# CodeQL vs Checkmarx One: which do AI models recommend for SAST, October 2026

IT AI Recommendation Index, October 2026 Edition, Static application security testing. Zero of fourteen models named CodeQL first on the direct prompt; zero named Checkmarx One. Page: https://it-ai-index.com/developer/static-application-security-testing/codeql-vs-checkmarx-one/

| | First-choice share | Rank | Negative rate | Labels | Models naming it |
|---|---|---|---|---|---|
| CodeQL | 4% | #5 of 10 | 11% | 27 | 11 of 14 |
| Checkmarx One | 2% | #8 of 10 | 45% | 44 | 14 of 14 |

## The direct prompt, model by model

- GPT-5.4 mini: neither first, one named (first choices: Semgrep) (alternatives: Checkmarx One, Snyk Code, Veracode Static Analysis)
- Perplexity Sonar: neither first, one named (first choices: Semgrep) (alternatives: Checkmarx One, Snyk Code)
- GLM 4.7 FlashX: neither first, one named (first choices: SonarQube) (alternatives: Checkmarx One, Codacy, DeepSource, GitHub Advanced Security, OpenText Fortify, Semgrep, Snyk Code, Veracode Static Analysis)
- Claude Haiku 4.5: neither named
- Gemini 3.5 Flash: neither named (first choices: Aikido Security) (alternatives: GitHub Advanced Security, GitLab SAST, Semgrep, Snyk Code)
- Grok 4.1 Fast: neither named (first choices: Snyk Code) (alternatives: Semgrep, SonarQube)
- Mistral Small: neither named (first choices: Snyk) (alternatives: Aikido Security, Semgrep)
- DeepSeek V4 Flash: neither named (first choices: Snyk Code) (alternatives: CodeAnt.ai, Semgrep, SonarQube)
- Llama 4 Maverick: neither named (first choices: Snyk Code)
- Qwen 3.7 Flash: neither named (first choices: SonarQube) (alternatives: Semgrep, Snyk Code)
- Kimi K2: neither named (first choices: Semgrep, Snyk Code) (alternatives: SonarQube)
- MiniMax M2.5: neither named (first choices: Snyk) (alternatives: Semgrep, Veracode Static Analysis)
- GPT-6 Luna: neither named (first choices: Semgrep) (alternatives: GitHub Advanced Security, Snyk)
- Muse Glimmer 30B: neither named (first choices: SonarQube) (alternatives: Semgrep)

## What the models said about CodeQL

- "CodeQL requires a subscription to GitHub Advanced Security (GHAS), which is highly enterprise-focused and expensive" (Gemini 3.5 Flash, budget prompt, soft negative)
- "CodeQL (A "Proceed with Caution" for Closed-Source Teams) ... GHAS licensing, which can be prohibitively expensive" (Gemini 3.5 Flash, negative prompt, soft negative)
- "CodeQL if your stack or plan doesn't fit: Check language support before adopting it" (GPT-6 Luna, negative prompt, soft negative)
- "GitHub CodeQL – Free for public repositories ... It provides deep code analysis and is widely used for finding vulnerabilities in multiple languages." (Mistral Small, budget prompt, first choice)
- "my default recommendation would be GitHub CodeQL if you already use GitHub" (GPT-5.4 mini, paraphrase prompt, first choice)
- "Lower noise: CodeQL (semantic, low FPs)" (Grok 4.1 Fast, negative prompt, first choice)

## What the models said about Checkmarx One

- "What I'd Avoid for Mid-Market: Checkmarx and Veracode are powerful but typically start at $60K–$100K+/year" (Kimi K2, paraphrase prompt, hard negative)
- "be especially cautious with legacy configurations of Checkmarx, Fortify/OpenText, and SonarQube for security use cases unless they are heavily tuned" (Muse Glimmer 30B, negative prompt, soft negative)
- "Legacy enterprise | Checkmarx, Veracode, Fortify | Deep compliance... unless you have specific compliance requirements that mandate legacy tools" (Kimi K2, scale prompt, soft negative)
- "Checkmarx One (Gartner/Forrester Leader): Enterprise-grade with deep dataflow/taint analysis... Checkmarx for banks" (Grok 4.1 Fast, comparative prompt, first choice)
- "an enterprise-grade scanner (like Checkmarx or Fortify) for nightly full-scan reports and compliance auditing." (Qwen 3.7 Flash, comparative prompt, first choice)
- "Gartner Magic Quadrant Leader for 7 consecutive years... Best For: Large enterprises, portfolio-level governance" (Kimi K2, comparative prompt, first choice)

Share is the count of first choices across the direct, paraphrase, budget and scale prompts over all fourteen models, for a mid-market B2B company; rank is within the category. Comparisons are drawn for the top eight products in each category. Published under CC BY 4.0; the output is the models' output, and nothing here is a recommendation by the index.