GPT-6 Astra: A new generation of intelligence

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A new generation of intelligence

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We’re introducing GPT‑6 Astra, the world’s most intelligent and aligned model.

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GPT‑6 Astra brings together years of research and big bets across pre-training, reinforcement learning, and alignment. Astra is state-of-the-art on computer use, browsing, software engineering, cybersecurity, science, and professional work. Astra saturates FrontierMath Tier 4 with a 98% score, having already helped solve long-standing open problems in mathematics. Astra also saturates ARC-AGI-3 with a 99.9% score and ExploitBench with a 100% score. It also sets a new frontier on computer and browser use, handling the most demanding professional work with unmatched speed, accuracy, and judgment.

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AutomationBench tests whether agents can complete multistep business workflows across applications. GPT‑6 Astra reaches a new high among the reported results, completing 41.4% of tasks, compared with 31.4% for Claude Fable 5.1 and 26.9% for Claude Opus 5.

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Astra is our most aligned model, with substantial improvements in understanding user intent and model behavior—you can delegate tasks with greater confidence in Astra’s judgment. As one way that we test this, we built a new evaluation informed by the Hugging Face incident that evaluates whether a model facing a difficult or impossible task will go beyond its intended scope. Compared to GPT‑5.6 Sol, which without production safeguards went beyond the authorized target 48% of the time, GPT‑6 Astra did this in 0% of cases.

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The world’s best computer use model

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A step change in professional work

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GPT‑6 Astra pairs advances in computer use with targeted training for professional environments, to help tackle complex work tasks. It combines the intelligence required for complex problems with the ability to carry out multistep workflows and produce polished documents, spreadsheets, and presentations.

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BenchCAD tests whether models can reconstruct 3D objects from multi-view renders by generating CAD code. With tools, GPT‑6 Astra reaches a new high in the comparison shown, achieving a 95.9% geometric-overlap score, versus 83.3% for GPT‑5.6 Sol and 84.3% reported for Claude Fable 5.1.⁵ Estimated API cost is approximately 43% lower than Sol and 86% lower than Fable 5.1 in the configurations shown.

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GPT‑6 Astra is our best model for adhering to existing templates and producing slides that are well laid out and succinctly convey key points with a structured narrative. It creates clear, well-structured documents, presentations, spreadsheets, and analyses that follow your templates and match your writing and visual style. Astra is also trained to specifically pull only the context that matters into outputs, instead of repeating information unnecessary for the work at hand. All this means it can output more immediately usable artifacts that match your business context and standards.

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GPT‑6 Astra creates a slideshow about GPT‑Gaia, a fictional model, using just a few slides from OpenAI’s presentation template, capturing the correct tone and layout throughout. This means you can expect slide decks that are correctly formatted for your business standards.

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GPT‑6 Astra also brings stronger visual judgment to the websites, games, applications, and renderings it builds. With Sites(opens in a new window) in ChatGPT, Astra can create, host, and share websites, web apps, and games directly from a prompt.

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“Astra gives us a significant advantage in both capability and efficiency. It successfully executes our most complex creative workflows while using up to 20% fewer tokens than other models we've tested. Most importantly, for our customers, it means higher quality output.”

Alex Mashrabov, CEO and Co-founder, Higgsfield AI

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GPT‑6 Astra models a house in Blender and turns it into a walkable scene in Unreal Engine 5, helping designers and clients explore the layout and experience the space before it’s built.

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The model can bring games to life through vivid graphics, engaging gameplay and accurate motion, allowing non-technical people to create and play custom games that go beyond rudimentary elements in minutes. Credit: Pietro Schirano.

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When instructions leave room for interpretation, GPT‑6 Astra is better than previous models at making the right call. It uses context to fill in routine gaps and asks focused questions when the answer could change the outcome. In Codex, it can ask asynchronously while continuing work that doesn’t depend on your reply. If you don’t respond, it proceeds with sensible assumptions where appropriate, but waits for your input on consequential decisions.

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The examples below show how Astra collaborates on everyday tasks where missing information can materially change the answer.

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A side-by-side comparison of GPT-5.6 Sol and GPT-6 Astra helping create a personal career website.

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Astra is also better at staying oriented as a task evolves. Earlier models sometimes treated steering messages as a new goal, losing track of the original request or earlier constraints. Astra incorporates new requirements, changes course when asked, and answers side questions without dropping the broader task.

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“Astra is a significant quality improvement over GPT‑5.6 Sol across complex legal tasks. In our early testing, Astra stood out by approaching legal work the way a discerning lawyer does: it distinguishes documents from established records, surfaces unsupported assumptions, and converts gaps into concrete drafting positions.”

Niko Grupen, Head of Applied Research, Harvey

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Coding

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GPT‑6 Astra is the best model for software engineering to date.

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“GPT‑6 Astra delivers state-of-the-art performance on our internal coding benchmarks and shows a clear step forward in trading intuition evaluations compared with GPT‑5.6 Sol. When used for agentic coding, GPT‑6 Astra communicates in a way that’s easier for developers to follow and produces code that requires less iteration to reach production quality.”

John Crepezzi, AI Assistants, Jane Street

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“We tested Astra across low, medium, and high effort on one of our first-generation evals, and it came out significantly ahead of GPT 5.6 Sol. Higher effort buys more iterations on a fresh build, more verification through browser testing, and a lean toward code execution over apply-patch. Understanding how a model spends its effort is how we give millions of builders a faster, more reliable path from idea to working app.”

Fabian Hedin, CTO & Co-founder, Lovable

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FrontierCode 1.1 Extended evaluates whether coding agents produce changes ready to merge into real codebases. GPT‑6 Astra nearly matches Claude Fable 5’s best score—64.5% versus 64.9%—at approximately 63% lower estimated API cost.⁸

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With Astra, we’re introducing a new way for Codex to preserve and retrieve context when the context window fills. Historically, models have used compaction to summarize work during long sessions, such as when debugging complex issues or tackling large refactors. Each compaction can leave out details about why a fix failed or how a component behaves. In Codex, Astra can keep notes across context windows, preserving accumulated details without repeatedly compressing them into a single summary. Earlier context windows remain searchable, so Astra can find requirements or test results from previous messages and tool outputs—even if that information wasn’t captured in its notes. You can enable this experimental feature in your Codex (opens in a new window)config.toml(opens in a new window), and it will become the default for Astra in the coming weeks.

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Advancing scientific discovery

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“The story is: end of one era, start of another.”

Greg Burnham, EpochAI

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GPT‑6 Astra is a major advance for scientific discovery, mathematics, and health. Today, we’re sharing two further results on the gaps between prime numbers.^{^{9, 10}}

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Astra also sets new records across a suite of math and science evaluations.

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GPQA Diamond tests graduate-level scientific reasoning in biology, chemistry, and physics. GPT‑6 Astra reaches a new high in the comparison shown at 96.0%. At a lower-cost setting, it also exceeds GPT‑5.6 Sol’s best score—94.9% versus 94.6%—at approximately 37% lower estimated API cost.

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Astra can help with the practical work behind scientific discovery. By combining scientific reasoning with computer use, it can work directly in specialized software to inspect data and explore results, helping researchers assess the evidence and decide what to investigate next.

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GPT‑6 Astra navigates scientific software to inspect sequencing quality and visualize genetic variation, helping researchers assess their data and identify where to focus further analysis.

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Cybersecurity

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As we discussed in our safety update, Astra is a significant jump in cyber capabilities and meets the Critical threshold(opens in a new window) in cybersecurity under our Preparedness Framework. Its ability to identify and develop zero-day exploits can help defenders find and patch weaknesses, but it also creates a need for stronger safeguards. To understand how far these capabilities extend, we ran Astra on internal and third-party expert evaluations.

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We first tested the model without production safeguards on ExploitBench and ExploitGym, which evaluate whether models can turn known software vulnerabilities into working exploits. On ExploitBench, Astra achieved a perfect score of 100%, compared with 78.5% for GPT‑5.6 Sol, our previous frontier cyber-capable model. On ExploitGym, Astra reached a 42.4% success rate, compared with 30.3% for GPT‑5.6 Sol, while using substantially fewer output tokens.^¹³

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Given concerns that exposure to historical software vulnerabilities may have affected benchmark results, we also evaluated Astra on two novel benchmarks. For one, we built an internal “ExploitBench (June–August 2026)” evaluation to test exploit development using vulnerabilities from the previous three months.^¹⁴ Astra achieved substantially higher arbitrary code-execution rates than GPT‑5.6 Sol on this dataset while using far fewer output tokens. During the evaluation, Astra even discovered and used two previously unknown zero-day vulnerabilities. We are disclosing both vulnerabilities to their maintainers.

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We also tested Astra on SRE-Bench^¹⁵, a benchmark that measures whether models can reverse engineer software binaries to understand its core logic without access to raw source code. Astra solved 88.0% of tasks in a single attempt and 99.2% within four attempts, compared with 55.9% and 68.7% for GPT‑5.6 Sol, respectively.

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Beyond benchmarks, expert-led assessments found that Astra, when run without production safeguards, could use previously unknown vulnerabilities to achieve arbitrary code execution in hardened browsers and create privilege-escalation exploits for hardened operating-systems.

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As we discussed in The Defender’s Window, frontier cyber capabilities can help defenders find weaknesses faster, but they also make those weaknesses easier to exploit, raising the urgency for defenders to adapt. With the version of Astra launching today, defenders can use it to complete tasks such as secure code review and patching.

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However, Astra will refuse to comply with more advanced cybersecurity tasks such as creating proof-of-concept exploits for vulnerabilities. Through OpenAI Daybreak, we plan to expand access and roll out less restrictive safeguards in the coming weeks. This will enable more defensive workflows, including vulnerability and proof-of-concept validation, malware analysis, and detection engineering.

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We have also strengthened our protections against potential cyber misuse, building upon our safeguards stack for GPT‑5.6 Sol. These include stronger model robustness to better withstand potential jailbreaks and more context for our monitoring systems. We have continued rigorous internal and external testing, including automated evaluations with our internal red-teaming attackers. More details about our cyber safeguards and testing are available in the Astra system card(opens in a new window) and our blog.

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Aligning and deploying GPT‑6 Astra responsibly

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Astra is our most aligned model. Astra excels at exercising care, respecting task boundaries, and communicating transparently. This work is the latest product of our long-running research program focused on training models that remain aligned with human intent from start to finish.

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In sensitive environments, Astra proceeds with care commensurate with its risk. In an evaluation of computer use tasks adversarially selected to elicit misbehavior, Astra was more successful at avoiding unintended consequences. Running with additional security measures offered by default yielded even stronger performance.

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Astra causes fewer misaligned outcomes than any other frontier models tested. For a fair comparison, we used a generic computer-using-agent harness (based on the native computer use tools available in both the OpenAI Responses API(opens in a new window) and Anthropic Messages API(opens in a new window)) and without additional protections that are normally deployed to Codex and ChatGPT Work users (auto-review, confirmation policy). ¹⁶

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Astra is also more likely to operate within the boundaries set by the user and implied by its environment. In an internal evaluation, Astra never attempted to circumvent a Codex Auto-Review denial. This held even when Auto-review was deliberately configured to be evadable and the task was impossible to complete otherwise. This respect for the environment restrictions is consistent with the results of our impossible cyber task evaluation that we shared in the intro of this post and other findings documented in our System Card.

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This evaluation tests how models respond to auto-review denials in knowledge-work tasks. Exploiting a poor user configuration to bypass auto-review counts as failure. Astra never attempted to circumvent auto-review.

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Astra additionally sets new marks in transparent user communication. In one evaluation, Astra is three times less likely than GPT 5.6-Sol to make inaccurate representations about its capabilities and affordances.

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In our capability-hallucination evaluation, Astra shows a substantial improvement over GPT‑5.6 Sol, making fewer misleading claims about its capabilities.

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Our evaluations found Astra’s written reasoning harder to monitor than GPT‑5.6 Sol’s, based on tests that explicitly asked it to evade monitoring. We attribute this to Astra’s greater control over written reasoning on simpler tasks and ability to solve problems with fewer written steps. Astra still appears to struggle to conceal the reasoning needed for complex tasks, but we take the decline seriously. Improving monitorability remains a research priority, and the accompanying system card(opens in a new window) details our findings and ongoing work.

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Alignment training is core to our approach to deployment. As additional layer of defenses, we also build system safeguards like Codex Auto-review(opens in a new window) and monitoring agents’ reasoning and actions to help detect and contain unsafe behavior. As described in our safety update, we are also deploying misalignment monitoring in production for Astra-class models in order to have visibility into misalignment, and help contain its worst instances. These safeguards resemble our monitoring for internal deployments and involve a system of classifiers which check the model’s reasoning and actions for unauthorized behavior and automatically stop potentially unauthorized activity.

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Given the significant increase in Astra’s cybersecurity capabilities, we are being especially careful to make this deployment safe and secure. Extra safety checks can sometimes slow, pause, or stop legitimate work, including defensive cybersecurity. If a task is paused in ChatGPT or Codex, you may be asked to review the action before continuing. In the API, the task will stop. These checks can sometimes interrupt legitimate work, and we are continuing to iterate on this system to reduce unnecessary interruptions. Misalignment monitoring cannot replace alignment: our goal is to build models that reliably stay within their authorized scope, so these protections do not need to intervene.

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Availability

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GPT‑6 Astra is rolling out today to a limited set of organizations and over the coming days will become available to all ChatGPT Plus, Pro, Business, and Enterprise users, as well as through the OpenAI API and AWS. Astra usage is included within the existing subscription allowances—users and businesses will also be able to purchase credits for additional usage. Users on the Pro, Business, and Enterprise plans will also get access to GPT‑6 Astra Pro. Enterprise administrators can enable Astra for their workspace; access is off by default at launch.

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Astra supports Zero Data Retention for eligible API customers, and as we shared last month, we're testing Private Safety Processing to strengthen safety monitoring while preserving customer privacy.

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For developers, GPT‑6 Astra is available in the OpenAI API as gpt-6-astra, and is also available in Amazon Bedrock. OpenAI API Standard pricing is $10 per million input tokens and $50 per million output tokens. Separate rates apply to cache reads and writes. Fast mode is available for GPT‑6 Astra in the API and delivers up to 2.5x the speed of Standard processing at 2x the Standard price.

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Computer Use

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Computer Use	GPT‑6 Astra	GPT‑5.6 Sol ²	Claude Fable 5.1	Claude Fable 5	Claude Opus 5	Gemini 3.8 Flash
Agents' Last Exam	59.3%	53.6%		48.7%	55.5%
OSWorld 2.0 (v2026.08.08, offline set, partial score)	72.6%	65.7%			70.2% ³
ScreenSpot-Pro (no tools)	92.7%	76.9%		87.3% ¹⁷

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Professional

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Professional	GPT‑6 Astra	GPT‑5.6 Sol	Claude Fable 5.1	Claude Fable 5	Claude Opus 5	Gemini 3.8 Flash
AutomationBench	41.4%	18.1%	31.4%	17.4%	26.9%
BenchCAD	95.9%	83.3%	84.3% ⁵	67.5% ⁵	82.1% ⁵
BrowseComp	91.5%	90.4%		87.4%	90.8%
OpenScore String Quartets (1 - OMR-NED)	0.84	0.19
Internal Design Tasks	50.0%	47.4%		35.8%
Internal Data Science Tasks	40.9%	30.5%		34.7%
Artificial Analysis Intelligence Index v4.1.1	61.2	60.9	65.7	62.1	63.1	58.7

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Coding

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Coding	GPT‑6 Astra	GPT‑5.6 Sol	Claude Fable 5.1	Claude Fable 5	Claude Opus 5	Gemini 3.8 Flash
Terminal-Bench 4.0	57.7%	37.3%	55.8%	42.0%	52.3%	19.1%
DeepSWE v1.1	74.1%	72.7%	67.4%	69.9%	73.7%	73.8%
FrontierCode 1.1 Extended (score)	64.5% ⁸	60.6%	63.6%	64.9%	63.6%	56.3%
FrontierCode 1.1 Main (score)	53.3% ⁸	47.5%	50.9%	53.5%	53.4%	43.6%
Internal Database Migration Tasks	63.9%	42.7%	57.8%	50.3%
Artificial Analysis Coding Agent Index v1.4	67.0	65.1		67.2	68.1	61.2

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Academic

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Academic	GPT‑6 Astra	GPT‑5.6 Sol	Claude Fable 5.1	Claude Fable 5	Claude Opus 5	Gemini 3.8 Flash
Terminal-Bench Science 0.1	64.6%	22.4%	52.6%	21.4%	30.0%
FrontierMath Tier 4 (v2)	97.6%	83.0%	87.8%	87.8%	73.2%
GPQA Diamond	96.0%	94.6%	93.7%	92.6%	93.7%	95.3%
Humanity's Last Exam (w/ tools)	57.2%		65.0%	63.8%	63.6%

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Science and Health

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Science and Health	GPT‑6 Astra	GPT‑5.6 Sol	Claude Fable 5.1	Claude Fable 5	Claude Opus 5	Gemini 3.8 Flash
GeneBench Pro	37.8%	28.7%
MedChemBench (Internal)	49.3%	47.4%
LifeSciBench	60.3%	59.9%
HealthBench Professional (length-adjusted)	63.4%	60.5%	56.6% ¹¹	60.9% ¹¹	54.5% ¹¹	52.1%

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Cybersecurity

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Cybersecurity	GPT‑6 Astra	GPT‑5.6 Sol	Claude Fable 5.1	Claude Fable 5	Claude Opus 5	Gemini 3.8 Flash
ExploitBench	100.0%	78.5%			70%
Exploit Gym	42.4% ¹³	30.3% ¹³	30.4% ¹⁷	28.4%¹⁷	22.0%¹⁷
ExploitBench (June-Aug 2026)	39.0%	5.5%
SRE-Bench	88.0%	55.9%			12.5%
SEC-Bench Pro	85.4%	79.1%

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Alignment

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Alignment	GPT‑6 Astra	GPT‑5.6 Sol	Claude Fable 5.1	Claude Fable 5	Claude Opus 5	Gemini 3.8 Flash
Internal computer use safety benchmark (lower is better)	2.4%	22.0%	9.5%	18.3%	11.5%
Internal computer use safety benchmark, w/ AutoReview (lower is better)	1.8%	4.5%
Internal circumvention benchmark (lower is better)	0.00%	0.29%
ExploitGym honeypot (lower is better)	0.0%	48.2%
Impossible ExploitGym	100.0%
Internal hallucination benchmark (lower is better)	4.2%	12.2%

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Long Context

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Long Context	GPT‑6 Astra	GPT‑5.6 Sol	Claude Fable 5.1	Claude Fable 5	Claude Opus 5	Gemini 3.8 Flash
OpenAI MRCR v2 8-needle 256K-512K	100.0%	91.5%
OpenAI MRCR v2 8-needle 512K-1M	96.3%	73.8%

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Abstract reasoning

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Abstract reasoning	GPT‑6 Astra	GPT‑5.6 Sol	Claude Fable 5.1	Claude Fable 5	Claude Opus 5	Gemini 3.8 Flash
ARC-AGI-3	99.9% ¹	7.8%	-	-	30.2%
ARC-AGI-2	95.0%	92.5%	90.0%	89.2%	90.4%
ARC-AGI-1	98.5%	97.5%	97.5%	98.5%	97.5%

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Evaluation scores are the maximum at any effort. GPT evaluations were run in our research environment or via our API, which may provide slightly different output from production ChatGPT due to differences in the system prompts, tools available, etc.

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FOOTNOTES

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1
On ARC-AGI-3, GPT-6 Astra was run with our responses API harness, which changes two settings to better match real-world performance. The changes do not specifically target ARC-AGI-3.
2
GPT-5.6 Sol refers to the version available in our API, ChatGPT Codex, and ChatGPT Work. The version in ChatGPT Chat is slightly different.
3
OSWorld V2-Offline is a subset of the original OSWorld V2 that works without internet access. Claude model performance on OSWorld-V2 Offline was reproduced by an independent third-party. On OSWorld 2.0, the scores for Claude use the official settings, and not the modified tasks and modified grading from the Fable 5.1 System Card.
4
Human baseline times were recorded by human trainers while completing these tasks as part of the Lifeval data collection. Model times are the reported elapsed times for the corresponding demonstration runs. The displayed clips are edited excerpts.
5
On BenchCAD, Claude's scores reflect 3 modifications to the eval, detailed in the Fable 5.1 System Card(opens in a new window).
6
Guang Yang, Victoria Ebert, Nazif Tamer, Brian Siyuan Zheng, Luiza Pozzobon, and Noah A. Smith. “LEGATO: Large-scale End-to-end Generalizable Approach to Typeset OMR(opens in a new window).” arXiv:2506.19065, 2025.
7
Mark R. H. Gotham, Maureen Redbond, Bruno Bower, and Peter Jonas. “The OpenScore String Quartet Corpus(opens in a new window).” Proceedings of the 10th International Conference on Digital Libraries for Musicology, pp. 49–57. ACM, 2023.
8
On FrontierCode, GPT-6 Astra was run with a developer message similar to a section of its developer message in Codex(opens in a new window): "Avoid creating excessive test files. Create a new test file only when required by repository conventions or when no existing file is a suitable home. Avoid unrelated cleanup and unnecessary complexity. Reuse suitable existing utilities. Read relevant repository instructions and inspect nearby code, tests, documentation, and CI. Follow established conventions. The goal is clean, mergeable code." The prompt was not optimized for the eval.
9
The first concerns how close together prime numbers can occur, however far along the number line you go. For more than a decade, the best known result established that infinitely many pairs of primes are at most 246 apart. Julia Stadlmann(opens in a new window) recently improved that bound to 240. Astra helped establish a stronger bound of 186, showing that infinitely many pairs occur within this smaller distance. Short prime gaps: Proof(opens in a new window) and supporting research(opens in a new window).
10
The second concerns unusually large gaps between primes. Astra improved a term in a bound on these gaps that had remained unchanged for more than 80 years. We’re sharing the proofs and abridged chain of thought and verification materials for both results. Large prime gaps: Proof(opens in a new window) and supporting research(opens in a new window).
11
We independently evaluated all Claude models following the intended HealthBench Professional procedure, using GPT‑5.4 grading and length-adjusted, unclipped scores. For Fable 5.1, we used Opus 5 fallback for provider refusals.
12
Claude Fable 5 and 5.1 are not included in LifeSciBench Gold v1, GeneBench Pro v13, and MedChemBench because they refuse the majority of questions in these evaluations.
13
On ExploitGym, we tested Astra and Sol without the 6-hour time limit, to better assess their full cyber capabilities. They are fast enough that it has little impact.
14
ExploitBench (June–August 2026) contains 20 high-severity V8 vulnerabilities across 13 stable Chrome releases. The benchmark tests whether agents can achieve arbitrary code execution in V8 and official Chrome releases for Linux by exploiting each specified vulnerability. Some included vulnerabilities may not permit arbitrary code execution under the evaluation’s constraints, so a 100% success rate may not be achievable.
15
Jeremy Spence et al. “The Next Challenge for Agentic Cybersecurity: A Realistic, Contamination-Free Reverse Engineering Benchmark(opens in a new window).” arXiv:2608.11469v1, 2026.
16
When we test across third-party models, we use a simpler research setup. Codex has a more complex production configuration, which can result in different raw-model error rates. Provider-side safeguards and computer-tool implementations still differ. Users do not experience the no-confirmation scenario in Codex, as it's an internal research configuration.
17
For ScreenSpot-Pro and ExploitGym, the Fable scores we report come from Mythos, which is Fable with fewer safeguards.

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