GitHub Copilot’s Custom Code Completion Model Cuts Latency by 35%—Here’s What Changed

TL;DR GitHub Copilot rolled out a new custom code completion model with 20% more accepted characters, 12% higher acceptance rate, 3x faster token throughput, and 35% lower latency . For .NET developers shipping on GitHub, this means faster inline suggestions, fewer context-switching delays, and a measurably snappier IDE experience. No code changes required—it’s live now.

The Numbers That Matter

The enhanced GPT-4.1 Copilot code completion model improves the model’s ability to infer intent from code context, resulting in more accurate and contextually relevant inline code suggestions . But let’s talk performance:

35% latency reduction: Suggestions arrive faster, keeping you in flow state.
3x throughput: The model processes tokens three times quicker, especially valuable at scale.
12% higher acceptance rate: Developers accept more suggestions, reducing manual typing.
20% more retained characters: Accepted completions are more substantial and useful.

Why does this matter? Latency is the enemy of developer experience. A 500ms suggestion delay breaks your rhythm; a 325ms delay doesn’t. The average wall clock time per-task dropped 25%, with the median dropping 45%, and Copilot now responds more concisely to simple questions, keeping developers focused on the task at hand .

For .NET Developers: Real-World Impact

If you’re building .NET applications in VS Code or Visual Studio, this improvement compounds across your day. Consider a typical scenario:

// You start typing a LINQ query
var activeUsers = dbContext.Users
    .Where(u => u.IsActive)
    .OrderBy(u => u.LastLogin)
    .Select(u => new { u.Id, u.Email })
    
// Copilot now suggests the .ToListAsync() completion 
// 35% faster than before
    .ToListAsync();

The old model might take 650ms to surface that suggestion. The new one? ~420ms. Across hundreds of completions per day, that’s reclaimed focus and fewer context switches to your browser or documentation.

How It Works (The Engineering Story)

GitHub worked with language experts to improve overall completion quality, finding that language-specific evaluations lead to better outcomes along quality and style preferences—a unique approach compared to execution-based testing and A/B testing alone .

The team didn’t just chase raw speed; they optimized for semantic usefulness. Reward functions were refined for build/test success, semantic usefulness (edits that advance the user’s intent without bloat), and API modernity preference for up-to-date, idiomatic libraries and patterns .

Translation: The model learned to prefer modern .NET idioms—async/await over callbacks, LINQ over loops, nullable reference types—without bloating suggestions.

Availability & What You Need to Do

This update is available for all users on all plans . If you’re on GitHub Copilot Pro, Business, or Enterprise, you’re already using it. No toggles, no opt-ins, no API version bumps. It’s a silent upgrade that just works.

For .NET developers using the next-generation v1 Azure OpenAI APIs (available since August 2025), you get ongoing access to the latest features with no need to specify new api-version’s each month, faster API release cycles, and OpenAI client support with minimal code changes —so if you’re integrating Copilot completions into your own tools, the foundation is already there.

The Bigger Picture: Agentic Copilot is Evolving

Copilot coding agent is an asynchronous, autonomous background agent where you delegate a task and it opens a draft pull request, makes changes in the background, and requests a review from you . Copilot can now search the web to gather extra context and supplement its existing knowledge, debug unusual error messages, and understand recent changes to languages and libraries since the model’s training cut-off .

The latency win on code completions is the foundation for this agentic layer to work smoothly. Faster suggestions → faster reasoning → faster PRs.

One Small Caveat: Model Deprecations

Selected Claude, OpenAI, and Gemini models were deprecated on October 23, 2025 across all GitHub Copilot experiences (Copilot Chat, inline edits, ask and agent modes, and code completions) . If you’ve pinned an older model in your Copilot settings or integrations, check your configuration. Copilot Enterprise administrators may need to enable access to alternative models through their model policies in Copilot settings .

GitHub Copilot's Custom Code Completion Model Cuts Latency by 35%—Here's What Changed