Feature flags · September 2026 Edition
LaunchDarkly vs ConfigCat
Seven of twelve models named LaunchDarkly first on the direct prompt; four named ConfigCat. Both were named by all twelve models and LaunchDarkly carries 61 labels and ConfigCat 36, so the shares are not directly comparable.
Named in one category this edition.
Named in one category this edition.
First-choice share30%17%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate31%11%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#1#3A position in a field of 9; printed, not drawn.
Labels6136A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, LaunchDarkly reading right to left. Rank and label count are printed, not drawn.Flagsmith was named alongside these two in ten of the twelve direct answers. LaunchDarkly vs Flagsmith · Flagsmith vs ConfigCat
Share is the count of first choices across the direct, paraphrase, budget and scale prompts over all twelve 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
How many of the twelve models made each the first choice, per way of asking, and how many argued against it.
LaunchDarklyFirst choices, of twelve modelsConfigCat
Direct744 against LaunchDarkly
Paraphrase521 against LaunchDarkly
Comparative90
Budget-constrained023 against LaunchDarkly
Scale-constrained201 against LaunchDarkly
Negative0010 against LaunchDarkly · 4 against ConfigCat
Bars are first choices, 0 to 12 each sideModels that argued againstA model can name both, so the two sides of a row do not sum to twelve.
The direct prompt
The plain question, one answer per model, grouped by where LaunchDarkly and ConfigCat stood in it.
Both were the first choice
1 of 12 modelsThe answer named them together, and the judge labeled each a first choice.MiniMax M2.5ConfigCat, LaunchDarkly alternatives: Flagsmith, GrowthBook
LaunchDarkly first, ConfigCat an alternative
6 of 12 modelsConfigCat was named in the answer but not as the choice, or not at all.Claude Haiku 4.5Flagsmith, LaunchDarkly alternatives: Statsig
Perplexity SonarLaunchDarkly alternatives: Flagsmith, GrowthBook, Statsig, Unleash
Grok 4.1 FastLaunchDarkly alternatives: ConfigCat, Flagsmith, Statsig, Unleash
Mistral SmallLaunchDarkly alternatives: Flagsmith, Statsig, Unleash
Llama 4 MaverickLaunchDarkly
GLM 4.7 FlashXLaunchDarkly alternatives: Flagsmith, GrowthBook, Split, Statsig
ConfigCat first, LaunchDarkly an alternative
3 of 12 modelsLaunchDarkly was named in the answer but not as the choice, or not at all.GPT-5.4 miniConfigCat alternatives: Flagsmith, LaunchDarkly
Qwen 3.7 FlashConfigCat alternatives: Flagsmith, Split, Unleash
Kimi K2ConfigCat, Statsig
Neither was the first choice, one was named
2 of 12 modelsThe answer put something else first and named one of the two as an alternative.Gemini 3.5 FlashFlagsmith alternatives: ConfigCat, GrowthBook, PostHog, Schematic
DeepSeek V4 FlashGrowthBook alternatives: ConfigCat, Flagsmith, Statsig, Unleash
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
ConfigCat leads by forty-one points.
ConfigCat43%#1 of 10LaunchDarkly2%#9 of 10Mid-marketThe figures above
The order flips: LaunchDarkly leads at mid-market.
LaunchDarkly30%#1 of 9ConfigCat17%#3 of 9Enterprise
LaunchDarkly leads by forty-six points.
LaunchDarkly59%#1 of 7ConfigCat13%#2 of 7What the models said about LaunchDarkly
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“Caution level: High (especially for new adopters or scale-ups)... Avoid if... Microservices, startups, regulated industries, cost-sensitive” DeepSeek V4 Flash · negative prompt · hard negative
“Avoid for tight budgets: LaunchDarkly/Split (free tiers cap quickly, then $10+/MAU or seats).” Grok 4.1 Fast · budget prompt · hard negative
“Avoid: LaunchDarkly... they are notoriously expensive for startups.” Qwen 3.7 Flash · budget prompt · hard negative
“LaunchDarkly: The market giant. Best-in-class for enterprise governance, automated APM rollbacks, and security—but typically the most expensive, using a strict MAU pricing model.” Gemini 3.5 Flash · scale prompt · first choice
“LaunchDarkly — The Enterprise Benchmark ... Choose LaunchDarkly if release governance, approvals, and auditability are your top priorities.” GLM 4.7 FlashX · comparative prompt · first choice
“The most complete feature management platform... LaunchDarkly suits enterprises needing governance and audit trails” Claude Haiku 4.5 · comparative prompt · first choice
What the models said about ConfigCat
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“Platforms with significant limitations or red flags: - ConfigCat: ... its pricing model is criticized for being expensive as you scale” Mistral Small · negative prompt · hard negative
“Lighterweight option may lack advanced governance, stats, and enterprise features for regulated environments.” GLM 4.7 FlashX · negative prompt · soft negative
“ConfigCat - Operational Limitations - Limited A/B testing capabilities” Kimi K2 · negative prompt · soft negative
“the best default choice is usually ConfigCat if you want the simplest low-cost managed service” Perplexity Sonar · budget prompt · first choice
“The Best "All-Rounder": ConfigCat ... strikes the best balance between power and simplicity” Qwen 3.7 Flash · direct prompt · first choice
“ConfigCat is probably the best default choice for a mid-market B2B company.” GPT-5.4 mini · direct prompt · first choice
Comparisons are drawn for the top three products in each category. The output is the models' output; nothing here is a recommendation by the index.