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, Microsoft Azure, and AWS Bedrock.
title: "GPT-6 Astra: A new generation of intelligence" url: "https://openai.com/index/gpt-6-astra/"
We’re introducing GPT‑6 Astra, the world’s most intelligent and aligned model.
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.
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, Microsoft Azure, and AWS Bedrock.
Terminal-Bench Science 0.1 tests whether agents can complete scientific research workflows using code and terminal tools, including analyzing data, running simulations, and fitting models. GPT‑6 Astra reaches a new high among the models compared at 64.6%, versus 52.6% for Claude Fable 5.1, at approximately 31% lower estimated API cost. At a lower-cost setting, Astra scores 61.1%, versus GPT‑5.6 Sol’s best result of 22.4%, at approximately 27% lower estimated API cost.
“On ARC-AGI-3, Astra surpassed our human action-efficiency baseline on 96% of levels, effectively reaching human parity on the benchmark. Not only is this the best model we’ve ever tested, but it also represents a meaningful step change in frontier-model performance - not only in its ability to navigate and solve novel environments, but also in how efficiently it learns to do so.”
Greg Kamradt, ARC Prize Foundation
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.
The world’s best computer use model
GPT‑6 Astra marks a new frontier in the speed, accuracy, and safety of computer use. It can take care of tedious tasks like filling out online forms, updating customer records in a CRM, and organizing your calendar. It can conduct online research and draft summaries in your email or in your document editor. It can analyze scientific data, generate plots, create a website, and run frontend QA checks to make sure all the features on that site work. It can help you autonomously install and test software, and troubleshoot problems you see on screen. These improvements are also reflected in our state-of-the-art evaluation results.
Agents’ Last Exam tests agents on complex professional tasks in real software, from financial modeling to engineering and media production. GPT‑6 Astra reaches a new high in the comparison shown, scoring 59.3%, compared with 55.5% for Claude Opus 5 and 53.6% for GPT‑5.6 Sol. At these highest-scoring settings, Astra also uses approximately 65% fewer output tokens than Opus 5.
These improvements also result in significant efficiency gains in real knowledge-work tasks. In latency simulations on OSWorld 2.0, Astra achieves higher computer-use performance in about 47% less time per task than GPT‑5.6 Sol, scoring 72.6% at roughly 40 minutes per task, compared with 65.7% at roughly 75 minutes.3
GPT‑6 Astra’s computer-use capabilities can be seen in outputs across domains, including game development, electrical engineering, and everyday knowledge work:
This is a 15-second condensed playback of GPT‑6 Astra performing printed circuit board (PCB) layout in KiCad, turning an electronic schematic into a manufacturable PCB by placing components and routing copper connections. Integral to every electronic device today, PCB layout is a manual task and common source of latency in the electronics design process. Accelerating it means freeing engineers to invent, optimize, and test their next idea at a significantly higher cadence.
Alongside Astra, we are also updating the Codex harness to significantly improve the speed of computer use. Combined with Astra’s efficiency, this translates to a 1.9x faster task completion compared to the current GPT‑5.6 Sol experience, on the Mind2Web benchmark. The model’s improvements on speed mean it can take on many time-consuming life tasks for you, faster than you can.4
GPT‑6 Astra: 2 min 54 sec
“We’re integrating GPT‑6 Astra into Devin’s harness on launch day, where it delivers state-of-the-art performance on our internal testing benchmark. Its excellent computer use, writing, and codebase understanding improved testing right out of the box: videos are noticeably easier to follow, and reports are clearer and more concise”
Silas Alberti, SVP Research, Cognition
A step change in professional work
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.
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.
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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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.↩
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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. ↩ ↩
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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 the authors on the official leaderboard . 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.↩ ↩
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Model times are the reported elapsed times for the corresponding demonstration runs. The displayed clips are edited excerpts.↩ ↩ ↩ ↩
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On BenchCAD, Claude's scores reflect 3 modifications to the eval, detailed in the Fable 5.1 System Card.↩
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Mark R. H. Gotham, Maureen Redbond, Bruno Bower, and Peter Jonas. “ The OpenScore String Quartet Corpus.” Proceedings of the 10th International Conference on Digital Libraries for Musicology, pp. 49–57. ACM, 2023.↩ ↩
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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 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 and supporting research.↩
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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 and supporting research.↩ ↩ ↩ ↩
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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.↩ ↩
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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.↩
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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.↩
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Jeremy Spence et al. “ The Next Challenge for Agentic Cybersecurity: A Realistic, Contamination-Free Reverse Engineering Benchmark.” arXiv:2608.11469v1, 2026.↩
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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.↩ ↩ ↩ ↩ ↩
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