Github Copilot: What model to use for what use?

Débage, refactoring, documentation … The language models present in Github Copilot each have their strengths.

Faced with the diversity of artificial intelligence models offered (11!) By Github Copilot, choosing the one that best corresponds to your task can be complex. Each model offers distinct capacities in terms of performance, speed and specialization. Github has published a specific guide on the distinct capacities of each model, allowing developers to make enlightened choices. We have synthesized the main strengths of these conclusions in a table to allow you to see more clearly.

GPT-4.1, Claude 3.7, Gemini 2.5 PRO… What model to use for what use?

For the occasion, we have retained 8 different criteria that allow you to differentiate the models:

• There documentation is the ability to generate the explanation of the code, whether in the form of a textual document or commentary in the code.
• THE reasoning In step which allows you to carry out complex actions that require sequencing in step.
• THE refactoring designates the capacity of the model to make a code to make it more efficient without changing the final behavior.
• THE debugging Includes the capacity of the model to identify bugs or error in the code and apply relevant fixes
• There multimodality Allows you to send the images model. For example a screenshot to make it understand a visual element of an interface.
• There speed Or latency means the capacity of the model to respond quickly. In general, the models with several hundred billion parameters are slower, and the small models much faster.
• THE Cost / efficiency is a key indication for many developers. Using a precise but heavy model can be quickly expensive. For the generation of simple code, an economic model is often sufficient.
• Context : This is the criterion that comes up most regularly in recent months. The longer it is, the more the model will be able to understand code projects in their entirety.

What are the best models with Github Copilot?

Three models emerge as undisputed champions of the code generation: GPT-4.1, Claude 3.7 Sonnet and Gemini 2.5 Pro. GPT-4.1 is distinguished by its precision in technical documentation and its very good multimodal capacities. Claude 3.7 Sonnet, meanwhile, shines with his multi-stage reasoning and by his ability to carry out complex refactoring. Finally Gemini 2.5 Pro is positioned as the king of speed and efficiency. It is the most versatile code model available in Github Copilot.

Clearly, use GPT-4.1 for complex documentation, Claude 3.7 Sonnet for advanced refactoring, and Gemini 2.0 Flash (or 2.5 pro for complex cases) for quick code generation tasks. To be more precise, you can also benchmark the models yourself: systematically test several code suggestions with different models, compare their outputs and select the model that lines up best with the complexity and constraints of your project (large efficient models for example better on rarer programming languages).