Index › Products › WikiLeaks · October 2026 Edition

1 category · Named, not ranked

WikiLeaks

3Judge labels

0First choices

2Negative labels

3 / 14Models named it

1Category

October 2026 Edition. Every number here is derived from the raw labels under vendor table v2026-10.7, every buyer segment counted.

Standing

2 labels, too few to rank

A product needs 10 labels in a category before a share or quadrant is stated. WikiLeaks was named 2 times in Whistleblowing, where FaceUp led with 25%. The labels and the evidence are below, counted exactly.

By buyer segmentRead the same way at every buyer size.

Small businessnot named

No model named it for this buyer.

Mid-market78 of 107

under the label threshold

Enterprise85 of 88

under the label threshold

In whistleblowing · each standing computed within its segment · bars are 0 to 100 · the accent bar is the product's own best reading

Standing by category

Every category where a model named WikiLeaks 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	Since September 2026
Whistleblowing and investigations	Core HR and payroll	0%	78 of 107	50%	2	under 10 labels · led by FaceUp at 25%

Movement

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

By model

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

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Model	Alternative	Negative	Labels
Claude Haiku 4.5	0	0	0
GPT-5.4 mini	0	0	0
Gemini 3.5 Flash	0	0	0
Perplexity Sonar	0	0	0
Grok 4.1 Fast	0	0	0
Mistral Small	0	0	0
DeepSeek V4 Flash	0	0	0
Llama 4 Maverick	0	0	0
Qwen 3.7 Flash	0	0	0
Kimi K2	0	1	1
GLM 4.7 FlashX	0	0	0
MiniMax M2.5	1	0	1
GPT-6 Luna	0	0	0
Muse Glimmer 30B	0	0	0

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

Direct0 labelsNone

Paraphrase0 labelsNone

Comparative1 labelNone

Budget-constrained0 labelsNone

Scale-constrained0 labelsNone

Negative2 labelsNone

First choiceAlternativeMentionNegative3 labels in all, every segment counted; 0 of the 0 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.

“For document publication & global exposure: WikiLeaks” MiniMax M2.5 · Whistleblowing · comparative 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.

“Platforms with Significant Concerns ... WikiLeaks - Red flags: Allegations of working with or being exploited by foreign governments” Kimi K2 · Whistleblowing · negative prompt · hard negative

Named alongside

The products named in the same answers as WikiLeaks, over the 3 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and WikiLeaks was named but was not.

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ProductSame answerTook the first choice insteadHead to head

SecureDrop2 of 32Not among the top eight

GlobaLeaks2 of 31Not among the top eight

Formalize1 of 30Not among the top eight

OpenLeaks1 of 30Not among the top eight

Signal1 of 30Not among the top eight

A head-to-head page exists where both products are among a category's top eight. 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 WikiLeaks. A fact about retrieval, not a lever on the model.

Citations exist only for the models that return a source list, five of the fourteen in this edition, so these counts come from 3 of the 3 answers that named WikiLeaks and are not a share of its labels.

Domains cited

ceur-ws.org3

darkowl.com2

gijn.org2

giz.de2

knowledgehub.transparency.org2

torwiki.org2

academic.oup.com1

bigideasdb.com1

brookings.edu1

cambridge.org1

No domain is on file for WikiLeaks, so its own site is not marked.

Pages cited

ceur-ws.org/Vol-2749/paper5.pdf3

darkowl.com/blog-content/whistleblower-sites-1012

giz.de/en/downloads/giz2023-en-overview-of-whistleblowing-software.pdf2

torwiki.org/services/whistleblowing2

academic.oup.com/oxford-law-pro/book/57814/chapter/4713027091

bigideasdb.com/problems/whistleblowing-problems1

brookings.edu/articles/how-to-combat-fake-news-and-disinformation1

cambridge.org/core/journals/american-journal-of-international-law/article/ethical-consider1

Pages are listed as the models cited them.

Follow WikiLeaks

An email the morning each edition publishes: where this product moved, where it held, and by how much against the noise floor. One address, confirmed by a click; a stop link in every email.

Already following? Everything you follow, with a stop for each.

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 WikiLeaks'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 WikiLeaks, applied by version and listed in the change log; and a one-line description supplied by the vendor and marked as such.

What a new claim receivesHide what a new claim receives

A new claim receives the current edition's vendor brief for WikiLeaks by email, built from the raw record of the edition. It shows:

where WikiLeaks is named, by buyer and by framing, and which cells hold its first choices;
the claims the models make when they name it, ranked, with the strongest and the weakest quoted;
its vocabulary against the segment leader's, and the pages the models cited;
who was chosen in the answers that did not name WikiLeaks, and every reason the record gives;
a battlecard for each top rival: the head-to-head split, why they win, and the reservation quoted against them;
one page of published figures cleared to show a buyer.