How to Use GPT-5 Codex: Complete Guide (2026)

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:

1. An OpenAI account with API access.
2. Python 3.9+ (for the examples in this guide).
3. A payment method on your OpenAI account (Codex is not available on the free tier).

Step 1: Get Your API Key

1. Go to platform.openai.com/api-keys.
2. Click Create new secret key.
3. Name it (e.g., "codex-dev") and copy the key.
4. 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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