AppFollow vs MobileAction
Zero of fourteen models named AppFollow first on the direct prompt; zero named MobileAction. AppFollow was named by fourteen of the fourteen models and MobileAction by thirteen and AppFollow carries 45 labels and MobileAction 34, so the shares are not directly comparable.
Named in three categories this edition.
Named in two categories this edition.
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
AppFollowFirst choices, of fourteen modelsMobileAction
Across every category in the November 2026 Edition, AppFollow and MobileAction were named in the same answer sixty-one times, of the 149 answers naming AppFollow and the 107 naming MobileAction. In those answers MobileAction took the first choice six times and AppFollow eight.
Every model, every framing
| Model | Direct | Paraphrase | Comparative | Budget-constrained | Scale-constrained | Negative |
|---|---|---|---|---|---|---|
| Claude Haiku 4.5 | | | | | ||
| GPT-5.4 mini | | | | |||
| Gemini 3.5 Flash | | | | | ||
| Perplexity Sonar | | | | | ||
| Grok 4.20 | | | | | ||
| 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 MobileAction first choice named as an alternative argued againstblank: not namedEach cell is one answer, AppFollow on the left and MobileAction on the right.The direct prompt
Neither was the first choice, one was named
6 of 14 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
8 of 14 modelsThe answer made no first choice from these two in this category.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
AppFollow8%#5 of 12MobileAction8%#6 of 12AppFollow10%#2 of 10MobileAction10%#3 of 10AppFollow12%#3 of 11MobileAction2%#6 of 11What the models said about AppFollow
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Three of four in this category shown.
“The best app store optimization tool for a company with a limited budget is AppFollow, which has a free plan and paid pricing.” Llama 4 Maverick · budget prompt · first choice
“AppFollow is best for end-to-end review management and automation paired with ASO analytics and competitor research” Claude Haiku 4.5 · comparative prompt · first choice
“Begin with AppFollow since it offers the most comprehensive free suite covering all essential ASO functions.” GLM 4.7 FlashX · budget prompt · first choice
What the models said about MobileAction
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of seven in this category shown.
“Avoid premium tools like AppTweak (~$79+/month), Sensor Tower, or MobileAction unless scaling up” Grok 4.20 · budget prompt · hard negative
“almost all major ASO tools (e.g., AppTweak, MobileAction, Sensor Tower, AppFigures, Astro) offer proprietary metrics... these metrics are not transparent.” Llama 4 Maverick · negative prompt · soft negative
“MobileAction (from $12.50/mo), but these tend to be more suitable for smaller teams or indie developers” GLM 4.7 FlashX · direct prompt · soft negative
“I'd recommend AppTweak or MobileAction as the top choices for a mid-sized B2B company” Claude Haiku 4.5 · paraphrase prompt · first choice
“If your budget is limited, MobileAction Lite is probably the best value starting point.” GPT-5.4 mini · budget prompt · first choice
“Best low-cost paid pick: MobileAction Lite. It's $15/month” GPT-6 Luna · budget prompt · first choice
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.