Index › Products › Read.me · October 2026 Edition

1 category · Named, not ranked

Read.me

1Judge labels

0First choices

0Negative labels

1 / 14Models named it

1Category

October 2026 Edition. Every number here is derived from the raw labels under vendor table v2026-10.8, every buyer segment counted.

Standing

1 label, too few to rank

A product needs 10 labels in a category before a share or quadrant is stated. Read.me was named 1 time in IT documentation, where Hudu led with 17%. 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-market67 of 92

under the label threshold

Enterprisenot named

No model named it for this buyer.

In it documentation · 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 Read.me 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
IT documentation	IT operations and endpoint	0%	67 of 92	0%	1	under 10 labels · led by Hudu at 17%

Movement

This is the first edition on this tier, so no move can be computed for Read.me 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 Read.me across every prompt where it was named for a mid-market B2B company. Fourteen models, six prompts per category.

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

Negative0 labelsNone

First choiceAlternativeMentionNegative1 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.

No positive label carried a quote.

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.

No model argued against it.

Named alongside

The products named in the same answers as Read.me, over the 1 answer that named it. Took the first choice instead counts the answers where the other product was the first choice and Read.me was named but was not.

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ProductSame answerTook the first choice insteadHead to head

Confluence1 of 10Not among the top eight

Document3601 of 10Not among the top eight

GitBook1 of 10Not among the top eight

Mintlify1 of 10Not among the top eight

ProProfs Knowledge Base1 of 10Not 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 Read.me. 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 1 of the 1 answers that named Read.me and are not a share of its labels.

Domains cited

aimultiple.com1

capterra.co.uk1

document360.com1

eyer.ai1

faddom.com1

gitbook.com1

helpdocs.io1

linkedin.com1

ones.com1

proprofskb.com1

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

Pages cited

aimultiple.com/it-documentation-software1

capterra.co.uk/directory/33755/it-documentation/software1

document360.com/blog/it-documentation1

document360.com/blog/it-documentation-software1

eyer.ai/blog/10-best-it-documentation-software-20241

faddom.com/it-documentation-software1

gitbook.com/blog/best-documentation-collaboration-tools1

helpdocs.io/knowledge-base-comparison/confluence-alternative1

Pages are listed as the models cited them.

Follow Read.me

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 Read.me'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 Read.me, 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 Read.me by email, built from the raw record of the edition. It shows:

where Read.me 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 Read.me, 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.