Short answer
Choose GitHub Copilot CLI if you prefer a terminal-centered, prompt-driven workflow or want to use Copilot from command-line scripts. Choose GitHub Copilot in VS Code if you want inline coding suggestions as you type, plus chat and agent tools alongside your code.
The two aren’t limited to different task sizes: both can handle multi-step coding work. The practical distinction is how you want to interact with Copilot and review its work. VS Code offers suggestions without starting a chat; CLI work starts from a terminal session or prompt. Neither is, by itself, a general-purpose model-serving API for your application.
GitHub lists Copilot CLI as available with all Copilot plans. For organization-provided Copilot, the organization’s CLI policy must also be enabled. Access to other features and metered usage still depends on the plan and its settings. (See GitHub Copilot CLI and GitHub Copilot policies.)
Copilot CLI vs VS Code Copilot at a glance
| Need | Better starting point | What’s different |
|---|---|---|
| Get code suggestions while writing | VS Code | Inline suggestions appear as you work in the editor; you can accept, partially accept, or dismiss them without opening chat. (See VS Code inline suggestions.) |
| Work from a terminal | Copilot CLI | Use an interactive session in a project folder, or send a prompt from the command line. CLI can work with files and tools subject to permissions and approval settings. (See GitHub CLI documentation.) |
| Ask an agent to handle a multi-step task | Either | Both support agent workflows. VS Code Agent mode can plan file changes, edit across a project, and run commands; CLI works in the terminal against a trusted folder. Task size alone doesn’t determine the choice. (See VS Code Agent mode.) |
| Reuse a specialized role | Either | CLI lets you select an available custom agent with /agent; VS Code lets you select a custom agent from the agent picker. You need a configured profile for your own custom agent. (See CLI custom agents and VS Code custom agents.) |
| Run independent subtasks in parallel | CLI /fleet |
Consider it when work can be split into independent pieces. Parallel agents use model resources; don’t assume they will make every task faster or cheaper. (See GitHub /fleet documentation.) |
| Call a model from your product backend | Neither by itself | Use a model provider’s API for direct inference. If you want to embed Copilot’s agent workflow, evaluate the separate Copilot SDK and its integration requirements. (See Copilot SDK documentation.) |
What is the practical difference?
Copilot in VS Code includes more than Agent mode. Inline suggestions can complete code, comments, or tests while you type; VS Code also offers chat and agent-driven development. You review suggestions and decide whether to accept them. The available access and allowance depend on your plan. (See VS Code inline-suggestion documentation.)
Copilot CLI is a terminal interface. You can ask questions or request changes in an interactive session, or provide a prompt from the command line. Before it works in a folder, you must trust that folder; commands and file changes may require approval depending on the mode and permissions. (See GitHub CLI documentation.)
For a feature that touches application code and tests, either tool can be a starting point. CLI keeps the interaction in the shell; VS Code lets you follow suggestions and agent edits alongside the editor. Choose the workflow that makes it easiest for you to inspect and validate the changes.
Which should you choose for your coding workflow?
- Want help as you write code? Start with VS Code inline suggestions. They appear in the editor as you type, without requiring a chat prompt. (See VS Code documentation.)
- Want to prompt Copilot from a terminal or command-line workflow? Start with CLI. Review its folder-trust and tool-approval settings before allowing file changes or command execution. (See GitHub CLI documentation.)
- Want an agent to tackle a larger, multi-file task? Either can fit. Use VS Code if you want to watch and steer changes in the editor; use CLI if you prefer to direct the work from the terminal. GitHub documents VS Code Agent mode for high-level, iterative tasks, so it isn’t limited to small edits. (See VS Code Agent mode.)
- Repeating a team task? Consider a custom agent with shared instructions and tools. Select it explicitly when the role matters, then keep tests and review as deliberate workflow steps. A reusable agent can standardize the instructions, but it doesn’t guarantee identical results or replace review. (See VS Code custom agents and CLI custom agents.)
Selecting a custom agent
In Copilot CLI, use /agent in the interactive session to choose from available agents. You can also refer to an agent in your prompt for Copilot to infer which one you mean. Custom profiles can be defined for a user, repository, organization, or enterprise; organization-provided CLI access also depends on the organization’s policy. (See GitHub CLI custom-agent documentation.)
In VS Code, select a custom agent from the agent picker in Chat. To use your own agent, configure a profile for the workspace or user. The VS Code documentation describes custom agents as reusable instructions, tools, and optionally a model for a role such as planning or code review. (See VS Code custom-agent documentation.)
Explicit selection helps make the intended role clear; natural-language delegation can be more flexible but may not invoke the specialist you intended. Either way, a person can forget a review or test step—make important quality checks explicit.
Parallel work and usage
Copilot CLI’s /fleet can break a plan into independent tasks for parallel subagents. For example, API changes, tests, and documentation may be separable parts of a feature. It is a better fit when those tasks can proceed independently than when each step depends on the latest result from another. (See GitHub /fleet documentation.)
Copilot usage is measured in AI credits based on model and token use; 1 AI credit equals $0.01 USD. GitHub documents a CLI session-limit feature for capping credits per session. The limit is currently in public preview, is a soft limit, and may be exceeded slightly if a response is already in progress. Parallel work can increase model usage, so consider the applicable plan allowance and spending controls. (See AI-credit billing for individuals and CLI session limits.)
Production model APIs: what these tools do—and don’t—replace
If you mean direct model inference from your application, Copilot CLI and the VS Code extension are clients, not your app’s model-serving endpoint. Use the intended model provider’s production API for direct inference, and confirm that provider’s deployment, authentication, and billing terms.
If you want to use your own model inside Copilot CLI, GitHub documents local BYOK for external model providers and compatible endpoints. The supported models and setup depend on the configuration; the model must support tool calling and streaming. This configures CLI to use a provider; it does not turn CLI into an API for your product. (See GitHub CLI BYOK documentation.)
GitHub also documents organization-managed custom models for Copilot Chat, CLI, and IDEs. This feature is in public preview and subject to change. Organization owners can add provider API keys; GitHub says this lets organizations use their existing provider payment arrangements and monitor usage through the provider’s dashboards and billing. Confirm provider compatibility, governance, and payment arrangements for the intended configuration. (See Organization custom models and BYOK overview.)
A separate option is the Copilot SDK for applications that need Copilot’s agent workflow. GitHub documents an architecture in which the application uses the SDK, which communicates with the Copilot CLI over JSON-RPC. That is an agent-runtime integration, not the same thing as calling a raw model inference endpoint. Check the SDK’s current setup and terms for your application. (See Copilot SDK and SDK setup paths.)
Plans and cost
The CLI and VS Code are Copilot surfaces; neither has a universal “cheaper” cost advantage. GitHub lists individual plans at $10 USD per calendar month for Pro, $39 for Pro+, and $100 for Max. It lists Copilot Business at $19 USD per user per month, billed monthly. Individual-plan AI-credit allowances vary. Business and Enterprise include credits pooled across the organization or enterprise; usage beyond the pool is billed separately. Organization access to features and spending controls can depend on administrator policy. (See GitHub Copilot licenses and prices, individual AI-credit allowances, and organization billing.)
For organization and enterprise accounts, GitHub’s billing documentation says inline code completions and next-edit suggestions are not billed in AI credits; Copilot Chat and Copilot CLI are among the features that do consume credits. The individual billing documentation describes AI-credit usage by model and tokens but does not establish the same inline-suggestion exception there, so don’t assume the organization billing distinction applies to every account type. If you configure your own provider key, that provider’s usage and billing arrangements also matter. (See organization usage-based billing, individual billing, and BYOK overview.)
Bottom line
- Choose VS Code Copilot for inline suggestions while writing, or if you want to steer agent work within your editor.
- Choose Copilot CLI for terminal-first, prompt-driven work and CLI-specific parallel
/fleettasks. - Consider both if you move between terminal and editor; their agent workflows overlap, while their everyday entry points differ.
- For a production model API, identify whether you need direct provider inference or an embedded Copilot agent workflow. Confirm provider compatibility, access policy, and billing for that specific integration.
Sources
GitHub Copilot CLI — GitHub Docs
GitHub Copilot policies for enterprises and organizations — GitHub Docs
Usage-based billing for individuals — GitHub Docs
Usage-based billing for organizations and enterprises — GitHub Docs
GitHub Copilot licenses — GitHub Docs
Enabling custom models for GitHub Copilot in your organization — GitHub Docs
Bring your own key for GitHub Copilot — GitHub Docs
Enabling custom models for GitHub Copilot in your enterprise — GitHub Docs