OpenAI Releases GPT-5.1 Prompting Guide

OpenAI has published a comprehensive prompting guide for GPT-5.1, their newest flagship model. The guide documents technical patterns developed through internal testing and production deployments with enterprise partners. Here's what changed and what it means for teams building AI systems.

Major Model Improvements

Here's what's new.

Reasoning Token Calibration

GPT-5.1 dynamically adjusts reasoning token consumption based on query difficulty. Simple questions use fewer tokens; complex problems receive deeper computational resources. This represents a shift from GPT-5's more uniform token allocation, with direct implications for operational costs at scale.

Enhanced Steerability

The model now responds more reliably to instructions about:

• Output verbosity and length
• Personality and communication style
• Response formatting preferences
• Update frequency during multi-step tasks

New "None" Reasoning Mode

GPT-5.1 introduces a none reasoning setting that completely eliminates reasoning tokens, making it functionally equivalent to GPT-4.1 for low-latency applications. Critically, this mode still supports hosted tools like web search and file search—a capability previous minimal reasoning modes lacked.

Migration Guidance from GPT-5

OpenAI provides specific migration recommendations:

• Emphasize persistence: GPT-5.1 can be overly concise at the expense of completeness. Prompts should explicitly encourage thorough solutions.
• Specify output formatting: Be explicit about desired verbosity and detail levels.
• Update coding tools: Migrate from custom apply_patch implementations to the new named tool format.
• Clarify instructions: GPT-5.1 excels at instruction-following, so resolve any conflicting guidance in your prompts.

Agentic Capabilities

Here are the agentic updates.

User Updates (Preambles)

The guide details how to configure agent progress reports during long-running tasks:

• Frequency: Updates every 6 execution steps or 8 tool calls
• Content: Concrete outcomes, not just next steps
• Format: 1-2 sentences for routine updates, longer for initial plans and final recaps
• Timing: Post immediate commentary before extended tool-calling sequences

Personality Shaping

OpenAI demonstrates how to define agent personas for customer-facing applications, with examples showing how to balance warmth, directness, and efficiency. The guide includes specific prompting patterns for controlling acknowledgment phrases and conversational rhythm.

Solution Persistence

To prevent premature task termination, the guide recommends prompting for:

• Autonomous, end-to-end completion within a single turn
• Proactive context gathering without waiting for user prompts
• Bias toward action on ambiguous directives
• Full implementation, verification, and explanation before ending

Tool Usage Optimization

Parallel Tool Calling

GPT-5.1 executes parallel tool calls more efficiently. The guide recommends:

• Enabling parallelism in tool definitions
• Batching reads and edits to improve speed
• Including parallelism examples in system prompts

New Named Tools

apply_patch: Creates, updates, and deletes files using structured diffs instead of full rewrites. Available as a named tool type without custom descriptions. Internal testing showed 35% reduction in failure rates compared to custom implementations.

shell: Enables controlled command-line interactions through a plan-execute loop. The model proposes commands; your system executes them and returns outputs.

Both tools use the Responses API and require specific implementation patterns documented in the guide.

Coding Workflow Enhancements

For coders:

Plan Tool Implementation

For medium-to-large coding tasks, OpenAI recommends implementing a planning tool that maintains task status. Requirements include:

• 2-5 milestone items (no micro-steps)
• Exactly one item "in_progress" at any time
• Status updates at least every 8 tool calls
• All items completed or explicitly deferred before ending

Design System Enforcement

The guide includes patterns for constraining visual outputs to match brand guidelines, particularly for frontend development using Tailwind CSS. Instructions emphasize token-based color systems over hard-coded values.

None Mode Prompting Strategies

When using none reasoning mode:

• Encourage explicit planning before function calls
• Request verification of outputs for better instruction-following
• Emphasize task completion to prevent premature termination
• Use few-shot prompting and detailed tool descriptions

The guide notes that none mode substantially improves custom function-calling performance compared to GPT-5's minimal reasoning setting.

Metaprompting Methodology

OpenAI documents a two-step process for systematic prompt debugging:

Step 1: Diagnosis

Provide the model with your system prompt and failure logs. Request root-cause analysis identifying contradictions and problematic phrasing.

Step 2: Revision

Use the analysis to generate surgical prompt edits that resolve conflicts without complete rewrites. Focus on clarifying ambiguous guidance and removing redundancies.

This approach treats prompt optimization as an iterative engineering process rather than guesswork.

Practical Implications

For teams running AI systems in production:

• Token economics improve for high-volume applications through better calibration
• None mode expands deployment options for latency-sensitive use cases
• Named tools reduce implementation overhead for common patterns
• Steerability improvements make agent behavior more predictable
• Metaprompting accelerates debugging for complex multi-agent systems

The guide represents operational refinement rather than architectural change. Teams currently using GPT-5 can migrate incrementally; those on GPT-4.1 may find none mode offers a cleaner upgrade path.