How to Use GPT-5.3 Codex API ?

TL;DR

GPT-5.3 Codex is finally available via API, weeks after its initial release. You can access it two ways:

1. OpenRouter - Model ID openai/gpt-5.3-codex, competitive pricing
2. OpenAI Developers Platform - Model ID gpt-5.3-codex, direct access

To get started: sign up at OpenRouter or OpenAI's platform, grab your API key, and make your first request using the standard Chat Completions endpoint.

Introduction

For weeks, developers wanted to integrate GPT-5.3 Codex into their applications but there was a catch. OpenAI released the model through the Codex App, CLI, and IDE extensions, yet the API remained inaccessible. Teams building AI-powered development tools, automation pipelines, and coding assistants were left waiting.

That wait is over.

GPT-5.3 Codex is now available via API, giving developers the programmatic access they've been requesting since the model's release. You have two options:

1. OpenRouter - Access via openrouter.ai with competitive pricing and unified API
2. OpenAI Developers Platform - Direct access via developers.openai.com

Whether you're building a SaaS product, automating internal tools, or integrating AI capabilities into your existing applications, the GPT-5.3 Codex API provides a straightforward path to leverage OpenAI's most capable coding model. With pricing starting at just $0.00125 per million input tokens and a context window that can handle massive codebases, it's never been more accessible.

In this guide, we'll walk through everything you need to know to integrate GPT-5.3 Codex into your development workflow. From setting up your OpenRouter account to making production-ready API calls, you'll have the knowledge to start building smarter, faster.

What is GPT-5.3 Codex?

Released by OpenAI, GPT-5.3 Codex is specifically optimized for code generation, understanding, and debugging tasks. Unlike general-purpose language models, Codex has been trained on vast amounts of programming code, making it exceptionally good at:

• Code Generation: Write functions, classes, and even entire applications from descriptions
• Code Understanding: Explain what unfamiliar code does and suggest improvements
• Debugging: Identify bugs and suggest fixes
• Documentation: Generate docstrings and API documentation
• Refactoring: Improve code structure without changing functionality

The version available through OpenRouter (openai/gpt-5.3-codex) supports a 400,000 token context window—enough to upload an entire medium-sized codebase in a single request. This makes it ideal for tasks that require understanding broad code relationships across multiple files.

Why Use OpenRouter?

OpenRouter serves as a unified API gateway that provides access to multiple AI models from various providers through a single, consistent interface.

Here's why developers choose OpenRouter for accessing GPT-5 Codex:

1. Unified API: One API key accesses dozens of models
2. Competitive Pricing: Often cheaper than direct API access
3. No Rate Limits: Flexible quotas based on usage
4. Easy Switching: Swap models without changing your code
5. Free Credits: New accounts receive $1 in free credits to start

If you're already using other models through OpenRouter, adding GPT-5 Codex requires just changing the model ID in your existing API calls.

OpenRouter vs OpenAI Developers Platform

You have two options to access GPT-5.3 Codex via API:

Choose OpenRouter if: You want unified access to multiple LLM providers, competitive pricing, and flexibility to switch models.

Choose OpenAI Developers Platform if: You prefer direct access, already use OpenAI APIs, and want official support.

Both options provide the same underlying GPT-5.3 Codex model—the difference is in pricing, convenience, and your existing setup.

Access Option 1: OpenAI Developers Platform

If you prefer direct access through OpenAI's official API, here's how to get started:

Step 1: Create an OpenAI Account

Navigate to platform.openai.com and sign up or log in.

Step 2: Generate Your API Key

1. Go to API Keys in the left sidebar
2. Click Create new secret key
3. Copy and save your key (shown only once)

Step 3: Make Your First Request

curl -X POST https://api.openai.com/v1/chat/completions \
  -H "Authorization: Bearer YOUR_OPENAI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-5.3-codex",
    "messages": [
      {
        "role": "user",
        "content": "Write a Python function that calculates the factorial of a number."
      }
    ]
  }'

Replace YOUR_OPENAI_API_KEY with your actual API key.

Python Example (OpenAI Direct)

import os
from openai import OpenAI

client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))

response = client.chat.completions.create(
    model="gpt-5.3-codex",
    messages=[
        {
            "role": "user",
            "content": "Create a REST API endpoint in FastAPI for user authentication"
        }
    ],
    temperature=0.7,
    max_tokens=2000
)

print(response.choices[0].message.content)

Access Option 2: OpenRouter

Step 1: Create Your Account

Navigate to openrouter.ai and sign up with your email. The registration process takes less than two minutes.

Step 2: Get Your API Key

After logging in, click your profile icon and select "API Keys." Create a new key and copy it immediately—keys are only shown once for security reasons.

Step 3: Add Credits

While new accounts receive $1 in free credits, you'll want to add more for sustained usage. Navigate to "Credits" and add funds via credit card or other supported methods. A minimum of $5-$10 is recommended for regular development.

Step 4: Verify Model Availability

In the OpenRouter dashboard, search for "gpt-5.3-codex" to confirm it's available. The model ID you'll use is openai/gpt-5.3-codex.

Making Your First API Call

The simplest way to test your setup is with curl. Open your terminal and run:

curl -X POST https://openrouter.ai/api/v1/chat/completions \
  -H "Authorization: Bearer YOUR_OPENROUTER_API_KEY" \
  -H "Content-Type: application/json" \
  -H "HTTP-Referer: https://your-site.com" \
  -d '{
    "model": "openai/gpt-5.3-codex",
    "messages": [
      {
        "role": "user",
        "content": "Write a Python function that calculates the factorial of a number."
      }
    ]
  }'

Replace YOUR_OPENROUTER_API_KEY with your actual key and https://your-site.com with your website URL (required by OpenRouter for analytics).

You should receive a JSON response containing the generated code. Congratulations—you've made your first GPT-5.3 Codex API call.

Python Integration

For Python applications, you can use the OpenAI SDK with a custom base URL:

Installation

pip install openai requests python-dotenv

Basic Usage

import os
from openai import OpenAI
from dotenv import load_dotenv

load_dotenv()

client = OpenAI(
    base_url="https://openrouter.ai/api/v1",
    api_key=os.getenv("OPENROUTER_API_KEY"),
)

def generate_code(prompt: str) -> str:
    """Generate code using GPT-5.3 Codex via OpenRouter."""
    response = client.chat.completions.create(
        model="openai/gpt-5.3-codex",
        messages=[
            {
                "role": "system",
                "content": "You are an expert programmer. Write clean, well-documented code."
            },
            {
                "role": "user",
                "content": prompt
            }
        ],
        temperature=0.7,
        max_tokens=2000
    )

    return response.choices[0].message.content

# Example usage
code = generate_code("Create a REST API endpoint in FastAPI for user authentication")
print(code)

Streaming Responses

For longer code generation, streaming provides a better user experience:

def generate_code_streaming(prompt: str):
    """Generate code with streaming responses."""
    response = client.chat.completions.create(
        model="openai/gpt-5.3-codex",
        messages=[{"role": "user", "content": prompt}],
        stream=True,
        temperature=0.7
    )

    for chunk in response:
        if chunk.choices[0].delta.content:
            print(chunk.choices[0].delta.content, end="", flush=True)

# Example usage
generate_code_streaming("Write a React component for a file upload button")

Error Handling

Always implement proper error handling for production applications:

import json

def generate_code_safe(prompt: str) -> dict:
    """Generate code with proper error handling."""
    try:
        response = client.chat.completions.create(
            model="openai/gpt-5.3-codex",
            messages=[{"role": "user", "content": prompt}],
            temperature=0.7,
            max_tokens=2000
        )

        return {
            "success": True,
            "code": response.choices[0].message.content,
            "usage": {
                "prompt_tokens": response.usage.prompt_tokens,
                "completion_tokens": response.usage.completion_tokens,
                "total_tokens": response.usage.total_tokens
            }
        }

    except Exception as e:
        return {
            "success": False,
            "error": str(e)
        }

# Check token usage
result = generate_code_safe("Write a Python decorator for logging")
if result["success"]:
    print(f"Token usage: {result['usage']['total_tokens']} tokens")

Node.js Integration

JavaScript and TypeScript developers can integrate GPT-5.3 Codex using the OpenAI SDK or native fetch:

Installation

npm install openai

Basic Usage

import OpenAI from "openai";

const openai = new OpenAI({
  baseURL: "https://openrouter.ai/api/v1",
  apiKey: process.env.OPENROUTER_API_KEY,
  defaultHeaders: {
    "HTTP-Referer": "https://your-site.com",
    "X-Title": "Your App Name",
  },
});

async function generateCode(prompt) {
  const completion = await openai.chat.completions.create({
    model: "openai/gpt-5.3-codex",
    messages: [
      {
        role: "system",
        content: "You are an expert full-stack developer. Write production-ready code.",
      },
      {
        role: "user",
        content: prompt,
      },
    ],
    temperature: 0.7,
    max_tokens: 2000,
  });

  return completion.choices[0].message.content;
}

// Example usage
const code = await generateCode("Create a Python function for binary search");
console.log(code);

Using Native Fetch

async function generateCodeFetch(prompt) {
  const response = await fetch(
    "https://openrouter.ai/api/v1/chat/completions",
    {
      method: "POST",
      headers: {
        "Authorization": `Bearer ${process.env.OPENROUTER_API_KEY}`,
        "Content-Type": "application/json",
        "HTTP-Referer": "https://your-site.com",
        "X-Title": "Your App Name",
      },
      body: JSON.stringify({
        model: "openai/gpt-5.3-codex",
        messages: [{ role: "user", content: prompt }],
        temperature: 0.7,
        max_tokens: 2000,
      }),
    }
  );

  const data = await response.json();
  return data.choices[0].message.content;
}

Advanced Parameters and Options

GPT-5.3 Codex supports several parameters to fine-tune your API calls:

Temperature

Controls randomness. Lower values (0.1-0.3) produce more deterministic output—ideal for code generation where consistency matters:

response = client.chat.completions.create(
    model="openai/gpt-5.3-codex",
    messages=[{"role": "user", "content": "Write a sorting algorithm"}],
    temperature=0.2,  # Low for consistent, predictable code
)

Max Tokens

Limit response length to control costs:

response = client.chat.completions.create(
    model="openai/gpt-5.3-codex",
    messages=[{"role": "user", "content": "Explain this entire codebase"}],
    max_tokens=4000,  # Limit response length
)

Top P

Alternative to temperature for controlling output diversity:

response = client.chat.completions.create(
    model="openai/gpt-5.3-codex",
    messages=[{"role": "user", "content": "Write a function"}],
    top_p=0.9,
)

Stop Sequences

Specify strings that stop generation:

response = client.chat.completions.create(
    model="openai/gpt-5.3-codex",
    messages=[{"role": "user", "content": "Write Python code"}],
    stop=["```", "###"],  # Stop at code blocks
)

Validating Generated Code with Apidog

This is where many developers stumble. You ask GPT-5.3 Codex to "build an API," it generates what looks like valid code, and then you spend hours debugging why it doesn't work. The solution: validate before you deploy.

The Workflow

1. Generate the Specification: Ask Codex for an OpenAPI specification, not just code
2. Import to Apidog: Validate the spec and generate test cases
3. Test the Implementation: Run automated tests against the generated code

Example: Validating an API Specification

# Ask Codex to generate an OpenAPI spec, not just code
prompt = """
Create a REST API for a task management application.
Output the complete OpenAPI 3.0 specification in YAML format.
Include:
- Endpoints for CRUD operations on tasks
- Authentication using Bearer tokens
- Error responses for 400, 401, 404, 500
- Request/response examples
"""

After receiving the specification, import it into Apidog:

1. Open Apidog and create a new project
2. Go to Import → OpenAPI/Swagger
3. Paste the YAML from Codex
4. Apidog automatically generates test cases
5. Run the tests to validate the spec

This "trust but verify" approach saves hours of debugging and ensures your AI-generated code meets professional standards.

Pricing Breakdown

Here's what you need to know about costs for GPT-5.3 Codex:

OpenRouter Pricing

OpenAI Developers Platform Pricing

Note: OpenRouter offers significantly lower input pricing , making it more cost-effective for code generation tasks that involve sending large codebases as context. Both platforms share the same output pricing at $14.00 per million tokens.

Cost Comparison Examples

Context Window

Both platforms support a 400,000 token context window, allowing you to upload entire codebases in a single request.

Troubleshooting Tips

Rate Limiting

If you hit rate limits, implement exponential backoff:

import time

def generate_code_with_retry(prompt, max_retries=3):
    for attempt in range(max_retries):
        try:
            return generate_code(prompt)
        except Exception as e:
            if attempt < max_retries - 1:
                wait_time = 2 ** attempt
                print(f"Rate limited. Waiting {wait_time}s...")
                time.sleep(wait_time)
            else:
                raise e

Invalid API Key

Ensure your key starts with "sk-or-" for OpenRouter:

# Wrong
api_key = "sk-xxxx"  # This is an OpenAI key

# Correct
api_key = "sk-or-v1-xxxx"  # This is an OpenRouter key

Model Not Found

Double-check the model ID: openai/gpt-5.3-codex (not "gpt-5" or "codex" alone).

Conclusion

Accessing GPT-5.3 Codex through OpenRouter opens up powerful AI-assisted development capabilities for every developer. With straightforward API access, competitive pricing, and a massive context window, you can integrate intelligent code generation into any application.

The key to success lies in the workflow: generate code with GPT-5.3 Codex, validate with Apidog, and deploy with confidence. This combination gives you the speed of AI generation with the reliability of professional testing.