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Developer documentation platforms

ProductivityAugust 2026 edition

Which questions do you win vs lose?

Get your brand’s prompt-level results

Recommendation share

How often each brand is named, across unbranded questions and all five engines

BRANDS

SHAREShare of answers naming this brand, averaged across the five engines

ChatGPTChatGPTShare of ChatGPT answers that name this brand
ClaudeClaudeShare of Claude answers that name this brand
GeminiGeminiShare of Gemini answers that name this brand
PerplexityPerplexityShare of Perplexity answers that name this brand
Google AI OverviewsGoogle AI OverviewsShare of Google AI Overviews answers that name this brand
GitBook
73.1
92.6
59.3
63.9
86.0
Docusaurus
54.6
88.9
79.6
60.2
49.5
Mintlify
48.1
92.6
40.7
57.4
62.6
4
ReadMe
60.2
61.1
54.6
32.4
36.4
5
Redocly
36.1
75.0
45.4
24.1
25.2
6
Stoplight
15.7
35.2
17.6
13.0
15.0
7
Document360
7.4
25.0
6.5
17.6
29.9
8
Archbee
0.0
3.7
10.2
2.8
10.3
How we measurePowered by daydream

Every question changed this edition. The previous editions asked a set we wrote for the category; this one asks questions derived from what buyers actually searched or posted, so a month-over-month figure would compare two different questions and read as movement. Comparison returns next edition, when there are two mined runs to set against each other.

Framing sensitivity

The same question asked at each company size

Share of answers naming the brand

SMB50-person startupENTERPRISE2,000-person companyDocusaurus60.6%40.0%ReadMe59.4%56.7%Mintlify73.3%45.6%Document36011.7%30.6%Redocly30.6%45.0%Stoplight16.1%25.0%GitBook77.8%69.4%Archbee6.7%7.2%

BRANDS

SMBShare of answers naming this brand when the question is asked at this company size50-person startup

ENTERPRISEShare of answers naming this brand when the question is asked at this company size2,000-person company

SPREADThe gap between the highest and lowest column in this row, in percentage points

Mintlify
73.3%
45.6%
27.8%
Docusaurus
60.6%
40.0%
20.6%
Document360
11.7%
30.6%
18.9%
Redocly
30.6%
45.0%
14.4%
Stoplight
16.1%
25.0%
8.9%
GitBook
77.8%
69.4%
8.3%
ReadMe
59.4%
56.7%
2.8%
Archbee
6.7%
7.2%
0.6%
How we measurePowered by daydream

Model divergence

The engines that name a brand least and most often, and the gap between them

BRANDS

LOWEST ENGINEThe engine that names this brand least often, and its share

HIGHEST ENGINEThe engine that names this brand most often, and its share

SPREADThe gap between the highest and lowest column in this row, in percentage points

Mintlify
40.7GeminiGemini
92.6ClaudeClaude
51.9
Redocly
24.1PerplexityPerplexity
75.0ClaudeClaude
50.9
Docusaurus
49.5Google AI OverviewsAI Overviews
88.9ClaudeClaude
39.4
GitBook
59.3GeminiGemini
92.6ClaudeClaude
33.3
ReadMe
32.4PerplexityPerplexity
61.1ClaudeClaude
28.7
Document360
6.5GeminiGemini
29.9Google AI OverviewsAI Overviews
23.4
Stoplight
13.0PerplexityPerplexity
35.2ClaudeClaude
22.2
Archbee
0.0ChatGPTChatGPT
10.3Google AI OverviewsAI Overviews
10.3
How we measurePowered by daydream

Citation trail

The sites the engines linked to when they answered

SOURCES

REACHShare of all answers that cited this source at least once% of all answers

LINKSEvery link to this source, counted across all answerstotal

DEPTHLinks to this source per answer that cited itlinks/answer

mintlify.com
887
1.9
gitbook.com
734
1.8
ferndesk.com
493
2.1
reddit.com
227
1.2
docsio.co
247
1.7
github.com
237
2.1
featurebase.app
178
1.6
redocly.com
160
1.5
clickhelp.com
119
1.1
atlassian.com
110
1.0
buildwithfern.com
169
1.7
docsie.io
174
1.8
dev.to
131
1.3
readme.com
102
1.2
herothemes.com
166
1.9
apidog.com
129
1.5
documentation.ai
96
1.2
happysupport.ai
192
2.4
docusaurus.io
87
1.2
youtube.com
118
1.7
How we measurePowered by daydream

Which questions do you win vs lose?

Get your brand’s prompt-level results

How we measure this3 of 12 questions published

We write a fixed set of category questions, none of which names a brand, and count how often each brand comes up. A brand is present in an answer only when one of its names literally appears in the text, because a string match cannot hallucinate.

Every question starts from one somebody already asked. Some come from search demand, where the figure is how many people typed that phrasing in a month. The rest come from a public forum post, and we link to the page it was written on. We rewrite each one into a plain question, because a search string is a fragment and a forum post is written the way people type, and neither is a fair thing to ask an engine. We change the wording and never the subject, and the original is published beside every question we disclose. The set is frozen before a single answer is collected, so every engine and every edition is asked exactly the same thing.

12questions scored
3buyer framings each
36prompts run
5engines

The 3 we publish, of 12

Chosen to span both where the questions come from and how they are shaped, so the sample describes the battery rather than one corner of it. That is 25% of the questions the ranking comes from.

  • What free or low-cost documentation software would you recommendas asked: Any recommendations on free or low cost documentation software?reddit.comuseCaseconsideration
  • What is the best technical documentation softwareas asked: best technical documentation software40/mocategoryawareness
  • How do you create your documentation pages for your projectsas asked: How do you create your documentation pages for your projects?reddit.compainconsideration

Each is asked 3 ways

  • for a 50-person startup50-person startup
  • for a 2,000-person enterprise2,000-person company
  • at the lowest cost or free, including open-source optionsCheapest / free

Why the rest stays private

A published battery invites brands to write pages against the exact wording, at which point a score moves without the brand’s actual standing moving and the measurement stops describing anything. Benchmark suites keep a held-out set for the same reason. The method is public so it can be judged, a quarter of the questions are public so it can be checked, and the rest stays private so the numbers stay worth checking.

What the numbers are, and aren’t

  • The August 2026 edition is one dated run, not a rolling average.
  • Share is averaged across the engines rather than pooled, so an engine that returned fewer answers cannot look like a brand losing ground.
  • A move is marked only when a two-proportion test puts it outside what the sample size can explain. A few points between neighbouring brands is a tie.
  • Rankings reflect how often AI engines name a brand. They are not endorsements by daydream.
  • 16 further questions name competitors directly. None of them feeds the ranking, because a question that already names the contenders cannot measure who gets recommended. They do count towards the citation trail, which is measured over every answer we collected rather than the unbranded ones alone.