Antigravity 2.0 vs Cursor: Multi-Agent IDE Workflows Compared

By the middle of 2026 the two most visible agentic development environments had converged on the same idea: stop treating the assistant as one composer you talk to, and start treating it as a fleet of agents you dispatch. Google relaunched Antigravity at I/O on May 19, 2026, and Cursor reworked its interaction model around parallel agents in the same window. The interesting question is no longer which one has agents. It is how each one hands you control of many agents at once, where that work runs, and what the bill looks like when several agents are busy. Here is the shape of the decision before the detail.

Dimension	Antigravity 2.0	Cursor
Form factor	Five surfaces: desktop app, CLI, SDK, Managed Agents API, Enterprise Agent Platform	Editor first, plus cloud agent environments
Default model	Gemini 3.5 Flash, other Gemini tiers selectable	Model agnostic, frontier models selectable
Multi-agent model	Custom subagent workflows, scheduled background tasks	Parallel agents with a dedicated Agents view
Where work runs	Local plus Managed Agents API for hosted runs	Local editor or isolated cloud copies of the repo
Base pricing	Pro tier included with Google AI Pro	Subscription tiers per seat
Higher tier	Ultra at 100 dollars per month, 5x Pro limits	Usage based on top of the seat

What changed in Antigravity 2.0

The original Antigravity launched in November 2025 alongside Gemini 3 as a free, agent first IDE, positioned squarely against Cursor. The 2.0 relaunch is a much bigger claim. Instead of one editor, Google now ships a platform with five surfaces: the redesigned desktop application, a new Antigravity CLI (the same tool that absorbed the old Gemini CLI), an SDK for building custom agents, a Managed Agents API for hosted execution, and an Enterprise Agent Platform for teams. The centerpiece of the desktop app is orchestration. You can set several agents working on a problem at once, design custom subagent workflows, and schedule tasks that run automatically in the background without you sitting in the loop.

That last capability, scheduled background agents, is the real tell about Google's intent. A composer helps you write the next function. A scheduler that fires agents on a cadence is infrastructure for work that happens while you are asleep: nightly dependency upgrades, recurring test triage, batch refactors across a large repo. The default model is Gemini 3.5 Flash, which is the cheap, fast tier, and that choice matters because background orchestration only makes economic sense when the per token cost of the workhorse model is low enough to run many agents continuously.

What changed in Cursor

Cursor arrived at parallelism from the editor side rather than the platform side. Its big release reworked the core interaction away from a single composer toward multiple agents running at once, with a dedicated Agents view for launching and tracking them. The second headline is cloud agent environments: agents can run on isolated, configured copies of your repository instead of only inside your local editor. That is the same architectural instinct as Antigravity's Managed Agents API, arrived at from the opposite direction. Cursor kept the editor as the center of gravity and grew execution outward into the cloud; Antigravity built a platform and kept an editor as one of five surfaces.

The practical consequence is where your attention lives. In Cursor, the editor remains the place you supervise, review diffs, and steer. In Antigravity, the desktop app is one control surface among several, and the CLI and Managed Agents API invite you to drive orchestration from a terminal or a script. If your team already thinks in terms of editor centric review, Cursor's model asks less of you to adopt. If your team thinks in terms of pipelines and scheduled jobs, Antigravity's surfaces map more naturally onto how you already work. The broader field of agentic IDEs including Claude Code and Windsurf has been moving this way all year, and the polyglot editor comparison in Cursor, Windsurf, Zed, and Aider on the Aider Polyglot benchmark is a useful companion if raw coding quality is your first filter.

Execution model: the decision that actually costs money

The feature that separates these two in production is not the editor chrome, it is where the agents execute and how that is billed. Cloud execution, whether Cursor's cloud agent environments or Antigravity's Managed Agents API, removes the constraint that agent work has to fit on your laptop and inside your local review loop. That is genuinely useful for long horizon tasks: a migration that touches hundreds of files, a test suite repair that runs for an hour, a refactor you want to kick off and walk away from. The same per worker model and multi-agent automation trade-offs we covered for Claude Code and Codex apply here. More agents running in parallel means more concurrent token spend, and cloud execution means you are also paying for the isolated environment.

The cost math is where the default model choice bites. Antigravity defaults to Gemini 3.5 Flash, a cheap tier, which is a deliberate move to make continuous background orchestration affordable. Cursor is model agnostic, so your spend depends on which frontier model you point the agents at. If you run parallel agents on a top tier model in Cursor, you can generate a large bill quickly, because parallelism multiplies token consumption by the number of concurrent workers. Neither approach is wrong. The rule is simple: match the model tier to the task. Cheap, fast models for wide parallel fan out and background jobs; expensive, careful models for the small number of steps where a mistake is costly.

Pricing in practice

Antigravity's base Pro tier is included with existing Google AI Pro subscriptions, and the new Ultra plan is 100 dollars per month for 5x the Pro limits. That bundling is a real advantage if your team already pays for Google AI, because the incremental cost of adding the IDE is low. Cursor is priced per seat with usage on top, which is the familiar model for editor tools and easy to reason about at the individual level, but the usage component grows with how aggressively your team runs parallel and cloud agents.

For a small team the deciding factor is often what you already buy. If you are inside the Google ecosystem, Antigravity's bundling and its cheap default model make heavy agent use cheaper to sustain. If you want model flexibility and an editor first review loop, Cursor's per seat clarity is easier to budget and to adopt without changing how developers work day to day.

FAQ

Is Antigravity 2.0 a replacement for Cursor? Not exactly. Antigravity 2.0 is a five surface platform that includes an IDE, while Cursor is an editor that grew cloud execution. If you only want a better editor with parallel agents, Cursor is the closer fit. If you want to orchestrate and schedule fleets of agents across surfaces, Antigravity is built for that.

Which one is cheaper to run heavy agent workloads on? Antigravity's default Gemini 3.5 Flash makes continuous and background agent use cheaper per token, and the Pro tier is bundled with Google AI Pro. Cursor's cost depends on the frontier model you select and how much parallel and cloud execution you use.

Can I use frontier models other than Gemini in Antigravity? Antigravity defaults to Gemini 3.5 Flash and lets you select other Gemini tiers. Cursor is model agnostic and lets you pick across providers, which is the main reason teams that want model choice lean toward Cursor.

Do I need cloud agents at all? Only for work that outgrows your local review loop: long migrations, hour long test repairs, or scheduled background jobs. For interactive coding with review on every diff, local execution in either tool is fine and cheaper.

When this applies to your stack

If your team is standardizing on how it runs many agents, not just whether it uses one, the choice comes down to your existing ecosystem and your review culture. Google shops that want scheduled, cheap, background orchestration will find Antigravity 2.0's surfaces and default model a natural fit. Teams that want model flexibility and an editor centric loop will move faster on Cursor. Either way, the expensive mistake is running parallel agents on a frontier model when a cheap tier would do, because parallelism multiplies whatever per token cost you chose.

If you are designing an agentic engineering workflow and want help sizing the model tiers, the parallelism, and the cost envelope before you commit a team to one platform, that is the kind of AI integration and agent engineering work we do at Contra Collective. The goal is a workflow that gets faster without a bill that grows faster than the output.