Artificial Intelligence1 min read

Gemini 4 Hydrogen and Orchestrator

By · Published by Everything Blog

In short

Google Antigravity has launched Gemini 4 Hydrogen alongside Orchestrator, a system that pairs Hydrogen with Gemini 4 Carbon in a dynamic, bidirectional workflow. Instead of relying on static model routers, Orchestrator delegates tasks like planning and refactoring to Carbon while routing execution steps like edits and testing to Hydrogen. On Terminal-Bench 4.1 testing, this coordinated setup matches near-Carbon success levels while finishing 19% faster and utilizing 24% less quota. Gemini 4 Hydrogen is also available as a standalone coding model featuring four reasoning depth settings ranging from low to xhigh.

Key points

  • Developers can use the Antigravity SDK to manually build custom pipelines dividing tasks…: Developers can use the Antigravity SDK to manually build custom pipelines dividing tasks between Hydrogen and Carbon.
  • Paid Google AI Pro and Ultra subscribers receive access to Gemini 4 Hydrogen starting tod…: Paid Google AI Pro and Ultra subscribers receive access to Gemini 4 Hydrogen starting today.
  • Orchestrator is initially restricted to Google AI Ultra users, with broader tier availabi…: Orchestrator is initially restricted to Google AI Ultra users, with broader tier availability and enterprise access planned.

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Gemini 4 Hydrogen and Orchestrator

Today, we’re rolling out Gemini 4 Hydrogen inside Google Antigravity alongside a new model dropdown option called Orchestrator, which pairs Hydrogen with Gemini 4 Carbon in an optimized balance of quality, efficiency, and latency.

Put Gemini 4 Carbon and Gemini 4 Hydrogen to work with Orchestrator

Earlier this week, we introduced Gemini 4 Carbon alongside /swarm

for tackling massive, multi-worktree engineering tasks with parallel agent teams. In just a couple of days, tens of thousands of swarms have been launched on Antigravity, pushing frontier intelligence on the hardest problems.

For everyday feature work and debugging inside a single session, running a frontier reasoning model with multiple parallel agents on every step of every routine task slows down your loop and burns frontier quota. A common industry approach is to use a static “model router” that guesses task difficulty from your first prompt and routes the entire task to the cheapest model that…

Artificial Intelligence