o1-mini is a smaller reasoning model in the OpenAI o1 series, focused on programming, mathematics, and multi-step logic tasks. It is suitable for work with clearly defined problem conditions that requires derivation rather than broad knowledge coverage: from analyzing the cause of errors to proposing algorithmic solutions and organizing verifiable solution explanations. You can integrate it into applications 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-mini",
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 and output, and invocation methods before choosing a model.
o1-mini is natively positioned for programming and reasoning under text-based conditions. Platform text interactions are submitted in the Chat Completions or Responses format, and relevant history is maintained by the application.
Learn what o1-mini can bring to your work.
o1-mini's standout focus is programming, making it suitable for analyzing requirements, code, and errors together. When using it, you can ask it to explain its assessment of the issue first, then provide modification plans and testing recommendations, making it easier to distinguish genuine logic fixes from rewrites that merely make the code appear reasonable. Generated code still needs to be verified in the actual environment.
It continues the o1 series design of thinking before answering, making it suitable for mathematical and logical tasks that require combining conditions and comparing strategies. Provide the complete problem, variable definitions, and constraints, and request key derivations, conclusions, and verification methods to turn answers into work materials that are easy to review, rather than merely a final numerical value.
o1-mini is better suited to organizing clear text conditions, code, and failure examples into reasoning tasks. When solving problems continuously, you can first establish current hypotheses and candidate solutions, then revise them based on new examples; for complex projects, do not retain only the final response, but preserve the input conditions and actual verification results that affect judgment.
Start with specific tasks to find where the model can be effective.
Submit minimal reproducible code, error logs, expected behavior, and the runtime environment, and have o1-mini analyze possible fault locations, organize fix code, and provide a regression test checklist. Suitable for developers investigating edge cases and algorithmic logic issues; the deliverable is a fix proposal for review and execution, not a test report that has already been run.
Provide data structures, input size, constraints, and goals, and have the model compare candidate algorithms, explain trade-offs, and provide an implementation draft. You can further ask about extreme inputs, complexity, and test cases to produce an algorithm description and an initial code draft. The more complete the task materials, the easier it is to evaluate whether a solution meets real engineering conditions.
Provide the problem statement, existing derivations, and specific questions, and ask o1-mini to identify conditions that need to be added, explain key steps, or compare solution methods. Suitable for learning support and organizing derivations in technical calculations; the output can serve as an initial draft of a solution explanation. Important conclusions should be verified through substitution, independent calculation, or formal proof.
Choose based on task complexity, input materials, and expected results.
At launch, o1-mini was positioned as a faster, more economical reasoning option than o1-preview, especially suited for code. If a task mainly depends on provided code, formulas, and clear constraints rather than broad world knowledge, it may be the preferred choice. They are different models, so o1-preview evaluation results should not be used directly to judge o1-mini.
When the work focuses on code analysis in text, mathematical conditions, and logical problem-solving, o1-mini has a clearer specialized positioning. If a task is mainly about image understanding or general interaction, consider models such as GPT-4o that are suited to the relevant input. Do not treat o1-mini as a general-purpose multimodal assistant, and there is no need to prioritize a reasoning model for simple rewriting tasks.
Based on the characteristics of o1-mini, first validate small tasks whose results can be checked.
You can ask directly: Design an algorithm according to the constraints, explain why it meets the requirements, and list test cases for minimum input, maximum input, and duplicate data. Do not rely on external materials or images.
Programming and logic problems should include complete conditions; run generated code in the actual environment before deciding whether it is complete.
Use the full model ID o1-mini, first confirm the public request format and available parameters on the API page, then connect your application. Preserve result parsing, error 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 scope of capabilities.
Answers to common questions about using o1-mini.
No. o1-mini and o1-preview are different models in the o1 series. The former has a smaller, programming-oriented reasoning focus. When making calls, explicitly specify o1-mini, and do not directly apply evaluations, features, or parameters from other o1 models to it.
It is recommended to provide minimal reproducible code, the complete error message, expected output, and runtime environment, and clearly state whether you want a diagnosis or a code fix. You can ask the answer to include reasons for the changes and testing suggestions, then validate the generated content in a local environment rather than only pasting a single error message.
When using it, prioritize submitting text-based questions and formulas, and do not treat screenshot recognition as the default workflow. You can first convert the image content into text, check symbols and conditions, and then ask your question; if the problem depends on graphical details, a vision model is usually more suitable.
When using Chat Completions, put relevant history into messages; when using Responses, organize input and related conversation content according to the documentation. In each round, provide the latest materials, revision goals, and key constraints; for longer tasks, retain interim summaries and a final version that can be checked independently.
Responses and Chat Completions use input and messages respectively; choosing a protocol does not itself mean an o1-mini capability upgrade. Existing applications can continue using their data structures. For multi-turn tasks, retain necessary premises, candidate solutions, and the latest failure examples, then submit them in the corresponding request format.
Choose a development approach or application suited to this model and follow its setup guide.
↗OpenAI Chat Completions API
SDK installation, authentication and examples↗OpenAI Responses API
SDK installation, authentication and examples
↗Using Ace Data Cloud with Cherry Studio
Chat client setup · Base URL, API key and model
↗Using Ace Data Cloud with ChatBox
Chat client setup · Base URL, API key and modelThis is only a basic call example. See the full docs for more parameters and advanced usage.
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