OpenAI has GPT‑5.1-Codex-Max introducedA new Frontier agentive coding model is now available in its Codex developer environment. This release is a significant step forward in AI-assisted software engineering, offering improved long-term reasoning, efficiency, and real-time interactive capabilities. GPT‑5.1-Codex-Max will now replace GPT‑5.1-Codex as the default model on Codex-integrated surfaces.
The new model is designed to act as a continuous, high-context software development agent, capable of managing complex refactors, debugging workflows, and project-scale tasks across multiple context windows.
This comes after Google released its powerful new Gemini 3 Pro model yesterday, yet it still outperforms or matches it on key coding benchmarks:
But SWE-Bench Verified, GPT‑5.1-Codex-Max achieved 77.9% accuracy In the extra-high logic effort, the Gemini 3 came out ahead of the Pro by 76.2%.
also led Terminal-Bench 2.0, with 58.1% accuracy versus Gemini’s 54.2%, And that matches the Gemini’s score of 2,439 on the competitive coding Elo benchmark, LiveCodeBench Pro.
When measured against the Gemini 3 Pro’s most advanced configuration – its Deep Thinking model – Codex-Max also has a slight edge in the agentive coding benchmarks.
Performance Benchmarks: Incremental Gains in Key Functions
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 IC SWE, it achieved 79.9% accuracy, a significant increase from 66.3% for the GPT‑5.1 codec. In SWE-Bench verified (n=500), it reached 77.9% accuracy on extra-high logic effort, outperforming the GPT‑5.1 codec’s 73.7%.
A more modest improvement in performance was seen on Terminal Bench 2.0 (n=89), with GPT‑5.1-codecs-max achieving 58.1% accuracy compared to 52.8% for GPT‑5.1-codecs.
All evaluations were run with compaction and extra-high logic effort enabled.
These results indicate that the new model provides high bounds on both benchmark accuracy and real-world utility under extended logic loads.
Technical Architecture: Long-Horizon Reasoning through Condensation
A major architectural improvement in GPT‑5.1-Codex-Max is its ability to effectively reason over extended input-output sessions using a mechanism. condensation,
This enables the model to retain important relevant information while discarding irrelevant details as it approaches its context window boundary – effectively allowing it to operate continuously on millions of tokens without performance degradation.
The model has been observed internally to complete tasks lasting more than 24 hours, including multi-step refactors, test-driven iteration, and autonomous debugging.
Condensation also improves token efficiency. At medium logic effort, GPT‑5.1-codecs-max used approximately 30% fewer think tokens than GPT‑5.1-codecs for comparable or better accuracy, which has an impact on both cost and latency.
Platform integration and use cases
GPT‑5.1-Codex-Max is currently available in several codecs-based environments, which refers to OpenAI’s own integrated tools and interfaces built specifically for codex-centric AI agents. These include:
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codex cliOpenAI’s official command-line tool (@openai/codex), where GPT‑5.1-Codex-Max is already live.
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IDE extensionPossibly developed or maintained by OpenAI, although no specific third-party IDE integration was named.
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Interactive Coding EnvironmentSuch as those used to demonstrate frontend simulation apps such as Cartpoll or Snell Law Explorer.
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internal code review toolingUsed by OpenAI’s engineering teams.
For now, GPT‑5.1-Codex-Max is not yet available via public API, although OpenAI says it is coming soon. Users who want to work with models in a terminal environment today can do so by installing and using the Codex CLI.
At the moment it is not confirmed whether the model will integrate into third-party IDEs, unless they are built on top of the CLI or future APIs.
The model is able to interact with live tools and simulations. Examples shown in the release include:
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An interactive Cartpol policy gradient simulator that visualizes reinforcement learning training and activations.
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A Snell Law Optics Explorer supports dynamic ray tracing across refractive indices.
These interfaces exemplify the model’s ability to reason in real time while maintaining an interactive development session – effectively connecting computation, visualization, and implementation within a single loop.
Cybersecurity and Security Barriers
While GPT‑5.1-Codex-Max does not meet OpenAI’s “high” capability threshold for cybersecurity under its readiness framework, it is the most capable cybersecurity model currently deployed by OpenAI. It supports use cases such as automated vulnerability detection and remediation, but with strict sandboxing and disabled network access by default.
OpenAI reports no increase in large-scale malicious use, but has introduced advanced monitoring systems, including activity routing and disruption mechanisms for suspicious behavior. Unless developers opt for broader access, the codec remains isolated from the local scope, reducing risks such as early injection of untrusted content.
Deploy context and developer usage
GPT‑5.1-Codex-Max is currently available to users ChatGPT Plus, Pro, Business, Edu and Enterprise Plans. It will also become the new default in codecs-based environments, replacing GPT‑5.1-Codex, which was a more general-purpose model.
OpenAI says 95% of its internal engineers use Codex weekly, and since adoption, these engineers have sent ~70% more pull requests on average – highlighting the tool’s impact on internal development velocity.
Despite its autonomy and persistence, OpenAI emphasizes that Codex-Max should be treated as a coding aid, not a replacement for human review. The model produces terminal logs, test quotes, and tool call output to support transparency into the generated code.
Outlook
GPT‑5.1-Codex-Max represents a significant evolution in OpenAI’s strategy toward agentive development tools, offering greater reasoning depth, token efficiency, and interactive capabilities in software engineering tasks. By extending its context management and compaction strategies, the model is deployed to handle tasks at the scale of full repositories, rather than individual files or snippets.
With a continued emphasis on agentic workflows, secure sandboxes, and real-world evaluation metrics, Codex-Max sets the stage for the next generation of AI-assisted programming environments – while underscoring the importance of observability in increasingly autonomous systems.