Monolithic codebase
AI Models & Assistants / Knowledge library
GPT-5.5
A new class of intelligence for coding and professional work.
Why learn GPT-5.5?
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-5.5: A new class of intelligence for coding and professional work. Published aliases: gpt-5.5-2026-04-23. 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-5.5 does.
- Identify a use case for GPT-5.5.
- Describe a limitation of GPT-5.5.
- Name a compatible tool for GPT-5.5.
- Compare GPT-5.5 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 Financial Code Refactoring
Large-scale financial institutions managing millions of lines of legacy COBOL and C++ codebases.
The business problem
Manual refactoring of monolithic legacy systems is prone to human error, creates significant technical debt, and requires excessive developer hours to ensure compliance with modern security standards.
How this topic solves it
GPT-5.5 utilizes its high-reasoning capabilities and extensive 1,050,000 context window to ingest entire legacy modules, map dependencies, and suggest modular refactoring patterns that align with current security protocols.
- Ingest the legacy codebase into the context window using the Chat Completions API.
- Define structural constraints and security requirements as system instructions.
- Utilize function calling to trigger automated unit testing suites for each refactored module.
- Apply patches iteratively using the apply_patch tool to maintain version control integrity.
- Validate the output against the original logic using the Responses API.
The flow of legacy code through the GPT-5.5 processing engine.
Reasoning and refactoring
Validation logic
Refactored output
- Legacy Source → Ingest → GPT-5.5 Engine
- GPT-5.5 Engine → Verify → Test Suite
- Test Suite → Feedback Loop → GPT-5.5 Engine
- GPT-5.5 Engine → Deploy → Modernized Code
Implementation example
This snippet initializes the refactoring process by sending the legacy code to the model with high reasoning effort to ensure accurate structural analysis.
client.chat.completions.create(
model='gpt-5.5-2026-04-23',
messages=[{'role': 'user', 'content': legacy_code_blob}],
reasoning_effort='high' # Use high effort for complex refactoring logic
)Expected business outcome
A modernized, modular codebase that maintains functional parity with the original system while significantly reducing the surface area for security vulnerabilities.
Limitations and checks
The model may experience increased latency and higher token costs when processing inputs exceeding 272K tokens; developers should monitor token usage and implement prompt caching to mitigate overhead.