# Snyk Code vs CodeQL: which do AI models recommend for SAST, October 2026

IT AI Recommendation Index, October 2026 Edition, Static application security testing. Four of fourteen models named Snyk Code first on the direct prompt; zero named CodeQL. Page: https://it-ai-index.com/developer/static-application-security-testing/snyk-code-vs-codeql/

| | First-choice share | Rank | Negative rate | Labels | Models naming it |
|---|---|---|---|---|---|
| Snyk Code | 14% | #3 of 10 | 2% | 41 | 14 of 14 |
| CodeQL | 4% | #5 of 10 | 11% | 27 | 11 of 14 |

## The direct prompt, model by model

- Grok 4.1 Fast: snyk code first (first choices: Snyk Code) (alternatives: Semgrep, SonarQube)
- DeepSeek V4 Flash: snyk code first (first choices: Snyk Code) (alternatives: CodeAnt.ai, Semgrep, SonarQube)
- Llama 4 Maverick: snyk code first (first choices: Snyk Code)
- Kimi K2: snyk code first (first choices: Semgrep, Snyk Code) (alternatives: SonarQube)
- GPT-5.4 mini: neither first, one named (first choices: Semgrep) (alternatives: Checkmarx One, Snyk Code, Veracode Static Analysis)
- Gemini 3.5 Flash: neither first, one named (first choices: Aikido Security) (alternatives: GitHub Advanced Security, GitLab SAST, Semgrep, Snyk Code)
- Perplexity Sonar: neither first, one named (first choices: Semgrep) (alternatives: Checkmarx One, Snyk Code)
- Qwen 3.7 Flash: neither first, one named (first choices: SonarQube) (alternatives: Semgrep, 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
- Mistral Small: neither named (first choices: Snyk) (alternatives: Aikido Security, Semgrep)
- 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 Snyk Code

- "Choose Snyk Code only if you already use Snyk Open Source/Container SCA...The limitation is fewer supported languages than SonarQube/Semgrep and no custom rule authoring." (Muse Glimmer 30B, direct prompt, soft negative)
- "I'd recommend Snyk Code as a top choice based on recent analyst reports (Forrester Leader), reviews, and pricing." (Grok 4.1 Fast, direct prompt, first choice)
- "Choose Snyk Code if your primary goal is developer adoption and you want a frictionless, speedy security tool" (Gemini 3.5 Flash, comparative prompt, first choice)
- "using a developer-first tool (like Snyk or GitHub Advanced Security) for fast, early feedback during coding" (Qwen 3.7 Flash, comparative prompt, first choice)

## 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)

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.