How to Use GPT-5 Codex: Complete Guide (2026)
A practical guide to OpenAI's GPT-5 Codex for code generation and development
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How to Use GPT-5 Codex: Complete Guide (2026)
GPT-5 Codex is OpenAI's latest model optimized for code generation, understanding, and transformation. Built on the GPT-5 architecture, Codex brings significant improvements over previous coding models: better long-range reasoning, more accurate multi-file generation, native support for 50+ programming languages, and the ability to run code in a sandboxed environment and iterate on the results.
This guide covers everything from getting your API key to advanced usage patterns, with working code examples throughout.
What Is GPT-5 Codex?
GPT-5 Codex is a specialized variant of GPT-5 fine-tuned for software development tasks. It is available through the OpenAI API and is the model powering the latest version of GitHub Copilot.
| Feature | GPT-5 Codex | GPT-4o | GPT-5 |
|---|---|---|---|
| Code generation quality | Excellent | Very Good | Good |
| Context window | 256K tokens | 128K tokens | 256K tokens |
| Multi-file understanding | Yes | Limited | Partial |
| Sandboxed code execution | Yes | No | No |
| Function calling | Yes | Yes | Yes |
| Supported languages | 50+ | 30+ | 30+ |
| Latency (avg) | ~2s first token | ~1s first token | ~3s first token |
| Price (input) | $3/M tokens | $2.50/M tokens | $10/M tokens |
| Price (output) | $12/M tokens | $10/M tokens | $30/M tokens |
Prerequisites
You need:
- An OpenAI account with API access.
- Python 3.9+ (for the examples in this guide).
- A payment method on your OpenAI account (Codex is not available on the free tier).
Step 1: Get Your API Key
- Go to platform.openai.com/api-keys.
- Click Create new secret key.
- Name it (e.g., "codex-dev") and copy the key.
- Set it as an environment variable:
export OPENAI_API_KEY="sk-your-api-key-here"
Step 2: Install the OpenAI SDK
pip install openai
Verify:
import openai
print(openai.__version__)
Step 3: Basic Code Generation
Here is a simple example that generates a Python function:
from openai import OpenAI
client = OpenAI()
response = client.chat.completions.create(
model="gpt-5-codex",
messages=[
{
"role": "system",
"content": "You are an expert software engineer. Write clean, well-documented, production-ready code."
},
{
"role": "user",
"content": "Write a Python function that validates an email address using regex. Include type hints, docstring, and unit tests."
}
],
temperature=0.2,
max_tokens=2000
)
print(response.choices[0].message.content)
Output:
import re
from typing import Optional
def validate_email(email: str) -> bool:
"""
Validate an email address using a comprehensive regex pattern.
Args:
email: The email address string to validate.
Returns:
True if the email is valid, False otherwise.
Examples:
>>> validate_email("user@example.com")
True
>>> validate_email("invalid@")
False
"""
pattern = r'^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$'
return bool(re.match(pattern, email))
# Unit tests
def test_validate_email():
# Valid emails
assert validate_email("user@example.com") is True
assert validate_email("first.last@company.co.uk") is True
assert validate_email("user+tag@gmail.com") is True
# Invalid emails
assert validate_email("") is False
assert validate_email("not-an-email") is False
assert validate_email("@example.com") is False
assert validate_email("user@") is False
assert validate_email("user@.com") is False
print("All tests passed!")
if __name__ == "__main__":
test_validate_email()
Step 4: Multi-File Code Generation
GPT-5 Codex can generate entire project structures. Use a structured prompt:
response = client.chat.completions.create(
model="gpt-5-codex",
messages=[
{
"role": "system",
"content": """You are a senior software engineer. When asked to create
a multi-file project, output each file with its path in a markdown
code block with the filename as the language identifier."""
},
{
"role": "user",
"content": """Create a minimal Express.js REST API for a todo app with:
- TypeScript
- Routes for CRUD operations
- Zod validation
- In-memory storage
- Error handling middleware
Output each file separately with its file path."""
}
],
temperature=0.2,
max_tokens=4000
)
print(response.choices[0].message.content)
Step 5: Code Review and Refactoring
GPT-5 Codex is excellent at reviewing existing code:
code_to_review = """
def process_data(data):
result = []
for i in range(len(data)):
if data[i] != None:
if data[i] > 0:
result.append(data[i] * 2)
else:
result.append(0)
return result
"""
response = client.chat.completions.create(
model="gpt-5-codex",
messages=[
{
"role": "system",
"content": "Review the code for bugs, performance issues, and style problems. Provide a refactored version."
},
{
"role": "user",
"content": f"Review and refactor this Python code:\n\n```python\n{code_to_review}\n```"
}
],
temperature=0.2,
max_tokens=2000
)
print(response.choices[0].message.content)
Step 6: Function Calling for Code Tasks
GPT-5 Codex supports function calling, which is useful for structured code operations:
tools = [
{
"type": "function",
"function": {
"name": "create_file",
"description": "Create a new file with the given content",
"parameters": {
"type": "object",
"properties": {
"path": {
"type": "string",
"description": "File path relative to project root"
},
"content": {
"type": "string",
"description": "File content"
},
"language": {
"type": "string",
"description": "Programming language"
}
},
"required": ["path", "content"]
}
}
},
{
"type": "function",
"function": {
"name": "run_command",
"description": "Run a shell command",
"parameters": {
"type": "object",
"properties": {
"command": {
"type": "string",
"description": "Shell command to execute"
}
},
"required": ["command"]
}
}
}
]
response = client.chat.completions.create(
model="gpt-5-codex",
messages=[
{
"role": "user",
"content": "Set up a new Vite + React + TypeScript project with Tailwind CSS"
}
],
tools=tools,
tool_choice="auto"
)
# Process function calls
for tool_call in response.choices[0].message.tool_calls:
print(f"Function: {tool_call.function.name}")
print(f"Arguments: {tool_call.function.arguments}")
print("---")
Step 7: Streaming Responses
For real-time code generation in a UI or CLI:
stream = client.chat.completions.create(
model="gpt-5-codex",
messages=[
{
"role": "user",
"content": "Write a React component for an infinite scroll list with loading states"
}
],
temperature=0.2,
stream=True
)
for chunk in stream:
if chunk.choices[0].delta.content:
print(chunk.choices[0].delta.content, end="", flush=True)
Best Practices
Prompt Engineering for Code
| Technique | Example | Why It Helps |
|---|---|---|
| Specify the language | "Write a TypeScript function..." | Prevents language ambiguity |
| Request type hints | "Include type annotations..." | Produces safer code |
| Ask for tests | "Include unit tests..." | Validates correctness |
| Set constraints | "Use no external dependencies..." | Controls output scope |
| Provide context | "This is part of a Next.js 14 project..." | Aligns with your stack |
| Request error handling | "Handle all edge cases..." | Produces robust code |
Temperature Settings
| Temperature | Use Case |
|---|---|
| 0.0 - 0.2 | Code generation (deterministic, consistent) |
| 0.3 - 0.5 | Code review and explanation |
| 0.6 - 0.8 | Creative naming, brainstorming solutions |
| 0.9 - 1.0 | Experimental, exploring alternatives |
For production code generation, keep temperature at 0.2 or lower.
Using Codex with Popular Tools
| Tool | How to Use Codex |
|---|---|
| GitHub Copilot | Automatically uses Codex as the backend |
| Cursor | Select "GPT-5 Codex" from the model dropdown |
| Continue.dev | Set model to gpt-5-codex in config |
| Aider | aider --model gpt-5-codex |
| LangChain | ChatOpenAI(model="gpt-5-codex") |
Supported Languages
GPT-5 Codex has been trained on code from 50+ languages. Here are the top-tier supported languages ranked by output quality:
| Tier | Languages |
|---|---|
| Tier 1 (Best) | Python, TypeScript, JavaScript, Go, Rust, Java |
| Tier 2 (Very Good) | C++, C#, Ruby, PHP, Kotlin, Swift, SQL |
| Tier 3 (Good) | Scala, Haskell, Elixir, Dart, Lua, R, MATLAB |
| Tier 4 (Functional) | Perl, Assembly, COBOL, Fortran, Lisp |
Pricing and Rate Limits
| Plan | Rate Limit | Price (Input) | Price (Output) |
|---|---|---|---|
| Tier 1 (new accounts) | 500 RPM | $3/M tokens | $12/M tokens |
| Tier 2 | 2,000 RPM | $3/M tokens | $12/M tokens |
| Tier 3 | 5,000 RPM | $3/M tokens | $12/M tokens |
| Tier 4 | 10,000 RPM | $3/M tokens | $12/M tokens |
Cost estimation for common tasks:
| Task | Input Tokens | Output Tokens | Estimated Cost |
|---|---|---|---|
| Generate a function | ~500 | ~1,000 | ~$0.01 |
| Code review (500 lines) | ~5,000 | ~2,000 | ~$0.04 |
| Generate a full module | ~1,000 | ~5,000 | ~$0.06 |
| Analyze a codebase (10K lines) | ~50,000 | ~5,000 | ~$0.21 |
Common Errors and Fixes
| Error | Cause | Fix |
|---|---|---|
model_not_found |
Wrong model name | Use gpt-5-codex exactly |
rate_limit_exceeded |
Too many requests | Implement exponential backoff |
context_length_exceeded |
Input too large | Reduce context or split the request |
insufficient_quota |
No credits | Add payment method at platform.openai.com |
timeout |
Complex generation | Increase timeout, split into smaller tasks |
Conclusion
GPT-5 Codex is the most capable code generation model available from OpenAI in 2026. Its combination of a large context window, multi-file understanding, and sandboxed execution makes it suitable for everything from generating individual functions to scaffolding entire projects. Use it through the API for maximum flexibility, or through tools like GitHub Copilot and Cursor for a more integrated experience.
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