Claude vs. Gemini in Antigravity

Antigravity is an AI-powered development tool that lets you choose the underlying model — including Claude and Gemini — for the tasks it runs. That choice affects how accurately the model follows your project brief, how much context it uses in a long session, and how reliably it applies accepted changes on repeat runs. A tool like fenbs keeps a shared task brief and a record of accepted changes outside the model, so the work is available when you switch between Claude and Gemini or hand the project to a teammate.

For coding-assistant comparisons beyond Antigravity, see Gemini CLI vs Claude Code. For a general Claude and Gemini comparison, see Claude vs. Gemini. This page focuses on the Antigravity-specific workflow.

What model choice changes in Antigravity

When you switch models inside Antigravity, you are changing which language model processes your prompt and returns a result. The Antigravity tooling, file access, and session structure remain the same. What changes is how the model interprets your instructions, how much of the project context it uses effectively, and how it handles ambiguous or incomplete requirements.

Claude tends to follow multi-part instructions precisely and performs well on structured reasoning and code review with detailed criteria. Gemini handles larger context windows on current models and accepts image inputs natively if your workflow includes screenshots or diagrams alongside code.

Neither is universally better for Antigravity tasks. The right choice depends on the specific task, the size of your codebase context, and whether you need features like image input. Test both on a representative task from your project before committing to one for a full workflow.

How to evaluate models on the same Antigravity task

The clearest comparison runs the same task through both models with the same brief and the same review criteria. Start with a bounded task — a single function, a specific bug fix, or a clearly scoped refactor — rather than an open-ended one. Open-ended tasks are harder to evaluate consistently.

• Write the task description precisely: what the function must do, what the inputs and outputs are, and any constraints on style or dependencies.
• Run each model on the same task without changing the requirements between runs.
• Review the output against the same criteria: does it meet the requirement, does it introduce any side effects, and is the code readable?
• Note where each model needed correction and what kind of correction it was — wrong logic, missed constraint, or stylistic issue.

Keep the results from both runs. After a few tasks, patterns emerge: one model might consistently miss a specific type of constraint, or produce more verbose output that takes longer to review. That is useful information for deciding which model to use for which kind of task going forward.

Context windows in a live Antigravity project

Antigravity supplies the model with file context from your project. As your codebase grows, the amount of context available for each call becomes a practical limit. Claude 3.5 Sonnet and later Anthropic models support up to 200,000 tokens. Gemini 1.5 Pro and later models support up to one million tokens, which can handle a larger codebase in a single call.

In practice, most individual tasks in Antigravity do not require the full codebase in context — only the relevant files and their immediate dependencies. The larger context ceiling becomes relevant when a task requires reasoning across many files simultaneously, or when you want to supply a long project brief alongside the code.

Recording accepted changes when models switch

One risk of running both Claude and Gemini on the same project is that a change accepted from one model gets undone by the other in a later session. This happens when the second model does not know the first one's output was reviewed and approved.

The solution is a record of accepted changes that both models can read. Write what was changed, why it was accepted, and any follow-on constraints the change introduces. "We switched from inline styles to CSS modules in the header component (session on October 14)" is enough to stop the next model from reverting the approach.

This is the same problem in every multi-model development workflow. The model does not remember a previous session; the project record has to carry that memory.

When to use Claude vs Gemini for Antigravity tasks

• Use Claude when the task has many simultaneous constraints — a function that must satisfy five requirements at once, or a refactor with strict style rules alongside correctness requirements.
• Use Gemini when the task involves a very large codebase that exceeds Claude's context window, or when you want to supply screenshots or diagrams alongside code.
• Use Claude Code directly for longer coding sessions or when you want terminal integration alongside Antigravity.
• Use either when cost is a factor: compare the per-token rates for your expected task volume on anthropic.com and ai.google.dev before choosing.

How to do this with fenbs

1. Create a free account and add a project for your Antigravity work.
2. Write the project brief as a context note: the goal, the tech stack, and the code style rules.
3. Record accepted code decisions in Decisions and rules: what was changed, when, and why. Add constraints that subsequent sessions must follow.
4. Add tasks for the work you are tracking, with their current status and the next step.
5. Connect Claude Code from Integrations to read the project context and decisions at the start of each session. When you switch models in Antigravity, the shared record stays consistent.