15 - AI Models in Copilot: Selection Guide

AI Models in GitHub Copilot: Complete Guide to Choosing

GitHub Copilot has adopted a multi-model architecture that represents a fundamental change in how AI development tools operate. Instead of depending on a single language model, Copilot offers access to a range of models from three of the leading AI providers: OpenAI, Anthropic and Google. Each model has distinct characteristics, strengths and limitations, and choosing the right one for the specific task can make a significant difference in result quality and work speed.

In this fifteenth article of the series, we will explore in detail all the models available in Copilot, their technical characteristics, the criteria for selection, the Auto Mode system for automatic selection, the cost implications with premium requests, and the best practices for getting the most out of each model.

Available Models

GitHub Copilot provides access to models from three main families, each with distinct strengths. Understanding the differences is the first step to using them effectively.

OpenAI Family

OpenAI models were the first to power Copilot and remain a solid choice for many development tasks. They are hosted on OpenAI and Microsoft Azure infrastructure, ensuring high availability and low latency for users in North America and Europe.

GPT-4.1

GPT-4.1 is the workhorse of the OpenAI family in Copilot. It offers an excellent balance between speed and quality, making it suitable for most daily development tasks. It has a deep understanding of code structure, programming best practices and conventions of major frameworks.

Compared to its predecessor GPT-4o, GPT-4.1 shows significant improvements in structured code generation, long context comprehension and the ability to follow complex instructions. It is particularly effective in boilerplate generation, scaffolding and translating requirements into code.

GPT-4.5

GPT-4.5 represents OpenAI's flagship model, with superior reasoning capabilities compared to GPT-4.1. It is the model to choose for tasks requiring in-depth analysis, complex reasoning or understanding of intricate systems. The quality of responses is notably superior, but at the cost of higher latency and the consumption of premium requests.

GPT-4.5 excels in scenarios such as debugging complex problems, analyzing distributed architectures, resolving non-trivial merge conflicts and generating optimized algorithms. The trade-off between response time and quality makes it ideal for tasks where accuracy is more important than speed.

o3-mini

The o3-mini model is designed for speed. It is significantly faster than other OpenAI models, with response quality that, while being inferior to GPT-4.1 for complex tasks, remains adequate for many daily scenarios. Being classified as a standard model, it does not consume premium requests.

It is the ideal choice for repetitive tasks, boilerplate code generation, quick completions and fast iterations where feedback speed takes priority over result perfection.

Anthropic Family (Claude)

Anthropic's Claude models are hosted on AWS, Anthropic and Google Cloud Platform. They are known for their excellent in-depth analysis capabilities, long context comprehension and production of well-structured and detailed responses.

Claude Haiku 4.5

Haiku 4.5 is the fastest model in the Claude family, designed to respond quickly while maintaining adequate quality for routine tasks. Its 200K token context window makes it particularly useful when working with many open files or using Repository Indexing on large codebases.

It is an excellent choice for quick completions, simple code questions, basic test generation and operations where immediate feedback is the priority. The combination of speed and wide context makes it an ideal complement to more powerful but slower models.

Claude Sonnet 3.7

Sonnet 3.7 represents the balanced model in the Claude family and is one of the most appreciated models among developers for overall response quality. It combines good response speed with advanced analytical capabilities, making it suitable for a wide range of tasks.

It is particularly strong in code review, complex bug analysis, code generation following specific patterns and production of detailed technical documentation. Its ability to understand broad context makes it effective when working with large codebases where relationships between files are important.

Claude Opus 4

Opus 4 is Anthropic's flagship model, with top-of-class reasoning capabilities. It is the model to choose for the most complex and challenging tasks: deep architectural analysis, debugging systemic problems, designing solutions for ambiguous requirements and refactoring legacy systems.

The combination of advanced reasoning and a 200K token context window makes it unique in the landscape of models available in Copilot. The cost is one premium request per interaction, so it should be used strategically for tasks where superior quality justifies the consumption.

Google Family (Gemini)

Google's Gemini models are hosted on Google Cloud Platform and bring distinctive capabilities, particularly in the multimodal domain and in processing very large context windows.

Gemini 2.0 Flash

Flash 2.0 is the fastest model in the Gemini family, optimized for quick responses with a good level of quality. Its distinctive feature is the 1 million token context window, enormously larger than any other model available in Copilot. This makes it particularly suited for working with very large repositories or when you need to include many files in the context.

Gemini 3 Flash and Gemini 3 Pro

The third generation of Gemini brings significant improvements in both speed and quality. Gemini 3 Flash offers excellent quality for a fast model, while Gemini 3 Pro competes with the flagship models from OpenAI and Anthropic in accuracy and reasoning capability.

Gemini 3 Pro stands out for its advanced multimodal capability: it can analyze not only images but also video and audio, making it unique for scenarios such as analyzing bug screencasts, understanding complex architectural diagrams and transcribing technical meetings.

Gemini 2.5 Pro

Gemini 2.5 Pro is a model with excellent reasoning and coding capabilities, positioned as an alternative to GPT-4.5 and Claude Opus 4. The combination of high quality and a 1M token context window makes it particularly suited for analyses that require understanding large amounts of code simultaneously.

Complete Comparison Table

To facilitate selection, here is a direct comparison of all available models on the most relevant parameters for software development.

Auto Mode: Automatic Model Selection

Auto Mode is Copilot's default mode that automatically selects the most suitable model based on the type of task requested. Instead of having to manually choose which model to use for each interaction, Auto Mode analyzes your request and decides which model will deliver the best result.

How Auto Mode Works

The automatic selection system evaluates several factors to determine the optimal model:

When to Use Auto Mode

Auto Mode is recommended in most scenarios. The main advantages are:

• No decision needed: You don't have to spend time choosing the model
• Cost optimization: The system balances quality and premium request consumption
• Dynamic adaptation: If the request evolves in complexity, the model can change
• Built-in best practices: The selection system incorporates GitHub's best practices

Manual Model Selection

In supported IDEs (VS Code, JetBrains, Visual Studio), you can manually select the model to use for each conversation. Selection happens through a dropdown menu in the Copilot Chat window.

Where to Select the Model

When Manual Selection is Better

Manual selection is particularly useful in these scenarios:

Recommendations by Use Case

Based on the characteristics of each model, here are the recommendations for the most common use cases in software development.

Daily Coding Tasks

Speed and Prototyping

Complex Reasoning

Code Review

Documentation

Premium Requests: Understanding Costs

Copilot's multi-model architecture introduces the concept of premium requests: some interactions consume additional resources and are counted separately from the user's monthly budget. Understanding this system is essential for effectively managing your usage.

What Counts as a Premium Request

Not all interactions with Copilot are equal in terms of computational cost. Premium requests correspond to the use of top-tier models that require more resources to generate responses.

Allocation by Plan

Each Copilot plan includes a different number of monthly premium requests. Once the budget is exhausted, the user can continue using only standard models or purchase additional premium requests.

Premium Request Management Strategy

To optimize premium request usage without sacrificing work quality, follow these strategies.

SCENARIO: Developing a new feature (API endpoint for reporting)

PHASE 1 - Scaffolding (Standard model: o3-mini)
"Generate the scaffolding for a new report module:
controller, service, repository, DTO, test file"
>> Cost: 0 premium requests

PHASE 2 - Base implementation (Standard model: GPT-4.1)
"Implement the service with methods:
generateDailyReport, generateWeeklyReport, exportToCsv"
>> Cost: 0 premium requests

PHASE 3 - Complex logic (Premium model: Opus 4)
"Analyze this SQL query for the aggregated report.
There are 3 joins and 2 subqueries. Optimize it to
handle 10M+ rows in under 2 seconds"
>> Cost: 1 premium request

PHASE 4 - Testing (Standard model: Sonnet 3.7)
"Generate unit tests for ReportService covering
all methods, edge cases and error handling"
>> Cost: 0 premium requests

PHASE 5 - Code Review (Premium model: Opus 4)
"Complete review of the report module: security,
performance, best practices, potential bugs"
>> Cost: 1 premium request

TOTAL: 2 premium requests for a complete feature

Model Availability by Plan

Not all models are available on all plans. Here is the complete availability matrix.

Multimodal Capabilities

An increasingly important aspect in model selection is the support for multimodal input, meaning the ability to analyze not only text and code but also images, video and other formats.

Multimodal Use Cases in Development

Multimodal capabilities open up interesting scenarios for software development that go beyond traditional code generation.

Best Practices for Model Selection

After analyzing all models and their use cases, here are the general guidelines for optimizing selection.

The 80-15-5 Rule

A good rule of thumb for model usage distribution is:

Quick Selection Workflow

Experiment and Compare

An often overlooked aspect is the importance of experimenting with different models on the same prompt. Different models can produce qualitatively different solutions for the same problem, and comparing responses can lead to valuable insights.

PROMPT: "How would you implement a rate limiting system
for a REST API with Node.js?"

GPT-4.1: Suggests express-rate-limit with Redis store,
         practical and immediate implementation.

Sonnet 3.7: First analyzes the different algorithms
            (token bucket, sliding window, fixed window),
            then suggests sliding window log with Redis.

Opus 4: Produces a complete architectural analysis
        with distributed rate limiting, grace period,
        retry-after headers, and monitoring integration.

Gemini 2.5 Pro: Proposes a solution with API Gateway
                pattern, comparison of managed solutions
                (Cloudflare, AWS WAF) vs custom.

CONCLUSION: Each model brings a different perspective.
The "best" depends on what you need at that moment.

Model Evolution

The AI model ecosystem is rapidly evolving. New models are released regularly and existing ones are improved. It is important to stay updated on new developments to make the most of the latest capabilities.

IDE Configuration for Specific Models

For those who want to configure model preferences at the IDE level, here is how to set preferences in VS Code.

{
  // Default model for Copilot Chat
  "github.copilot.chat.defaultModel": "auto",

  // Model for inline completion (code suggestions)
  // Not all models support inline completion
  "github.copilot.advanced": {
    "model": "gpt-4.1"
  },

  // Configuration for Agent Mode
  "github.copilot.chat.agent.defaultModel": "claude-sonnet-3.7",

  // Enable/disable specific models
  "github.copilot.chat.models.enabled": [
    "gpt-4.1",
    "claude-sonnet-3.7",
    "claude-opus-4",
    "gemini-2.0-flash"
  ]
}

Conclusion

GitHub Copilot's multi-model architecture represents a significant advantage over tools that depend on a single model. The ability to choose the most suitable model for each specific task, or to let Auto Mode make the choice for you, offers unprecedented flexibility in the developer's toolkit.

The key to making the most of this flexibility is understanding the strengths of each model and aligning them with your tasks. There is no "absolute best" model: the best is the one that produces the optimal result for the specific task you are facing.

The practical advice is: start with Auto Mode as default, use standard models (GPT-4.1, Sonnet 3.7) for daily work, and reserve premium requests for moments where superior quality truly makes a difference. Over time, you will develop a natural intuition about which model to choose for each situation.