Doofinder vs Klevu
Zero of fourteen models named Doofinder first on the direct prompt; one named Klevu. Doofinder was named by twelve of the fourteen models and Klevu by thirteen and Doofinder carries 25 labels and Klevu 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 ecommerce search and merchandising page.
By framing
DoofinderFirst choices, of fourteen modelsKlevu
Across every category in the October 2026 Edition, Doofinder and Klevu were named in the same answer forty-four times, of the 80 answers naming Doofinder and the 105 naming Klevu. In those answers Klevu took the first choice five times and Doofinder nineteen.
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 | | | | |
Doofinder Klevu first choice named as an alternative argued againstblank: not namedEach cell is one answer, Doofinder on the left and Klevu on the right.The direct prompt
Klevu first, Doofinder not the choice
1 of 14 modelsDoofinder was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
6 of 14 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
7 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
Doofinder32%#1 of 13Klevu11%#3 of 13Doofinder0%#– of 9Klevu0%#9 of 9What the models said about Doofinder
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Three of four in this category shown.
“Doofinder - Offers conversion-focused search with quick-setup AI search for SMB and mid-market, starting at $49/month.” Llama 4 Maverick · paraphrase prompt · first choice
“Doofinder appears to be the best ecommerce search and merchandising platform for companies with limited budgets” MiniMax M2.5 · budget prompt · first choice
“Doofinder – Best All-Around for SMBs ... public, transparent pricing starting around $35–$49/month” Qwen 3.7 Flash · budget prompt · first choice
What the models said about Klevu
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“Klevu's G2 review sample is very small, so individual complaints—including one about renewal terms—aren't enough to establish a general pattern” GPT-6 Luna · negative prompt · soft negative
“Klevu has less robust analytics and recommendations compared to other tools... may not scale well or support large catalogs” Claude Haiku 4.5 · negative prompt · soft negative
“Limited advanced merchandising — Strong on search basics, but users note gaps in complex merchandising features.” DeepSeek V4 Flash · negative prompt · soft negative
“Often top for mid-market B2B/B2C hybrids... "Mid-market value champion" with 32% conversion lifts” Grok 4.1 Fast · direct prompt · first choice
“Klevu: Best AI-First Option for Modern B2B ... Klevu is the gold standard” Gemini 3.5 Flash · paraphrase prompt · first choice
“I recommend Klevu as the best onsite product search engine” Mistral Small · 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.