Categorizes code complexity
AI Models & Assistants / Knowledge library
GPT-5
Previous intelligent reasoning model for coding and agentic tasks with configurable reasoning effort
Why learn GPT-5?
Offers the documented OpenAI model capabilities through hosted.
The problem it solves
Provides model inference for supported text / image inputs.
Where it came from
GPT-5: Previous intelligent reasoning model for coding and agentic tasks with configurable reasoning effort Published aliases: gpt-5-2025-08-07. Input: text, image. Output: text. Checked 2026-10-13. Model capabilities are distinct from tools available in a particular agent.
Build your understanding
Work through these exercises in order. Try the task before opening a reference.
- Explain what GPT-5 does.
- Identify a use case for GPT-5.
- Describe a limitation of GPT-5.
- Name a compatible tool for GPT-5.
- Compare GPT-5 against an alternative.
Documentation and sources
- Primary source / model card (developers.openai.com)
Catalog research notes
- See the AI Workbench for source dates, pricing evidence and configuration details.
Connect the ideas
Built on
Real World Example
Automated Legacy Code Refactoring Pipeline
Software maintenance firms managing massive, undocumented enterprise codebases.
The business problem
Engineers spend excessive time manually deciphering complex, legacy logic, leading to high technical debt and slow feature delivery cycles.
How this topic solves it
Utilizing GPT-5's configurable reasoning effort allows the system to scale its cognitive depth based on the complexity of the code module being refactored, ensuring deep analysis for critical business logic while maintaining efficiency for routine boilerplate.
- Identify legacy modules requiring refactoring and categorize them by cyclomatic complexity.
- Configure the reasoning_effort parameter to high for complex logic and low for standard utility functions.
- Submit code snippets to the v1/chat/completions endpoint with structured output schemas.
- Validate the refactored code against existing unit test suites to ensure functional parity.
- Iteratively adjust reasoning effort based on the success rate of automated test passes.
The flow shows how code complexity dictates the reasoning effort level applied by the model.
Sets reasoning_effort parameter
Performs refactoring logic
Validates output
- Code Analyzer → Complexity Score → Reasoning Controller
- Reasoning Controller → Configures effort → GPT-5 Model
- GPT-5 Model → Refactored Code → Test Suite
Implementation example
This demonstrates how to explicitly set the reasoning_effort parameter within the API call to handle complex refactoring tasks.
client.chat.completions.create(
model='gpt-5-2025-08-07',
messages=[{'role': 'user', 'content': 'Refactor this function'}],
reasoning_effort='high' # Configures deep reasoning for complex legacy logic
)Expected business outcome
Improved developer velocity and reduced technical debt through consistent, automated refactoring that adapts to the nuance of the underlying code.
Limitations and checks
The model's reasoning effort is a trade-off between latency and depth; high effort increases response time significantly, which can be monitored by tracking the time-to-first-token in the API response headers.