Source code and docs
AI Models & Assistants / Knowledge library
GPT-5.4
A more affordable model for coding and professional work.
Why learn GPT-5.4?
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-5.4: A more affordable model for coding and professional work. Published aliases: gpt-5.4-2026-03-05. 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-5.4 does.
- Identify a use case for GPT-5.4.
- Describe a limitation of GPT-5.4.
- Name a compatible tool for GPT-5.4.
- Compare GPT-5.4 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
Legacy Codebase Modernization at FinTech Corp
Financial software firms maintaining massive, multi-decade monolithic codebases.
The business problem
High operational costs and slow iteration cycles when attempting to refactor legacy modules due to the sheer volume of documentation and interdependent code files that exceed standard context windows.
How this topic solves it
Utilize GPT-5.4's 1,050,000 token context window to ingest entire legacy modules, allowing the model to understand global dependencies and suggest refactoring patterns that maintain system integrity without manual cross-referencing.
- Aggregate legacy source files and technical documentation into a unified context package.
- Configure the Chat Completions API using the gpt-5.4 model identifier.
- Implement prompt caching for frequently referenced core library headers to reduce input costs.
- Execute refactoring requests using structured outputs to ensure the generated code adheres to modern syntax standards.
- Validate the output against existing unit tests to ensure functional parity.
The flow of data from legacy storage to the model for refactoring.
Cached headers
Refactoring engine
Refactored output
- Legacy Repo → Ingests full module → GPT-5.4
- Context Cache → Provides cached context → GPT-5.4
- GPT-5.4 → Generates refactored files → Modernized Code
Implementation example
This snippet demonstrates calling the GPT-5.4 model with a large code blob, utilizing high reasoning effort to ensure the model understands the complex dependencies inherent in legacy systems.
client.chat.completions.create(
model='gpt-5.4', # Using the flagship model for complex refactoring
messages=[{'role': 'user', 'content': 'Refactor this module: ' + large_code_blob}],
reasoning_effort='high' # Ensuring deep analysis of dependencies
)Expected business outcome
Improved developer velocity and reduced technical debt by enabling automated, context-aware refactoring across large-scale codebases.
Limitations and checks
The model's reasoning effort can lead to increased latency; developers should monitor response times and adjust the reasoning effort parameter if real-time feedback is required.