GPT-5.2 vs Opus 4.5: The Ultimate Coding Benchmark

Introduction

This tutorial provides a comprehensive breakdown of the coding benchmark comparison between GPT-5.2 and Claude Opus 4.5. We will explore the setup, execution, and results of the benchmark using a production-grade Product Requirements Document (PRD). By following these steps, you will gain insights into evaluating AI coding models effectively and understanding their communication styles.

Step 1: Understand the Benchmark Setup

• Design a PRD: Create a complex PRD that includes:
  • Multiple detail pages
  • AI-powered features like “Scoop” and “Alchemy”
  • Data management for cast and crew
  • Season and episode hierarchies
  • Integrations with streaming services
• Avoid Simple Problems: Focus on realistic, production-grade scenarios instead of cherry-picked problems to ensure a thorough evaluation.

Step 2: Prepare the Models for Testing

• Select AI Models: Choose the AI models to compare:
  • GPT-5.1 Codex Max Extra High
  • GPT-5.2 Medium
  • GPT-5.2 Extra High
  • Claude Opus 4.5
• Set Up Testing Environment: Ensure that all models are ready to interact with the PRD and can be tested under similar conditions.

Step 3: Execute Initial Testing

• Conduct First-Pass Builds: Run each model to generate code based on the PRD. Focus on:
  • Time taken to complete initial builds
  • Feature completion rates for each model
• Document Results: Record the outcomes and any notable behaviors during the coding process.

Step 4: Analyze Side-by-Side Results

• Comparison Metrics: Evaluate the models based on:
  • Code quality
  • Completion speed
  • Clarity of communication with the user
• Feature Completion Analysis: Analyze which features were successfully implemented by each model and identify gaps.

Step 5: Use the Delta Document Technique

• Refinement Pass: Implement the “Delta Document” technique to improve completion rates:
  • Create a document outlining the differences between the initial output and expected results.
  • Use this to guide the models in making necessary adjustments.
• Achieve High Completion Rates: Aim for a completion rate of 90-95% through iterative refinements.

Step 6: Assess Communication Styles

• Evaluate Feedback Mechanisms: Pay attention to how each model communicates:
  • Opus 4.5 provides a to-do list and explains its reasoning.
  • GPT-5.2 starts coding without user feedback.
• Understand the Importance: Recognize that communication style can significantly impact the development process.

Step 7: Review Feature Builds

• Focus on Notable Features: Take a closer look at standout features like the Alchemy build:
  • Analyze what made this feature particularly effective.
  • Consider how each model approached complex features differently.

Conclusion

This tutorial has taken you through the essential steps to benchmark AI coding models effectively. By focusing on a robust PRD, preparing models comprehensively, and analyzing both results and communication styles, you can gain valuable insights into their capabilities. As a next step, consider applying these methods to your own AI benchmarking projects to evaluate their performance in real-world scenarios.