Antigravity vs Cursor: the short answer
Consider Antigravity for delegated scaffolding or exploratory drafts; consider Cursor for developer-directed edits or refactoring tied to repository conventions. That is a conditional recommendation based partly on one author’s November 2025 account—not a finding that either product is generally faster, more accurate, or safer. Current documentation shows the distinction is not absolute: both products support agent workflows, and both provide ways to manage or direct agent activity. [1][2][3][4]
A coding tool can help you build an application that calls a production model API. That is different from using Antigravity or Cursor programmatically; neither product’s agent API should be assumed to be a general-purpose model-inference API. [5][6]
How the current workflows compare
| Product | What its documentation describes | Practical implication |
|---|---|---|
| Antigravity | Agents can perform sensitive operations subject to configurable Deny, Ask, and Allow permissions. Its permission presets govern approval for terminal commands, file access, and other agent actions. [2][3] | Delegated work can still be bounded by permissions and approval prompts; the degree of autonomy depends in part on the settings you choose. |
| Cursor | Cursor Agent can complete complex coding tasks independently, run terminal commands, and edit code. For larger work, a coordinator agent can plan and delegate to other agents. [4] | Cursor is not limited to inline, manually directed edits: its documented agent workflow also supports delegated tasks. |
So “Antigravity is autonomous; Cursor is guided” is too categorical as a description of today’s capabilities. The more useful distinction is the specific workflow and controls you want to use—not an assumption that only one product can delegate work. The product-to-task recommendation below is the Skywork author’s dated, anecdotal judgment. [1][2][3][4]
Which one might fit your coding work?
- Consider Antigravity for broad scaffolding or exploratory drafts if you want an agent to take a first pass across several parts of a task and you can review its work. In a Skywork author’s profile-page example, Antigravity reportedly created a route and stubbed a form with little supervision, but guessed the ORM configuration and added an unrequested migration. [1]
- Consider Cursor for constrained edits or repository-sensitive refactoring if you want to supply context and steer changes against existing conventions. In that same account, Cursor asked about the framework and state manager before scaffolding. The author also said Cursor handled a form task after receiving an existing pattern, while Antigravity assumed a different validation library. [1]
These are one author’s observations, not general performance findings. Delegation can reduce hands-on direction, but assumptions and diffs still need review. A guided approach depends more on your input and context. Review code and run tests either way; the anecdotes do not establish that either tool is inherently safer. [1]
Production model API: two different questions
“Production model API” can mean two things:
- Using a coding tool to build an application that calls a model API. The model provider is a separate service. Consult that provider’s documentation for API access, credentials, model availability, limits, and billing.
- Using Antigravity or Cursor programmatically. Antigravity’s documentation describes a Python SDK for building agents and points to a managed REST/gRPC agent API in Gemini API documentation. Cursor documents APIs for running Cursor agent workflows, including Cloud Agent operations. Cursor explicitly distinguishes these from a standalone model-inference or chat-completions API. Check the purpose, access conditions, and charges for the specific API. [5][6]
The Skywork article’s example of wiring a page to a REST endpoint is a coding task, not documentation of a production model API. [1]
What do the reported speed and quality comparisons show?
The Skywork author says they tried the tools on client tasks and ran three small exercises using a Node/React TypeScript stack, Docker, and a Postgres test database. In one CRUD example, the author reports 30 minutes to Antigravity’s first PR plus 12 minutes of cleanup, compared with 38 minutes to Cursor’s first pass. The author says Antigravity missed a rate-limit check and assumed an unfamiliar logger; Cursor reportedly had no broken imports in that run. For a legacy refactor, the article reports Cursor finishing in 24 minutes with green tests, but gives no comparable Antigravity completion time. [1]
These are author-reported observations, not a controlled or reproducible benchmark. The article provides no prompts, code, product or model versions, repeated trials, or independent validation. Its FAQ gives separate approximate timings—about 14 minutes plus 8 minutes of review for Antigravity and about 18 minutes for Cursor—without explaining how they relate to the scenario timings. Keep the reports separate: they do not establish general speed, defect rates, or which tool is safer. [1]
For your own evaluation, try a bounded change in a repository you know. Inspect the diffs, run the same tests, and track review and cleanup effort. That can help you assess fit for your work, but it is not evidence of a universal winner.
Cost and company-code checks
Cursor: The US pricing page lists Free at $0/month, Premium at $30/month, Super at $100/month, and Ultra at $200/month. Those displayed monthly rates do not by themselves establish whether the commitment is month-to-month or billed on another schedule; confirm checkout terms before comparing totals. Cursor’s usage documentation says paid plans include a weekly pool for Cursor models. Third-party models are not included and, on Premium and above, are billed at API list price from prepaid credit. [7][8]
Antigravity: Google’s pricing page lists a $0/month individual option. Its plans documentation says Google AI Pro users receive a higher quota refreshed every five hours until the weekly limit is reached. It also says Google AI Pro and Ultra users can use purchased AI credits or promotional credits for additional usage above the baseline quota. Credit use is controlled by a setting that can prevent automatic use or allow it after the baseline quota is exhausted; credits are consumed at standard Gemini Enterprise consumption pricing. The available pricing information here does not establish Google AI Pro’s current US price or billing commitment, so check Google One before comparing subscription totals. [9][10]
Pricing note: reviewed November 9, 2026. Prices, quota terms, and billing commitments can change; confirm the applicable checkout and usage terms before purchase. [7][9][10]
For company code, check the settings and terms that apply to your account and deployment. Antigravity documents permission choices that can deny an action, allow it, or require approval. Cursor says Cloud Agents run in a virtual machine in Cursor’s cloud; its documentation says that, in Privacy Mode, code is not used for training and is retained only for running the agent. These statements do not establish an immediate deletion deadline or replace your organization’s security review. [2][11][12]
Practical recommendation
For scaffolding or exploratory drafts, consider Antigravity, with permissions and review set to fit the task. For controlled edits or refactoring tied to repository conventions, consider Cursor, while remembering that Cursor also supports delegated agent work. Those choices reflect a November 2025 author’s examples, not proof of a lasting capability edge or benchmark winner. [1][2][3][4]
If API access or subscription cost is decisive, compare those separately from coding performance: agent SDK/API access, model inference, and product-plan usage are different questions. [5][6][7][8][9][10]
Sources
Antigravity vs Cursor: Which AI Coding Tool Is Better?
Permissions | Google Antigravity Docs
Agent settings | Google Antigravity Docs
Cursor APIs Overview | Cursor Docs
Usage and limits | Cursor Docs
Plans | Google Antigravity Docs