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Antigravity lists Claude Sonnet 4.6 and Claude Opus 4.6 as selectable models in its own Antigravity model documentation. So Claude Code vs Antigravity is not a contest between two models.
It is a contest between two harnesses. One runs in your terminal. The other wraps an agent in an IDE with a review queue.
This comparison covers how each one plans, executes, and evidences its work. It also covers the check neither tool performs on itself.
TL;DR
Claude Code and Antigravity are agentic coding harnesses rather than competing models, and Antigravity can run Claude models directly. The choice turns on where you work and how much supervision you want, not on which vendor has the stronger model this quarter.
Claude Code is a terminal-first agent you drive from the command line, while Antigravity is a full IDE built around an agent manager. The real split is harness design, not underlying model quality.
The table below compares both tools across the five dimensions that change day-to-day work.
| Dimension | Claude Code | Antigravity |
|---|---|---|
| Primary surface | Terminal CLI, plus VS Code, JetBrains, desktop, and web | Standalone IDE, plus an Antigravity CLI |
| Work review | Inline diffs and plan review per session | Review pane for plans, diffs, and recordings |
| Models | Claude models, with third-party providers on some surfaces | Gemini 3 family, Claude Sonnet 4.6, Claude Opus 4.6, GPT-OSS-120b |
| Extensibility | Skills, hooks, subagents, MCP, and CLAUDE.md | Slash commands, allowlists, and separate Chrome profiles |
| Unattended runs | Headless flag, pipes, GitHub Actions, GitLab CI/CD | Built around interactive supervision |
Read that table as a workflow question. Claude Code optimises for composability, and Antigravity optimises for oversight.
Both tools run multi-step work without you approving every keystroke. What differs is where the checkpoints sit.
Claude Code puts its extension points in the filesystem, which is why the same setup travels between the terminal, an IDE, and CI.
Antigravity puts its checkpoints in the interface instead. Its documentation calls the outputs Antigravity artifacts, meaning structured deliverables the agent produces to communicate progress.
The steering step is the genuine difference. You edit the plan before code lands, rather than reviewing a diff afterwards.
Both agents write competent test code. The question that decides a release is different, and it is whether anyone independent confirmed those tests were meaningful.
Each harness handles the authoring step well and stops short of independent proof.
A code review is the useful comparison. You would not accept a pull request whose only approval came from its own author.
This is where an external verifier belongs. Kane CLI from TestMu AI is a deterministic browser agent that validates rendered UI in a real Chrome browser from a natural-language objective.
It returns an evidence-backed pass or fail instead of a summary. It also installs into a coding agent as a skill, so the system that writes the code is not the system that clears it.
Setup, authentication, and the full command reference live in the Kane CLI introduction documentation.
Give both agents an identical brief and the code they produce will look broadly similar. The evidence trail will not.
The list below tracks what each harness leaves behind at each stage of the same task.
Every row above is self-reported, which is the gap the previous section covered. None of it comes from a system outside the agent that did the work.
Public benchmark evidence is thin on one side. The SWE-rebench agent leaderboard scored four coding harnesses on the same 111 problems from 65 repositories, in an evaluation window running from May to July 2026.
Antigravity does not appear on that board at all. A ten-point spread between harnesses is the clearest published sign that the wrapper matters, and Google has posted no third-party score to sit against it.
For a wider view of how these harnesses sit next to one another, our roundup of agentic coding CLI tools covers the terminal-native field in more depth.
Claude Code leaves thin evidence of what it verified, and Antigravity confines browser checks to Chrome. Both agents grade their own work, which is the failure mode neither one documents.
Start with the limits that show up first in Claude Code.
Antigravity trades those problems for a different set.
The Enterprise model restriction deserves a second look during procurement. A team that standardised on Claude Opus 4.6 during a Pro trial will lose it on upgrade.
The Chrome limit has a straightforward answer. Export the agent-written flows to Playwright, then run that suite across browsers on the TestMu AI automation cloud.
Grid capabilities and framework setup are covered in the Playwright testing documentation.
Yes, mechanically. Claude Code is a terminal CLI, so it runs in the Antigravity integrated terminal like any other command. Nothing connects the two harnesses once it does.
You end up with two agents sharing one working directory. The list below covers what is not shared between them.
Treat one agent as the author and keep the other idle. Then give both the same external gate, which is the pattern we documented in our Claude Code Kane CLI verification write-up.
The two tools price access differently, and the shape of the plan matters more than the headline number.
The comparison below covers the access model each vendor documents. Check the vendor page for current figures before you budget.
| Access question | Claude Code | Antigravity |
|---|---|---|
| Free entry point | No, a subscription or Console account is required | Yes, a Free tier is documented |
| Paid tiers | Claude subscription plans and Anthropic Console usage | Google AI Plus, Pro, and Enterprise |
| Bring your own provider | Yes, on terminal, VS Code, and JetBrains | Not documented |
| Quota structure | Subscription and API usage limits | Weekly and five-hour limits, split by model family |
A free tier looks decisive until the five-hour limit interrupts a migration. Budget on quota shape, not on entry price.
Throttling shapes agentic work more than context window size does. A long refactor dies at a rate limit, not at a token ceiling.
The two tools throttle on different axes.
Pick Claude Code when your work lives in the terminal and a CI pipeline. Pick Antigravity when you want a visual review queue and a reviewable implementation plan before any code lands.
Neither choice is permanent. Antigravity runs Claude models, and Claude Code runs in the Antigravity terminal.
Teams evaluating the underlying models rather than the harness will get more from our breakdown of the best LLM for coding.
The Claude Code vs Antigravity question resolves faster once you stop treating it as a model comparison. Antigravity ships Claude models, so the real decision is about workflow shape.
Choose the terminal when you automate. Choose the IDE when you supervise. Then work through the four steps below.
The tool choice matters less than that last point. Whichever agent you run, something other than that agent has to confirm the software works.
Author
Anubhav Singhmaar is an AI Product Manager at TestMu AI driving Kane CLI, the command-line tool that brings browser automation to the terminal, turning natural-language flows into runs in a real Chrome browser that return pass or fail with shareable proof. He owns the roadmap and prioritization and works with engineering to ship developer-facing features. Before TestMu AI, he spent over four years at Sprinklr owning enterprise voice AI across APAC and EMEA. A mechanical engineer turned product manager, he grounds guidance in real QA workflows.
Reviewer
Srinivasan Sekar is Director of Engineering at TestMu AI (formerly LambdaTest), where he leads engineering and open-source initiatives behind the Selenium and Appium automation grid and owns TestMu AI's MCP Server. A committer to Appium and a contributor to Selenium, WebdriverIO, Taiko, and AppiumTestDistribution, he brings over 15 years of experience in quality engineering and open-source technologies. He is the author of the Apress book 'The MCP Standard: A Developer's Guide to Building Universal AI Tools with the Model Context Protocol,' a Certified Kubernetes and Cloud Native Associate, and an international conference speaker. Before TestMu AI he spent over eight years at Thoughtworks as a Principal Consultant and Quality Architect. Srinivasan holds a B.Tech in Information Technology from Anna University.
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