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Static application security testing · October 2026 Edition

Snyk Code vs CodeQL

Four of fourteen models named Snyk Code first on the direct prompt; zero named CodeQL. Snyk Code was named by fourteen of the fourteen models and CodeQL by eleven and Snyk Code carries 41 labels and CodeQL 27, so the shares are not directly comparable.

Snyk Code

accepted challenger

Named in one category this edition.

CodeQL

accepted challenger

Named in one category this edition.

First-choice share14%4%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate2%11%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#3#5A position in a field of 10; printed, not drawn.
Labels4127A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Snyk Code reading right to left. Rank and label count are printed, not drawn.Semgrep was named alongside these two in twelve of the fourteen direct answers. Semgrep vs Snyk Code · Semgrep vs CodeQL · SonarQube vs Snyk Code

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; every quote names the model and the prompt it came from. Both figures come from the static application security testing page.

By framing

How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
Snyk CodeFirst choices, of fourteen modelsCodeQL
Direct401 against Snyk Code
Paraphrase31
Comparative30
Budget-constrained011 against CodeQL
Scale-constrained00
Negative012 against CodeQL
Bars are first choices, 0 to 14 each sideModels that argued againstA model can name both, so the two sides of a row do not sum to fourteen.

Across every category in the October 2026 Edition, Snyk Code and CodeQL were named in the same answer forty times, of the 118 answers naming Snyk Code and the 74 naming CodeQL. In those answers CodeQL took the first choice two times and Snyk Code five.

Every model, every framing

The eighty-four answers behind the chart above, one cell each: where Snyk Code and CodeQL stood in it.
ModelDirectCOParaphraseCOComparativeCOBudget-constrainedCOScale-constrainedCONegativeCO
Claude Haiku 4.5
GPT-5.4 miniCOCO
Gemini 3.5 FlashCOCO
Perplexity SonarCO
Grok 4.1 FastCOCOCO
Mistral SmallCO
DeepSeek V4 FlashCOCOCOCO
Llama 4 Maverick
Qwen 3.7 FlashCOCO
Kimi K2COCO
GLM 4.7 FlashX
MiniMax M2.5
GPT-6 LunaCOCOCOCO
Muse Glimmer 30BCOCO
Snyk CodeCO CodeQL first choice named as an alternative argued againstblank: not namedEach cell is one answer, Snyk Code on the left and CodeQL on the right.

The direct prompt

The plain question, one answer per model, grouped by where Snyk Code and CodeQL stood in it.

Snyk Code first, CodeQL not the choice

4 of 14 modelsCodeQL was named in the answer but not as the choice, or not at all.
Grok 4.1 FastSnyk Code alternatives: Semgrep, SonarQube
DeepSeek V4 FlashSnyk Code alternatives: CodeAnt.ai, Semgrep, SonarQube
Llama 4 MaverickSnyk Code
Kimi K2Semgrep, Snyk Code alternatives: SonarQube

Neither was the first choice, one was named

5 of 14 modelsThe answer put something else first and named one of the two as an alternative.
GPT-5.4 miniSemgrep alternatives: Checkmarx One, Snyk Code, Veracode Static Analysis
Gemini 3.5 FlashAikido Security alternatives: GitHub Advanced Security, GitLab SAST, Semgrep, Snyk Code
Perplexity SonarSemgrep alternatives: Checkmarx One, Snyk Code
Qwen 3.7 FlashSonarQube alternatives: Semgrep, Snyk Code
GLM 4.7 FlashXSonarQube alternatives: Checkmarx One, Codacy, DeepSource, GitHub Advanced Security, OpenText Fortify, Semgrep, Snyk Code, Veracode Static Analysis

Neither was named

5 of 14 modelsThe answer made no first choice from these two in this category.
Claude Haiku 4.5no first choice
Mistral SmallSnyk alternatives: Aikido Security, Semgrep
MiniMax M2.5Snyk alternatives: Semgrep, Veracode Static Analysis
GPT-6 LunaSemgrep alternatives: GitHub Advanced Security, Snyk
Muse Glimmer 30BSonarQube alternatives: Semgrep

Bold names in an answer are the products the judge labeled a first choice; a model naming several gives each of them that label. The full answer text for every row is in the record.

By buyer segment

The same question asked on behalf of a different buyer. Each standing is computed within its segment and they are never added together. The figures above are the mid-market standing, which is the one the category orders by.
Small business
Snyk Code leads by eleven points.
Snyk Code11%#3 of 13
CodeQL0%#8 of 13
The full small business standing →
Mid-marketThe figures above
Snyk Code leads by ten points.
Snyk Code14%#3 of 10
CodeQL4%#5 of 10
The full mid-market standing →
Enterprise
Snyk Code leads by two points.
Snyk Code4%#4 of 10
CodeQL2%#7 of 10
The full enterprise standing →

What the models said about Snyk Code

Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Four of five in this category shown.

“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

Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of seven in this category shown.

“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
Also compared
Semgrep vs Snyk CodeSemgrep vs CodeQLSonarQube vs Snyk CodeSonarQube vs CodeQLSnyk Code vs SnykSnyk Code vs GitHub Advanced SecuritySnyk Code vs Veracode Static AnalysisSnyk Code vs Checkmarx OneSnyk vs CodeQLCodeQL vs GitHub Advanced SecurityCodeQL vs Veracode Static AnalysisCodeQL vs Checkmarx OneAll twenty-eight head-to-head pages and the full SAST standing

Comparisons are drawn for the top eight products in each category, each against each. The output is the models' output; nothing here is a recommendation by the index.