Overview Leaderboard Methodology
Industry Report

Prototyping Platforms

Tools that let designers create high-fidelity, interactive prototypes for web and mobile apps to validate flows and interactions before development.

Brands tracked: 37
Brands analyzed: 37
Last updated: 2026-04-22
Model: OpenAI GPT-5
Prompts: 282
Total responses: 1,460
Top Brand Overall?
Figma
96/100

Highest overall AI Visibility Score in this industry.

LBA Leader?
Bubble
89

Highest score on the LBA metric in this industry.

Authority Leader?
Figma
100

Highest score on the Authority metric in this industry.

TOM Leader?
Figma
100

Highest score on the TOM metric in this industry.

Figma is the default answer in AI responses for Prototyping Platforms

When users ask ChatGPT, Claude, or Gemini about Prototyping Platforms, Figma is the brand that surfaces first - unprompted, consistently, and usually at the top of any list the model generates. Framer is a close second, but the gap between them is meaningful. If you're competing in this space and you're not in the top handful, you're effectively invisible to AI-driven discovery.

Brand Leaderboard All 37 Prototyping 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
Figma
figma.com
 88
100
100
96
2
Framer
framer.com
 75
76
93
81
3
Axure
axure.com
 79
51
76
67
4
ProtoPie
protopie.io
 67
54
74
64
5
Adobe
adobe.com
 87
14
62
44
6
Sketch
sketch.com
 76
15
62
43
7
I
InVision
invisionwfm.com
 67
7
43
29
8
Marvel
marvelapp.com
 83
8
23
27
9
Webflow
webflow.com
 86
9
18
25
10
Bubble
bubble.io
 89
9
11
23
11
Balsamiq
balsamiq.com
 72
7
11
20
12
Miro
miro.com
 82
8
8
20
13
Raspberry Pi
raspberrypi.com
 85
8
8
20
14
UXPin
uxpin.com
 75
8
10
20
15
Retool
retool.com
 80
8
8
19
16
FlutterFlow
flutterflow.io
 74
7
7
18
17
Uizard
uizard.io
 72
7
7
18
18
Maze
maze.co
 71
7
7
17
19
Origami Studio
origami.design
 63
6
6
16
20
Penpot
penpot.app
 66
7
7
16
21
Justinmind
justinmind.com
 57
6
7
15
22
MockFlow
mockflow.com
 58
6
6
15
23
Moqups
moqups.com
 61
6
6
15
24
Fluid UI
fluidui.com
 55
5
5
14
25
Mockplus
mockplus.com
 56
6
6
14
26
Proto.io
proto.io
 58
6
6
14
27
Flinto
flinto.com
 52
5
5
13
28
P
Principle
principleformac.com
 50
5
5
13
29
Visily
visily.ai
 47
5
5
12
30
Wondershare Mockitt
mockitt.com
 48
5
5
12
31
Pidoco
pidoco.com
 42
4
4
11
32
Pixso
pixso.net
 43
4
4
11
33
Prott
prottapp.com
 38
4
4
10
34
H
HotGloo
hotgloo.com
 33
3
3
9
35
Quant-UX
quant-ux.com
 30
3
3
8
36
Kite Compositor
kiteapp.co
 4
0
0
2
37
UX-App
ux-app.com
 0
0
0
0

How is this calculated? Methodology

Every brand in this leaderboard is scored against the same set of 282 shared Prototyping 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 282 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 →