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OpenAI ARTICLE ARTIKEL 19 November 2025 19 november 2025

GPT-5.1-Codex-Max System Card GPT-5.1-Codex-Max-systeemkaart

Title: GPT-5.1-Codex-Max System Card Titel: GPT-5.1-Codex-Max-systeemkaart

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AI maker AI-maker OpenAI Type Type Article Artikel Published Gepubliceerd 19 November 2025 19 november 2025 Updates Updates Videos Video's View original article Bekijk origineel artikel
Why it matters Waarom dit telt

Quick editorial signal Snelle redactionele duiding

2 min
Impact Impact

Relevant if you build with AI tools, APIs, or coding agents. Relevant als je bouwt met AI-tools, API's of coding agents.

Audience Voor wie Developers Developers
Level Niveau Expert Expert
  • Track this as a OpenAI update, not just a standalone headline. Bekijk dit als OpenAI-update, niet alleen als losse headline.
  • Useful for builders who need to understand API, coding, or workflow changes. Nuttig voor bouwers die API-, code- of workflowwijzigingen willen begrijpen.
  • Likely worth revisiting after people have used the release in practice. Waarschijnlijk de moeite waard om opnieuw te bekijken zodra mensen het in praktijk gebruiken.
model apps developers safety

GPT-5.1-Codex-Max System Card | OpenAI

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November 19, 2025

PublicationSafety

GPT‑5.1‑Codex‑Max System Card

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Introduction

GPT‑5.1‑Codex‑Max is our new frontier agentic coding model. It is built on an update to our foundational reasoning model trained on agentic tasks across software engineering, math, research, medicine, computer use and more. It is our first model natively trained to operate across multiple context windows through a process called compaction, coherently working over millions of tokens in a single task. Like its predecessors, GPT‑5.1‑Codex‑Max was trained on real-world software engineering tasks like PR creation, code review, frontend coding and Q&A.

This system card outlines the comprehensive safety measures implemented for GPT‑5.1-Codex-Max. It details both model-level mitigations, such as specialized safety training for harmful tasks and prompt injections, and product-level mitigations like agent sandboxing and configurable network access.

GPT‑5.1‑Codex‑Max was evaluated under our Preparedness Framework. It is very capable in the cybersecurity domain but does not reach High capability on cybersecurity. We expect current trends of rapidly increasing capability to continue, and for models to cross the High cybersecurity threshold in the near future. Like other recent models, it is being treated as High capability on biology, and is being deployed with the corresponding suite of safeguards we use for GPT‑5. It does not reach High capability on AI self-improvement.

PublicationSafety

GPT‑5.1‑Codex‑Max System Card

Author

OpenAI

GPT‑5.1‑Codex‑Max is our new frontier agentic coding model. It is built on an update to our foundational reasoning model trained on agentic tasks across software engineering, math, research, medicine, computer use and more. It is our first model natively trained to operate across multiple context windows through a process called compaction, coherently working over millions of tokens in a single task. Like its predecessors, GPT‑5.1‑Codex‑Max was trained on real-world software engineering tasks like PR creation, code review, frontend coding and Q&A.

This system card outlines the comprehensive safety measures implemented for GPT‑5.1-Codex-Max. It details both model-level mitigations, such as specialized safety training for harmful tasks and prompt injections, and product-level mitigations like agent sandboxing and configurable network access.

GPT‑5.1‑Codex‑Max was evaluated under our Preparedness Framework. It is very capable in the cybersecurity domain but does not reach High capability on cybersecurity. We expect current trends of rapidly increasing capability to continue, and for models to cross the High cybersecurity threshold in the near future. Like other recent models, it is being treated as High capability on biology, and is being deployed with the corresponding suite of safeguards we use for GPT‑5. It does not reach High capability on AI self-improvement.

* 2025

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Author

OpenAI

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