# App Radar vs MobileAction: which do AI models recommend for app store optimization, October 2026

GTM AI Recommendation Index, October 2026 Edition, App store optimization tools. Two of fourteen models named App Radar first on the direct prompt; zero named MobileAction. Page: https://gtm-ai-index.com/marketing/app-store-optimization/app-radar-vs-mobileaction/

| | First-choice share | Rank | Negative rate | Labels | Models naming it |
|---|---|---|---|---|---|
| App Radar | 7% | #4 of 9 | 6% | 47 | 14 of 14 |
| MobileAction | 5% | #6 of 9 | 6% | 32 | 12 of 14 |

## The direct prompt, model by model

- Qwen 3.7 Flash: app radar first (first choices: App Radar, AppTweak) (alternatives: AppFollow)
- MiniMax M2.5: app radar first (first choices: App Radar, AppTweak) (alternatives: ASO.dev, AppFollow, MobileAction)
- GPT-5.4 mini: neither first, one named (first choices: AppTweak) (alternatives: App Radar, MobileAction, SplitMetrics Optimize)
- Gemini 3.5 Flash: neither first, one named (first choices: AppTweak) (alternatives: App Radar, AppFollow, MobileAction)
- Perplexity Sonar: neither first, one named (first choices: AppTweak) (alternatives: MobileAction)
- Grok 4.1 Fast: neither first, one named (first choices: AppTweak) (alternatives: App Radar, AppFollow, Mobile Action)
- Mistral Small: neither first, one named (first choices: Guideflow) (alternatives: AppFollow, Applytics, MobileAction)
- DeepSeek V4 Flash: neither first, one named (first choices: AppTweak) (alternatives: App Follow, MobileAction)
- Kimi K2: neither first, one named (first choices: AppTweak) (alternatives: AppFollow, MobileAction)
- GLM 4.7 FlashX: neither first, one named (first choices: AppTweak) (alternatives: App Radar, AppFollow, Appfigures)
- Muse Glimmer 30B: neither first, one named (first choices: AppTweak) (alternatives: App Radar, Applytics)
- Claude Haiku 4.5: neither named (first choices: AppTweak, Appfigures) (alternatives: AppFollow, Lengreo, Sensor Tower)
- Llama 4 Maverick: neither named (first choices: Applytics) (alternatives: AppFollow, AppTweak, Guideflow)
- GPT-6 Luna: neither named (first choices: MobileAction ASO Intelligence Pro) (alternatives: AppTweak Grow)

## What the models said about App Radar

- "starts at €69/month (~$75) ... Better if you have a slightly larger budget." (DeepSeek V4 Flash, budget prompt, soft negative)
- "the results do not position it as strongly for mid-market B2B as AppTweak" (Perplexity Sonar, direct prompt, soft negative)
- "Lacks the deep market intelligence B2B needs" (DeepSeek V4 Flash, direct prompt, soft negative)
- "Reputable options include: AppTweak, App Radar, ASODesk, and AppFollow" (Kimi K2, negative prompt, first choice)
- "points to AppTweak and AppRadar as the leading choices" (Qwen 3.7 Flash, direct prompt, first choice)
- "suggested: AppTweak, App Radar, or Sensor Tower for enterprise" (Kimi K2, scale prompt, first choice)

## What the models said about MobileAction

- "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)
- "More agency-oriented; strong ASA automation but less B2B-specific brand/competitor intelligence." (DeepSeek V4 Flash, paraphrase prompt, soft negative)
- "This is a great starting point for small teams or startups that need basic ASO insights without a financial commitment." (Mistral Small, budget prompt, first choice)
- "I'd start with MobileAction Pro for a mid-sized B2B company focused on app-store rankings." (GPT-6 Luna, paraphrase prompt, first choice)
- "shortlist AppTweak or MobileAction for hands-on ASO" (GPT-6 Luna, comparative prompt, first choice)

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. Comparisons are drawn for the top eight products in each category. Published under CC BY 4.0; the output is the models' output, and nothing here is a recommendation by the index.