Any harness, any model
Choose the harness you want the agent to run in, then pair that agent with the model that fits the task.
A setup that adapts to the task
Keep the harness you know, pick the model that fits the task, and decide where that model runs.
Work in a familiar harness
Use the agent harness you already know, such as GitHub Copilot, Claude Code, or Codex.
Pick the best model for the task
Choose from multiple providers, including Anthropic, OpenAI, Google, and Microsoft, plus open weight models, and match speed, reasoning, and context window to the work at hand.
Choose where your model runs
Bring your own key to connect a provider account directly, route through infrastructure you control, or run a model locally to work offline or keep code on your machine.
Start with the harness that fits
The agent harness determines where work runs, how it interacts with your workspace, and when it asks for approval. Switch harnesses mid-session and VS Code carries your conversation history and context along with it.
Learn about code isolation and handoff
Pair it with the right model
Switch models as your needs change, compare how they handle the same task, and adopt new releases as they land.
See supported language modelsGet more from every AI credit
Different models consume AI credits at different rates, so the model you pick shapes what a task costs.
Optimize AI credit usage
Bring your own key
Add your own API key to use a provider account directly, or configure a custom endpoint to route requests through infrastructure you control.
Configure your own model provider
And much more
Language models
See which models are available, how to add a provider, and how to switch between them.
Auto model selection
Let VS Code route each request to an efficient model based on task complexity and service health.
Configure thinking effort
Dial reasoning up for complex problems, or down to reduce latency and AI credit use.
Cache Explorer
Diagnose prompt cache misses between requests to reduce token cost and latency.
Custom agents
Pin a specific model to a purpose-built agent so the right one is always used for the job.
Pick the agent and model that fit the work
Bring the agents and models you already use, then switch either one as your needs change.
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