GTM AI Index
Index Marketing Experimentation › Convert vs Kameleoon
Experimentation and personalization · September 2026 Edition

Convert vs Kameleoon

Zero of twelve models named Convert first on the direct prompt; one named Kameleoon. Convert was named by ten of the twelve models and Kameleoon by twelve and Convert carries 17 labels and Kameleoon 27, so the shares are not directly comparable.

Convert

accepted challenger

Named in one category this edition.

Kameleoon

accepted challenger

By Q56318461, Paris, France, founded 2012. Named in one category this edition.

First-choice share8%4%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate0%4%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#3#6A position in a field of 14; printed, not drawn.
Labels1727A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Convert reading right to left. Rank and label count are printed, not drawn.VWO was named alongside these two in eleven of the twelve direct answers. VWO vs Convert · VWO vs Kameleoon · Mida vs Convert

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 experimentation and personalization page.

By framing

How many of the twelve models made each the first choice, per way of asking, and how many argued against it.
ConvertFirst choices, of twelve modelsKameleoon
Direct01
Paraphrase41
Comparative00
Budget-constrained00
Scale-constrained00
Negative111 against Kameleoon
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.

Across every category in the September 2026 Edition, Convert and Kameleoon were named in the same answer nineteen times, of the 53 answers naming Convert and the 68 naming Kameleoon. In those answers Kameleoon took the first choice three times and Convert three.

Every model, every framing

The seventy-two answers behind the chart above, one cell each: where Convert and Kameleoon stood in it.
ModelDirectParaphraseComparativeBudget-constrainedScale-constrainedNegative
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
Convert Kameleoon first choice named as an alternative argued againstblank: not namedEach cell is one answer, Convert on the left and Kameleoon on the right.

The direct prompt

The plain question, one answer per model, grouped by where Convert and Kameleoon stood in it.

Kameleoon first, Convert not the choice

1 of 12 modelsConvert was named in the answer but not as the choice, or not at all.
Qwen 3.7 FlashKameleoon, VWO alternatives: Mutiny, Personizely

Neither was the first choice, one was named

6 of 12 modelsThe answer put something else first and named one of the two as an alternative.
GPT-5.4 miniOptimizely CMS alternatives: Kameleoon, VWO
Perplexity SonarVWO alternatives: Convert, Mutiny
Grok 4.1 FastVWO alternatives: Convert Experiences, Kameleoon, Personyze
DeepSeek V4 FlashVWO alternatives: Convert Experiences, Kameleoon, Mutiny
GLM 4.7 FlashXConvert Experiences alternatives: Kameleoon, VWO
MiniMax M2.5Personizely, Personyze alternatives: Convert

Neither was named

5 of 12 modelsThe answer made no first choice from these two in this category.
Claude Haiku 4.5Mutiny alternatives: AB Tasty, Abmatic AI, Mida, VWO
Gemini 3.5 FlashVWO alternatives: Abmatic AI, Markettailor, Userled, Webflow Optimize
Mistral SmallPersonyze alternatives: Abmatic AI, VWO
Llama 4 MaverickPersonizely, Personyze, VWO
Kimi K2VWO alternatives: Intellimize, Mutiny

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
Convert leads by eight points.
Convert8%#4 of 15
Kameleoon0%#12 of 15
The full small business standing →
Mid-marketThe figures above
Convert leads by four points.
Convert8%#3 of 14
Kameleoon4%#6 of 14
The full mid-market standing →
Enterprise
The order flips: Kameleoon leads at enterprise.
Kameleoon6%#5 of 11
Convert2%#8 of 11
The full enterprise standing →

What the models said about Convert

Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Three of four in this category shown.

“I would recommend Convert.com if you want a strong balance of reliability, privacy, and straightforward pricing” Perplexity Sonar · paraphrase prompt · first choice
“I would recommend Convert.com as the best A/B testing and personalization tool for a mid-sized B2B company” Llama 4 Maverick · paraphrase prompt · first choice
“Privacy-first (excellent GDPR), affordable (~$299+/month), up to 50 goals/test.” Grok 4.1 Fast · negative prompt · first choice

What the models said about Kameleoon

Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Four of five in this category shown.

“Too few independent buyer reviews to verify claims; developer-heavy approach may alienate marketers” Kimi K2 · negative prompt · soft negative
“For most mid-sized B2B companies, I'd recommend Kameleoon because it offers the best combination of A/B testing and personalization capabilities” Claude Haiku 4.5 · paraphrase prompt · first choice
“VWO (Visual Website Optimizer) and Kameleoon are consistently rated as top choices for this segment in 2026” Qwen 3.7 Flash · direct prompt · first choice
“Kameleoon (France) - GDPR compliant, no personal data collection” Mistral Small · negative prompt · first choice
Also compared

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.