Merge pull request #39424 from github/repo-sync

Repo sync

Author: docs-bot

content/copilot/concepts/copilot-billing/understanding-and-managing-requests-in-copilot.md

@@ -85,7 +85,7 @@ Each model has a premium request multiplier, based on its complexity and resourc
 
 {% data variables.copilot.copilot_gpt_41 %} and {% data variables.copilot.copilot_gpt_4o %} are the included models, and do not consume any premium requests if you are on a **paid plan**.
 
-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_o3_mini %} model, your interaction will consume **one premium request**, not 0.33 premium requests.
+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 %}
 
@@ -101,7 +101,6 @@ If you use **{% data variables.copilot.copilot_free_short %}**, you have access
 | {% 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_o3_mini %}                            | 0.33                           | 1                     |
 | {% data variables.copilot.copilot_o4_mini %}                            | 0.33                           | Not applicable        |
 
 {% endrowheaders %}

content/copilot/reference/ai-models/choosing-the-right-ai-model-for-your-task.md

@@ -25,7 +25,6 @@ Use this table to find a suitable model quickly, see more detail in the sections
 | {% data variables.copilot.copilot_gpt_41 %} | General-purpose coding and writing | Fast, accurate code completions and explanations | Agent mode, visual |
 | {% data variables.copilot.copilot_gpt_4o %} | General-purpose coding and writing | Fast completions and visual input understanding | Agent mode, visual |
 | {% data variables.copilot.copilot_o3 %} | Deep reasoning and debugging | Multi-step problem solving and architecture-level code analysis | Reasoning |
-| {% data variables.copilot.copilot_o3_mini %} | Fast help with simple or repetitive tasks | Quick responses for code snippets, explanations, and prototyping | Lower latency |
 | {% 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 %} | Deep reasoning and debugging | Complex problem-solving challenges, sophisticated reasoning | Reasoning, vision |
 | {% data variables.copilot.copilot_claude_sonnet_35 %} | Fast help with simple or repetitive tasks | Quick responses for code, syntax, and documentation | Agent mode |
@@ -68,7 +67,6 @@ These models are optimized for speed and responsiveness. They’re ideal for qui
 | 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_o3_mini %} | Provides low-latency, accurate responses. Great for real-time suggestions and code walkthroughs. |
 | {% 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. |
 

content/copilot/reference/ai-models/supported-ai-models-in-copilot.md

@@ -37,7 +37,6 @@ This table lists the AI models available in {% data variables.product.prodname_c
 | {% data variables.copilot.copilot_gpt_41 %} | OpenAI | GA | {% octicon "check" aria-label="Included" %} | {% octicon "check" aria-label="Included" %} | {% octicon "check" aria-label="Included" %} |
 | {% data variables.copilot.copilot_gpt_4o %} | OpenAI | GA | {% octicon "check" aria-label="Included" %} | {% octicon "check" aria-label="Included" %} | {% octicon "check" aria-label="Included" %} |
 | {% data variables.copilot.copilot_o3 %} | OpenAI | {% data variables.release-phases.public_preview_caps %} | {% octicon "x" aria-label="Not included" %} | {% octicon "check" aria-label="Included" %} | {% octicon "check" aria-label="Included" %} |
-| {% data variables.copilot.copilot_o3_mini %} | OpenAI | GA | {% octicon "x" aria-label="Not included" %} | {% octicon "check" aria-label="Included" %} | {% octicon "check" aria-label="Included" %} |
 | {% data variables.copilot.copilot_o4_mini %} | OpenAI | {% data variables.release-phases.public_preview_caps %} | {% octicon "x" aria-label="Not included" %} | {% octicon "check" aria-label="Included" %} | {% octicon "check" aria-label="Included" %} |
 | {% data variables.copilot.copilot_claude_opus %} | Anthropic | GA | {% octicon "x" aria-label="Not included" %} | {% octicon "check" aria-label="Included" %} | {% octicon "check" aria-label="Included" %} |
 | {% data variables.copilot.copilot_claude_sonnet_35 %} | Anthropic | GA | {% octicon "check" aria-label="Included" %} | {% octicon "check" aria-label="Included" %} | {% octicon "check" aria-label="Included" %} |
@@ -60,7 +59,6 @@ The following table shows which models are available in each client.
 | {% data variables.copilot.copilot_gpt_41 %}                   | {% octicon "check" aria-label="Included" %}           | {% octicon "check" aria-label="Included" %}       | {% octicon "check" aria-label="Included" %}              | {% octicon "check" aria-label="Included" %}       |  {% octicon "check" aria-label="Included" %}     |    {% octicon "check" aria-label="Included" %}       |
 | {% data variables.copilot.copilot_gpt_4o %}                    | {% octicon "check" aria-label="Included" %}          | {% octicon "check" aria-label="Included" %}       | {% octicon "check" aria-label="Included" %}              | {% octicon "check" aria-label="Included" %}       | {% octicon "check" aria-label="Included" %}      |   {% octicon "check" aria-label="Included" %}        |
 | {% data variables.copilot.copilot_o3 %}              | {% octicon "check" aria-label="Included" %}          | {% octicon "check" aria-label="Included" %}       | {% octicon "check" aria-label="Included" %}              | {% octicon "check" aria-label="Included" %}       | {% octicon "check" aria-label="Included" %}      |   {% octicon "check" aria-label="Included" %}        |
-| {% data variables.copilot.copilot_o3_mini %}                   | {% octicon "check" aria-label="Included" %}          | {% octicon "check" aria-label="Included" %}       | {% octicon "check" aria-label="Included" %}              | {% octicon "check" aria-label="Included" %}       | {% octicon "check" aria-label="Included" %}      |   {% octicon "check" aria-label="Included" %}        |
 | {% data variables.copilot.copilot_o4_mini %}         | {% octicon "check" aria-label="Included" %}          | {% octicon "check" aria-label="Included" %}       | {% octicon "check" aria-label="Included" %}              | {% octicon "check" aria-label="Included" %}       | {% octicon "check" aria-label="Included" %}      |   {% octicon "check" aria-label="Included" %}        |
 | {% data variables.copilot.copilot_claude_opus %}  | {% octicon "check" aria-label="Included" %}           | {% octicon "check" aria-label="Included" %}         | {% octicon "check" aria-label="Included" %}              | {% octicon "check" aria-label="Included" %}       | {% octicon "check" aria-label="Included" %}      | {% octicon "check" aria-label="Included" %}          |
 | {% data variables.copilot.copilot_claude_sonnet_35 %}         | {% octicon "check" aria-label="Included" %}          | {% octicon "check" aria-label="Included" %}       | {% octicon "check" aria-label="Included" %}              |  {% octicon "check" aria-label="Included" %}      | {% octicon "check" aria-label="Included" %}      |  {% octicon "check" aria-label="Included" %}         |
@@ -91,7 +89,6 @@ For more information about premium requests, see [AUTOTITLE](/copilot/managing-c
 | {% data variables.copilot.copilot_gpt_41 %} | 0 | 1 |
 | {% data variables.copilot.copilot_gpt_4o %} | 0 | 1 |
 | {% data variables.copilot.copilot_o3 %} | 1 | Not applicable |
-| {% data variables.copilot.copilot_o3_mini %} | 0.33 | 1 |
 | {% data variables.copilot.copilot_o4_mini %} | 0.33 | Not applicable |
 | {% data variables.copilot.copilot_claude_opus %} | 10 | Not applicable |
 | {% data variables.copilot.copilot_claude_sonnet_35 %} | 1 | 1 |

content/copilot/tutorials/comparing-ai-models-using-different-tasks.md

@@ -75,15 +75,15 @@ def grant_editor_access(user_id, doc_id):
 * {% data variables.copilot.copilot_gpt_4o %} can recognize the pattern and provide a clear, concise explanation.
 * The task doesn't require deep reasoning or complex logic.
 
-## {% data variables.copilot.copilot_o3_mini %}
+## {% data variables.copilot.copilot_o4_mini %}
 
-OpenAI {% data variables.copilot.copilot_o3_mini %} is a fast, cost-effective reasoning model designed to deliver coding performance while maintaining lower latency and resource usage. {% data variables.product.prodname_copilot_short %} is configured to use OpenAI's "medium" reasoning effort.
+OpenAI {% data variables.copilot.copilot_o4_mini %} is a fast, cost-efficient model designed for simple or repetitive coding tasks. It delivers reliable, concise answers with very low latency, making it ideal for real-time suggestions and lightweight development workflows. {% data variables.copilot.copilot_o4_mini %} is optimized for speed and responsiveness, so you can quickly iterate on small code changes or get instant feedback on straightforward prompts.
 
 ### Example scenario
 
-Consider a scenario where you are working on a software licensing system. You have a list of user records, each stored as a dictionary with fields like `name`, `active`, and `signup_date`. You want to find only the active users, sorted by their signup date, so that the newest users appear last.
+Suppose you are building a utility script and need to filter a list of user records to include only active users, sorted by their signup date. The task is simple and doesn’t require deep reasoning or multi-step logic.
 
-Below is list of users you are working with:
+Here’s a sample list of users:
 
 ```python
 users = [
@@ -111,10 +111,11 @@ active_users_sorted = sorted(active_users, key=lambda user: user["signup_date"])
 print(active_users_sorted)
 ```
 
-### Why o3-mini is a good fit
+### Why {% data variables.copilot.copilot_o4_mini %} is a good fit
 
-* The task involves simple filtering and sorting, which doesn’t require deep reasoning.
-* Its fast responses make it ideal for quick iteration during development.
+* The task is straightforward and benefits from fast, low-latency responses.
+* {% data variables.copilot.copilot_o4_mini %} is optimized for cost and speed, making it ideal for quick edits, prototyping, and utility code.
+* Use this model when you want reliable answers for simple coding questions without waiting for unnecessary depth.
 
 ## {% data variables.copilot.copilot_gemini_flash %}
 

content/graphql/overview/rate-limits-and-node-limits-for-the-graphql-api.md

@@ -286,4 +286,6 @@ If {% data variables.product.github %} takes more than 10 seconds to process an
 
 You can check the status of the GraphQL API at [githubstatus.com](https://www.githubstatus.com/) to determine whether the timeout is due to a problem with the API. You can also try to simplify your request or try your request later. For example, if you are requesting a large number of objects in a single request, you can try requesting fewer objects split over multiple queries.
 
+If a timeout occurs for any of your API requests, additional points will be deducted from your primary rate limit for the next hour to protect the speed and reliability of the API.
+
 {% endif %}