The ChatGPT 5.1 Prompting Guide for Code Agents

The release of ChatGPT 5.1 marks a significant paradigm shift for developers, moving beyond simple text generation to the foundation for a new era of agentic programming. This model is engineered not just to respond, but to reason, execute, and persist as a capable collaborator.

This article serves as a definitive ChatGPT prompting guide for developers on how to effectively prompt and control GPT-5.1's advanced capabilities, framing its features as pillars supporting this new paradigm. We will cover the groundbreaking concept of Adaptive Reasoning, new executors like the apply_patch AI tool, powerful economic models like Extended Prompt Caching, and the blueprints for building with the new Agents SDK.

1. The Engine: Architecting Intelligence with Adaptive Reasoning

The core innovation driving GPT-5.1 is Adaptive Reasoning, a mechanism that allows the model to dynamically adjust its computational effort based on a task's complexity, treating intelligence as a configurable resource.

What is Adaptive Reasoning?

Adaptive Reasoning means GPT-5.1 calibrates the "thinking" tokens and processing time it dedicates to a request.

• For simple queries, it responds almost instantly, conserving resources.
• For complex problems, it engages in more persistent, deeper thought to ensure a reliable answer.

This efficiency translates directly to real-world performance gains:

• A command lookup that previously took 10 seconds can now be completed in just 2.
• An analysis from Balyasny Asset Management reported that, across its evaluation suite, GPT-5.1 operated "2–3 times faster than GPT-5".
• On simpler tasks, this can lead to token usage reductions of up to 88%, making it economically viable to use a single frontier model across an entire application stack.

Controlling the Trade-off with reasoning_effort

Developers can directly control this behavior via the reasoning_effort API parameter, allowing for a fine-tuned balance between speed and intelligence.

2. The Economics: Maximizing Efficiency with Extended Prompt Caching

GPT-5.1 introduces powerful economic levers that, when used correctly, can dramatically reduce operational costs for stateful, agentic applications.

The 90% Discount: How Caching Works

Extended Prompt Caching is a feature that allows prompts to remain active in the model's cache for up to 24 hours.

• Cached input tokens receive a 90% discount.
• They are priced at just $0.125 per 1 million tokens compared to the standard $1.25 per 1 million uncached tokens.
• This 24-hour context persistence is the economic backbone for the long-running, multi-hour agent loops enabled by the Agents SDK and models like GPT-5.1-Codex-Max.

Structuring Your ChatGPT 5.1 Prompts for Cache Hits

To maximize the financial benefit of caching, developers must structure their ChatGPT 5.1 prompts to facilitate cache hits.

• The caching mechanism relies on exact prefix matching.
• This means static content, such as system instructions or few-shot examples, must be placed at the beginning of the prompt.
• Dynamic content, like user input, should be appended at the end.

The Critical Compliance Trade-off: Caching vs. ZDR

A critical strategic decision for enterprise developers is that Extended Prompt Caching is fundamentally incompatible with Zero Data Retention (ZDR) requirements.

• Caching, by its nature, requires storing session state for up to 24 hours.
• This directly violates the principle of ZDR, which mandates that all data be deleted immediately after a request is processed.
• This forces organizations to make a strategic choice between maximizing cost efficiency with caching and maintaining strict data compliance with ZDR.

3. The Executors: Agentic Prompting with New AI Tools

GPT-5.1 moves beyond generating text to executing tasks through a new suite of specialized AI tools, enabling true agentic workflows.

The shell Tool: Interacting with the System

The shell AI tool allows the model to propose shell commands to be executed in the developer's environment.

• The developer's integration runs these commands and returns the output to the model.
• This creates a powerful "plan-execute" loop, allowing the agent to inspect the local system, run tests, install dependencies, and gather real-time context to inform its next steps.

The apply_patch Tool: Reliable Code Modification

Instead of suggesting code changes in plain text, the apply_patch AI tool allows the model to generate structured diffs for creating, updating, or deleting files.

• This structured approach, implemented as a named freeform function call instead of a standard JSON format, yielded a 35% decrease in apply_patch failure rates during testing, prioritizing the high reliability essential for autonomous agents.
• Developers can integrate these tools by including them in the tools array of the Responses API call (e.g., {"type": "shell"} or {"type": "apply_patch"}).

4. The Blueprint: Building with the Agents SDK

The Agents SDK provides the necessary framework for orchestrating GPT-5.1's agentic capabilities into robust applications, turning a language model into a functional software engineering collaborator.

Setting Up the Agent

To build an agent, developers use the Agent class from the SDK. The setup involves:

• Defining the agent with a set of instructions.
• Specifying the model (e.g., "gpt-5.1").
• Providing a list of the tools it is authorized to use, such as shell_tool and apply_patch_tool.

The Importance of a Secure Executor

When using the shell AI tool, security is paramount.

• The recommended best practice is to implement a ShellExecutor class that isolates all command execution within a dedicated and restricted workspace directory.
• This prevents the agent from interacting with files outside of its intended scope.
• The official guidance warns: "In production, always execute shell commands in a sandboxed environment".

For Long-Horizon Tasks: GPT-5.1-Codex-Max

For extremely complex and long-running tasks, such as project-scale refactors or multi-hour agent loops, developers should use the specialized GPT-5.1-Codex-Max model.

• It employs a process called "compaction" to intelligently prune its history while preserving critical context.
• This compaction process allows it to work coherently over millions of tokens and sustain its reasoning for extended periods.

5. Understanding the Model Landscape

GPT-5.1 is available through several API aliases, each optimized for different use cases.

Core Specifications

• Context Window: 400,000 tokens.
• Maximum Output: 128,000 tokens.

Frequently Asked Questions (FAQs)

This section covers the essential context for using these powerful AI tools.

Sources and References:

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