GPT-5.4 lands in GitHub Copilot with native computer control, 1M token context, two variants (Thinking/Pro). Full breakdown for VS Code, JetBrains, Xcode.
What GPT-5.4 Brings to Copilot
GitHub Copilot now includes GPT-5.4, with two clear upgrades that change day-to-day use: native computer control and a one-million-token context window. Computer control means the agent can operate more of your environment than pure chat—opening files, running commands, navigating UI, and applying multi-step edits without you pasting every intermediate result. The large context window means long histories, multi-file diffs, and large codebases can stay in a single session instead of being chopped into short prompts that lose surrounding detail.
Two variants ship for different tradeoffs. Thinking is aimed at deliberate, multi-step work where you want the model to reason carefully before acting. Pro is oriented toward higher-capacity, higher-stakes sessions when you need stronger output quality and can afford more latency or cost. Pick Thinking when the path is ambiguous and you care about intermediate reasoning; pick Pro when the task is large, high-risk, or you need the strongest available pass on a complex change.
Computer Control in Practice
Treat computer control as an operator, not only a code generator. Good prompts name the goal, the constraints, and the stop conditions: which repo or project, which branch, what may be edited, what must not run, and how you will verify success. Prefer small loops—inspect, propose, apply, verify—over one-shot “rewrite the system” requests. Keep secrets out of the environment the agent can reach, and review every command and file change before you accept it.
Computer control is strongest for tasks that span tools: reproducing a bug, updating related files, running tests, and adjusting config until something green. It is weaker when the environment is poorly defined, permissions are too broad, or you have not stated what “done” looks like. Always retain the ability to undo: version control, clear diffs, and a habit of rejecting partial or surprising actions.
Using the 1M Context Window Well
A million-token window does not mean dump everything in by default. Useful context is targeted: the modules under change, adjacent interfaces, recent failing tests, relevant docs, and the conversation that led to the current task. Put the stable background first, then the live problem, then the question or action. When sessions get long, restate the goal and current state so the model does not lean on stale assumptions buried early in the thread.
Large context helps with cross-cutting work—refactors, API migrations, and security reviews that need many files in view—but it also makes noise expensive. Strip unrelated logs, generated assets, and full dependency trees unless they are required. If quality drops, shrink to the critical paths and re-open a focused session rather than stacking more history on top of a confused one.
VS Code, JetBrains, and Xcode
Across editors, the same model features land with different UI surfaces. In VS Code, expect Copilot chat, inline suggestions, and agent-style flows that can drive the editor and terminal under your approval. In JetBrains IDEs, the same capabilities show up through that product’s Copilot integration: chat panels, completion, and project-aware assistance tied to the open project structure. In Xcode, usage centers on Apple-platform workflows—Swift/UI, project files, build and test feedback—with the same model options where Copilot is available.
- VS Code: best fit for multi-file agent loops, terminal-driven verify, and web/backend stacks already living in that editor.
- JetBrains: lean on project indexing and refactoring tools; ask Copilot for the plan and code, then apply changes with IDE renames and inspections.
- Xcode: keep prompts concrete about schemes, targets, and platforms; use computer control carefully around builds so you do not burn time on accidental clean/rebuild cycles.
Wherever you work, start with the smaller variant for exploration, switch to Pro when the design is settled and the change is large, and keep computer control behind explicit review. The value is not the headline numbers alone—it is shorter loops from intent to verified change when you scope tasks tightly and stay in the loop.