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GPT-5.1-Codex-Max System Card
GPT‑5.1-Codex-Max 是我们最新一代具有 agentic 能力的编程代理模型。它建立在我们对基础推理模型的更新之上，该模型在软件工程、数学、科研、医学、计算机使用等多个领域的 agentic 任务上接受训练。它也是我们首个通过名为 compaction 的过程原生训练、能够在多个上下文窗口间协同工作的模型，能在单项任务中连贯处理数百万个标记。像以往型号一样， GPT‑5.1-Codex-Max 在真实世界的软件工程任务上接受训练，包括 PR 创建、代码审查、前端开发和 Q&A。
该系统说明概述了为 GPT‑5.1-Codex-Max 实施的全面安全措施。内容既包括模型层面的缓解手段——例如针对有害任务和 prompt injections 的专项安全训练——也包括产品层面的防护措施，如 agent sandboxing 和可配置的网络访问。
我们在 Preparedness Framework 下对 GPT‑5.1-Codex-Max 进行了评估。该模型在网络安全领域能力很强，但尚未达到网络安全方面的 High capability 水平。我们预计能力迅速提升的态势将持续，模型在不久的将来可能跨越这一门槛。与其他近期模型一样，它在生物学领域被视为 High capability ，并部署了与我们对 GPT‑5 使用的相应防护套件。它在 AI 自我改进方面则未达到 High capability 。
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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-CodexMax. 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.
via OpenAI News
GPT‑5.1-Codex-Max 是我们最新一代具有 agentic 能力的编程代理模型。它建立在我们对基础推理模型的更新之上，该模型在软件工程、数学、科研、医学、计算机使用等多个领域的 agentic 任务上接受训练。它也是我们首个通过名为 compaction 的过程原生训练、能够在多个上下文窗口间协同工作的模型，能在单项任务中连贯处理数百万个标记。像以往型号一样， GPT‑5.1-Codex-Max 在真实世界的软件工程任务上接受训练，包括 PR 创建、代码审查、前端开发和 Q&A。
该系统说明概述了为 GPT‑5.1-Codex-Max 实施的全面安全措施。内容既包括模型层面的缓解手段——例如针对有害任务和 prompt injections 的专项安全训练——也包括产品层面的防护措施，如 agent sandboxing 和可配置的网络访问。
我们在 Preparedness Framework 下对 GPT‑5.1-Codex-Max 进行了评估。该模型在网络安全领域能力很强，但尚未达到网络安全方面的 High capability 水平。我们预计能力迅速提升的态势将持续，模型在不久的将来可能跨越这一门槛。与其他近期模型一样，它在生物学领域被视为 High capability ，并部署了与我们对 GPT‑5 使用的相应防护套件。它在 AI 自我改进方面则未达到 High capability 。
----------------------
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-CodexMax. 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.
via OpenAI News
