Every category where a model named CodeQL for a mid-market B2B company. Share is first choices across the direct, paraphrase, budget and scale prompts; rank is within every product named in that category.

Category	Function	Share	Rank	Negative rate	Labels	Quadrant
Static application security testing	Developer platform	2%	6 of 47	8%	26	accepted challenger

Movement

This is the first edition on this tier, so no move can be computed for CodeQL yet. From the next edition this section shows, per buyer segment, whether its share moved by more than the measured noise floor.

By model

How each model treated CodeQL across every prompt where it was named for a mid-market B2B company. Twelve models, six prompts per category.

Model	First choice	Alternative	Mention	Negative	Labels
Claude Haiku 4.5	0	0	1	0	1
GPT-5.4 mini	1	1	1	0	3
Gemini 3.5 Flash	0	1	0	0	1
Perplexity Sonar	0	1	0	1	2
Grok 4.1 Fast	1	2	1	0	4
Mistral Small	0	1	1	0	2
DeepSeek V4 Flash	0	3	1	1	5
Llama 4 Maverick	0	0	1	0	1
Qwen 3.7 Flash	0	0	0	0	0
Kimi K2	1	2	0	0	3
GLM 4.7 FlashX	0	2	0	0	2
MiniMax M2.5	0	2	0	0	2

By framing

Which of the six questions produced the naming. By model says how often; this says asked what. The first-choice count on the right carries the marks of the models that produced it.

FramingLabels by classFirst choices

Direct3 labelsNone

Paraphrase6 labelsNone

Comparative20 labels1not counted in share

Budget-constrained13 labels1

Scale-constrained3 labelsNone

Negative18 labels3not counted in share

First choiceAlternativeMentionNegative63 labels in all, every segment counted; 1 of the 5 first choices count toward share, since the comparative and negative framings do not. The bar is one segment per label class, to scale within the framing.

What the models said for it

Verbatim evidence the judge attached to positive labels.

“Look for modern alternatives - Tools with semantic analysis (like CodeQL) show lower false positive rates” Kimi K2 · SAST · negative prompt · first choice

“the best default choice is usually GitHub CodeQL if your code is already on GitHub” GPT-5.4 mini · SAST · budget prompt · first choice

“Prioritize reachability/taint analysis (CodeQL, Veracode <1-10% FP tuned)” Grok 4.1 Fast · SAST · negative prompt · first choice

“Powerful semantic analysis, backed by GitHub/Microsoft... Best for: Teams already using GitHub” Kimi K2 · SAST · budget prompt · alternative

And against it

Verbatim evidence attached to negative labels. A warning on a product with few labels is a warning; on a product with many, it is one voice among them.

“CodeQL is powerful, but Checkmarx notes it requires expertise to write queries” Perplexity Sonar · SAST · negative prompt · soft negative

“no steep query-DSL learning curve like CodeQL” DeepSeek V4 Flash · SAST · budget prompt · soft negative

Named alongside

The products named in the same answers as CodeQL, over the 63 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and CodeQL was named but was not.

ProductSame answerTook the first choice insteadHead to head

Semgrep59 of 6332Not in the top three

SonarQube52 of 635Not in the top three

Snyk Code44 of 634Not in the top three

Checkmarx One42 of 638Not in the top three

Veracode33 of 633Not in the top three

OpenText Fortify29 of 630Not in the top three

Bandit19 of 630Not in the top three

Aikido Security15 of 630Not in the top three

Brakeman12 of 630Not in the top three

Black Duck Coverity11 of 630Not in the top three

A head-to-head page exists where both products are in a category's top three. The other rows are the same fact without a page behind them, so they link to the product instead.

What carried it into the answer

The sites and pages cited by the answers that named CodeQL. A fact about retrieval, not a lever on the model.

Citations exist only for the models that return a source list, four of the twelve in this edition, so these counts come from 57 of the 63 answers that named CodeQL and are not a share of its labels.

Domains cited

appsecsanta.com37

corgea.com33

dev.to33

checkmarx.com29

zeropath.com28

ox.security24

pixee.ai24

endorlabs.com23

augmentcode.com21

safeguard.sh21

273 of the 273 domain citations in answers naming CodeQL came from somebody else's page.

Pages cited

dev.to/rahulxsingh/i-reviewed-32-sast-tools-the-ones-worth-using-283e28

zeropath.com/blog/best-sast-tools28

appsecsanta.com/sast-tools27

corgea.com/learn/best-sast-tools27

ox.security/blog/static-application-security-sast-tools24

checkmarx.com/learn/sast/open-source-vs-premium-sast-tools23

pixee.ai/blog/best-sast-tools-202619

endorlabs.com/learn/best-sast-tools18

Pages are listed as the models cited them.

Names read as CodeQL

What the judge wrote, as written, with how often. The vendor table decides that these count as CodeQL; a claim can dispute any of them.

GitHub CodeQL 6CodeQL (GitHub Advanced Security) 1CodeQL (GitHub) 1GitHub CodeQL (Advanced Security) 1

Is this your product?

Claim this page

Claiming is free and changes nothing in the data. A claimed page shows a verified contact who is told when each edition publishes and when CodeQL's standing changes by more than the noise floor; the right to propose corrections to the vendor table, meaning names the judge wrote that should or should not read as CodeQL, applied by version and listed in the change log; and a one-line description supplied by the vendor and marked as such.

It does not get any change to labels, shares or verdicts, any preview, or any say over which quotes appear. A verification link goes to your work email; an address at codeql.com is approved on the spot, any other address is reviewed by hand.

Your name and company appear on the claimed page, or the company alone if you ask below. A title and a LinkedIn address appear there too if you give them, and are left off if you do not. Your email address is never published.