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Copy file name to clipboardExpand all lines: content/copilot/concepts/auto-model-selection.md
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Experience less rate limiting and reduce the mental load of choosing a model by letting {% data variables.copilot.copilot_auto_model_selection %} automatically choose the best available model.
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In {% data variables.product.prodname_vscode_shortname %}, {% data variables.copilot.copilot_auto_model_selection %} chooses from {% data variables.copilot.copilot_gpt_41 %}, {% data variables.copilot.copilot_gpt_5_mini %}, {% data variables.copilot.copilot_gpt_5 %}, {% data variables.copilot.copilot_claude_sonnet_35 %}, and {% data variables.copilot.copilot_claude_sonnet_45 %}, based on availability and to help reduce rate limiting. Included models may change over time.
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In {% data variables.product.prodname_vscode_shortname %}, {% data variables.copilot.copilot_auto_model_selection %} chooses from {% data variables.copilot.copilot_gpt_41 %}, {% data variables.copilot.copilot_gpt_5_mini %}, {% data variables.copilot.copilot_gpt_5 %}, {% data variables.copilot.copilot_claude_haiku_45 %}, and {% data variables.copilot.copilot_claude_sonnet_45 %}, based on availability and to help reduce rate limiting. Included models may change over time.
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Automatically selected models **won't** include these models:
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* Models with premium request multipliers greater than one. See [AUTOTITLE](/copilot/reference/ai-models/supported-models#model-multipliers).
Copy file name to clipboardExpand all lines: content/copilot/reference/ai-models/model-comparison.md
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| {% data variables.copilot.copilot_claude_haiku_45 %} | Fast help with simple or repetitive tasks | Fast, reliable answers to lightweight coding questions | Agent mode | Not available |
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| {% data variables.copilot.copilot_claude_sonnet_45 %} | General-purpose coding and agent tasks | Complex problem-solving challenges, sophisticated reasoning | Agent mode |[{% data variables.copilot.copilot_claude_sonnet_45 %} model card](https://assets.anthropic.com/m/12f214efcc2f457a/original/Claude-Sonnet-4-5-System-Card.pdf)|
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| {% data variables.copilot.copilot_claude_opus_41 %} | Deep reasoning and debugging | Complex problem-solving challenges, sophisticated reasoning | Reasoning, vision |[{% data variables.copilot.copilot_claude_opus_41 %} model card](https://assets.anthropic.com/m/4c024b86c698d3d4/original/Claude-4-1-System-Card.pdf)|
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| {% data variables.copilot.copilot_claude_sonnet_35 %} | Fast help with simple or repetitive tasks | Quick responses for code, syntax, and documentation | Agent mode, vision |[{% data variables.copilot.copilot_claude_sonnet_35 %} model card](https://www-cdn.anthropic.com/fed9cc193a14b84131812372d8d5857f8f304c52/Model_Card_Claude_3_Addendum.pdf)|
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| {% data variables.copilot.copilot_claude_sonnet_40 %} | Deep reasoning and debugging | Performance and practicality, perfectly balanced for coding workflows | Agent mode, vision |[{% data variables.copilot.copilot_claude_sonnet_40 %} model card](https://www-cdn.anthropic.com/6be99a52cb68eb70eb9572b4cafad13df32ed995.pdf)|
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| {% data variables.copilot.copilot_gemini_25_pro %} | Deep reasoning and debugging | Complex code generation, debugging, and research workflows | Reasoning, vision |[{% data variables.copilot.copilot_gemini_25_pro %} model card](https://storage.googleapis.com/model-cards/documents/gemini-2.5-pro.pdf)|
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| {% data variables.copilot.copilot_grok_code %} | General-purpose coding and writing | Fast, accurate code completions and explanations | Agent mode |[{% data variables.copilot.copilot_grok_code %} model card](https://data.x.ai/2025-08-20-grok-4-model-card.pdf)|
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| {% data variables.copilot.copilot_claude_haiku_45 %} | Balances fast responses with quality output. Ideal for small tasks and lightweight code explanations. |
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| {% data variables.copilot.copilot_claude_sonnet_35 %} | Balances fast responses with quality output. Ideal for small tasks and lightweight code explanations. |
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* {% data variables.copilot.copilot_claude_haiku_45 %}
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* {% data variables.copilot.copilot_claude_sonnet_45 %}
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* {% data variables.copilot.copilot_claude_opus_41 %}
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* {% data variables.copilot.copilot_claude_sonnet_35 %}
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* {% data variables.copilot.copilot_claude_sonnet_40 %}
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{% data variables.copilot.copilot_claude_haiku_45 %} and {% data variables.copilot.copilot_claude_opus_41 %} are hosted by Anthropic PBC. {% data variables.copilot.copilot_claude_sonnet_40 %} is hosted by Anthropic PBC and Google Cloud Platform. {% data variables.copilot.copilot_claude_sonnet_45 %} is hosted by Amazon Web Services, Anthropic PBC, and Google Cloud Platform. {% data variables.copilot.copilot_claude_sonnet_35 %} is hosted exclusively by Amazon Web Services. {% data variables.product.github %} has provider agreements in place to ensure data is not used for training. Additional details for each provider are included below:
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{% data variables.copilot.copilot_claude_haiku_45 %} and {% data variables.copilot.copilot_claude_opus_41 %} are hosted by Anthropic PBC. {% data variables.copilot.copilot_claude_sonnet_40 %} is hosted by Anthropic PBC and Google Cloud Platform. {% data variables.copilot.copilot_claude_sonnet_45 %} is hosted by Amazon Web Services, Anthropic PBC, and Google Cloud Platform. {% data variables.product.github %} has provider agreements in place to ensure data is not used for training. Additional details for each provider are included below:
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* Amazon Bedrock: Amazon makes the [following data commitments](https://docs.aws.amazon.com/bedrock/latest/userguide/data-protection.html): _Amazon Bedrock doesn't store or log your prompts and completions. Amazon Bedrock doesn't use your prompts and completions to train any AWS models and doesn't distribute them to third parties_.
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* Anthropic PBC: {% data variables.product.github %} maintains a [zero data retention agreement](https://privacy.anthropic.com/en/articles/8956058-i-have-a-zero-retention-agreement-with-anthropic-what-products-does-it-apply-to) with Anthropic.
Copy file name to clipboardExpand all lines: content/copilot/tutorials/compare-ai-models.md
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* It can interpret visual assets, such as UML diagrams, wireframes, or flowcharts, to generate code scaffolding or suggest architecture.
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* It can be useful for reviewing screenshots of UI layouts or form designs and generating.
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## {% data variables.copilot.copilot_claude_sonnet_35 %}
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## {% data variables.copilot.copilot_claude_haiku_45 %}
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{% data reusables.copilot.model-use-cases.claude-35-sonnet %}
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{% data reusables.copilot.model-use-cases.claude-haiku-45 %}
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### Example scenario
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Consider a scenario where you are implementing both unit tests and integration tests for an application. You want to ensure that the tests are comprehensive and cover any edge cases that you may and may not have thought of.
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For a complete walkthrough of the scenario, see [AUTOTITLE](/copilot/tutorials/writing-tests-with-github-copilot).
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### Why {% data variables.copilot.copilot_claude_sonnet_35 %} is a good fit
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### Why {% data variables.copilot.copilot_claude_haiku_45 %} is a good fit
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* It performs well on everyday coding tasks like test generation, boilerplate scaffolding, and validation logic.
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* The task leans into multi-step reasoning, but still stays within the confidence zone of a less advanced model because the logic isn’t too deep.
{% data variables.copilot.copilot_claude_haiku_45 %} is a good choice for everyday coding support—including writing documentation, answering language-specific questions, or generating boilerplate code. It offers helpful, direct answers without over-complicating the task. If you're working within cost constraints, {% data variables.copilot.copilot_claude_haiku_45 %} is recommended as it delivers solid performance on many of the same tasks as {% data variables.copilot.copilot_claude_sonnet_45 %}, but with lower resource usage.
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