OpenAI Shares Best Practices for Prompting O-Series Reasoning Models

OpenAI has published a detailed guide on how to effectively prompt its o-series reasoning models, such as o1 and o3-mini, emphasizing simpler, more direct instructions over traditional chain-of-thought prompting. The guide also outlines the differences between reasoning models and GPT models, helping developers determine which model best fits their needs.

O-Series Models vs. GPT Models: Key Differences

OpenAI categorizes its models into two distinct families:

O-Series Models ("The Planners")

• Thinks longer about complex tasks

• Designed for complex reasoning, strategy, and decision-making.

• Excels at math, science, engineering, finance, and legal analysis.

• Processes large volumes of ambiguous information with high accuracy.

GPT Models ("The Workhorses")

• Optimized for fast execution and cost efficiency.

• Best suited for well-defined, straightforward tasks.

• Used alongside o-series models to carry out planned steps.

Choosing the Right Model for Your Use Case

Selecting the best AI model depends on your priorities and task complexity:

• Speed & Cost Efficiency → GPT models are faster and more cost-effective.

• Executing Well-Defined Tasks → GPT models excel at handling straightforward, structured tasks.

• Accuracy & Reliability → O-series models are better for decision-making and high-stakes applications.

• Complex Problem-Solving → O-series models navigate ambiguity and multi-step reasoning.

If your primary need is speed and cost-efficiency for clear-cut tasks, GPT models are the best fit. However, if you need high accuracy, logical reasoning, or multi-step problem-solving, the o-series models are the ideal choice.

In many cases, the most effective approach combines o-series models for strategic decision-making with GPT models for executing tasks efficiently.

When to Use O-Series Models

OpenAI outlines key scenarios where o-series models excel:

• Navigating Ambiguous Tasks – Handles incomplete instructions, filling in gaps intelligently, often seeking clarification before making assumptions or attempting to fill in missing information.

• Extracting Key Information – Analyzes large unstructured datasets to find critical details.

• Understanding Complex Relationships – Synthesizes insights from legal, financial, insurance, or technical documents. Reasoning models excel at identifying connections between documents, interpreting nuanced policies and rules, and applying them to tasks to make informed, context-aware decisions.

• Multi-Step Agentic Planning – Acts as a planner, generating a comprehensive, multi-step solution to a problem and then choosing the appropriate GPT model (“the doer”) for each step, depending on whether advanced reasoning or faster response time is needed.

• Advanced Visual Reasoning – Interprets complex charts, tables, and poor-quality images. o1 can recognize patterns across multiple images by interpreting a legend from one page of an architectural drawing and accurately applying it to another page without explicit guidance.

• Reviewing & Debugging Code – Conducts in-depth code reviews in the background, detecting errors across files, often giving the models higher latency.

• Evaluating AI Model Responses – Assesses the accuracy, consistency, and reliability of outputs generated by other AI models. They are particularly effective in grading, validating, and benchmarking responses in fields like healthcare, finance, and legal analysis, where precision is critical. By identifying nuanced differences, contextual inconsistencies, and errors, these models enhance data quality and decision-making across AI-driven workflows.

Best Practices for Prompting O-Series Models

To maximize performance, OpenAI recommends the following:

• Use developer messages instead of system messages for better command execution.

• Keep prompts simple and direct—avoid unnecessary step-by-step or chain of thought instructions.

• Use delimiters like markdown, XML tags or section titles to clarify input structure.

• Try zero-shot prompting first before adding few-shot examples. Reasoning models are designed to handle tasks with minimal instruction, so it's best to begin with zero-shot prompting—providing only a direct request without examples. If the model's output needs refinement, introduce few-shot examples to guide its responses. When using examples, ensure they closely align with your instructions, as inconsistencies between the prompt and examples can lead to poor results or unintended biases in responses.

• Clearly define guidelines, constraints and success criteria to guide model responses. Provide specific parameters for the desired response and prompt the model to refine its output until it meets the goal.

• Signal markdown formatting preferences using "Formatting re-enabled" in prompts.

Looking Ahead: The Future of AI-Driven Reasoning

OpenAI’s emphasis on dedicated reasoning models reflects the growing need for more advanced, decision-oriented AI. As AI systems take on complex problem-solving roles, businesses and developers must optimize their approach to prompting and model selection.

By combining o-series models for strategic reasoning with GPT models for execution, OpenAI envisions AI workflows that are more efficient, accurate, and scalable across industries like finance, law, healthcare, and engineering.

OpenAI hopes these guidelines will help users get the most out of its reasoning models, ensuring they are applied effectively for tasks that require deep analytical thinking, structured decision-making, and multi-step problem-solving.

Editor’s Note: This article was created by Alicia Shapiro, CMO of AiNews.com, with writing, image, and idea-generation support from ChatGPT, an AI assistant. However, the final perspective and editorial choices are solely Alicia Shapiro’s. Special thanks to ChatGPT for assistance with research and editorial support in crafting this article.