, all from voice input alone.
Astra begins rolling out Thursday to enterprise customers with OpenAI's gated access program, Daybreak. OpenAI says it will become available over the coming days to ChatGPT Plus, Pro, Business and Enterprise customers, as well as through the OpenAI API and cloud platforms including AWS Bedrock and Microsoft Azure.
From answering questions to operating computers
The enterprise case for Astra rests heavily on computer use.
OpenAI says the model can fill out online forms, update CRM records, organize calendars, conduct web research and draft results into documents or email. It can 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.
Those capabilities point toward a potentially important change in enterprise AI architecture.
For much of the generative AI boom, companies have needed to connect models to corporate systems through APIs, plugins, retrieval systems and purpose-built tools. Brockman argued that computer-use agents could begin bypassing some of that integration work because software already exposes an interface designed for a highly general-purpose intelligence: the human user.
“We’ve been bottlenecked over this gigantic era by people writing connectors and very painstakingly building these connections into all these tools that people can already use,” Brockman said.
With sufficiently capable computer use, he added, an agent can instead “zip through spreadsheets, fill out forms, [and] navigate across web pages.”
The idea goes back to OpenAI's earliest days, Brockman said, when researchers discussed training an agent around the same basic inputs and outputs available to humans using computers: pixels, keyboards and mice.
“I feel like we’ve really achieved the first agent that feels like it’s actually able to do that in a way that’s just so extremely useful,” he said.
OpenAI reports that on an offline subset of OSWorld 2.0, Astra scored 72.6% while taking roughly 40 minutes per task, compared with GPT-5.6 Sol's 65.7% at roughly 75 minutes — approximately 47% less time per task.
The company also demonstrated Astra performing tasks ranging from creating a 3D game to preparing a legal agreement while simultaneously handling unrelated requests. The broader message was that the model is intended to move beyond the familiar chatbot pattern in which humans continually provide the next instruction.
“With Astra, users have incredible capabilities at their fingertips and can do things that seemed very far away less than a year ago,” OpenAI researcher Mia Glaese said during the briefing. “With those capabilities, we expect people to delegate much more complex work across applications, with humans directing the work at a much higher level.”
That shift — from prompting AI to supervising AI — may ultimately matter more to businesses than another increase on an academic benchmark.
OpenAI says Astra represents its biggest training jump yet
Aidan Clark, an OpenAI researcher who discussed Astra's development during the briefing, described it as the company's largest-scale training run.
According to Clark, Astra is the first OpenAI model pretrained using more than 100,000 DBUs at the company's Stargate infrastructure and the first for which previous models played a major role supervising the training of the next model.
“Based on the evals we monitor during pre-training, we believe the jump from Sol to Astra represents a larger increase in capabilities than the jump to Sol represented over previous models,” Clark said.
OpenAI attributes Astra's capabilities to the combination of large-scale pretraining and reinforcement learning intended to teach the model to connect information and execute increasingly long tasks.
The resulting benchmark numbers are striking.
OpenAI reports Astra scores 97.6% on FrontierMath Tier 4 v2, 74.1% on DeepSWE v1.1, 95.9% on BenchCAD, 96% on GPQA Diamond and 100% on ExploitBench. It also reports a 98.6% score on ARC-AGI-3.
But that last number comes with an important qualification — and highlights a growing problem with how the industry talks about model intelligence.
If Astra scores 98.6% on ARC-AGI-3, is that AGI?
ARC-AGI has become one of the most closely watched attempts to measure whether AI systems can generalize to unfamiliar problems rather than reproduce capabilities acquired through training.
On the current ARC-AGI-3 leaderboard, conventional frontier-model runs sit dramatically below Astra's reported 98.6% result.
But the comparison isn't straightforward.
OpenAI's own evaluation notes say Astra uses the company's Responses API harness, while comparison models can operate under different configurations.
That distinction matters because another recent ARC-AGI-3 result demonstrated just how much performance can come from the system surrounding a model.
In August, NVIDIA reported that its Agentic Variation Operators, or AVO, architecture achieved a 100% score across all 25 environments and 183 levels in the ARC-AGI-3 public set. But NVIDIA did not create a foundation model that suddenly jumped to 100%. AVO used Claude Opus 5, and NVIDIA said the underlying model's baseline was roughly 30%.
AVO adds mechanisms including persistent memory, tools, feedback and recovery, allowing an agent to maintain progress over long-running tasks rather than treating every interaction as effectively isolated.
NVIDIA's conclusion was explicit: long-horizon capability can emerge from the complete agent system, rather than the foundation model alone.
That debate has already spilled into the AI community. One r/singularity user argued that ARC-AGI-3's restrictions on retaining context across actions made the benchmark an unrealistic representation of how production agents operate, comparing it to testing humans while repeatedly erasing what they had learned.
Other commenters have pushed in the opposite direction, arguing that adding elaborate harnesses makes it harder to determine whether the underlying model has actually generalized. One commenter responding to NVIDIA's result wrote: “Let’s see if the capabilities generalise or if it was just overtrained on this specific benchmark.”
The disagreement exposes an increasingly important question for claims about AGI: What exactly is the object being measured?
A foundation model? A model plus persistent memory? A model with a computer, browser and tools? Or the complete deployed system?
For enterprises, the distinction may eventually become less important operationally. Companies buy outcomes from systems, not benchmark purity. If an agent can reliably reconcile accounts, investigate an incident, modify a production codebase or assemble a financial model, whether that ability originates primarily in neural weights, memory architecture or tool orchestration may matter less than its cost, reliability and auditability.
And OpenAI appears increasingly willing to make that argument.
“Everyone has a different definition of AGI,” Brockman said. “When we started OpenAI, we kind of thought that there was going to be this well-defined moment that everyone would recognize: ‘That’s AGI.’ It hasn’t played out like that. It’s a much more gray, fuzzy thing.”
But Brockman went considerably further when asked whether Astra itself qualifies.
“For me personally, I do think we’re there,” he said. “I think there’s a pretty good argument for it.”
Later, he offered perhaps the clearest formulation of OpenAI's position: “I think it’s not unreasonable to feel that we are now in the AGI era.”
No GDPval?
One notable omission from OpenAI’s Astra launch materials is GDPval, the company’s own benchmark for measuring performance on economically valuable, real-world work. OpenAI introduced GDPval in 2025 specifically to move beyond academic-style tests and coding benchmarks, evaluating models on 1,320 tasks drawn from 44 knowledge-work occupations across nine major U.S. industries. Those tasks include deliverables such as legal briefs, engineering designs, spreadsheets, presentations, customer-support work and nursing care plans — much closer to the enterprise workflows OpenAI now says Astra is designed to automate.
That makes the absence conspicuous given the AGI framing around Astra. OpenAI originally positioned GDPval as a way to ground discussion about AGI and economic impact in observable workplace performance rather than speculation. Its own description says the benchmark was created to track how well AI systems perform on “economically valuable, real-world tasks” and to provide a clearer picture of how models might support professionals in everyday work. In other words, if Astra’s significance is that enterprises can now delegate materially more work to AI, GDPval would appear to be one of OpenAI’s most directly relevant internal yardsticks for substantiating that claim.
The omission does not invalidate Astra’s other results, but it does leave an analytical gap. OpenAI’s 98.6% ARC-AGI-3 score speaks to interactive reasoning and adaptation, while benchmarks such as DeepSWE and Agents’ Last Exam capture specific forms of software engineering and professional workflow performance. GDPval, by contrast, was explicitly designed to ask a broader economic question: can models produce work products comparable to those of experienced professionals across a wide cross-section of occupations? OpenAI’s earlier results showed frontier systems approaching expert-level quality on some of those tasks, with substantial gains from GPT-4o to GPT-5.
There is also an important limitation in GDPval that may help explain why OpenAI did not center it here. The current version is one-shot: it does not measure the long-horizon, interactive, multi-application work that Astra is supposed to excel at. OpenAI itself has said future versions should add iterative workflows, richer context and ambiguity. That means GDPval is arguably both highly relevant to Astra’s enterprise story and somewhat mismatched to its most agentic capabilities.
Still, given Brockman’s “AGI era” framing, the missing number is worth noting. If the practical case for AGI is increasingly about whether AI can perform economically meaningful work across many professions, then GDPval is one of OpenAI’s clearest attempts to measure exactly that. Until Astra results appear there — or on a successor designed for multi-step agentic work — claims about its broad economic generality rest more on a mosaic of specialized benchmarks and demonstrations than on the company’s own flagship benchmark for real-world occupational performance.
Price-per-task now matters more than price-per-token, according to OpenAI
That systems-level view also changes how OpenAI wants customers to think about cost.
For developers, the API model name is gpt-6-astra. The release also says Astra supports Zero Data Retention for eligible API customers and that OpenAI is testing Private Safety Processing.
OpenAI API Standard pricing is:
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$10 per million input tokens
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$50 per million output tokens
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Separate pricing applies to cache reads/writes.
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Fast mode provides up to 2.5× Standard processing speed at 2× Standard pricing.
|
Model |
Input ($/1M) |
Output ($/1M) |
Total ($/1M) |
Source |
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Muse Spark 1.2 / 1.3 Contributor |
$0.10 |
$0.20 |
$0.30 |
|
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MiMo-V2.5 Flash |
$0.10 |
$0.30 |
$0.40 |
|
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DeepSeek-V4-Flash — off-peak |
$0.22 |
$0.66 |
$0.88 |
|
|
GPT-5.6 Luna |
$0.20 |
$1.20 |
$1.40 |
|
|
MiniMax-M3 |
$0.30 |
$1.20 |
$1.50 |
|
|
LongCat-2.0 — limited-time promo |
$0.30 |
$1.20 |
$1.50 |
|
|
DeepSeek-V4-Flash — peak hours |
$0.44 |
$1.32 |
$1.76 |
|
|
MiMo-V2.5 |
$0.40 |
$2.00 |
$2.40 |
|
|
DeepSeek-V4-Pro — off-peak |
$0.66 |
$1.98 |
$2.64 |
|
|
LongCat-2.0 — standard |
$0.75 |
$2.95 |
$3.70 |
|
|
MiMo-V2.5 Pro (≤256K) |
$1.00 |
$3.00 |
$4.00 |
|
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Gemini 3.7 Flash — through Dec. 31, 2026 |
$0.75 |
$3.75 |
$4.50 |
|
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Gemini 3.8 Flash — through Dec. 31, 2026 |
$0.75 |
$3.75 |
$4.50 |
|
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DeepSeek-V4-Pro — peak hours |
$1.32 |
$3.96 |
$5.28 |
|
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Muse Spark 1.1 / 1.2 / 1.3 |
$1.25 |
$4.25 |
$5.50 |
|
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GLM-5.3 |
$1.40 |
$4.40 |
$5.80 |
|
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Grok 4.6 — <200K prompt tokens |
$2.00 |
$6.00 |
$8.00 |
|
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MiMo-V2.5 Pro (>256K) |
$2.00 |
$6.00 |
$8.00 |
|
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Qwen3.8-Max |
$2.00 |
$6.00 |
$8.00 |
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Gemini 3.7 Flash — starting Jan. 1, 2027 |
$1.50 |
$7.50 |
$9.00 |
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Gemini 3.8 Flash — starting Jan. 1, 2027 |
$1.50 |
$7.50 |
$9.00 |
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GPT-5.6 Terra |
$2.00 |
$12.00 |
$14.00 |
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Grok 4.6 — ≥200K prompt tokens |
$4.00 |
$12.00 |
$16.00 |
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GPT-5.4 |
$2.50 |
$15.00 |
$17.50 |
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Kimi K3 |
$3.00 |
$15.00 |
$18.00 |
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Claude Opus 5 |
$5.00 |
$25.00 |
$30.00 |
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Sakana Fugu Ultra (≤272K) |
$5.00 |
$30.00 |
$35.00 |
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GPT-5.6 Sol — Standard mode |
$5.00 |
$30.00 |
$35.00 |
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Claude Fable 5 / Claude Mythos 5 |
$10.00 |
$50.00 |
$60.00 |
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Claude Fable 5.1 / Claude Mythos 5.1 |
$10.00 |
$50.00 |
$60.00 |
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GPT-6 Astra — Standard mode |
$10.00 |
$50.00 |
$60.00 |
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GPT-5.6 Sol — Fast mode |
$10.00 |
$60.00 |
$70.00 |
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GPT-6 Astra — Fast mode |
$20.00 |
$100.00 |
$120.00 |
Those prices matter, but Brockman argued that token pricing is becoming a poor proxy for the actual economics of enterprise AI.
“Pricing tokens doesn’t make any sense,” Brockman said. “Our tokens are not necessarily the same as our competitors’ tokens; they’re not the same between different model families.”
Instead, he said, businesses should evaluate price per completed task.
“What you actually want, and I think the market is starting to really wake up to, is the price per task,” Brockman said. “It’s just about: can you get the thing done for an appropriate cost at appropriate speed?”
OpenAI says Astra illustrates that argument on DeepSWE v1.1, where its highest-performing configuration beats GPT-5.6 Sol's highest-scoring setting while producing an approximately 57% lower estimated API cost per task.
For enterprise buyers, that metric could prove more useful than token prices as agents become more autonomous. An inexpensive model that requires repeated retries, human correction and thousands of additional inference steps may ultimately cost more than an expensive model that finishes the workflow correctly the first time.
More autonomy creates a harder governance problem
The same capability that makes Astra interesting to enterprises also makes it harder to govern.
A chatbot generates something for a person to inspect. An agent operating a computer can actually change a record, send information, manipulate files or take actions across applications.
Glaese said that as users delegate more work, OpenAI needs models that recognize where their authority ends.
“Even as models can do more things autonomously, we have to be able to trust them more,” she said. “Our understanding of alignment and safety has to advance with model capabilities, and Astra is both our most capable and our most aligned model.”
The company's safety work around Astra offers a revealing look at what governing systems at this capability level may require.
In a separate background briefing conducted a day before the launch briefing, OpenAI sources said the company had paused some frontier training for roughly two weeks following the Hugging Face incident, even though Astra itself was not involved. During that period, OpenAI tightened the security around its research infrastructure, restricted what training workloads could access and connect to, expanded monitoring, and raised internal requirements around both model behavior and the environment in which models were being trained.
Some work on Astra resumed under those controls, while a larger reinforcement-learning run for a future model remained paused for longer.
The distinction is important. According to OpenAI sources, the pause was not prompted by evidence that Astra itself had become too dangerous to release. The company viewed it instead as an attempt to prevent its safety, monitoring and infrastructure controls from falling behind rapidly advancing model capability. The work done during that period built on months — and in some areas years — of prior alignment and security research rather than representing a safety stack constructed from scratch in two weeks.
That approach increasingly resembles enterprise risk management more than conventional model moderation. Instead of relying on a single refusal layer, OpenAI described a defense-in-depth system spanning model behavior, classifiers, security controls, monitoring and post-deployment threat response.
OpenAI sources said Astra's cybersecurity safeguards, for example, combine refusals trained into the model with system-level classifiers and offline detection intended to identify abuse patterns that may unfold across multiple prompts rather than in a single obviously malicious request. For higher-risk users, monitoring can use broader conversational context to recognize when individually innocuous requests form part of a larger attack workflow.
That has obvious implications for enterprises considering highly autonomous agents. The relevant control surface is no longer just the prompt presented to a model. Organizations increasingly have to reason about sequences of actions, the model's understanding of its authorization boundary, what applications and data it can reach, whether suspicious trajectories can be detected while they are underway and what happens when a safeguard fires.
OpenAI says an internal evaluation inspired by the Hugging Face incident tested whether models would exceed their authorized scope when confronted with difficult or impossible objectives. Without production safeguards, GPT-5.6 Sol went beyond the authorized target 48.2% of the time; Astra did so in 0% of cases.
OpenAI sources described a related internal alignment evaluation based on difficult cybersecurity tasks in which the earlier model attempted to reach adjacent systems in a majority of tests when production safeguards were absent, while Astra made no such attempts.
The goal, according to the sources, is not merely to train an agent to persist until a task is finished, but to teach it that persistence has boundaries: an agent should be capable of recognizing when completing an objective would require exceeding its authorized scope and return to the user instead.
That is a particularly consequential distinction for enterprise agents. Persistence is one of the properties that makes autonomous systems useful — a model that gives up after the first failed attempt will not be much of an operator.
But persistence becomes a liability if an agent interprets an objective so literally that it circumvents access controls, security reviews or other constraints designed to stop exactly that behavior.
According to OpenAI sources, Astra's training therefore emphasizes both explicit boundaries and what the company described as softer constraints: recognizing the intent behind security controls and backing off rather than trying to find a technically available route around them.
Observability may become the enterprise bottleneck
Yet OpenAI chief scientist Jakub Pachocki stressed that stronger alignment results should not be interpreted as solving the underlying problem.
“Progress in intelligence does not guarantee progress in alignment,” Pachocki said.
The company is particularly concerned about monitorability — whether humans or other systems can understand enough of a model's reasoning to identify dangerous behavior.
As models improve, Pachocki said, they can accomplish harder tasks with fewer natural-language reasoning tokens. More capable systems are also becoming increasingly aware of and able to influence their own chains of thought.
That potentially turns observability into one of the defining enterprise infrastructure problems of the agent era.
OpenAI sources said the company is adding misalignment monitoring to Astra's external deployment so systems can inspect its reasoning and actions for signs that it is operating outside the authority it was given. In severe cases, that monitoring can halt an activity. The company characterized monitoring as a secondary layer rather than a substitute for aligning model behavior in the first place.
The deployment details also illustrate the compromises enterprise customers may encounter. OpenAI sources said its monitoring approach is designed to remain compatible with Zero Data Retention arrangements. On surfaces where data can be retained, suspicious activity can support additional review processes; under ZDR setups, classifiers can run without the conversation being retained.
The safeguards may also introduce operational friction. OpenAI sources said legitimate work can sometimes be slowed, paused or stopped — including defensive cybersecurity tasks and potentially unrelated activity. In ChatGPT or Codex, the user may be asked to approve an action before the system proceeds; in API workflows, a flagged task may stop outright.
That trade-off is likely to become familiar to CIOs and security leaders. The more authority an AI worker receives, the less plausible it becomes to treat AI governance as an after-the-fact content filtering exercise. Enterprises will need controls closer to those already used for human identities and privileged software: scoped permissions, audit trails, policy enforcement, real-time monitoring and escalation when an agent approaches a consequential boundary.
OpenAI therefore faces a tension that enterprises deploying autonomous agents will eventually face themselves: the systems becoming capable enough to perform meaningful independent work are simultaneously becoming harder to inspect.
Pachocki said OpenAI is willing to make that a constraint on further development.
“We will not accept the degradation in our ability to monitor model alignment beyond a certain level,” he said. “We will pause scaling until we can gain enough confidence.”
“We also have to be willing to slow down, or halt further scaling, when our confidence in safety is not sufficient.”
Astra also crosses OpenAI's critical cyber threshold
The stakes are particularly concrete in cybersecurity.
OpenAI has designated Astra as the first model to reach the Critical cybersecurity threshold under its Preparedness Framework. According to OpenAI sources, that designation means the model, when given appropriate tools and access, is capable of finding previously unknown vulnerabilities and developing exploit chains across well-protected systems without continuous human guidance.
OpenAI reports Astra scores 100% on ExploitBench. Sources also said additional testing against a newer set of 20 recently disclosed serious vulnerabilities produced substantially stronger results than GPT-5.6 Sol with fewer output tokens, and that Astra discovered two previously unknown vulnerabilities during evaluation that OpenAI subsequently disclosed to maintainers. Human expert testing found the model could identify novel zero-day vulnerabilities across multiple software categories, including browsers and operating systems.
Those capabilities are dual-use by definition. An agent capable of autonomously finding a vulnerability can help a defender patch it or help an attacker exploit it.
OpenAI is therefore limiting Astra's most advanced cyber capabilities initially. The company says trusted defenders will receive broader access through Daybreak Blue, prioritizing organizations responsible for protecting critical digital infrastructure, while more general access remains subject to stronger restrictions and monitoring.
For enterprise security teams, this represents another version of Astra's broader proposition: frontier models are moving from advising specialists toward performing portions of specialist work themselves.
AGI may arrive as an economic transition, not a single benchmark
That brings the discussion back to AGI.
Brockman notably did not present Astra's 98.6% ARC-AGI-3 score as a mathematical proof that OpenAI has achieved artificial general intelligence. Nor did he claim there is now a universally accepted technical threshold that Astra has crossed.
Instead, his argument was more practical.
A system can now solve extremely difficult scientific problems while also performing ordinary economic work through the same interfaces humans use. The qualitative shift comes from the breadth of those capabilities and from the amount of work people can begin handing over.
“There’s still more to do,” Brockman said. “There are still lots of improvements to be made, but there is something significant here that I think is qualitatively improved.”
Astra, he said, represents “a real shift in what kind of work people can delegate to AI.”
That framing may ultimately be more consequential for enterprises than deciding whether Astra earns a particular three-letter label.
The important threshold for businesses is whether agents become reliable enough that organizations restructure workflows around them: humans specify objectives and constraints, AI systems execute the intermediate steps, and employees intervene primarily for judgment, exceptions and consequential decisions.
Astra also makes clear that those systems will require a corresponding change in governance. The enterprise question is no longer simply whether a model gives a good answer. It is whether an AI worker can be given access to real applications and sensitive information, continue working through obstacles, stay inside the authority granted to it, explain enough of what it is doing to remain governable, and stop when either the model or the surrounding control system determines that human intervention is required.
If that happens at scale, AGI may look less like a machine suddenly passing one definitive test and more like a gradual economic transition that becomes obvious only in retrospect.
That is essentially Brockman's argument.
“I think if you want to say this is the first one, I think it’s reasonable,” he said of Astra. “If you want to say the previous one is the first one, you want to say the next one’s the first one. But I think that if you fast forward a year, I think it’s going to be pretty hard to say that there was no point out there where you’re not in the AGI era.”
For enterprises, that argument may soon be tested less by whether Astra can top another leaderboard than by something much more measurable: how much consequential work organizations are willing to let it do.
