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Main test runner function that validates GPT-5 readiness by running comprehensive tests against multiple OpenAI models (GPT-5 and GPT-4o) and provides production readiness recommendations.

Purpose

This function orchestrates a complete validation suite to determine if GPT-5 is ready for production deployment. It instantiates a GPT5Validator, runs all tests against configured models (TEST_MODELS), collects results, prints a summary, and provides actionable recommendations based on test outcomes. The function compares GPT-5 performance against GPT-4o as a baseline and returns appropriate exit codes for CI/CD integration.

Source Code

def main():
    """Main test runner"""
    print("GPT-5 Readiness Validation")
    print("=" * 60)
    
    validator = GPT5Validator()
    
    # Test all models
    for model in TEST_MODELS:
        try:
            results = validator.run_all_tests(model)
            validator.results[model] = results
        except Exception as e:
            print(f"\n✗ CRITICAL ERROR testing {model}: {e}")
            validator.results[model] = {'error': (False, str(e))}
    
    # Print summary
    all_passed = validator.print_summary()
    
    # Recommendation
    print(f"\n{'='*60}")
    print("RECOMMENDATION")
    print(f"{'='*60}\n")
    
    gpt5_results = validator.results.get('gpt-5', {})
    gpt5_passed = all(success for success, _ in gpt5_results.values())
    
    gpt4o_results = validator.results.get('gpt-4o', {})
    gpt4o_passed = all(success for success, _ in gpt4o_results.values())
    
    if gpt5_passed and gpt4o_passed:
        print("✓ GPT-5 is PRODUCTION READY")
        print("\nTo upgrade:")
        print("  1. Set USE_GPT5=true in .env")
        print("  2. Restart application")
        print("  3. Monitor for 24 hours")
        return 0
    
    elif gpt5_passed and not gpt4o_passed:
        print("⚠ GPT-5 passed but GPT-4o failed (unexpected)")
        print("Please check API key and connectivity")
        return 1
    
    elif not gpt5_passed and gpt4o_passed:
        print("✗ GPT-5 is NOT ready for production")
        print("\nRecommendation: Stay with GPT-4o")
        print("\nGPT-5 Issues:")
        for test_name, (success, message) in gpt5_results.items():
            if not success:
                print(f"  • {test_name}: {message}")
        return 1
    
    else:
        print("✗ CRITICAL: Both models failing")
        print("Please check API configuration and connectivity")
        return 1

Return Value

Returns an integer exit code: 0 if GPT-5 is production ready (all tests passed for both models), 1 if GPT-5 is not ready, if there are unexpected failures, or if both models fail. The exit code is suitable for use in automated deployment pipelines.

Dependencies

• openai
• tiktoken

Required Imports

import os
import sys
import time
from openai import OpenAI
from typing import Dict, List, Tuple
import tiktoken
import traceback

Usage Example

import os
import sys
from openai import OpenAI
from typing import Dict, List, Tuple
import tiktoken
import traceback

# Set required environment variable
os.environ['OPENAI_API_KEY'] = 'your-api-key-here'

# Define TEST_MODELS global
TEST_MODELS = ['gpt-5', 'gpt-4o']

# Define GPT5Validator class (simplified example)
class GPT5Validator:
    def __init__(self):
        self.results = {}
    
    def run_all_tests(self, model):
        return {'test1': (True, 'Passed'), 'test2': (True, 'Passed')}
    
    def print_summary(self):
        return True

# Run the main function
if __name__ == '__main__':
    exit_code = main()
    sys.exit(exit_code)

Best Practices

• Ensure OPENAI_API_KEY is set before calling this function to avoid authentication errors
• Define TEST_MODELS as a module-level constant containing the models you want to test
• The GPT5Validator class must implement run_all_tests() and print_summary() methods
• Use the return code in CI/CD pipelines to gate deployments (0 = success, 1 = failure)
• Monitor the console output for detailed test results and specific failure reasons
• Consider wrapping the function call in a try-except block for additional error handling
• The function expects GPT-4o as a baseline comparison model; ensure it's included in TEST_MODELS
• Review the printed recommendations carefully before upgrading to GPT-5 in production
• Follow the 24-hour monitoring recommendation after upgrading to GPT-5

Tags

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