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GPT-5.1-Codex-Max - An intelligent programming model launched by OpenAI

GPT-5.1-Codex-Max is an intelligent programming model from OpenAI, designed for complex, long-cycle development tasks. Based on a newer inference architecture, the model uses a "compression" technique to span multiple context windows, enabling it to process large amounts of data...

What is GPT-5.1-Codex-Max?

GPT-5.1-Codex-Max is an intelligent programming model launched by OpenAI, designed specifically for complex, long-cycle development tasks. Based on a newer inference architecture, the model uses "compression" technology to span multiple context windows, enabling it to handle large-scale tasks with millions of tokens, such as project-level refactoring and deep debugging. The model performs exceptionally well on real-world software engineering tasks, such as code review and front-end development, and runs smoothly in Windows environments. It significantly improves token efficiency and reduces development costs. GPT-5.1-Codex-Max is integrated into Codex, supporting CLI, IDE extensions, cloud services, and code review; its API will be available soon.

Main functions of GPT-5.1-Codex-Max

  • Long-cycle task processingGPT-5.1-Codex-Max is the first model that can span multiple context windows, maintaining consistency in long-cycle tasks through "compression" technology, supporting project-level refactoring, deep debugging, and continuous development for many hours.
  • High-efficiency code generationIt performs exceptionally well in real-world software engineering tasks, such as code review, front-end development, and pull request creation, generating high-quality code while significantly reducing development costs.
  • Multi-environment supportThis update provides the first support for the Windows environment, optimizes collaboration capabilities within the Codex CLI, and is suitable for various development scenarios.
  • Improved reasoning efficiencyAt the same inference strength, GPT-5.1-Codex-Max uses fewer think tokens while providing higher accuracy and performance compared to its predecessor.
  • Security and credibilityImproved performance in long-cycle inference tasks such as cybersecurity, and enhanced defense capabilities through projects such as Aardvark.

Technical Principles of GPT-5.1-Codex-Max

  • Compression technology for multi-context windowsBy compressing historical information, the model can automatically clean up space when it approaches the context window limit, while retaining key context, supporting the continuous execution of long-term tasks.
  • Updated reasoning frameworkBased on OpenAI's latest fundamental reasoning model, and trained through extensive software engineering, mathematics, and research tasks, it performs more intelligently in complex tasks.
  • Efficient token managementBy optimizing the inference process and reducing unnecessary token usage, it provides higher efficiency and lower cost in non-latency-sensitive tasks.
  • Cross-platform optimizationThe model has been specially trained to adapt to the Windows environment and its collaboration capabilities have been optimized in the Codex CLI to make it more suitable for real-world development scenarios.

Performance of GPT-5.1-Codex-Max

In the SWE-Lancer IC SWE test, the accuracy of GPT-5.1-Codex-Max improved from 66.3% to 79.9%; in Terminal-Bench 2.0, the accuracy improved from 52.8% to 58.1%.

Project address for GPT-5.1-Codex-Max

  • Project official websitehttps://openai.com/index/gpt-5-1-codex-max/

Application scenarios of GPT-5.1-Codex-Max

  • Code refactoringThe model supports large-scale code refactoring, optimizes code structure, and improves code quality by using compression techniques across context windows.
  • Code debuggingThe model can perform in-depth code debugging, continuously track and fix complex problems, and reduce debugging time and manpower costs.
  • Code generationThe model can generate high-quality front-end and back-end code, optimize code performance, reduce development costs, and improve development efficiency.
  • Code reviewIt automatically reviews code, provides detailed feedback, and helps developers identify potential problems and improve code quality.
  • CI/CD processAutomatically fix code issues during continuous integration and continuous deployment, ensuring that the code passes tests smoothly and goes live quickly.
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