# CodeQL vs Veracode Static Analysis: 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 Veracode Static Analysis. Page: https://it-ai-index.com/developer/static-application-security-testing/codeql-vs-veracode-static-analysis/

| | First-choice share | Rank | Negative rate | Labels | Models naming it |
|---|---|---|---|---|---|
| CodeQL | 4% | #5 of 10 | 11% | 27 | 11 of 14 |
| Veracode Static Analysis | 2% | #7 of 10 | 28% | 39 | 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)
- 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)
- MiniMax M2.5: neither first, one named (first choices: Snyk) (alternatives: Semgrep, 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)
- Perplexity Sonar: neither named (first choices: Semgrep) (alternatives: Checkmarx One, 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)
- 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 Veracode Static Analysis

- "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)
- "Full scans can delay fast-moving dev cycles, there's no on-premise option with code upload raising security concerns, and pricing can be steep" (Claude Haiku 4.5, negative prompt, soft negative)
- "not necessarily the one with the most heavy-duty enterprise compliance rules (like legacy tools Veracode or Fortify)" (Gemini 3.5 Flash, direct prompt, soft negative)
- "the recommended static code security scanner for a mid-sized B2B company could be either Checkmarx or Veracode" (Llama 4 Maverick, paraphrase prompt, first choice)
- "Excels in broad language/framework support (including legacy/binary), whole-app analysis, and enterprise integrations" (Grok 4.1 Fast, comparative prompt, alternative)
- "Highly regulated environments, on-premises requirements, or teams needing very deep taint analysis." (GLM 4.7 FlashX, direct prompt, alternative)

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.