AppFollow vs Appfigures
Zero of fourteen models named AppFollow first on the direct prompt; zero named Appfigures. AppFollow was named by fourteen of the fourteen models and Appfigures by thirteen and AppFollow carries 45 labels and Appfigures 35, 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 modelsAppfigures
Across every category in the November 2026 Edition, AppFollow and Appfigures were named in the same answer sixty-six times, of the 149 answers naming AppFollow and the 98 naming Appfigures. In those answers Appfigures took the first choice nine times and AppFollow five.
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 Appfigures first choice named as an alternative argued againstblank: not namedEach cell is one answer, AppFollow on the left and Appfigures 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
Appfigures16%#2 of 12AppFollow8%#5 of 12AppFollow10%#2 of 10Appfigures8%#4 of 10AppFollow12%#3 of 11Appfigures0%#11 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 Appfigures
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 not transparent.” Llama 4 Maverick · negative prompt · soft negative
“More budget-friendly option that covers basics well, though may lack some advanced features needed for competitive B2B markets.” MiniMax M2.5 · direct prompt · soft negative
“Others like Appfigures or Asolytics are more budget-oriented but miss advanced features.” Grok 4.20 · direct prompt · soft negative
“Verdict: Start with AppFigures. Their $9.99 entry point allows you to monitor your real-time performance accurately” Qwen 3.7 Flash · budget prompt · first choice
“upgrading to Appfigures ($44.99/month) when you're ready for active keyword tracking” MiniMax M2.5 · budget prompt · first choice
“AppTweak or AppFigures would be the best starting points.” MiniMax M2.5 · paraphrase 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.