Antigravity 2.0 vs Cursor

Antigravity 2.0 and Cursor are both coding assistants tracked by AIDiveForge. Below is a side-by-side comparison of pricing, capabilities, platforms, and ownership — sourced from each tool's live website and verified before publishing.

Antigravity 2.0

The vendor describes Project IDX as a browser-based IDE where agents handle multi-step coding tasks end-to-end: writing code, executing it, observing what breaks in a live preview, and self-correcting before handing back control. Multi-model support means you are not locked to a single provider when one model handles your stack better than another. The free tier exists but carries usage caps that surface quickly on longer agentic runs — teams hitting those caps mid-task face a hard stop, not a graceful queue. Browser-based architecture removes local setup friction but also removes offline access and the deep editor customization that engineers who have spent years tuning their environment tend to miss.

Cursor

Cursor is an IDE-native coding agent that plans and executes multi-step tasks across entire codebases — editing files, running terminal commands, and spinning up parallel agents without requiring approval at every step. The vendor describes cloud agents that use their own compute to build, test, and demo features end to end, with the result queued for your review rather than interrupting your flow. That model works well for repetitive, well-scoped tasks: boilerplate generation, dependency migrations, test scaffolding. Where it starts to strain is open-ended architectural decisions — the agent can produce a plan, but if your codebase has undocumented assumptions baked into fifteen files, the output requires real scrutiny before it ships. Teams handling high-stakes refactors report adding review checkpoints that partially offset the autonomy gain.

Attribute	Antigravity 2.0	Cursor
Pricing	Paid	Paid
Price	$0-$200/month	$20/mo
Free trial	No	No
Open source	No	No
Has API	Yes	Yes
Self-hosted option	No	No
Platforms	macOS, Windows, Linux, Web-based	macOS 12+, Windows 10+, Linux (Ubuntu 20.04+, Fedora 36+, Debian 10+), Chrome OS (Linux dev environment)
Released	2025-11	2023-03
Pros	Self-verifying execution loop — the agent runs code, observes live browser output, and revises without waiting for you to relay what broke, which means you stop being the error-relay between your AI tool and your test environment. Multi-model support in a single environment, so switching the underlying model when one handles your framework better is a configuration change rather than a tool migration. Browser-based access with no local setup, which means onboarding a new developer or spinning up a fresh environment takes minutes rather than an afternoon of dependency resolution. Multi-agent task splitting lets separate agents handle discrete parts of a complex task in parallel, cutting the wall-clock time on multi-step workflows that a single-agent loop would process serially. API access means the agentic core can be called from external pipelines, so teams integrating AI into CI or build systems are not forced to use only the browser interface.	Multi-file context window with semantic codebase indexing, so the agent can trace a dependency chain across a project rather than hallucinating what exists outside the open file. Parallel cloud agents that execute simultaneously on separate tasks, which means a migration that would take a developer a full day of sequential edits can be split across agents and reviewed as a batch. Terminal command execution built into the agent loop, so tasks that require running tests or build steps to validate a change complete without switching context to a separate shell. Enterprise audit trail on paid tiers, so organizations with compliance requirements have a record of what the agent changed and when — removing the liability of autonomous code execution in regulated environments. CLI access in addition to the desktop IDE, so the same agent capabilities can be triggered inside CI/CD pipelines for repetitive tasks like boilerplate generation and dependency updates without manual IDE interaction.
Cons	Free tier usage caps terminate agentic runs mid-task when a multi-step job exceeds the allotment — there is no graceful queue, the session stops, and teams restart manually or upgrade to a paid tier before they have fully evaluated whether the tool fits. No self-hosted option and no offline access: teams with data residency requirements, air-gapped environments, or security policies restricting cloud-only tooling cannot use this at all, and those teams move to locally-deployable alternatives rather than filing exception requests. Browser-based execution means editor customization stops at what Google exposes in the interface — developers who depend on a specific plugin, language server configuration, or terminal workflow find the ceiling fast, and the path forward is maintaining a second local environment for the tasks IDX cannot handle. Complex conditional branching across more than a few agents strains the multi-agent coordination layer; community reports describe tasks with deep dependency chains producing inconsistent results, and teams handling those workflows add manual checkpoints that undercut the automation they bought the tool to achieve.	Open-ended architectural refactors in codebases with undocumented coupling produce output that requires line-by-line review — the agent cannot infer business logic that exists only in team memory, and at that point the review cost approaches the cost of writing the change manually. Self-hosting is not available, which means all codebase indexing and agent execution runs on Anysphere's infrastructure — teams with air-gapped environments or strict data residency requirements hit this wall immediately and move to a self-hosted alternative like a locally-run model with a compatible IDE. Parallel agent output arriving as a review batch creates a front-loaded review problem: when six agents complete simultaneously, the human checkpoint that was supposed to reduce bottlenecks becomes a concentrated review spike rather than a distributed one, which compounds on teams without a dedicated reviewer role.

Bottom line

Antigravity 2.0 and Cursor are closely matched on pricing model, openness, and API availability — pick by feature set and platform support in the table above.

Frequently asked questions

What is the difference between Antigravity 2.0 and Cursor?

Antigravity 2.0 is Paid, while Cursor is Paid. Compare pricing, free trial, API, platforms, and pros/cons in the table above on AIDiveForge.

Is Antigravity 2.0 better than Cursor?

It depends on your workflow. Use the side-by-side attributes (pricing, open source, API, self-hosted, platforms) to decide. AIDiveForge does not rank a universal winner — we publish verified facts so you can choose.