Statsig vs GrowthBook
Three of fourteen models named Statsig first on the direct prompt; zero named GrowthBook. Statsig was named by fourteen of the fourteen models and GrowthBook by thirteen and Statsig carries 47 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
StatsigFirst choices, of fourteen modelsGrowthBook
Across every category in the October 2026 Edition, Statsig and GrowthBook were named in the same answer eighty-two times, of the 131 answers naming Statsig and the 118 naming GrowthBook. In those answers GrowthBook took the first choice six times and Statsig eleven.
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 | | | | |
Statsig GrowthBook first choice named as an alternative argued againstblank: not namedEach cell is one answer, Statsig on the left and GrowthBook on the right.The direct prompt
Statsig first, GrowthBook an alternative
3 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
8 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
GrowthBook7%#4 of 8Statsig5%#5 of 8Statsig11%#5 of 8GrowthBook9%#6 of 8What the models said about Statsig
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“if you want a pure-play infrastructure flag tool, look elsewhere, as Statsig has pivoted heavily into Amplitude's analytics-first roadmap” Gemini 3.5 Flash · direct prompt · soft negative
“Statsig is still growing in enterprise governance, with RBAC and audit features that aren't as strong” Claude Haiku 4.5 · negative prompt · soft negative
“Free flags at any scale, but analytics-based pricing can add up” DeepSeek V4 Flash · scale prompt · soft negative
“If you want zero cost + zero maintenance: Go with Statsig. It's the only platform offering truly unlimited feature flags on a free tier” Kimi K2 · budget prompt · first choice
“Widely considered the best all-rounder for startups and mid-size teams in recent years” DeepSeek V4 Flash · comparative prompt · first choice
“LaunchDarkly (if budget allows) or Statsig (if experimentation is a priority)” MiniMax M2.5 · direct 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.