Deep reasoning model for mathematical programming and professional analysis
o1-pro is the reasoning choice in the OpenAI o1 series for difficult problems, focusing not on quickly completing everyday Q&A but on investing more thought in complex analysis. It is suitable for mathematics and science problems, programming, data science, and case material analysis, especially for tasks that require checking assumptions, comparing approaches, and delivering complete arguments. On this platform, it can be accessed through responsive, message-based, or managed session methods.
Clarify capacity, input and output, and invocation methods before selecting a model.
Model positioning
High-compute reasoning direction in the o1 series
Primary working mode
Text questions and material input, generating analysis, code, or argumentation text
Responses endpoint
POST /openai/responses, using model and input
Message-based endpoint
POST /openai/chat/completions, using model and messages
Managed session endpoint
/aichat/conversations and /aichat2/conversations
Session continuation
Continue discussions through stateful and id; the basic results are answer and id
The deep reasoning positioning comes from OpenAI's public introduction to o1 pro mode; input organization and session operations vary according to the selected platform endpoint.
Core capabilities
Learn what o1-pro can bring to your work.
Break difficult problems into verifiable arguments
The value of choosing o1-pro lies in complex reasoning, rather than simply making answers longer. When handling math, science, or problems with multiple constraints, you can ask it to state the conditions, compare solution methods, and explain the scope within which the conclusion holds. Input should include the complete problem statement and known assumptions, so that the deliverable becomes an analysis that is easy to review rather than an isolated answer.
Conduct in-depth analysis around code and data
Programming and data science are areas officially emphasized for o1 pro mode. It is suitable to provide code snippets, error symptoms, data definitions, and target constraints, and ask it to analyze causes, compare implementation paths, and propose validation plans. You can set output requirements such as fix recommendations, algorithm explanations, or test checklists, then verify the results in the actual environment.
Give professional materials a clear decision structure
For professional texts such as case law, o1-pro is suitable for organizing issues, facts, rules, and conclusions around the provided materials. It is recommended to submit key passages and the disputed issues to be answered at the same time, and ask it to distinguish facts in the materials from inferences. This makes it easier for reviewers to examine the basis of the argument and avoids mistaking complete expression for facts that have already been verified.
Use cases
Start with specific tasks to find where the model can be effective.
Reviewing mathematics and scientific research plans
Enter problems, draft derivations, experimental hypotheses, or plan constraints, and request solution comparisons, key derivations, and validation steps. This is suitable for the review stage before formal submission, focusing on finding missing conditions and skipped steps in reasoning. When experimental results or numerical conclusions are involved, provide the raw data and calculation methodology together.
Reviewing complex defects and algorithms
Submit minimal reproducible code, error messages, expected behavior, and resource constraints, and have o1-pro organize possible causes, provide modification ideas, and design regression tests. Deliverables can include review comments, candidate patches, or explanations of algorithm trade-offs. The model is responsible for analysis and generating recommendations; compilation, execution, and performance testing are still completed by the development environment.
Comparative analysis of case-law materials
Provide relevant case-law excerpts, factual background, and the legal issues to be compared, and request a table of disputed issues, rule-application analysis, and a list of materials that need to be supplemented. This is suitable for assisting research and preparing discussion drafts; it is not advisable to provide only a case name and request a definitive conclusion. Citations, applicable jurisdictions, and timeliness should be verified by professionals.
How to choose this model
Choose based on task complexity, input materials, and expected results.
When it is worth moving from o1 to o1-pro
When the challenge lies in multi-step reasoning, trade-offs between approaches, or answer reliability, and you can accept a longer wait, o1-pro is worth considering. OpenAI's public comparison of o1 pro mode shows that it outperforms o1 and o1-preview on difficult math, science, and programming evaluations; this supports its positioning for difficult tasks, but does not mean it will win on every problem.
Reserve deep reasoning for genuinely difficult parts
Simple rewriting, short-text extraction, and routine Q&A usually do not require high-compute reasoning. A more sensible approach is to organize the materials first, then hand the hard-to-judge parts to o1-pro, such as conflicting design constraints, difficult-to-reproduce code issues, or conclusions requiring a complete argument. If the input contains only a vague goal, filling in the conditions first is usually more effective than directly increasing reasoning effort.
Get started
From a small-scale task to full integration.
01
Prepare the task and materials
Clarify the goal, required inputs, and output requirements, using real business examples as a starting point.
02
Try it in the API playground
Open the trial page, confirm the parameters supported by this entry point, then submit a small-scale task to review the results.
03
Integrate according to the API documentation
Keep the complete model ID, use the request format specified in the documentation, and confirm billing rules on the Pricing page.
Usage boundaries
Before formal use, understand output quality and capability scope.
o1-pro is not primarily chosen for instant feedback. The official positioning of o1 pro mode includes longer thinking and generation times, making it suitable for reviews, research, and complex problem-solving; interactive interfaces should retain a waiting state rather than being designed for actions that must receive an immediate response.
Deeper reasoning does not mean results are necessarily correct. Mathematical conclusions require checking conditions, code requires running tests, and case analysis requires verifying citations and scope of applicability. Prompts should explicitly request assumptions and uncertainties, rather than accepting only a final conclusion stated with certainty.
Images, audio, files, tools, and reasoning parameters provided by the interface belong to configuration scopes for different workflows and cannot all be treated as default o1-pro capabilities. Tasks can first be organized around text analysis; when code execution, attachment reading, or real-time information is needed, the corresponding steps should be designed separately.
Frequently Asked Questions
Answers to common questions about using o1-pro.
What is the main difference between o1-pro and o1?
The core difference is the amount of reasoning effort applied to difficult tasks. OpenAI describes o1 pro mode as a version of o1 that uses more compute resources, with a focus on improving reliability and completeness for complex problems. When choosing, weigh task difficulty and waiting time rather than assuming every task is better suited simply because of the name's pro designation.
How should I ask questions to better suit o1-pro?
Clearly state the goal, known conditions, constraints, and expected deliverables. For example, when submitting an algorithm problem, also specify the input range, correctness requirements, and existing approaches, and ask for a comparison of solutions and verification of boundary conditions. Giving it the full problem structure is more likely to produce useful analysis than merely asking it to “think deeply.”
Which input method should I choose to integrate o1-pro?
When you need to organize content yourself, use Responses input; applications with existing message history can use Chat Completions messages. If you want to simplify multi-turn conversation management, use the managed conversation endpoint to submit a question, and continue the discussion through stateful and the returned id.
Can o1-pro directly run the code it generates?
Code analysis and code execution are separate steps. o1-pro can be used to discuss implementations, generate candidate changes, and devise test plans, but a single text request does not automatically compile or run code. Run tests in your development environment, then bring logs and failing cases back into the conversation so subsequent analysis is based on actual results.
Is o1-pro suitable for legal analysis?
Case-law analysis is one of the application areas mentioned officially. It is suitable for organizing issues in dispute based on provided text, comparing rules and facts, and creating research drafts. It cannot replace professional judgment; provide the applicable jurisdiction and key materials, and verify citations and the currency of rules—especially do not treat a draft directly as definitive legal advice.
Model information · Updated: 2026-10-01. For request parameters and billing rules, see the API and pricing sections.
