Overview Leaderboard Methodology
Industry Report

API Documentation Platforms

Services that generate, host, version, and present interactive API documentation and developer portals to streamline integration.

Brands tracked: 35
Brands analyzed: 35
Last updated: 2026-04-22
Model: OpenAI GPT-5
Prompts: 265
Total responses: 1,380
Top Brand Overall?
ReadMe
85/100

Highest overall AI Visibility Score in this industry.

LBA Leader?
Swagger
87

Highest score on the LBA metric in this industry.

Authority Leader?
ReadMe
81

Highest score on the Authority metric in this industry.

TOM Leader?
ReadMe
96

Highest score on the TOM metric in this industry.

ReadMe is the default answer in AI responses for API Documentation Platforms

ReadMe and Stoplight together dominate AI responses for API Documentation Platforms. Both brands consistently surface unprompted, with the model treating them as the default answers for most category queries. Brands outside the top two face a structural disadvantage: users are usually given these two before the model even considers alternatives.

Brand Leaderboard All 35 API Documentation Platforms brands ranked

Ranked by overall AI Visibility Score (smoothed geometric mean of LBA, Authority, and TOM with an LBA-based floor on Authority and TOM, see methodology). Click any brand for the full report.

# Brand LBA Authority TOM Overall
1
ReadMe
readme.com
 78
81
96
85
2
Stoplight
stoplight.io
 77
65
94
78
3
Redocly
redocly.com
 67
51
76
64
4
SwaggerHub
swaggerhub.com
 79
33
67
56
5
Swagger
swagger.io
 87
54
32
54
6
Postman
postman.com
 86
27
62
53
7
GitBook
gitbook.com
 78
12
29
31
8
Mintlify
mintlify.com
 70
16
23
30
9
Docusaurus
docusaurus.io
 80
8
23
27
10
Apigee
apigee.com
 87
9
12
23
11
Kong
konghq.com
 77
8
12
21
12
Microsoft Azure
azure.microsoft.com
 84
8
8
20
13
MuleSoft
mulesoft.com
 80
8
8
20
14
Read the Docs
readthedocs.io
 82
8
8
20
15
MkDocs
mkdocs.org
 76
8
8
19
16
APIMatic
apimatic.io
 68
7
7
17
17
Tyk
tyk.io
 71
7
7
17
18
Apiary
apiary.io
 66
7
7
16
19
Backstage
backstage.io
 64
6
6
16
20
Fern
buildwithfern.com
 53
5
10
16
21
Bump.sh
bump.sh
 62
6
6
15
22
Document360
document360.com
 61
6
6
15
23
Gravitee
gravitee.io
 60
6
6
15
24
S
Slate
slatedocs.com
 51
5
6
14
25
Stainless
stainless.com
 50
5
5
13
26
Archbee
archbee.com
 48
5
5
12
27
liblab
liblab.com
 36
4
4
10
28
Speakeasy
speakeasy.com
 40
4
4
10
29
Zuplo
zuplo.com
 37
4
4
10
30
Scalar
scalar.com
 33
3
3
9
31
Theneo
theneo.io
 32
3
3
8
32
DapperDox
dapperdox.io
 23
2
2
7
33
Konfig
konfigthis.com
 14
1
1
4
34
Nextra
nextra.site
 14
1
1
4
35
DeveloperHub
developerhub.io
 7
1
1
2

How is this calculated? Methodology

Every brand in this leaderboard is scored against the same set of 265 shared API Documentation Platforms prompts. The same prompts, same model, same iterations. So differences in scores reflect actual differences in AI visibility, not differences in measurement.

Overall AI Visibility Score
Smoothed geometric mean of LBA, Authority and TOM. Authority and TOM are floored at LBA × 0.1 before the geometric mean (the same floor used in the per-metric cards above, so brand cards and the composite tell the same story). Formula: composite = ((LBA + 5)(Authority + 5)(TOM + 5))^(1/3) - 5. The floor keeps brands the model clearly recognises but doesn't yet recommend from collapsing to zero, while a single genuinely weak metric still pulls the composite down. Full methodology.
Shared industry prompts
For Authority and TOM, all brands in the industry are scored against the same 265 category prompts (e.g. "best SEO tools for agencies"). This makes brand-to-brand comparisons valid - everyone faces identical inputs. LBA prompts are per-brand because they ask brand-specific questions.
Latent Brand Association (LBA)
5 brand probes + 1 control prompt, each run 5 times in recall mode (no web search). LBA = quality × meta × stability × share × recognition × 100. Read the full LBA methodology →
LLM Authority
50 organic category prompts (discovery, comparison, problem and transactional intents), each run once in recall mode and once in retrieval mode. Score = frequency × log-decayed prominence × intent weight, then 50/50 averaged across the two modes. Read the full Authority methodology →
Top of Mind (TOM)
15 high-volume discovery prompts (sourced from Keywords Everywhere search-volume data), each run 5 times in pure recall mode (no web). Score = frequency × (0.5 + 0.5 × log-prominence), volume-weighted. Read the full TOM methodology →