In this post, Jesse Houwing shares advice on choosing the most suitable GitHub Copilot model for large scale code refactoring, comparing GPT-4.5 and GPT-4o with practical recommendations.

Picking the Right GitHub Copilot Model

Author: Jesse Houwing

Selecting the best large language model (LLM) in GitHub Copilot can significantly affect productivity, especially as more models become available through GitHub Copilot and its previews. While official documentation summarizes the available AI models, it can be challenging to determine which one best fits a particular development scenario.

Guidance and Approach

Official guidance on model selection is provided by GitHub at:

To further streamline the process, I leveraged GitHub Copilot Chat itself to analyze the documentation and recommend the most suitable models for a specific real-world challenge.

My prompt to Copilot

# fetch https://docs.github.com/en/copilot/using-github-copilot/ai-models/choosing-the-right-ai-model-for-your-task

What model would I use to plan a large refactoring involving multiple files for which I do not yet have a working example?

Select the best 2 and explain the pros and cons between them

Copilot responded with a thoughtful suggestion, comparing the two leading models for this type of task: GPT-4.5 and GPT-4o.

Model Comparison

GPT-4.5

  • Pros:
    • Excels at handling complex logic and multi-step reasoning.
    • Retains and processes context across multiple files, aiding in understanding dependencies and relationships in large codebases.
    • Produces reliable, consistent, high-quality suggestions for improving maintainability and modularity.
    • Effective at handling ambiguous or incomplete requirements (useful when no working examples exist).
  • Cons:
    • Higher latency; responses are slightly slower than smaller models.
    • More resource-intensive, potentially costlier.

GPT-4o

  • Pros:
    • Faster response times, enabling quicker iteration during refactoring.
    • Balances reasoning ability with speed for lightweight or smaller refactoring tasks.
    • More cost-effective, fitting for budget-conscious scenarios.
  • Cons:
    • May struggle with complex, deeply nuanced, or interconnected refactoring tasks.
    • Handles multi-file or dependency-rich contexts less effectively compared to GPT-4.5.

Recommendation

  • Choose GPT-4.5 when dealing with complex logic, multiple file dependencies, or when higher quality, structured suggestions are needed.
  • Choose GPT-4o for faster iteration, smaller or less complex refactoring, or when working within tighter cost constraints.

Although both models are strong, the optimal choice depends on the complexity of your task and your priorities (depth and quality of suggestions vs. response speed and cost).

Summary

With the increasing diversity of available models in GitHub Copilot, developers should make intentional choices based on their specific coding needs. GPT-4.5 is preferred for complex, nuanced refactoring involving extensive dependencies, while GPT-4o is more suitable for smaller, rapid tasks or cost-sensitive projects.

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