o1-pro is the reasoning option in OpenAI's o1 series for difficult problems, focused not on quickly completing everyday Q&A, but on investing more thought in complex analysis. It is suited to mathematics and science problems, programming, data science, and case-law material analysis, especially for tasks that require checking assumptions, comparing options, and delivering complete arguments. Applications can integrate it using the public request format in this page's API section.
Point the Base URL to api.acedata.cloud, configure your platform API key and the model ID below, and use your compatible SDK or client.
import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ["ACEDATACLOUD_API_KEY"],
base_url="https://api.acedata.cloud/v1",
)
response = client.responses.create(
model="o1-pro",
input="Hello!",
)
print(response.output_text)Choose an available protocol for this model. OpenAI SDK uses a Base URL ending in /v1; Anthropic SDK uses the root URL. See each guide for protocol-specific parameters, tools and response formats.
Clarify capacity, input/output, and calling methods before selecting a model.
The deep reasoning positioning comes from OpenAI's public introduction to o1 pro mode; input organization and conversation operations vary according to the selected platform endpoint.
Learn what o1-pro can bring to your work.
The value of choosing o1-pro lies in complex reasoning, not simply in writing longer answers. When handling mathematics, science, or multi-constraint problems, you can ask it to state conditions, compare approaches, and explain the scope in which conclusions hold. Input should include the full problem statement and known assumptions, so the deliverable becomes an analysis that is easy to review rather than an isolated answer.
Programming and data science are areas officially emphasized for o1 pro mode. It is suitable for providing code snippets, error symptoms, data definitions, and target constraints, then asking it to analyze causes, compare implementation paths, and propose validation plans. You can specify the output as repair recommendations, algorithm explanations, or test checklists, and then verify the results in the actual environment.
For professional texts such as case law, o1-pro is well suited to organizing issues, facts, rules, and conclusions around the provided materials. It is recommended to submit both key passages and the disputed issues to be answered, 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 helps avoid mistaking polished expression for verified facts.
Start with specific tasks to find where the model can be effective.
Provide the problem, derivation draft, experimental hypotheses, or plan constraints, and request comparisons of approaches, key derivations, and validation steps. This is suitable for review before formal submission, focusing on identifying omitted conditions and gaps in reasoning. When experimental results or numerical conclusions are involved, provide the raw data and calculation methodology as well.
Submit minimal reproducible code, error messages, expected behavior, and resource limits, and have o1-pro organize possible causes, suggest modifications, and design regression tests. Deliverables can include review comments, candidate patches, or explanations of algorithm trade-offs. The model handles analysis and generates recommendations; compilation, execution, and performance testing are still completed in the development environment.
Provide relevant case-law excerpts, factual background, and the legal issues to be compared, and request a table of disputed issues, analysis of rule application, 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.
Choose based on task complexity, input materials, and expected results.
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 question.
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 give the hard-to-judge parts to o1-pro, such as conflicting design constraints, hard-to-reproduce code problems, or conclusions requiring a complete argument. If the input contains only vague goals, filling in the conditions first is usually more effective than directly increasing reasoning effort.
Based on o1-pro's characteristics, first validate small tasks with checkable results.
You can ask directly: Check the assumptions in this analysis item by item, propose scenarios that could overturn the conclusion, and then provide a sensitivity analysis plan. Separate conclusions that can be verified from the materials from parts that require experiments.
Clarify technical terms and the scope of evidence; longer reasoning still requires verification by people or computational tools.
Use the full model ID o1-pro, first confirm the public request format and available parameters on the API page, then connect your application. Keep result parsing, exception handling, and relevant evidence, and use the same set of real samples to evaluate whether it is suitable for continued use.
Before formal use, understand the output quality and capability scope.
Answers to common questions about using o1-pro.
The core difference is the amount of reasoning effort devoted 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 against wait time rather than assuming that pro in the name makes it better for every task.
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 edge cases. Giving it the complete problem structure is more likely to produce useful analysis than simply asking it to “think deeply.”
When you need to organize content yourself, use Responses input; applications with existing message history can use Chat Completions messages.
Code analysis and code execution are different steps. o1-pro can be used to discuss implementations, generate candidate changes, and propose test plans, but a single text request does not automatically compile or run code. Run tests in a development environment, then bring logs and failing cases back to the conversation so subsequent analysis is based on actual results.
Case-law analysis is one of the application areas mentioned by OpenAI. It is suitable for organizing issues in dispute based on provided text, comparing rules and facts, and producing research drafts. It cannot replace professional judgment; provide the applicable jurisdiction and key materials, and verify citations and whether rules remain current. In particular, do not treat a draft directly as definitive legal advice.
Choose a development approach or application suited to this model and follow its setup guide.
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