u/jukasper

We A/B tested different system prompts for GPT-5.5 in VS Code, and just changing the prompt made it faster and more token-efficient.

We figured this was worth sharing because we don't think people realize how much work continues after a model launches. Launch day isn't the finish line and our engineering teams keep iterating on prompts, validating changes, and improving things behind the scenes.

Check it out: https://code.visualstudio.com/blogs/2026/07/06/optimizing-vscode-coding-harness-model-providers

Previously available only on individual plans, it's now ready for teams at scale.

Built for coding and optimized for GitHub Copilot, MAI-Code-1-Flash delivers fast, low-latency responses. Making it well-suited for high-volume, iterative agentic coding workflows where speed and efficiency matter most.

Read the changelog

Over the last 6 months we ran a very simple say_hello task (part of our internal vscbench benchmark suite) over 50k times, and we wanted to share some insights we got from the data. It was super interesting to us how differently models approach even the simplest tasks.

Read the full post here

Let us know what you think :)