Antigravity's Orchestrator switches between a planning model and a cheaper executing model inside one session

Orchestrator is a new option in Antigravity's model selector. It pairs Gemini 4 Carbon with Gemini 4 Hydrogen, a model Google introduced the same day. The Antigravity post sets it against a static "model router" that "guesses task difficulty from your first prompt" and sends the whole task to one model. In the post's example, Carbon starts in a read-only phase, explores dependencies and builds a plan "with explicit re-planning triggers". Hydrogen then writes the edits, updates packages and runs tests. If Hydrogen "hits friction", it hands back to Carbon, which inspects the state, repairs the plan and resumes execution.

Google reports that Orchestrator reached "near-Carbon task success rates" while using 24% less quota and finishing 19% faster on average. Neither post gives the success rates themselves, and the two posts name different benchmark versions for the same result: Terminal-Bench 4.1 in the Antigravity post, Terminal-Bench 4.0 in the model announcement. For Hydrogen by itself, Google lists a flat price of $1.50 per million input tokens and $7.50 per million output tokens across a 1 million-token context window, and a score of 57% on Terminal-Bench 4.1 against 18.2% for Gemini 3.8 Flash. Hydrogen has four thinking levels: low, medium, high (the default) and a new xhigh. Every figure here is Google's own.

In Antigravity, Hydrogen is rolling out to paid Google AI Pro and Ultra subscribers. Orchestrator is rolling out to Ultra subscribers, with more models and plans to follow, and Google says enterprises can reach both through Gemini for business. For teams that want to assign models to roles themselves, the post points to the Antigravity SDK and shows an issue-triage demo in which Hydrogen agents triage and code while a Carbon agent takes larger refactors, new features and security checks.

Why it matters: A router that picks a model from the first prompt has to judge difficulty before the work starts. Orchestrator makes that choice during the run and lets the cheaper model hand the task back. If you run your own harness, the design the post describes is a planner that writes down when to re-plan and an executor that returns control when it gets stuck. The 24% quota saving comes from a benchmark for which Google does not report success rates, so measure cost per completed task on your own work before you count on it.