AppFollow vs Sensor Tower
Zero of fourteen models named AppFollow first on the direct prompt; zero named Sensor Tower. Both were named by all fourteen models and AppFollow carries 45 labels and Sensor Tower 54, so the shares are not directly comparable.
Named in three categories this edition.
United States, founded 2013. Named in three 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 modelsSensor Tower
Across every category in the November 2026 Edition, AppFollow and Sensor Tower were named in the same answer 108 times, of the 149 answers naming AppFollow and the 184 naming Sensor Tower. In those answers Sensor Tower took the first choice twenty-five times and AppFollow ten.
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 Sensor Tower first choice named as an alternative argued againstblank: not namedEach cell is one answer, AppFollow on the left and Sensor Tower on the right.The direct prompt
Neither was the first choice, one was named
5 of 14 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
9 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 12Sensor Tower0%#12 of 12AppFollow10%#2 of 10Sensor Tower2%#5 of 10Sensor Tower46%#1 of 11AppFollow12%#3 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 Sensor Tower
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight 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
“Avoid Sensor Tower for mid-market—it's enterprise-focused at $5,000+/month.” Kimi K2 · paraphrase prompt · hard negative
“Avoid enterprise-tier tools like Sensor Tower, AppTweak ($79+/mo)” DeepSeek V4 Flash · budget prompt · hard negative
“Sensor Tower: The industry leader for market intelligence. Best for revenue estimates, ad intelligence, and understanding high-level market trends.” Qwen 3.7 Flash · scale prompt · first choice
“AppTweak, Sensor Tower, or Mobile Action: These are frequently recommended in 2023–2024 reviews” Grok 4.20 · negative prompt · first choice
“More focused on large-scale market analysis than day-to-day listing execution; often excessive for small apps.” Perplexity Sonar · comparative prompt · alternative
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.