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App store optimization tools · October 2026 Edition

AppFollow vs ASOMobile

Zero of fourteen models named AppFollow first on the direct prompt; zero named ASOMobile. AppFollow was named by fourteen of the fourteen models and ASOMobile by nine and AppFollow carries 47 labels and ASOMobile 11, so the shares are not directly comparable.

AppFollow

accepted challenger

Named in three categories this edition.

ASOMobile

accepted challenger

Named in two categories this edition.

First-choice share7%5%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate2%9%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#3#7A position in a field of 9; printed, not drawn.
Labels4711A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, AppFollow reading right to left. Rank and label count are printed, not drawn.AppTweak was named alongside these two in twelve of the fourteen direct answers. AppTweak vs AppFollow · AppTweak vs ASOMobile · Appfigures vs AppFollow

Share is the count of first choices across the direct, paraphrase, budget and scale prompts over all fourteen models, for a mid-market B2B company; rank is within the category; every quote names the model and the prompt it came from. Both figures come from the app store optimization tools page.

By framing

How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
AppFollowFirst choices, of fourteen modelsASOMobile
Direct00
Paraphrase20
Comparative00
Budget-constrained231 against ASOMobile
Scale-constrained00
Negative201 against AppFollow
Bars are first choices, 0 to 14 each sideModels that argued againstA model can name both, so the two sides of a row do not sum to fourteen.

Across every category in the October 2026 Edition, AppFollow and ASOMobile were named in the same answer eighteen times, of the 136 answers naming AppFollow and the 30 naming ASOMobile. In those answers ASOMobile took the first choice five times and AppFollow five.

Every model, every framing

The eighty-four answers behind the chart above, one cell each: where AppFollow and ASOMobile stood in it.
ModelDirectParaphraseComparativeBudget-constrainedScale-constrainedNegative
Claude Haiku 4.5
GPT-5.4 mini
Gemini 3.5 Flash
Perplexity Sonar
Grok 4.1 Fast
Mistral Small
DeepSeek V4 Flash
Llama 4 Maverick
Qwen 3.7 Flash
Kimi K2
GLM 4.7 FlashX
MiniMax M2.5
GPT-6 Luna
Muse Glimmer 30B
AppFollow ASOMobile first choice named as an alternative argued againstblank: not namedEach cell is one answer, AppFollow on the left and ASOMobile on the right.

The direct prompt

The plain question, one answer per model, grouped by where AppFollow and ASOMobile stood in it.

Neither was the first choice, one was named

9 of 14 modelsThe answer put something else first and named one of the two as an alternative.
Claude Haiku 4.5AppTweak, Appfigures alternatives: AppFollow, Lengreo, Sensor Tower
Gemini 3.5 FlashAppTweak alternatives: App Radar, AppFollow, MobileAction
Grok 4.1 FastAppTweak alternatives: App Radar, AppFollow, Mobile Action
Mistral SmallGuideflow alternatives: AppFollow, Applytics, MobileAction
Llama 4 MaverickApplytics alternatives: AppFollow, AppTweak, Guideflow
Qwen 3.7 FlashApp Radar, AppTweak alternatives: AppFollow
Kimi K2AppTweak alternatives: AppFollow, MobileAction
GLM 4.7 FlashXAppTweak alternatives: App Radar, AppFollow, Appfigures
MiniMax M2.5App Radar, AppTweak alternatives: ASO.dev, AppFollow, MobileAction

Neither was named

5 of 14 modelsThe answer made no first choice from these two in this category.
GPT-5.4 miniAppTweak alternatives: App Radar, MobileAction, SplitMetrics Optimize
Perplexity SonarAppTweak alternatives: MobileAction
DeepSeek V4 FlashAppTweak alternatives: App Follow, MobileAction
GPT-6 LunaMobileAction ASO Intelligence Pro alternatives: AppTweak Grow
Muse Glimmer 30BAppTweak alternatives: App Radar, Applytics

Bold names in an answer are the products the judge labeled a first choice; a model naming several gives each of them that label. The full answer text for every row is in the record.

By buyer segment

The same question asked on behalf of a different buyer. Each standing is computed within its segment and they are never added together. The figures above are the mid-market standing, which is the one the category orders by.
Small business
AppFollow leads by four points.
AppFollow11%#3 of 11
ASOMobile7%#6 of 11
The full small business standing →
Mid-marketThe figures above
AppFollow leads by two points.
AppFollow7%#3 of 9
ASOMobile5%#7 of 9
The full mid-market standing →
Enterprise
AppFollow leads by fifteen points.
AppFollow15%#3 of 8
ASOMobile0%#– of 8
The full enterprise standing →

What the models said about AppFollow

Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Four of five in this category shown.

“AppFollow (Proceed with Caution)” GLM 4.7 FlashX · negative prompt · soft negative
“AppFollow: It is a comprehensive ASO platform that offers keyword research, competitor tracking, and market intelligence.” Llama 4 Maverick · paraphrase prompt · first choice
“I'd generally recommend AppFollow as the best fit if your main goal is mobile app rankings / ASO” GPT-5.4 mini · paraphrase prompt · first choice
“the strongest overall pick is AppFollow's free tier if you need the broadest no-cost coverage” Perplexity Sonar · budget prompt · first choice

What the models said about ASOMobile

Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Four of five in this category shown.

“solid but start paid and lack fully free equivalents matching AppFollow's breadth” Grok 4.1 Fast · budget prompt · soft negative
“The best app store optimization tool for a company with a limited budget is ASOMobile” Llama 4 Maverick · budget prompt · first choice
“Best All-Around Budget Pick: ASOMobile” Qwen 3.7 Flash · budget prompt · first choice
“ASOMobile - Best Overall Value” Kimi K2 · budget prompt · first choice
Also compared

Comparisons are drawn for the top eight products in each category, each against each. The output is the models' output; nothing here is a recommendation by the index.