# Semgrep vs Snyk Code: 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 Semgrep first on the direct prompt; four named Snyk Code. Page: https://it-ai-index.com/developer/static-application-security-testing/semgrep-vs-snyk-code/

| | First-choice share | Rank | Negative rate | Labels | Models naming it |
|---|---|---|---|---|---|
| Semgrep | 36% | #1 of 10 | 5% | 63 | 14 of 14 |
| Snyk Code | 14% | #3 of 10 | 2% | 41 | 14 of 14 |

## The direct prompt, model by model

- Kimi K2: both first (first choices: Semgrep, Snyk Code) (alternatives: SonarQube)
- GPT-5.4 mini: semgrep first (first choices: Semgrep) (alternatives: Checkmarx One, Snyk Code, Veracode Static Analysis)
- Perplexity Sonar: semgrep first (first choices: Semgrep) (alternatives: Checkmarx One, Snyk Code)
- GPT-6 Luna: semgrep first (first choices: Semgrep) (alternatives: GitHub Advanced Security, Snyk)
- Grok 4.20: 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)
- Gemini 3.5 Flash: neither first, one named (first choices: Aikido Security) (alternatives: GitHub Advanced Security, GitLab SAST, Semgrep, Snyk Code)
- Mistral Small: neither first, one named (first choices: Snyk) (alternatives: Aikido Security, Semgrep)
- 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)
- MiniMax M2.5: neither first, one named (first choices: Snyk) (alternatives: Semgrep, Veracode Static Analysis)
- Muse Glimmer 30B: neither first, one named (first choices: SonarQube) (alternatives: Semgrep)
- Claude Haiku 4.5: neither named

## What the models said about Semgrep

- "Semgrep OSS and Bandit are fast and low false-positive for obvious patterns, but miss vulnerabilities that span multiple functions... insufficient as sole nightly security scanners" (Muse Glimmer 30B, negative prompt, soft negative)
- "Semgrep Community Edition as your only deep security scanner: It can be useful for fast checks and custom rules, but its analysis is limited to a file/function context" (GPT-6 Luna, negative prompt, soft negative)
- "Checkmarx, Veracode, and Semgrep have all struggled with this because their rule-based engines flag anything that could be a vulnerability" (Claude Haiku 4.5, negative prompt, soft negative)
- "Semgrep as the primary scanner across all languages... For most budget-constrained companies, Semgrep OSS + SonarQube Community Edition... is the most commonly recommended starting point" (Muse Glimmer 30B, budget prompt, first choice)
- "Tools like Semgrep are generally preferred for modern CI/CD because they are lightweight, customizable, and run significantly faster." (GLM 4.7 FlashX, negative prompt, first choice)
- "look for developer-friendly alternatives like Semgrep, where rules are written in YAML and read like standard code" (Gemini 3.5 Flash, negative prompt, first choice)

## 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 4.20, 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)

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.