| title | AI model comparison | ||
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| shortTitle | Model comparison | ||
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| intro | Compare available AI models in {% data variables.copilot.copilot_chat_short %} and choose the best model for your task. | ||
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| contentType | reference |
{% data variables.product.prodname_copilot %} supports multiple AI models with different capabilities. The model you choose affects the quality and relevance of responses by {% data variables.copilot.copilot_chat_short %} and {% data variables.product.prodname_copilot_short %} inline suggestions. Some models offer lower latency, while others offer fewer hallucinations or better performance on specific tasks. This guide helps you pick the best model based on your task, not just model names.
Note
- Different models have different premium request multipliers, which can affect how much of your monthly usage allowance is consumed. For details, see AUTOTITLE.
- {% data reusables.copilot.auto-model-selection %}
Use this table to find a suitable model quickly, see more detail in the sections below.
| Model | Task area | Excels at (primary use case) | Further reading |
|---|---|---|---|
| {% for model in tables.copilot.model-comparison %} | |||
| {{ model.name }} | {{ model.task_area }} | {{ model.excels_at }} | {{ model.further_reading }} |
| {% endfor %} |
Use these models for common development tasks that require a balance of quality, speed, and cost efficiency. These models are a good default when you don't have specific requirements.
| Model | Why it's a good fit |
|---|---|
| {% data variables.copilot.copilot_gpt_53_codex %} | Delivers higher-quality code on complex engineering tasks like features, tests, debugging, refactors, and reviews without lengthy instructions. |
| {% data variables.copilot.copilot_gpt_5_mini %} | Reliable default for most coding and writing tasks. Fast, accurate, and works well across languages and frameworks. |
| {% data variables.copilot.copilot_grok_code %} | Specialized for coding tasks. Performs well on code generation, and debugging across multiple languages. |
| {% data variables.copilot.copilot_raptor_mini %} | Specialized for fast, accurate inline suggestions and explanations. |
Use one of these models if you want to:
- Write or review functions, short files, or code diffs.
- Generate documentation, comments, or summaries.
- Explain errors or unexpected behavior quickly.
- Work in a non-English programming environment.
If you're working on complex refactoring, architectural decisions, or multi-step logic, consider a model from Deep reasoning and debugging. For faster, simpler tasks like repetitive edits or one-off code suggestions, see Fast help with simple or repetitive tasks.
These models are optimized for speed and responsiveness. They’re ideal for quick edits, utility functions, syntax help, and lightweight prototyping. You’ll get fast answers without waiting for unnecessary depth or long reasoning chains.
| Model | Why it's a good fit |
|---|---|
| {% data variables.copilot.copilot_claude_haiku_45 %} | Balances fast responses with quality output. Ideal for small tasks and lightweight code explanations. |
Use one of these models if you want to:
- Write or edit small functions or utility code.
- Ask quick syntax or language questions.
- Prototype ideas with minimal setup.
- Get fast feedback on simple prompts or edits.
If you’re working on complex refactoring, architectural decisions, or multi-step logic, see Deep reasoning and debugging. For tasks that need stronger general-purpose reasoning or more structured output, see General-purpose coding and writing.
These models are designed for tasks that require step-by-step reasoning, complex decision-making, or high-context awareness. They work well when you need structured analysis, thoughtful code generation, or multi-file understanding.
| Model | Why it's a good fit |
|---|---|
| {% data variables.copilot.copilot_gpt_5_mini %} | Delivers deep reasoning and debugging with faster responses and lower resource usage than GPT-5. Ideal for interactive sessions and step-by-step code analysis. |
| {% data variables.copilot.copilot_gpt_54 %} | Great at complex reasoning, code analysis, and technical decision-making. |
| {% data variables.copilot.copilot_claude_sonnet_46 %} | Improves on Sonnet 4.5 with more reliable completions and smarter reasoning under pressure. |
| {% data variables.copilot.copilot_claude_opus_47 %} | Anthropic’s most powerful model. Improves on {% data variables.copilot.copilot_claude_opus_46 %}. |
| {% data variables.copilot.copilot_gemini_31_pro %} | Advanced reasoning across long contexts and scientific or technical analysis. |
| {% data variables.copilot.copilot_goldeneye %} | Complex problem-solving challenges and sophisticated reasoning. |
Use one of these models if you want to:
- Debug complex issues with context across multiple files.
- Refactor large or interconnected codebases.
- Plan features or architecture across layers.
- Weigh trade-offs between libraries, patterns, or workflows.
- Analyze logs, performance data, or system behavior.
For fast iteration or lightweight tasks, see Fast help with simple or repetitive tasks. For general development workflows or content generation, see General-purpose coding and writing.
Use these models when you want to ask questions about screenshots, diagrams, UI components, or other visual input. These models support multimodal input and are well suited for front-end work or visual debugging.
| Model | Why it's a good fit |
|---|---|
| {% data variables.copilot.copilot_gpt_5_mini %} | Reliable default for most coding and writing tasks. Fast, accurate, and supports multimodal input for visual reasoning tasks. Works well across languages and frameworks. |
| {% data variables.copilot.copilot_claude_sonnet_46 %} | Improves on Sonnet 4.5 with more reliable completions and smarter reasoning under pressure. |
| {% data variables.copilot.copilot_gemini_31_pro %} | Deep reasoning and debugging, ideal for complex code generation, debugging, and research workflows. |
Use one of these models if you want to:
- Ask questions about diagrams, screenshots, or UI components.
- Get feedback on visual drafts or workflows.
- Understand front-end behavior from visual context.
Tip
If you're using a model in a context that doesn’t support image input (like a code editor), you won’t see visual reasoning benefits. You may be able to use an MCP server to get access to visual input indirectly. See AUTOTITLE.
If your task involves deep reasoning or large-scale refactoring, consider a model from Deep reasoning and debugging. For text-only tasks or simpler code edits, see Fast help with simple or repetitive tasks.
Choosing the right model helps you get the most out of {% data variables.product.prodname_copilot_short %}. If you're not sure which model to use, start with a general-purpose option like {% data variables.copilot.copilot_gpt_41 %}, then adjust based on your needs.
