https://platform.openai.com/docs/guides/reasoning
FreeAdvanced reasoning models that think before answering
FreeFree tier
Inputs: textOutputs: text
About https://platform.openai.com/docs/guides/reasoning
OpenAI's reasoning models (including the o1 and o3 series) are designed to spend more time thinking through problems before generating responses, enabling them to handle complex reasoning tasks such as advanced mathematics, coding, and scientific analysis. This guide provides documentation on using these models via the API, including best practices for prompting, configuration options, and performance considerations.
Key Features
Step-by-step chain-of-thought reasoning before final response
Optimized for complex tasks in math, coding, and science
API support for reasoning_effort parameter to control thinking depth
Pros & Cons
Pros
- Produces well-reasoned, accurate answers for difficult problems
- Transparent thinking process enhances trust and interpretability
- Provides consistent, logical reasoning for multi-step tasks
Cons
- Higher latency due to internal reasoning steps
- Higher token usage leading to increased cost
- May overthink simple queries, making response time longer than necessary
Best For
Advanced mathematical problem solvingComplex code generation and debuggingScientific reasoning and analysis
FAQ
How do reasoning models differ from standard GPT models?
Reasoning models spend additional time thinking through problems step-by-step before generating a final answer, enabling them to handle more complex reasoning tasks that require careful logical deduction.
What is the reasoning_effort parameter?
The reasoning_effort parameter allows you to control how much thought the model puts into a response, balancing depth of reasoning with speed and cost.