App store optimization tools · October 2026 Edition
AppTweak vs Sensor Tower
Eleven of fourteen models named AppTweak first on the direct prompt; zero named Sensor Tower. Both were named by all fourteen models and AppTweak carries 57 labels and Sensor Tower 48, so the shares are not directly comparable.
Named in two categories this edition.
United States, founded 2013. Named in four categories this edition.
First-choice share39%7%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate9%40%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#1#5A position in a field of 9; printed, not drawn.
Labels5748A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, AppTweak reading right to left. Rank and label count are printed, not drawn.AppFollow was named alongside these two in nine of the fourteen direct answers. AppTweak vs Appfigures · AppTweak vs AppFollow · AppTweak vs App Radar
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.
AppTweakFirst choices, of fourteen modelsSensor Tower
Direct1107 against Sensor Tower
Paraphrase901 against AppTweak · 6 against Sensor Tower
Comparative932 against Sensor Tower
Budget-constrained001 against AppTweak · 1 against Sensor Tower
Scale-constrained241 against Sensor Tower
Negative303 against AppTweak · 2 against Sensor Tower
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, AppTweak and Sensor Tower were named in the same answer 144 times, of the 193 answers naming AppTweak and the 176 naming Sensor Tower. In those answers Sensor Tower took the first choice thirty-five times and AppTweak forty-two.
Every model, every framing
The eighty-four answers behind the chart above, one cell each: where AppTweak and Sensor Tower stood in it.
| Model | Direct | Paraphrase | Comparative | Budget-constrained | Scale-constrained | Negative |
|---|---|---|---|---|---|---|
| Claude Haiku 4.5 | | | | |||
| GPT-5.4 mini | | | | |||
| Gemini 3.5 Flash | | | | | | |
| Perplexity Sonar | | | | |||
| Grok 4.1 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 | | | | |
AppTweak Sensor Tower first choice named as an alternative argued againstblank: not namedEach cell is one answer, AppTweak on the left and Sensor Tower on the right.The direct prompt
The plain question, one answer per model, grouped by where AppTweak and Sensor Tower stood in it.
AppTweak first, Sensor Tower an alternative
11 of 14 modelsSensor Tower was named in the answer but not as the choice, or not at all.Claude Haiku 4.5AppTweak, Appfigures alternatives: AppFollow, Lengreo, Sensor Tower
GPT-5.4 miniAppTweak alternatives: App Radar, MobileAction, SplitMetrics Optimize
Gemini 3.5 FlashAppTweak alternatives: App Radar, AppFollow, MobileAction
Perplexity SonarAppTweak alternatives: MobileAction
Grok 4.1 FastAppTweak alternatives: App Radar, AppFollow, Mobile Action
DeepSeek V4 FlashAppTweak alternatives: App Follow, MobileAction
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
Muse Glimmer 30BAppTweak alternatives: App Radar, Applytics
Neither was the first choice, one was named
1 of 14 modelsThe answer put something else first and named one of the two as an alternative.Llama 4 MaverickApplytics alternatives: AppFollow, AppTweak, Guideflow
Neither was named
2 of 14 modelsThe answer made no first choice from these two in this category.Mistral SmallGuideflow alternatives: AppFollow, Applytics, MobileAction
GPT-6 LunaMobileAction ASO Intelligence Pro alternatives: AppTweak Grow
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
AppTweak leads by nine points.
AppTweak9%#5 of 11Sensor Tower0%#11 of 11Mid-marketThe figures above
AppTweak leads by thirty-two points.
AppTweak39%#1 of 9Sensor Tower7%#5 of 9Enterprise
The order flips: Sensor Tower leads at enterprise.
Sensor Tower47%#1 of 8AppTweak20%#2 of 8What the models said about AppTweak
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of seven in this category shown.
“Almost all major ASO tools (e.g., AppTweak, MobileAction, Sensor Tower, AppFigures, Astro) offer proprietary metrics... these metrics are entirely educated guesses” Gemini 3.5 Flash · negative prompt · soft negative
“Some like AppTweak or Adjust get flak on G2/Reddit for data inaccuracies, slow loads, high costs, or poor support, but they're not outright scams” Grok 4.1 Fast · negative prompt · soft negative
“Trade-off: More expensive at scale; better suited for companies prioritizing deep intelligence over execution workflows” Kimi K2 · paraphrase prompt · soft negative
“AppTweak is widely considered the most comprehensive platform... Choose AppTweak if you want the "Swiss Army Knife" approach to app growth. It is the best balance of features and usability.” GLM 4.7 FlashX · comparative prompt · first choice
“AppTweak: Widely considered the gold standard for dedicated ASO teams. Highly accurate, offers great ASA integration, and has robust multi-language features.” Gemini 3.5 Flash · scale prompt · first choice
“If you are an agency or full-time ASO manager: Use AppTweak. The accuracy of their semantic search tool and their historical data is critical” Gemini 3.5 Flash · comparative 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.
“Enterprise pricing (custom, typically $25K+/year), overkill for mid-market needs” Kimi K2 · direct prompt · hard negative
“Overkill and overpriced for a mid-sized B2B company focused on ASO execution.” DeepSeek V4 Flash · paraphrase prompt · hard negative
“high-tier Sensor Tower or MobileAction are incredible resources for massive marketing teams. However, they often lock their most useful keyword tools behind expensive enterprise-level subscriptions” Gemini 3.5 Flash · negative prompt · soft negative
“Leads in keyword research with the most comprehensive keyword database, real-time difficulty scores, and search volume estimates.” Mistral Small · comparative prompt · first choice
“Prioritize tools like Sensor Tower, data.ai (App Annie), AppTweak, or App Radar, which often suit enterprises with custom pricing” Grok 4.1 Fast · scale prompt · first choice
“The dominant player in mobile market intelligence... depth of market intelligence (Sensor Tower is the clear leader)” DeepSeek V4 Flash · comparative prompt · first choice
Also compared
AppTweak vs AppfiguresAppTweak vs AppFollowAppTweak vs App RadarAppTweak vs MobileActionAppTweak vs ASOMobileAppTweak vs SonarAppfigures vs Sensor TowerAppFollow vs Sensor TowerApp Radar vs Sensor TowerSensor Tower vs MobileActionSensor Tower vs ASOMobileSensor Tower vs SonarAll twenty-eight head-to-head pages and the full app store optimization standing
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.