It is open GPT-5.1-Introduced Codex Maxa new Frontier agentic coding model is now available in its Codex developer environment. The release marks a significant step forward in AI-related software engineering, offering long-horizon reasoning, performance and real-time interactive capabilities. GPT-5.1-CODEX MAX will now replace GPT-5.1-CODEX as the default model in codec-integrated levels.
The new model is designed to act as a persistent, high-context software development agent, capable of handling complex refactors, debugging workflows, and project-scale tasks in multiple context windows.
It comes on the heels of releasing its powerful new Gemini 3 Pro model yesterday, still outperforming or matching it on key coding benchmarks.
But SWE Bench Certificationfor , for , for , . GPT-5.1-Codex Max achieved an accuracy of 77.9% On additional reasoning efforts, the Gemini 3 is taking 76.2% past the Pro.
He also went ahead Terminal Bench 2.0, with 58.1% accuracy compared to Gemini’s 54.2%, And Gemini’s score was matched by a score of 2,439 on Leo Codec Pro, a competitive coding alu benchmark.
When it comes to Gemini 3 Pro’s latest configuration—its deep-thinking model—Codex Max also has a slight edge in the Agentic coding benchmark.
Performance standards: Additional benefits in critical tasks
GPT-5.1-CODEX MAX demonstrates measurable improvements over GPT-5.1-CODEX in a range of standard software engineering benchmarks.
On the SWE Lancer ICSWE, it achieved 79.9% accuracy, a significant increase from the GPT-5.1-CODEX’s 66.3%. In SWE Bench-validated (n = 500), it reached 77.9% accuracy on additional inference attempts, outperforming 73.7% of the GPT-5.1-codecs.
Performance on Terminal Bench 2.0 (n = 89) showed a more modest improvement, with GPT-5.1-CodexMax achieving 58.1% accuracy compared to 52.8% for GPT-5.1-Codex.
All evaluations were run with compression and extra high reasoning effort.
These results show that the new model offers a high ceiling on both benchmarked accuracy under extended reasoning loads and in real-world use.
Technical architecture: long-range reasoning by compression
A major architectural improvement in GPT-5.1-CodexMax is the ability to efficiently reason over extended input-output sessions using a single mechanism. Compression.
This enables the model to retain important contextual information while discarding irrelevant details as it nears the limit of its contextual window—allowing it to operate consistently across millions of tokens without performance degradation.
This model has been observed to internally complete tasks lasting more than 24 hours, including multi-part refactors, test-driven iterations, and autonomous debugging.
It also improves the performance of companion tokens. In a medium-argument effort, GPT-5.1-Codex Max used about 30% fewer thought tokens than GPT-5.1-Codex for comparable or better accuracy, which has both cost and latency implications.
Platform integration and use cases
GPT-5.1-CodexMax is currently available in several codec-based environments, which refer to OpenIQ’s own integrated tools and interfaces designed specifically for code-based AI agents. These include:
Codex CLIOpenai’s official command line tool (@openai/codex), where gpt-5.1-codexmax is already live.
IDE extensionpossibly developed or maintained by OpenAI, although no third-party IDE integration was named.
Interactive coding environmentas they are used to demonstrate front-end simulation apps such as KartPool or Steele’s Law Explorer.
Internal code review toolingused by Openai’s engineering teams.
For now, GPT-5.1-CodexMax is not yet available through the public API, although OpenAI says it’s coming soon. Users who want to work with models in a terminal environment today can do so by installing and using the Codex CLI.
Currently it cannot be confirmed if the model will be integrated into third party IDEs unless they are built on top of the CLI or future API.
The model has the ability to interact directly with tools and simulations. Examples shown in the release include:
An interactive cartpool policy gradient simulator, which visualizes reinforcement learning training and activities.
A steel law optics explorer, which supports dynamic ray tracing in refractive index.
These interfaces illustrate the ability to model in real time while maintaining an interactive development session.
Cybersecurity and safety barriers
Although GPT-5.1-CodexMax does not meet OpenAI’s “high” capability threshold for cybersecurity under its manufacturing framework, it is currently the most capable cybersecurity model OpenAI has deployed. It supports use cases such as automated threat detection and remediation, but defaults to sandboxing and disabled network access.
OpenAI has reported no increase in malicious use but has introduced improved monitoring systems, including activity routing and disruption mechanisms for suspicious behavior. Codex remains isolated from local workspaces unless developers choose wider access, reducing risks such as quick injection from untrusted content.
Deployment context and developer usage
GPT-5.1-CodexMax is currently available for users Chat GPT Plus, Pro, Business, Adv, and Enterprise Plans It will also become the new default in codec-based environments, replacing gpt-5.1-codecs, which was a general-purpose model.
OpenAI says 95% of its internal engineers use Codex weekly, and since adopting it, those engineers have sent an average of 70% more pull requests — highlighting the tool’s impact on internal development speed.
Despite its autonomy and persistence, Openei emphasizes that Codex Max should be considered as a coding assistant, not a replacement for human review. The model generates terminal logs, test references, and tool call output to support transparency in generated code.
Outlook
GPT-5.1-Codex Max represents a significant evolution in OpenAI’s strategy toward agentic development tools, offering greater reasoning depth, token efficiency, and interactive capabilities in software engineering tasks. By leveraging its context management and compression strategies, the model is positioned to handle tasks at the scale of entire collections rather than individual files or fragments.
With a continued emphasis on agent workflows, secure sandboxes, and real-world evaluation metrics, Codex Max sets the stage for the next generation of AI-assisted programming environments—while highlighting the importance of monitoring in increasingly autonomous systems.