How Cursor actually shows up when buyers ask ChatGPT, Claude, Gemini, and Perplexity for write and refactor code faster with AI assistance. Read the gaps, then run the same audit on your own brand.
Mention rate = how often Cursor appears in the response. Recommendation rate = how often Cursor is the answer the agent leads with.
How prompt responses split between Cursor and the named competitor set across all 15 prompts and 4 platforms.
Each gap is a prompt where the agent did not surface Cursor as the recommended answer. Severity reflects how close this prompt sits to a real buying decision.
“Compare Cursor and GitHub Copilot on workflow automation and reporting.”
Agents default to Copilot because Microsoft + GitHub trust signals dominate. Cursor's agent-mode advantages are referenced from third-party blogs, not first-party docs.
Ship a canonical /compare/copilot page maintained in-house with side-by-side capabilities, refreshed quarterly with date stamps.
“What is Cursor's pricing, refund policy, and support SLA?”
Plan names and prices are surfaced reliably but support SLA and refund terms missing.
Add a public support policy page and link it from pricing. Schema.org Offer.priceValidUntil + termsOfService.
“What are the best alternatives to Cursor?”
Continue.dev and Aider are now surfaced alongside Copilot in three of four agents — newer OSS entrants are getting indexed faster than Cursor's response pages.
Publish a /vs/open-source page acknowledging the OSS field and citing benchmarks. Defensible positioning > silence.
Same 15-prompt matrix. Real agent responses where an LLM key is configured; deterministic synthesis otherwise. Output is a scored audit with prompts, evidence gaps, and the fixes ranked by impact.
Sample report. Numbers above are illustrative of a real run on the same 15-prompt matrix — not a live audit. Real audits cite the verbatim response per platform.