Introduction
OpenAI has published the official guide for GPT-5.1, its flagship model designed to balance intelligence and speed for agentic and coding tasks. GPT-5.1 introduces a new reasoning mode called "none" for low-latency interactions and significantly improves calibration to prompt difficulty, consuming fewer tokens on simple inputs while handling complex ones more efficiently. This guide compiles prompt patterns that maximize performance in production environments, derived from extensive internal testing and collaborations with partners building production-ready agents.
Migrating to GPT-5.1 from Previous Models
For developers using GPT-4.1, GPT-5.1 with "none" reasoning mode represents a natural fit for most low-latency use cases that don't require reasoning. GPT-5 users should pay attention to several key aspects: GPT-5.1 has better-calibrated token consumption but can sometimes be excessively concise at the cost of answer completeness. It's important to emphasize through prompting the importance of persistence and completeness in instructions.
The model can occasionally be verbose, so it's advisable to be explicit about the desired level of output detail. For coding agents, OpenAI recommends migrating the apply_patch implementation to the new named tool version. GPT-5.1-codex has also been released, which behaves slightly differently and requires a specific prompting guide.
Agentic Steerability: Customizing Agent Behavior
Personality Definition
GPT-5.1 is a highly steerable model that allows robust control over agent behaviors, personality, and communication frequency. While verbosity can be controlled through a dedicated parameter, it's possible to shape the overall style, tone, and cadence through prompting. Defining a clear agent persona is particularly important for customer-facing agents that need emotional intelligence to handle various user situations and dynamics.
In practice, this can mean adjusting warmth and brevity to the conversation state, avoiding excessive acknowledgment phrases like "got it" or "thank you". OpenAI suggests balancing the right level of directness and warmth in problem-solving, defining when the agent should be more formal or more empathetic based on context.
User Updates Management
User updates, also called preambles, allow GPT-5.1 to share upfront plans and provide consistent progress updates during execution. These updates can be calibrated along four main axes: frequency, verbosity, tone, and content. The model has been trained to excel at keeping users informed with plans, important insights, decisions, and granular context about what and why it's doing certain actions.
When timed correctly, the model can share a point-in-time understanding that maps to the current state of execution. In prompting, it's possible to define which types of preambles would be useful and which wouldn't, specifying the desired frequency of updates (e.g., every 6 execution steps or 8 tool calls) and the appropriate level of detail for each work phase.
Optimizing Intelligence and Instruction-Following
Complete Solutions and Persistence
GPT-5.1 pays very close attention to provided instructions, including guidance on tool usage, parallelism, and solution completeness. On long agentic tasks, the model may terminate prematurely without reaching a complete solution, but this behavior is modifiable through prompting. OpenAI recommends instructing the model to treat itself as an "autonomous senior pair-programmer": once given a direction, it should proactively gather context, plan, implement, test, and refine without waiting for additional prompts at each step.
The suggested approach is to persist until the task is fully handled end-to-end in the current turn when feasible, avoiding stopping at analysis or partial fixes. It's important to be "extremely biased for action": if the user provides an ambiguous directive, assume you should proceed with the change. If the user asks "should we do x?" and the answer is "yes", the agent should also proceed with the action.
Tool-Calling Format and Parallelization
To make tool-calling most effective, OpenAI recommends describing functionality in the tool definition and how/when to use tools in the prompt. Descriptions should be concise but complete, specifying exactly what the tool does when invoked. In the prompt, it's advisable to include a section that references the tool with practical usage examples.
GPT-5.1 also executes parallel tool calls more efficiently. When scanning a codebase or retrieving from a vector store, enabling parallel tool calling and encouraging the model to use parallelism in the tool description represents a good starting point. In the system prompt, it's possible to reinforce parallel tool usage by providing examples of permissible parallelism, such as "Parallelize tool calls whenever possible. Batch reads (read_file) and edits (apply_patch) to speed up the process."
"None" Reasoning Mode for Improved Efficiency
GPT-5.1 introduces a new reasoning mode: "none". Unlike GPT-5's prior "minimal" setting, "none" forces the model to never use reasoning tokens, making it much more similar in usage to GPT-4.1, GPT-4o, and other prior non-reasoning models. Developers can now use hosted tools like web search and file search with "none", and custom function-calling performance is also substantially improved.
While GPT-5.1 doesn't use reasoning tokens with "none", OpenAI has found that prompting the model to think carefully about which functions it plans to invoke can improve accuracy. It's advisable to instruct the model to plan extensively before each function call and reflect extensively on the outcomes of previous function calls, ensuring the user's query is completely resolved.
OpenAI has also observed that on longer model executions, encouraging the model to "verify" its outputs results in better instruction following for tool use. The guidance for GPT-5.1 with "none" includes instructions to avoid premature termination, similar to those used for GPT-5, reminding the agent to continue until the user's query is completely resolved before ending the turn.
Maximizing Coding Performance
Planning Tool
For long-running tasks, OpenAI recommends implementing a planning tool. Although reasoning models plan within their reasoning summaries, it can be difficult to track where the model is relative to query execution. A dedicated planning tool allows creating and maintaining a lightweight plan before the first code or tool action.
The plan tool should include 2-5 milestone/outcome items, avoiding micro-steps and repetitive operational tasks. States should be maintained in the tool with exactly one "in_progress" item at a time, marking items as completed when done and posting timely status transitions. Before any non-trivial code change, the model should ensure the current plan has exactly one appropriate item marked "in_progress" corresponding to the work about to be done.
New Tool Types: apply_patch and shell
GPT-5.1 has been post-trained on specific tools commonly used in coding use cases. The predefined apply_patch tool allows creating, updating, and deleting files in the codebase using structured diffs. Instead of just suggesting edits, the model emits patch operations that the application applies and reports back on, enabling iterative, multi-step code editing workflows.
With GPT-5.1, apply_patch can be used as a new tool type without writing custom descriptions. The description and handling are managed via the Responses API. This implementation uses a freeform function call rather than a JSON format. In testing, the named function decreased apply_patch failure rates by 35%.
OpenAI has also built a new shell tool for GPT-5.1 that allows the model to interact with the local computer through a controlled command-line interface. The model proposes shell commands, the integration executes them and returns outputs. This creates a simple plan-execute loop that lets models inspect the system, run utilities, and gather data until task completion.
Design System Enforcement
When building frontend interfaces, GPT-5.1 can be steered to produce websites that match the visual design system. OpenAI recommends using Tailwind to render CSS, which can be further tailored to meet design guidelines. It's important to define a design system that constrains the colors generated by GPT-5.1, avoiding hard-coded colors and instead using global CSS variables and design system tokens.
Effective Metaprompting
Building prompts can be cumbersome, but it's the highest-leverage activity for resolving most model behavior issues. Small inclusions can unexpectedly steer the model undesirably. Metaprompting leverages GPT-5.1 itself to inspect its own instructions and traces, identifying failure modes and suggesting improvements.
The process consists of two phases: first, ask GPT-5.1 to diagnose failures by providing the system prompt and a batch of failure examples, asking it to identify distinct failure modes and specific prompt lines causing them. In the second phase, ask the model to propose a surgical revision of the system prompt that reduces observed issues while preserving positive behaviors.
This iterative approach allows clarifying conflicting rules, removing redundant or contradictory lines, and tightening vague guidance, making tradeoffs explicit. OpenAI recommends running queries again after each iteration to observe any regressions and repeating the process until failure modes have been identified and addressed.
Conclusion
GPT-5.1 builds on GPT-5's foundation by adding quicker thinking for easy questions, advanced steerability for model output, new tools for coding use cases, and the option to set reasoning to "none" when tasks don't require deep thinking. OpenAI's official guide represents a fundamental starting point for developers building production-ready agents, with prompt patterns derived from extensive testing and real deployments.
Prompting remains an iterative process, and the best results come from adapting these patterns to each project's specific tools and workflows. As agentic systems expand, considering metaprompting for desired additions helps maintain discrete boundaries for each tool and their appropriate usage conditions.
FAQ
What is GPT-5.1 and what are the main innovations?
GPT-5.1 is OpenAI's new flagship model that balances intelligence and speed, introducing the "none" reasoning mode for low latency and improving prompt calibration with optimized token consumption on simple and complex inputs.
How do I migrate from GPT-5 to GPT-5.1?
Migration from GPT-5 requires emphasizing persistence and completeness in prompting, specifying the desired level of output detail, and migrating apply_patch to the new named tool implementation for coding agents.
When should I use GPT-5.1's "none" reasoning mode?
The "none" mode is ideal for low-latency use cases that don't require deep reasoning, enabling the use of hosted tools like web search and improving custom function-calling performance compared to GPT-5.
How can I customize GPT-5.1 agent personality?
GPT-5.1 allows defining a clear agent persona through prompting, specifying style, tone, cadence, and appropriate acknowledgment level based on context and the type of user interaction.
What are user updates in GPT-5.1?
User updates are progress messages that GPT-5.1 shares during execution, calibrated on frequency, verbosity, tone, and content to keep the user informed about plans, decisions, and current work state.
What are the new coding tools in GPT-5.1?
GPT-5.1 introduces apply_patch as a named tool to create, update, and delete files with structured diffs (reducing failures by 35%) and shell tool to interact with the system through controlled command-line.
What is metaprompting and how does it work?
Metaprompting uses GPT-5.1 to analyze its own prompts, identifying failure modes and suggesting surgical revisions through a two-phase iterative process: problem diagnosis and specific improvement proposals.
How do I optimize GPT-5.1 for complete solutions?
Instruct GPT-5.1 to behave as an autonomous senior pair-programmer, persisting until end-to-end task completion and being extremely action-biased without waiting for confirmation at each step.