Category · Developer tools · live data
| # | Brand | Coverage | Presence | Avg rank | SoV | Score |
|---|---|---|---|---|---|---|
| 01 | GitHub Copilot |     | 95% | 2.1 | 5.9% | 99 |
| 02 | Cursor | | 86% | 3.5 | 4.9% | 81 |
| 03 | Windsurf | | 82% | 4.6 | 4.1% | 68 |
| 04 | Codeium | | 82% | 4.6 | 4.1% | 68 |
| 05 | GitHub | | 82% | 4.6 | 4.0% | 67 |
| 06 | Tabnine | | 67% | 6.6 | 2.6% | 43 |
| 07 | GitHub Copilot Enterprise | | 67% | 6.6 | 2.6% | 43 |
| 08 | SonarQube | | 67% | 6.5 | 2.6% | 44 |
| 09 | Aikido Security | | 67% | 6.6 | 2.5% | 42 |
| 10 | Greptile | | 66% | 6.8 | 2.4% | 40 |
| 11 | Qodo | | 66% | 6.8 | 2.4% | 40 |
| 12 | Veracode | | 66% | 6.9 | 2.3% | 39 |
| 13 | Amazon Q Developer | | 65% | 7.0 | 2.3% | 38 |
| 14 | Snyk | | 64% | 7.2 | 2.1% | 35 |
| 15 | CodeRabbit | | 63% | 7.6 | 1.8% | 30 |
| 16 | Sourcegraph | | 54% | 7.8 | 1.7% | 28 |
| 17 | Continue.dev | | 54% | 7.9 | 1.6% | 26 |
| 18 | Checkmarx One | | 54% | 7.8 | 1.7% | 28 |
| 19 | Semgrep Code | | 54% | 7.8 | 1.6% | 27 |
| 20 | DeepSource | | 54% | 7.9 | 1.6% | 26 |
Presence = share of prompts where the brand is cited · Avg rank = mean position when cited · SoV = share of voice across the category · First measured edition; trends begin next edition.
Which ai coding assistants brands are most cited by AI assistants?
GitHub Copilot ranks #1 with a score of 99/100, ahead of Cursor (81/100) and Windsurf (68/100). Measured across ChatGPT, Perplexity, Gemini and Claude for the query “best AI coding assistant for engineering teams 2026”.
Do AI assistants agree on which ai coding assistants brands to recommend?
1 of 20 brands in ai coding assistants are cited consistently across all four engines. ChatGPT cites the most engine-exclusive brands not mentioned by the others, suggesting it draws on a different source mix. Consensus brands tend to have stronger third-party coverage (reviews, comparisons, editorial mentions) rather than relying on a single source.
Which ai coding assistants brands are invisible to AI assistants?
5 of 20 brands are cited by one engine or fewer, including Sourcegraph, Continue.dev, Checkmarx One. Being functionally invisible to AI assistants ahead of a high-intent buyer query is a growing risk for vendors with otherwise solid market presence.
How is the AI visibility score calculated, and how is it different from SEO?
The score (0–100) combines five dimensions: coverage across engines, presence rate, average rank when cited, share of voice, and citation consistency. Unlike SEO, it measures whether a model names your brand inside its synthesized answer — not whether a link ranks on a results page. Full methodology at /methodology/.