Merge pull request #39719 from github/repo-sync

Repo sync

Author: docs-bot

content/code-security/securing-your-organization/introduction-to-securing-your-organization-at-scale/about-enabling-security-features-at-scale.md

@@ -48,6 +48,8 @@ You can customize {% data variables.product.prodname_security_configurations %},
 
 You will only ever see enablement settings for features that have been installed on your {% data variables.product.prodname_ghe_server %} instance by an enterprise administrator.
 
+To learn how to create {% data variables.product.prodname_custom_security_configurations %}, see [AUTOTITLE](/code-security/securing-your-organization/enabling-security-features-in-your-organization/creating-a-custom-security-configuration).
+
 {% endif %}
 
 {% data reusables.code-scanning.custom-security-configuration-enforcement-edge-cases %}

content/copilot/concepts/billing/copilot-requests.md

@@ -91,24 +91,7 @@ Each model has a premium request multiplier, based on its complexity and resourc
 
 If you use **{% data variables.copilot.copilot_free_short %}**, you have access to a limited number of models, and each model will consume one premium request when used. For example, if you make a request using the {% data variables.copilot.copilot_gemini_flash %} model, your interaction will consume **one premium request**, not 0.25 premium requests.
 
-{% rowheaders %}
-
-| Model                                                          | Multiplier for **paid plans**  | Multiplier for **{% data variables.copilot.copilot_free_short %}** |
-|----------------------------------------------------------------|--------------------------------|-----------------------|
-| {% data variables.copilot.copilot_gpt_41 %}                    | 0                              | 1                     |
-| {% data variables.copilot.copilot_gpt_4o %}                    | 0                              | 1                     |
-| {% data variables.copilot.copilot_claude_sonnet_35 %}          | 1                              | 1                     |
-| {% data variables.copilot.copilot_claude_sonnet_37 %}          | 1                              | Not applicable        |
-| {% data variables.copilot.copilot_claude_sonnet_37 %} Thinking | 1.25                           | Not applicable        |
-| {% data variables.copilot.copilot_claude_sonnet_40 %}          | 1                              | Not applicable        |
-| {% data variables.copilot.copilot_claude_opus_41 %}            | 10                             | Not applicable        |
-| {% data variables.copilot.copilot_claude_opus %}               | 10                             | Not applicable        |
-| {% data variables.copilot.copilot_gemini_flash %}              | 0.25                           | 1                     |
-| {% data variables.copilot.copilot_gemini_25_pro %}             | 1                              | Not applicable        |
-| {% data variables.copilot.copilot_o3 %}                        | 1                              | Not applicable        |
-| {% data variables.copilot.copilot_o4_mini %}                   | 0.33                           | Not applicable        |
-
-{% endrowheaders %}
+{% data reusables.copilot.model-multipliers %}
 
 ## Examples of premium request usage
 

content/copilot/how-tos/configure-personal-settings/configure-network-settings.md

@@ -74,13 +74,15 @@ If you have configured a proxy but are still encountering connection errors, see
 While {% data variables.product.prodname_copilot_short %} for {% data variables.product.prodname_vs %} reads the proxy settings from Windows, it does not retrieve authentication credentials from those Windows settings.
 
 If you need to authenticate to a proxy, you can try one of the below:
-1. Enable passing default proxy credentials by setting the environment variable `COPILOT_USE_DEFAULTPROXY` to `true`. 
-   - **Windows example**: Open the Command Prompt and run the following command:
-     ```
+1. Enable passing default proxy credentials by setting the environment variable `COPILOT_USE_DEFAULTPROXY` to `true`.
+   * **Windows example**: Open the Command Prompt and run the following command:
+
+     ```bash
      setx COPILOT_USE_DEFAULTPROXY true
      ```
+
      This sets the variable permanently for your user account. Restart any applications that need to use this variable.
-2. You can include your credentials in the proxy URL (for example: `http://USERNAME:[email protected]:5187/`), then set this URL as one of the supported environment variables listed in [Proxy settings for {% data variables.product.prodname_copilot_short %}](/copilot/concepts/network-settings-for-github-copilot#proxy-settings-for-copilot).
+1. You can include your credentials in the proxy URL (for example: `http://USERNAME:[email protected]:5187/`), then set this URL as one of the supported environment variables listed in [Proxy settings for {% data variables.product.prodname_copilot_short %}](/copilot/concepts/network-settings-for-github-copilot#proxy-settings-for-copilot).
 
 {% endvisualstudio %}
 

content/copilot/how-tos/use-chat/use-chat-in-github.md

@@ -140,6 +140,7 @@ You can attach an image to {% data variables.product.prodname_copilot_short %} a
 1. If you see the AI model picker at the top of the page, select one of the models that supports adding images to prompts:
 
    * {% data variables.copilot.copilot_gpt_41 %} (the default that's used if you don't see a model picker)
+   * {% data variables.copilot.copilot_gpt_5 %}
    * {% data variables.copilot.copilot_claude_sonnet_35 %}
    * {% data variables.copilot.copilot_claude_sonnet_37 %}
    * {% data variables.copilot.copilot_gemini_flash %}

content/copilot/reference/ai-models/model-comparison.md

@@ -26,6 +26,7 @@ Use this table to find a suitable model quickly, see more detail in the sections
 | Model                                                 | Task area | Excels at (primary use case) | Additional capabilities |
 |-------------------------------------------------------|-----------|-------------------------------|--------------------------|
 | {% data variables.copilot.copilot_gpt_41 %}           | General-purpose coding and writing | Fast, accurate code completions and explanations | Agent mode, vision |
+| {% data variables.copilot.copilot_gpt_5 %}            | Deep reasoning and debugging | Multi-step problem solving and architecture-level code analysis | Reasoning |
 | {% data variables.copilot.copilot_o3 %}               | Deep reasoning and debugging | Multi-step problem solving and architecture-level code analysis | Reasoning |
 | {% data variables.copilot.copilot_o4_mini %}          | Fast help with simple or repetitive tasks | Fast, reliable answers to lightweight coding questions | Lower latency |
 | {% data variables.copilot.copilot_claude_opus_41 %}   | Deep reasoning and debugging | Complex problem-solving challenges, sophisticated reasoning | Reasoning, vision |
@@ -44,8 +45,8 @@ Use these models for common development tasks that require a balance of quality,
 |-------|---------------------|
 | {% data variables.copilot.copilot_gpt_41 %} | Reliable default for most coding and writing tasks. Fast, accurate, and works well across languages and frameworks. |
 | {% data variables.copilot.copilot_claude_sonnet_37 %} | Produces clear, structured output. Follows formatting instructions and maintains consistent style. |
-| {% data variables.copilot.copilot_gemini_flash %} | Fast and cost-effective. Well suited for quick questions, short code snippets, and lightweight writing tasks. |
-| {% data variables.copilot.copilot_o4_mini %} | Optimized for speed and cost efficiency. Ideal for real-time suggestions with low usage overhead. |
+| {% data variables.copilot.copilot_gemini_flash %}     | Fast and cost-effective. Well suited for quick questions, short code snippets, and lightweight writing tasks. |
+| {% data variables.copilot.copilot_o4_mini %}          | Optimized for speed and cost efficiency. Ideal for real-time suggestions with low usage overhead. |
 
 ### When to use these models
 
@@ -66,11 +67,11 @@ These models are optimized for speed and responsiveness. They’re ideal for qui
 
 ### Recommended models
 
-| Model | Why it's a good fit |
-|-------|---------------------|
-| {% data variables.copilot.copilot_o4_mini %} | A quick and cost-effective model for repetitive or simple coding tasks. Offers clear, concise suggestions. |
-| {% data variables.copilot.copilot_claude_sonnet_35 %} | Balances fast responses with quality output. Ideal for small tasks and lightweight code explanations. |
-| {% data variables.copilot.copilot_gemini_flash %} | Extremely low latency and multimodal support (where available). Great for fast, interactive feedback. |
+| Model                                                     | Why it's a good fit                                                                                        |
+|-----------------------------------------------------------|------------------------------------------------------------------------------------------------------------|
+| {% data variables.copilot.copilot_o4_mini %}              | A quick and cost-effective model for repetitive or simple coding tasks. Offers clear, concise suggestions. |
+| {% data variables.copilot.copilot_claude_sonnet_35 %}     | Balances fast responses with quality output. Ideal for small tasks and lightweight code explanations.      |
+| {% data variables.copilot.copilot_gemini_flash %}         | Extremely low latency and multimodal support (where available). Great for fast, interactive feedback.      |
 
 ### When to use these models
 
@@ -92,14 +93,15 @@ These models are designed for tasks that require step-by-step reasoning, complex
 
 ### Recommended models
 
-| Model                                                 | Why it's a good fit                                                                                             |
-|-------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------|
-| {% data variables.copilot.copilot_o3 %}               | Strong at algorithm design, system debugging, and architecture decisions. Balances performance and reasoning.   |
-| {% data variables.copilot.copilot_claude_sonnet_37 %} | Provides hybrid reasoning that adapts to both fast tasks and deeper thinking.                                   |
-| {% data variables.copilot.copilot_claude_sonnet_40 %} | Improves on 3.7 with more reliable completions and smarter reasoning under pressure.                            |
-| {% data variables.copilot.copilot_claude_opus_41 %}   | Anthropic’s most powerful model. Improves on {% data variables.copilot.copilot_claude_opus %}.                  |
-| {% data variables.copilot.copilot_claude_opus %}      | Strong at strategy, debugging, and multi-layered logic.                                                         |
-| {% data variables.copilot.copilot_gemini_25_pro %}    | Advanced reasoning across long contexts and scientific or technical analysis.                                   |
+| Model                                                 | Why it's a good fit                                                                                           |
+|-------------------------------------------------------|---------------------------------------------------------------------------------------------------------------|
+| {% data variables.copilot.copilot_gpt_5 %}            | Great at complex reasoning, code analysis, and technical decision-making.                                     |
+| {% data variables.copilot.copilot_o3 %}               | Strong at algorithm design, system debugging, and architecture decisions. Balances performance and reasoning. |
+| {% data variables.copilot.copilot_claude_sonnet_37 %} | Provides hybrid reasoning that adapts to both fast tasks and deeper thinking.                                 |
+| {% data variables.copilot.copilot_claude_sonnet_40 %} | Improves on 3.7 with more reliable completions and smarter reasoning under pressure.                          |
+| {% data variables.copilot.copilot_claude_opus_41 %}   | Anthropic’s most powerful model. Improves on {% data variables.copilot.copilot_claude_opus %}.                |
+| {% data variables.copilot.copilot_claude_opus %}      | Strong at strategy, debugging, and multi-layered logic.                                                       |
+| {% data variables.copilot.copilot_gemini_25_pro %}    | Advanced reasoning across long contexts and scientific or technical analysis.                                 |
 
 ### When to use these models
 

content/copilot/reference/ai-models/model-hosting.md

@@ -19,10 +19,13 @@ contentType: reference
 Used for:
 
 * {% data variables.copilot.copilot_gpt_41 %}
+* {% data variables.copilot.copilot_gpt_5 %}
 * {% data variables.copilot.copilot_o3 %}
 * {% data variables.copilot.copilot_o4_mini %}
 
-{% data variables.copilot.copilot_gpt_41 %} is hosted by {% data variables.product.github %}'s Azure tenant when used in {% data variables.product.prodname_copilot %}. {% data variables.copilot.copilot_o3 %} and {% data variables.copilot.copilot_o4_mini %} models are hosted by OpenAI and {% data variables.product.github %}'s Azure tenant. OpenAI makes the [following data commitment](https://openai.com/enterprise-privacy/): _We [OpenAI] do not train our models on your business data by default_. {% data variables.product.github %} maintains a [zero data retention agreement](https://platform.openai.com/docs/guides/your-data) with OpenAI.
+{% data variables.copilot.copilot_gpt_41 %} is hosted by {% data variables.product.github %}'s Azure tenant when used in {% data variables.product.prodname_copilot %}.
+
+{% data variables.copilot.copilot_gpt_5 %}, {% data variables.copilot.copilot_o3 %} and {% data variables.copilot.copilot_o4_mini %} models are hosted by OpenAI and {% data variables.product.github %}'s Azure tenant. OpenAI makes the [following data commitment](https://openai.com/enterprise-privacy/): _We [OpenAI] do not train our models on your business data by default_. {% data variables.product.github %} maintains a [zero data retention agreement](https://platform.openai.com/docs/guides/your-data) with OpenAI.
 
 When using OpenAI's models, input requests and output responses continue to run through {% data variables.product.prodname_copilot %}'s content filters for public code matching, when applied, along with those for harmful or offensive content.