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Deprecating 8 low-usage Copilot models [GA] (#58053)

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Co-authored-by: Joe Clark <31087804+jc-clark@users.noreply.github.com>
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Sunbrye Ly
2025-10-23 11:38:51 -07:00
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parent 9d637ff293
commit 7a6596e43b
21 changed files with 638 additions and 117 deletions
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content/copilot/tutorials/compare-ai-models.md
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@@ -79,9 +79,9 @@ def grant_editor_access(user_id, doc_id):
* {% data variables.copilot.copilot_gpt_41 %} can recognize the pattern and provide a clear, concise explanation.
* The task doesn't require deep reasoning or complex logic.
## {% data variables.copilot.copilot_o4_mini %}
## {% data variables.copilot.copilot_gpt_5_mini %}
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.
OpenAI {% data variables.copilot.copilot_gpt_5_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_gpt_5_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
@@ -117,15 +117,15 @@ active_users_sorted = sorted(active_users, key=lambda user: user["signup_date"])
print(active_users_sorted)
```
### Why {% data variables.copilot.copilot_o4_mini %} is a good fit
### Why {% data variables.copilot.copilot_gpt_5_mini %} is a good fit
* 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.
* {% data variables.copilot.copilot_gpt_5_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 %}
## {% data variables.copilot.copilot_gpt_5 %}
{% data reusables.copilot.model-use-cases.gemini-20-flash %}
{% data reusables.copilot.model-use-cases.gpt-5 %}
### Example scenario
@@ -169,7 +169,7 @@ class Cart:
return Order("", None, 0)
```
### Why {% data variables.copilot.copilot_gemini_flash %} is a good fit
### Why {% data variables.copilot.copilot_gpt_5 %} is a good fit
* It can interpret visual assets, such as UML diagrams, wireframes, or flowcharts, to generate code scaffolding or suggest architecture.
* It can be useful for reviewing screenshots of UI layouts or form designs and generating.
@@ -189,9 +189,9 @@ For a complete walkthrough of the scenario, see [AUTOTITLE](/copilot/tutorials/w
* It performs well on everyday coding tasks like test generation, boilerplate scaffolding, and validation logic.
* 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_sonnet_37 %}
## {% data variables.copilot.copilot_claude_sonnet_45 %}
{% data reusables.copilot.model-use-cases.claude-37-sonnet %}
{% data reusables.copilot.model-use-cases.claude-sonnet-45 %}
### Example scenario
@@ -199,9 +199,9 @@ Consider a scenario where you're modernizing a legacy COBOL application by rewri
For a complete walkthrough of the scenario, see [AUTOTITLE](/copilot/tutorials/modernizing-legacy-code-with-github-copilot).
### Why {% data variables.copilot.copilot_claude_sonnet_37 %} is a good fit
### Why {% data variables.copilot.copilot_claude_sonnet_45 %} is a good fit
* {% data variables.copilot.copilot_claude_sonnet_37 %} handles complex context well, making it suited for workflows that span multiple files or languages.
* {% data variables.copilot.copilot_claude_sonnet_45 %} handles complex context well, making it suited for workflows that span multiple files or languages.
* Its hybrid reasoning architecture allows it to switch between quick answers and deeper, step-by-step problem-solving.
## Further reading