Codex Prompting Guide: Maximizing Efficiency and Autonomy with OpenAI’s Latest Updates

If you’re diving into the latest advancements in AI-driven coding, OpenAI’s Codex models are a must-explore. The gpt-5.3-codex model, available via the API, brings significant improvements in efficiency, intelligence, and autonomy. This guide is designed for developers who want to leverage these enhancements directly through the API for maximum customizability. If you prefer a simpler integration, consider using the Codex SDK.

Recent Improvements to Codex Models

• Faster and More Token Efficient: The latest Codex models use fewer tokens to accomplish tasks, making them more efficient. We recommend setting the reasoning effort to "medium" for a balanced approach that combines intelligence and speed.
• Higher Intelligence and Long-Running Autonomy: Codex is now capable of working autonomously for extended periods, handling complex tasks over hours without intervention. For your most challenging tasks, you can set the reasoning effort to high or xhigh.
• First-Class Compaction Support: This feature allows multi-hour reasoning sessions without hitting context limits, enabling longer continuous user conversations without needing to start new chat sessions.
• Enhanced PowerShell and Windows Support: Codex now performs better in PowerShell and Windows environments, making it a more versatile tool for developers working in these ecosystems.

Getting Started with gpt-5.3-codex

If you already have an existing Codex implementation, transitioning to the new model should be relatively smooth with minimal updates required. However, if you’re starting from scratch or optimizing your prompt and tools, here are some key points to consider:

Setting Up Your API Call

To use the gpt-5.3-codex model via the OpenAI API, you’ll need to specify it in your request. Here’s a basic example of how to set up an API call:

import openai

openai.api_key = 'your-api-key'

response = openai.Completion.create(
    engine="gpt-5.3-codex",
    prompt="Write a function that calculates the factorial of a number.",
    max_tokens=100,
    temperature=0.7,
    top_p=1,
    frequency_penalty=0,
    presence_penalty=0
)

print(response.choices[0].text.strip())

Optimizing Your Prompts

• Clear and Concise: Ensure your prompts are clear and concise to guide the model effectively.
• Contextual Information: Provide necessary contextual information to help the model understand the task better.
• Reasoning Effort: Adjust the reasoning effort based on the complexity of the task. Use medium for general tasks, high for more complex tasks, and xhigh for the most challenging ones.

Example: Writing a Function

Let’s say you want to write a function that calculates the factorial of a number. Here’s how you can structure your prompt:

prompt = """
Write a Python function named `factorial` that takes an integer `n` and returns the factorial of `n`.
The factorial of a non-negative integer `n` is the product of all positive integers less than or equal to `n`.
For example, the factorial of 5 (5!) is 1 * 2 * 3 * 4 * 5 = 120.
"""

response = openai.Completion.create(
    engine="gpt-5.3-codex",
    prompt=prompt,
    max_tokens=100,
    temperature=0.7,
    top_p=1,
    frequency_penalty=0,
    presence_penalty=0
)

print(response.choices[0].text.strip())

Advanced Features

Compaction Support

Compaction is a powerful feature that allows the model to manage long-running sessions without hitting context limits. This is particularly useful for tasks that require extended reasoning or multi-step processes.

To enable compaction, you can use the compaction parameter in your API call:

response = openai.Completion.create(
    engine="gpt-5.3-codex",
    prompt="Write a function that calculates the factorial of a number.",
    max_tokens=