ConfigCat vs GrowthBook
Four of fourteen models named ConfigCat first on the direct prompt; zero named GrowthBook. ConfigCat was named by twelve of the fourteen models and GrowthBook by thirteen and ConfigCat carries 39 labels and GrowthBook 43, so the shares are not directly comparable.
Named in one category this edition.
Named in one category 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 feature flags page.
By framing
ConfigCatFirst choices, of fourteen modelsGrowthBook
Across every category in the October 2026 Edition, ConfigCat and GrowthBook were named in the same answer sixty-eight times, of the 116 answers naming ConfigCat and the 118 naming GrowthBook. In those answers GrowthBook took the first choice four times and ConfigCat twenty-four.
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 4.1 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 | | | | |
ConfigCat GrowthBook first choice named as an alternative argued againstblank: not namedEach cell is one answer, ConfigCat on the left and GrowthBook on the right.The direct prompt
ConfigCat first, GrowthBook not the choice
4 of 14 modelsGrowthBook was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
7 of 14 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
3 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
ConfigCat32%#2 of 8GrowthBook7%#4 of 8ConfigCat16%#3 of 8GrowthBook9%#6 of 8ConfigCat12%#3 of 9GrowthBook0%#6 of 9What the models said about ConfigCat
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of seven in this category shown.
“It is noted as not a broad experimentation platform. Teams wanting measurement not only toggles tend to look elsewhere.” Muse Glimmer 30B · negative prompt · soft negative
“ConfigCat's hosted model may still be a risk for some regulated industries that require on-premises deployment” GPT-5.4 mini · negative prompt · soft negative
“ConfigCat has no built-in analytics” Claude Haiku 4.5 · negative prompt · soft negative
“I'd usually recommend ConfigCat if your main goal is reliable feature flags with lower complexity and predictable cost” GPT-5.4 mini · paraphrase prompt · first choice
“ConfigCat ⭐ Best for Mid-Market ... Start with ConfigCat if you want simple feature flags with predictable costs” Kimi K2 · direct prompt · first choice
“If I had to choose one for a budget-conscious company that still wants a polished product, I'd choose ConfigCat.” GPT-5.4 mini · budget prompt · first choice
What the models said about GrowthBook
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Three of four in this category shown.
“could be Flagsmith or GrowthBook, as they offer self-hosted alternatives with a typical budget of $0–$6000/month” Llama 4 Maverick · budget prompt · first choice
“For a mid-sized B2B company, GrowthBook is the strongest all-around recommendation.” DeepSeek V4 Flash · paraphrase prompt · first choice
“If you want a free, open-source option, GrowthBook is a great choice.” Mistral Small · 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.