OpenAI Release Notes

ChatGPT app experience updates

We’ve made it easier to study with interactive quizzes, manage project memory, and move between typing, dictation, and desktop work.

Learning, Projects, and personalized suggestions

• Practice with interactive quizzes. Ask ChatGPT to quiz you on a topic and answer questions directly in your conversation. Available to all consumer ChatGPT plans and Edu plans on web and mobile.
• Change an existing project’s memory setting. For eligible unshared projects, switch between default and project-only memory without starting a new project. Shared projects remain project-only.
• Get suggestions that fit how you use ChatGPT. Eligible paid users can see homepage suggestions based on their conversation history and connected tools.

Web

• Use Think on Free and Go. Select Think on the web when you want ChatGPT to reason through a harder question.

Android

• Switch between typing and dictation. The dictation button stays available after you add text or attachments, so you can continue building the same message.
• See suggestions that stay in sync. Composer suggestions update as you type or delete text.
• Keep typing with a physical keyboard. Connected keyboards keep the composer focused, preventing interruptions while you write.

Linux desktop

• Use ChatGPT and Codex on Linux. The desktop app is now available globally in public preview on Ubuntu 24.04 LTS and 26.04 LTS, Debian 13, and Fedora 43 and 44.
• Work in the built-in browser or Chrome. Browser actions are available in the Linux app; controlling other desktop apps is not yet supported.

Download ChatGPT for Linux.

Edit memory settings for existing projects

You can now change a project’s memory setting after you create it. Open the project, select the three-dot menu, choose Project settings, and select Default memory or Project-only memory under Memory.

With project-only memory, ChatGPT can use conversations from the same project for context, but it won’t reference memories or conversations from outside the project. Information from the project is also kept out of memory used in chats outside the project. Changes may take a few hours to take effect.

This update is available on all ChatGPT plans. Shared projects continue to use project-only memory and cannot be switched to default memory. ChatGPT Work is not available in projects using project-only memory.

Learn more about Projects in ChatGPT.

Original source

Computer History is an optional feature in the ChatGPT macOS app that lets members bring context from selected apps and websites into ChatGPT and Codex. It records interaction events—not screenshots, screen recordings, microphone input, or system audio—and private browsing is not included.

Computer History is off by default for ChatGPT Business. A workspace admin must enable access before members can choose to opt in. Members can pause Computer History, choose included apps and sites, and inspect or delete timeline items. Note that Computer History is not currently available in the EEA, UK or Switzerland.

Original source

Computer History is an opt-in feature in the ChatGPT desktop app on macOS that turns activity across apps and websites into memories and a timeline that ChatGPT and Codex can use. Choose which apps and websites contribute, pause collection, and review or delete your history at any time.

Computer History is available to ChatGPT Pro, Business, and Enterprise users. Business and Enterprise administrators must enable access before workspace members can turn it on. Initial availability excludes the European Economic Area (EEA), Switzerland, and the United Kingdom.

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Technical lessons from startups in production

GPT‑5.6 sets a new standard for price-performance

The GPT‑5.6 model family makes frontier-level agent performance dramatically more affordable, while also advancing the frontier of what is possible.

In this guide, we show how startups are using smarter model selection and new API controls that help with reasoning continuity, multi-agent orchestration, and programmatic tool calling to build faster, more capable agents at a fraction of the cost.

A better out-of-the-box experience

Since GPT‑5, each model generation has sought to tackle longer-horizon tasks with fewer tokens. GPT‑5.6 continues that trajectory: stronger agent performance, lower costs, with minimal changes to the underlying harness.

The improvements in top-line cost efficiency are compounded with increased accuracy at lower reasoning efforts. For example, on Agents’ Last Exam, GPT‑5.6 Sol at “low” reasoning outperformed GPT‑5.5 at “high” reasoning when the harness was kept constant. We’ve seen similar success stories in production testing where startups report seeing significant cost improvements across a range of workflows by reducing the reasoning effort from the prior defaults.

“We dropped GPT‑5.6 into our harness, and low reasoning effort gave us our best results. It knew when the data just wasn’t there, didn’t chase bad leads, and got to the right answer with fewer tokens.”
— Izzy Miller, AI Research Lead, Hex

Model Selection

Historically, upgrading to a flagship model at the highest reasoning available has been the best option for long-horizon use cases. This has been in large part due to these models being significantly more capable than cost-optimized models at handling longer contexts and tool calling. This has changed with the 5.6-family: with more test-time compute, Luna and Terra can often perform similar to GPT‑5.4 and 5.5 while being significantly cheaper.

Consider tasks in BrowseComp: a search-based benchmark that tests a model’s ability to search for obscure facts. Three months ago, GPT‑5.5 (Extra High) scored 84.36% on this benchmark for a total cost of $33.27. At launch, GPT‑5.6 Luna (Extra High) delivers essentially the same performance, scoring 84.04% at a cost of $1.33. We’ve since reduced prices further.

The smaller 5.6-family models are a strong fit for high-volume workloads, latency-sensitive interactions, and repeated steps within agentic workflows. For example, if you’re operating a legal-tech startup that parses handwritten memos prior to agentic analysis, instead of using a frontier model for the entire use case, you can now use Terra or Luna for extraction and register significant cost savings.

Evolving the Responses API to architect more efficient agents

In addition to making GPT‑5.6 more performant out of the box, we also shipped new primitives to the Responses API to unlock further gains. We trained GPT‑5.6 end-to-end with three complementary architectural interventions that enable agents to operate more efficiently:

1. Reuse work already performed: by allowing reasoning to be persisted across model turns and using native compaction to compress long-running conversations, the model can maintain coherence in its work across longer task horizons without getting confused or having to reconstruct prior context.
2. Parallel decomposition where appropriate: using native multi-agent orchestration allows coordinating multiple agents across parallel workstreams to finish complex tasks faster.
3. Move deterministic work into code: using programmatic tool calling to filter, aggregate, and orchestrate tool outputs outside the model’s context window, reserving model tokens for judgment and reducing cost, latency, and context rot.

Used together, the difference can be dramatic. For example, on ARC-AGI-3, GPT‑5.6 Sol scored 13.3% with the standard harness. After enabling retained reasoning and compaction, however, the score jumped to 38.3%—while using roughly 6× fewer output tokens. No changes to the model, but nearly three times the performance. You can read more on our ARC-AGI-3 harness investigation here.

Programmatic Tool Calling

Agentic workflows often involve two kinds of work:

1. Tasks that require judgment
2. Work that mostly requires moving, filtering, and combining data

When an agent retrieves 100 filings, filters them by date, and identifies relevant transactions, the model shouldn’t have to reason over every intermediate result in its context window. Programmatic Tool Calling lets GPT‑5.6 write JavaScript to orchestrate tools, run independent calls in parallel, and process their outputs outside the context window. The model is left to focus on what requires intelligence: applying judgment.

“For financial research, the hard part is reliably pulling filings, coordinating tools, and working through the numbers. In our evaluations, GPT‑5.6 using Programmatic Tool Calling matched our rubric quality while using 21% fewer input tokens. That’s the difference between an agent that can discuss financial research and one that can actually carry it out.”
— Alex Wang, Applied AI, Rogo

Multi-agent

On complex, parallelizable tasks, distributing actions and reasoning across multiple agent workstreams enables faster task completion as well as higher intelligence. In these setups, the primary agent is responsible for orchestrating the subagents and delegating tasks to them. The subagents pursue their objectives in parallel and finally pass back their output to the primary agent for final synthesis. Teams can start leveraging multi-agent natively by enabling multi-agent in the Responses API. This is also how the ultra capability setting in ChatGPT works.

“Qualia runs teams of agents on open-ended research problems, and GPT‑5.6 Sol just clicked. It showed a marked improvement over GPT‑5.5, finished faster than almost every other model we tested, and quickly became our go-to OpenAI model.”
— E Chi, Founder, Quadrillion

“GPT‑5.6 is the best orchestrator we’ve seen from OpenAI. We threw six specs at it at once (writing, building, and talking through all of them) and it kept track of everything without the quality falling apart.”
— Jon Bell, Co-founder and CPO, Obvious

Although GPT‑5.6 has a strong sense of the appropriate number of subagents and when to spawn them, multi-agent behavior is very steerable. Instructing the model on when to invoke subagents can increase the likelihood of spawning agents only in situations where the additional token expenditures would result in better performance.

Prompt Caching

Across the entire family of models, the prompt cache TTL has been extended to a minimum of 30 minutes and cache breakpoints can now be set deterministically within a model’s context window. This has enabled startups to significantly improve their cache hit rate.

“We added cache breakpoints and workspace-specific keys to a shared 29,000-token prompt and cut uncached input by 28%. The 30-minute cache window was a big unlock too: our agents could reuse the same context across runs instead of starting from scratch.”
— Lorenzo Gentile, AI Engineer, Ploy

In addition to setting cache breakpoints, continuing to use an appropriate prompt_cache_key increases the likelihood of requests landing on the same inference engine as one that previously served the same prefix, thereby reducing latency.

Conclusion

What stands out across these examples is how much the economics of building agents have changed.

Use cases that once required a frontier model at every step can now achieve comparable or better results at a fraction of the cost by using smaller models, tuning reasoning effort, and making efficient architectural choices.

We're excited to see what you all build!

Original source

Chat model defaults

Workspace owners and admins can configure the starting Chat model and reasoning level from Workspace settings > Models. Owners and admins can apply an admin default or allow User’s last choice for new chats - note that defaults do not grant access to unavailable models or override enforced workspace requirements.

The ChatGPT Desktop app should be upgraded to version 26.812.10818 or later for the new controls to take effect.

Updated model picker for Enterprise and Edu

Members in the Enterprise/Edu workspaces have updated model-picker and composer experience. The picker makes the available intelligence choices easier to compare while preserving workspace model availability and access controls.

Audit logs in the Global Admin Console

Workspace owners and admins can now review supported audit events from the Global Admin Console. Access and event coverage depend on the selected workspace and admin role. Read more.

Additive role-based access controls

Workspace owners in Enterprise and Edu workspaces can now configure eligible permissions in ordinary custom roles with Default, On, or Off. Default inherits the workspace setting, On explicitly grants the permission, and Off explicitly denies access. ChatGPT evaluates all applicable ordinary roles together: at least one role must grant the permission, and an explicit Off in any applicable role prevents access. Lockdown Mode is evaluated separately and can further restrict network-enabled capabilities. Learn more about role-based access controls.

Codex thread-level cost data

Members of eligible Enterprise workspaces with credit-based billing can review per-chat lifetime credit usage in Codex. Note that numbers present are estimates are planning aids and are not invoices. Members can also view their usage from the Settings pane in ChatGPT Desktop and by accessing the Usage & billing tab. This feature requires ChatGPT Desktop version 26.812.10818 or later. Read more.

Personal Analytics plugin

The Personal Analytics plugin is available for eligible workspaces. After a workspace owner or admin enables the plugin, members can install it in ChatGPT Desktop and ask questions about their own Codex activity and available aggregated workspace trends. Read more.

Codex service accounts

Eligible workspaces can create non-human service accounts for Codex automation. Workspace owners and admins can assign roles and groups, configure plugins, share account management, and issue scoped access tokens for CI runners and scheduled jobs. Read more.

Windows desktop performance improvements

The ChatGPT desktop app for Windows includes performance improvements. These improvements require ChatGPT Desktop version 26.803.41515 or later.

Computer History for ChatGPT Enterprise

Computer History is an optional feature in the ChatGPT macOS app that lets Enterprise members bring context from selected apps and websites into ChatGPT and Codex. It records interaction events—not screenshots, screen recordings, microphone input, or system audio—and private browsing is not included.

Computer History is off by default, and Enterprise admins can grant access by role before each member chooses whether to opt in. Members can pause Computer History, choose included apps and sites, and inspect or delete timeline items. Note that Computer History is not currently available in the EEA, UK or Switzerland.

Original source

Computer History is an optional feature in the ChatGPT macOS app that lets members bring context from selected apps and websites into ChatGPT and Codex. It records interaction events—not screenshots, screen recordings, microphone input, or system audio—and private browsing is not included.

Computer History is off by default for ChatGPT Business. A workspace admin must enable access before members can choose to opt in. Members can pause Computer History, choose included apps and sites, and inspect or delete timeline items. Note that Computer History is not currently available in the EEA, UK or Switzerland.

Original source

Google Drive is now in Library

If you have the Google Drive plugin connected, you can now see and browse your Google Drive files and folders directly from Library, including items shared directly with you. You can also quickly pull up a Drive file from the composer or with @mentions and add it to any chat—without uploading it again.

When you’re working with a file, you can now keep Google Docs, Sheets, and Slides open beside the conversation while asking ChatGPT to summarize, analyze, compare, or create something new from them. You can also select a folder and ask ChatGPT to work across the files it contains. Your content stays connected to Drive, so it’s easy to return to the original. Where supported and authorized, ChatGPT can update the source file directly.

Availability

The initial experience includes My Drive and files and folders shared directly with you; Shared Drives aren’t included yet. Some Google Drive editing and collaboration features aren’t yet available in ChatGPT.

Rolling out to Plus, Pro, Enterprise, Edu, Healthcare and Business users on the web in both the Chat and Work toggles. Mobile support will follow.

Computer History for macOS

Computer History is an optional feature in the ChatGPT macOS app that lets ChatGPT and Codex reference selected activity from apps and websites, helping users continue work without re-explaining every detail. It records interaction events—not screenshots, screen recordings, microphone input, or system audio—and private browsing is not included.

Computer History is off by default and available to Pro, Business, and Enterprise users. Business and Enterprise admins must grant access before members can opt in. Users can pause Computer History, choose included apps and sites, and inspect or delete timeline items. Note that Computer History is not currently available in the EEA, UK or Switzerland.

Original source

A new speed class for frontier intelligence, turning speed into a competitive advantage.

Today, we’re sharing an early look at Ultrafast, a new service tier that runs GPT‑5.6 Sol up to 14× faster than Standard processing, launching first in the OpenAI API. Powered by Cerebras, Ultrafast generates up to 750 output tokens per second, bringing our most intelligent model to products and workflows where every second matters.

With GPT‑5.6, we’re pushing the frontier on what our models can do and making them more efficient across every layer of our stack. Those improvements have made advanced intelligence more affordable and more useful to more people. Until now, getting real-time speed typically meant choosing a smaller or more specialized model. Ultrafast points to progress in a new direction: more useful work per second.

When speed no longer requires giving up intelligence, AI can move into the most time-sensitive parts of a business and new kinds of work become possible. We’ve already seen some encouraging scenarios for Ultrafast:

• Incident response and reliability: When a critical system fails, analyze application logs, recent code changes, and engineer reports to identify the likely cause and help prepare a fix while the outage is still unfolding.
• Financial research and security: Analyze market signals, assess transactions, and identify suspicious activity while conditions are still changing.
• Customer support and voice: Resolve complex customer issues in real time without interrupting the conversation, even when finding the answer requires multiple steps or systems.
• Commerce: Answer product questions, check inventory, personalize recommendations, and resolve checkout issues while the shopper is still deciding, before hesitation becomes an abandoned cart.
• Live research and experimentation: Turn research that previously took an overnight run into an interactive working session, letting teams test an idea, examine the results, adjust their approach, and run another experiment without breaking their flow.

During the preview period, we’re working with an initial group of customers to understand where this speed makes the biggest difference, and how those learnings can inform our products over time. If your business requires frontier intelligence at the highest speed, you can sign up to get notified when access expands.

GPT‑5.6 Sol Ultrafast and standard build a working 3D warehouse simulator from the same text prompt, side by side.

What early customers are experiencing

We’ve been testing GPT‑5.6 Sol on Ultrafast mode with an initial group of companies across coding, commerce, financial research, support, and other interactive applications. Starting with business workflows lets us study these conditions in real production environments. Their early work is helping us understand where an order-of-magnitude change in speed creates the most value and how products change when the model can keep pace with the person using it. We will use these findings to guide deployment as capacity grows.

“ The increase in speed brought by Cerebras is impressive. It enables different ways of using the models, and makes it practical for developers to work in a more focused and productive way alongside them. ”
—John Crepezzi, AI Assistants, Jane Street

How OpenAI is using Ultrafast

Inside OpenAI, a group of developers has been testing GPT‑5.6 Sol on Ultrafast mode to understand which workflows benefit from frontier intelligence that can answer in real-time.

Incident response is one example where our team is using Ultrafast. When an alert fires, engineers need to build an accurate picture while the system and the evidence are still changing. Teams use it to quickly read logs, analyze traces, synthesize conversations, identify the next checks, and help prepare or validate a fix—all in a fraction of the time with the intelligence of Sol. It reduces the delay between observing a signal, testing a hypothesis, and choosing the next action, while engineers remain responsible for judgment and deployment.

For research, our team uses Ultrafast to rapidly search knowledge sources, query data, and quickly gather, organize, and summarize information across connected tools. A common workflow in research is for our team members to launch a batch of experiments over night, and review the results in the morning. With Ultrafast, we see this loop tightening to support multiple iterations during the workday instead.

Powered by Cerebras

Ultrafast marks the next step in our partnership with Cerebras to bring ultra-low-latency inference to OpenAI’s platform. Now, with GPT‑5.6 Sol on Ultrafast mode, Cerebras is supporting OpenAI’s most intelligent model, delivering up to 750 output tokens per second, enabling businesses to build more responsive products, make faster decisions, and bring powerful AI directly into their most demanding workflows.

Availability

GPT‑5.6 Sol on Ultrafast mode is available in a limited preview today to a select group of customers. We’ll expand access as capacity grows. Sign up for updates.

Original source

Install the ChatGPT desktop app on Linux

The ChatGPT desktop app for Linux is available in preview for supported Ubuntu, Debian, and Fedora desktop distributions on x64 and ARM64 processors. Download the .deb or .rpm package for your distribution, then sign in to work with projects, local files, and Codex.

Import setup and recent work from other agents

The desktop app supports Claude Code, Claude Cowork, and Cursor. Import instructions, settings, skills, plugins, projects, and recent work, then turn on automatic updates in Settings > Import to keep imported work in sync.

Codex CLI can also import supported setup and recent chats from Claude Code and Cursor with /import.

Original source

Earlier this year, OpenAI frontier models and Codex became generally available on AWS, giving enterprises a new path to bring advanced AI into production. Today, we’re sharing the next step in our work with AWS: making Daybreak capabilities available through Amazon Bedrock.

With Daybreak Access

Defenders can use frontier cyber models within their existing AWS environments. Daybreak Blue and Daybreak Red access levels are both available in AWS:

• Daybreak Blue provides access to frontier general-purpose models, including GPT‑5.6 Sol, with safeguards tailored to authorized defensive security work.
• Daybreak Red provides access to our purpose-trained cybersecurity models for authorized vulnerability research, exploit validation, and security testing.

These models help accelerate vulnerability research, detection engineering, and incident response, from initial discovery through a validated fix. They also support complex workflows such as exploit reproduction and mitigation development.

Bringing frontier cybersecurity capabilities to AWS

For enterprises, adopting specialized cybersecurity capabilities requires more than model performance. It also requires security review, governance, procurement, access controls, and an operating model teams can support.

Through Amazon Bedrock, eligible customers can use Daybreak, including Daybreak Red and Daybreak Blue, within the AWS environments where they already build, secure, and operate software. This gives security teams a clearer path to apply frontier AI through familiar AWS security, governance, and operational workflows.

Together, OpenAI and AWS are helping more organizations put advanced cybersecurity capabilities to work in production.

Get started

Daybreak Red and Daybreak Blue require enrollment in Daybreak Access. Once approved, you can access the model through the Amazon Bedrock console or the Responses API using the bedrock-mantle endpoint. To learn more, see the documentation.⁠ (opens in a new window)

Learn more about Daybreak Red and Daybreak Blue.

Original source

Introducing new ways to unlock advanced cyber capabilities together with GPT‑5.6‑Cyber, our latest cybersecurity-specific model.

The cybersecurity world is rapidly changing—threat actors will increasingly use AI to conduct cyberattacks at unprecedented speed and scale, including in fully autonomous ways. As these capabilities spread, defenders have a narrowing window to prepare. Our answer is to put frontier intelligence in the hands of trusted defenders everywhere before attackers deploy offensive AI capabilities at scale.

We’re expanding OpenAI Daybreak with two access tiers designed to give approved defenders the right capabilities for their work:

• Daybreak Blue provides access to frontier general-purpose models, including GPT‑5.6 Sol, with safeguards tailored to authorized defensive security work. It is the recommended starting point for most defenders, supporting vulnerability discovery, secure code review, malware analysis, incident response, and patch validation.
• Daybreak Red provides access to our purpose-trained cybersecurity models for authorized vulnerability research, exploit validation, and security testing.

We’re also introducing GPT‑5.6‑Cyber, available through Daybreak Red. Built on GPT‑5.6 Sol, it is trained to improve capabilities on several specialized cybersecurity tasks (e.g., finding zero-day vulnerabilities and developing exploit chains) and to reduce refusals for certain higher-risk, dual-use cyber tasks.

Daybreak unlocks advanced cyber capabilities

As we previously shared⁠, GPT‑5.6 Sol delivers state-of-the-art performance on cybersecurity tasks. In production, we deploy system-level safeguards to screen cybersecurity-related requests to prevent misuse, but they can also block legitimate defensive work. Daybreak Blue access removes those guardrails, helping defenders get more out of the model in real-world security tasks, including incident detection and response, investigations, vulnerability management, and security assessments.

Even without system-level guardrails, there are still highly dual-use cybersecurity prompts (e.g., pentesting production systems) where GPT‑5.6 Sol will refuse to comply. To address this, we trained GPT‑5.6‑Cyber, available through Daybreak Red access, to further reduce refusals and improve performance on certain tasks. GPT‑5.6‑Cyber helps trusted defenders conduct legitimate security activities.

To measure the reduced rate of refusals that is provided by GPT‑5.6‑Cyber through Daybreak Red access, we created an internal evaluation (Advanced Cybersecurity Completion Rate) that measures how often models will respond to requests involving exploit-chain development, authentication bypass, privilege escalation, and other advanced cybersecurity scenarios1. GPT‑5.6‑Cyber completes 95.0% of these requests, compared with just 1.5% for GPT‑5.6 Sol, and 2.0% when used with Daybreak Blue access. It also completes more requests than GPT‑5.5‑Cyber, which completes only 57.3% of requests, addressing feedback from security researchers who encountered persistent refusals with the earlier model.

Below we show a series of cybersecurity prompts and the associated model responses from GPT‑5.6 Sol with system-level guardrails, GPT‑5.6 Sol (Daybreak Blue), GPT‑5.5‑Cyber (Daybreak Red), and GPT‑5.6‑Cyber (Daybreak Red).

Improving cybersecurity performance

The GPT‑5.6‑Cyber model is trained to improve performance on certain cybersecurity workflows involving exploit development and advanced security research. On ExploitGym2, which evaluates whether agents can turn known vulnerabilities into working exploits that achieve arbitrary code execution in controlled environments, GPT‑5.6‑Cyber outperforms both GPT‑5.6 Sol and GPT‑5.5 Cyber.

Another area that GPT‑5.6‑Cyber is aimed to improve is the ability to find and accurately calibrate the severity of novel zero-day vulnerabilities. We created an internal evaluation dataset in which we provide models with the current release of an open-source repository. We then ask them to generate proof-of-concept exploits with the maximum possible impact alongside a technical write-up of their findings. Models are evaluated on the severity and impact of their findings, as well as the calibration and quality of the accompanying technical write-up. GPT‑5.6‑Cyber (Daybreak Red) outperformed GPT‑5.6 Sol (Daybreak Blue) on this benchmark due to its specialized training.

We also evaluated GPT‑5.6‑Cyber on our internal Vulnerability Discovery and Report Writing evaluation, which gives an agent an open-ended prompt to find vulnerabilities in a repo with a known vulnerability. Models gain points on this evaluation by finding severe and actionable vulnerabilities (either novel or known vulnerabilities), developing a working proof-of-concept, and submitting a high-quality vulnerability report. Both GPT‑5.6 Sol and GPT‑5.6‑Cyber improve over GPT‑5.5‑Cyber. GPT‑5.6‑Cyber performs worse than GPT‑5.6 Sol on this evaluation, which we believe is due to the model sometimes producing shorter, less detailed vulnerability reports.

Finally, we measured exploit development capabilities on ExploitBench3, an evaluation testing an agent’s ability to develop a V8 vulnerability into a full exploit. This exploitation task is harder than ExploitGym — more defensive protections, such as the V8 sandbox, remain enabled, and the agent is given less information about the vulnerability to exploit. In the standard setting, which limits agents to 300 turns, GPT‑5.6 Sol (Daybreak Blue) solves tasks more token-efficiently and performs best. If we expand beyond the standard 300-turn setting to 600 turns, the performance gap between the two models narrows.

Aside from results on evaluation benchmarks, we also provided early access to GPT‑5.6‑Cyber to a group of trusted customer partners. These customers have successfully used the models to accelerate their defensive workflows to great success:

[GPT‑5.6 Cyber] is materially improving our specialist vulnerability-research workflows: it reasons more accurately about real exploit constraints, tracks complex state better, and has completed work in under a day that earlier models had not resolved after weeks of intermittent effort. In a governed Trusted Access environment, reducing unnecessary refusals helps authorized researchers preserve momentum and spend more time validating findings and turning them into defensive value.
—Jared Atkinson, CTO, SpecterOps

Finding and patching vulnerabilities in real-world software

GPT‑5.6‑Cyber’s capabilities extend beyond research benchmark performance to real-world vulnerability research. Real-world vulnerability research often requires sustained reasoning across large, unfamiliar codebases. Researchers must form and test hypotheses, trace interactions among multiple components, reproduce unexpected behavior, and determine whether a suspected vulnerability can be exploited in practice.

Since the GPT‑5.6‑Cyber model finished training, we have used it to extensively study and improve selected software projects. For example, we used GPT‑5.6‑Cyber to investigate V8, the JavaScript engine used by Chrome. We uncovered two previously unknown vulnerabilities that could be chained to corrupt memory and escape the V8 heap sandbox. Our researchers validated the findings and reported them to Google through coordinated vulnerability disclosure. Google fixed the vulnerability, assigning it as CVE-2026-15903.

CVE-2026-15903 is a high-severity vulnerability in V8, Chrome’s JavaScript engine. Its optimizing compiler incorrectly skipped a safety check when converting values to integers, allowing undefined values to produce an unexpectedly large number instead of the expected result.

If that number is used as an array index, the compiler may incorrectly assume it falls within the array’s bounds and omit the usual bounds check. An attacker can then read or overwrite memory belonging to other objects, potentially executing arbitrary code inside Chrome’s sandbox. Escaping the heap sandbox would generally require a second vulnerability, which GPT‑5.6‑Cyber found as well. The below diagram provides an overview of this high-severity vulnerability.

Aside from these V8 vulnerabilities, we have also used GPT‑5.6‑Cyber to identify high-severity issues in software that ranges from popular databases to mobile phones:

• At least five vulnerabilities in a popular mobile operating system, including a chain from an untrusted app to local privilege escalation.
• Three critical vulnerabilities in a popular database, including a remote path to code execution.
• Over 400 vulnerabilities that can lead to privilege escalation in a popular operating system kernel.

We are working closely with Daybreak partners and members of the open-source community to disclose and remediate these mobile OS, database, and kernel vulnerabilities.

Preparedness Evaluations

Under our Preparedness Framework, the GPT‑5.6 Sol model was assessed as High for cybersecurity capability and below the Critical threshold. Before launching GPT‑5.6‑Cyber, we also evaluated its frontier cyber capabilities and determined that it similarly reaches the High threshold but not the Critical threshold. The model improved over GPT‑5.6 Sol on some specialized cyber tasks that we directly trained for, but not sufficiently to reach our Critical threshold. Note that as we mentioned in our updates to the Hugging Face incident⁠, GPT‑5.6‑Cyber was not involved in exploiting Hugging Face, nor are any other models planned for an upcoming release.

We will publish a system card with further evaluations of GPT‑5.6‑Cyber at a later date.

Access and safeguards

Models running with reduced safeguards carry risks beyond standard model usage, whether from misuse or misalignment. Despite these risks, we believe that democratizing access to frontier intelligence for defenders is crucial to accelerating and automating cyber defense.

Daybreak Blue and Daybreak Red access are available for approved individuals⁠ (opens in a new window) and organizations conducting authorized work. We control access through identity verification, account security, monitoring, approved-use restrictions, and legal attestations.

We are also taking additional steps to enable safer use of cyber models:

• We are strongly encouraging Daybreak customers using Codex to switch from full-access mode to auto-review mode through app defaults and UI features. Auto-review evaluates actions requiring elevated permissions before execution and can block requests that pose a significant risk of destructive behavior.
• We are requiring all individual accounts in Daybreak to adopt hardware security keys, beginning September 1, 2026.
• We are actively working on additional security measures, including improved monitoring, which we intend to roll out in the coming weeks.
• We are prioritizing alignment training and testing for upcoming Daybreak releases.
• We’ve updated our Codex documentation on safety best practices to help teams keep cyber-capable agents within their intended security boundaries.

Best practices for using the Daybreak series include:

• Sandbox and isolate. Run security workflows in controlled environments without access to sensitive production systems or the open internet. Regularly test sandbox boundaries.
• Monitor agent actions. Use auto-review mode⁠ (opens in a new window) to review tool calls outside the Codex sandbox before they execute. Add further monitoring and human oversight for higher-risk workflows.
• Define the scope. Specify which systems and actions are authorized. Use scoped permission profiles⁠ (opens in a new window) to enforce those boundaries.

Organizations can also customize the review policy⁠ (opens in a new window) for their specific workflows.

We recommend Daybreak Blue as the starting point for most defenders. Teams whose authorized work includes advanced vulnerability research, exploit development, or red teaming can request Daybreak Red access for our most advanced cyber models. Apply to join the program at openai.com/daybreak/partners.

1. For all evaluations, we show the performance of each model using the highest publicly available reasoning level. Note that GPT‑5.6‑Cyber tends to be more extensive and comprehensive than GPT‑5.6 Sol in its reasoning budget, leading to higher token usage.
2. All ExploitGym evaluations were conducted using our new internal implementation in security-hardened, isolated environments, with strict monitoring for misaligned behaviors.
3. ExploitBench evaluations were conducted using our internal implementation in security-hardened, isolated environments.

Original source

Find earlier work with Computer History

Computer History turns activity across your apps and websites into a searchable timeline and memories that ChatGPT and Codex can use. Turn it on only if you want to share that context, then choose which apps and websites contribute, pause collection, and review or delete your history at any time.

Computer History is available in the ChatGPT desktop app on macOS for ChatGPT Pro, Business, and Enterprise customers. Business and Enterprise administrators must first enable access. Initial availability excludes the European Union, Switzerland, and the United Kingdom.

Find the document and Slack thread I was reviewing earlier, then summarize the decisions I still need to act on.

Use the ChatGPT desktop app on Linux

The ChatGPT desktop app for Linux is now available in preview. Install a .deb package on supported Ubuntu or Debian distributions, or an .rpm package on Fedora. Packages are available for both x64 and ARM64 processors.

Sign in with your ChatGPT account to work with projects, local files, and Codex. Some features, including Computer Use, aren’t yet available in the Linux preview.

Bring your existing agent setup and work with you

Import instructions, settings, skills, plugins, projects, and recent work from Claude Code, Claude Cowork, or Cursor into the ChatGPT desktop app. Turn on automatic updates in Settings > Import to keep your imported work in sync.

In Codex CLI, use /import to bring supported setup and recent chats from Claude Code or Cursor into your local session.

Read the August 11 desktop and CLI release notes.

Choose the right access for defensive security work

Daybreak now offers two tiers for approved defenders. Daybreak Blue supports general defensive work, such as secure code review, incident response, and patch validation. Daybreak Red requires its own approval and provides access to purpose-trained models for authorized security assessments.

Access requires Trusted Access for Cyber and applies only to the approved identity, workspace or organization, model, and product surface.

Read the August 10 Daybreak announcement.

Original source

We’re expanding our cybersecurity initiative Daybreak and introducing GPT-5.6-Cyber, a new model for advanced, authorized cybersecurity work.

As the threat landscape evolves, we’re putting frontier intelligence in the hands of trusted defenders before attackers can deploy offensive AI at scale.

Daybreak Blue provides access to frontier models, including GPT-5.6 Sol, with safeguards calibrated for broad defensive work.

It’s the recommended starting point for most defenders, supporting vulnerability discovery, secure code review, malware analysis, incident response, and patch validation.

AI Cybersecurity for teams

Daybreak Red provides access to purpose-trained cybersecurity models, including GPT-5.6-Cyber, for authorized vulnerability research, exploit validation, and security testing.

It’s designed for experienced defenders working on complex, authorized cybersecurity challenges.

We’ve used GPT-5.6-Cyber extensively in real-world vulnerability research, including work that uncovered previously unknown vulnerabilities in popular open-source software like Chrome’s v8 engine.

Advanced capabilities require strong safeguards. That’s why access is limited to approved defenders, with additional controls and monitoring for higher-risk cybersecurity work.

Expanding Daybreak official announcement post

Original source

Retiring individual-user sync for connected apps

Starting August 10, new individually authorized sync connections will no longer be available. On August 14, existing individual-user sync connections will be disabled, and deletion of associated synced data will begin. Administrator-managed sync is unaffected. By August 14, review your workspace’s connector settings and take the following actions where applicable:

Connector Required admin action Google Drive Enable the Google Drive plugin. If synced knowledge is required, configure admin-managed Google Drive sync using domain-wide delegation. SharePoint Enable the SharePoint plugin. If synced knowledge is required, configure admin sync. GitHub Enable the non-synced GitHub plugin. GitLab Issues We will communicate availability of the replacement plugin soon. Azure Boards (Azure DevOps) We will communicate availability of the replacement plugin soon. Basecamp We will communicate availability of the replacement plugin soon. Help Scout Identify affected users and workflows, and notify them that synced access will end. We are actively working with the partner to provide a replacement. Zoho Desk Identify affected users and workflows, and notify them that synced access will end. We are actively working with the partner to provide a replacement. Teamwork Identify affected users and workflows, and notify them that synced access will end. We are actively working with the partner to provide a replacement. Aha! Identify affected users and workflows, and notify them that synced access will end. We are actively working with the partner to provide a replacement. Zoho CRM Identify affected users and workflows, and notify them that synced access will end. We are actively working with the partner to provide a replacement. Pipedrive Identify affected users and workflows, and notify them that synced access will end. We are actively working with the partner to provide a replacement. Original source

Restaurant reservations in ChatGPT

ChatGPT can now help you find available restaurant reservations with OpenTable, Resy, and Yelp.

Ask in the chat and include where and when you’d like to eat, your party size, and any preferences, such as cuisine, budget, dietary needs, or atmosphere. You’ll see available reservations times directly in the response. You can also ask follow-up questions to narrow your options or check a specific restaurant. When you find a time that works, select it to book it.

Rolling out across all ChatGPT plans on mobile, web, and desktop. Reservations are available globally with OpenTable and in the US with Resy and in the US and Canada with Yelp. ChatGPT Work does not include restaurant reservation search.

Original source