Two of fourteen models named Algolia first on the direct prompt; one named Bloomreach Discovery. Algolia was named by fourteen of the fourteen models and Bloomreach Discovery by thirteen and Algolia carries 63 labels and Bloomreach Discovery 43, so the shares are not directly comparable.
Paris, France, founded 2012. 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.
Across every category in the October 2026 Edition, Algolia and Bloomreach Discovery were named in the same answer 119 times, of the 191 answers naming Algolia and the 132 naming Bloomreach Discovery. In those answers Bloomreach Discovery took the first choice eleven times and Algolia thirty-three.
| 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 |
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.
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“Be terrified of consumption-based pricing (Algolia and Coveo both have "cliff" pricing models that blow up as you grow).” DeepSeek V4 Flash · negative prompt · hard negative
“What to avoid on a limited budget: Algolia — starts free but escalates quickly into $300–$500+/month” DeepSeek V4 Flash · budget prompt · hard negative
“Algolia draws setup-complexity, cost, and support complaints... It is risky if you want predictable pricing and merchandiser-owned tooling without engineering tickets.” Muse Glimmer 30B · negative prompt · soft negative
“Algolia combines keyword and vector search within a unified engine, delivering typo-tolerant, semantic results with sub-second response times” Claude Haiku 4.5 · comparative prompt · first choice
“Best known for speed, developer friendliness, and API-first flexibility... If you want fast implementation + strong APIs: Algolia” GPT-5.4 mini · comparative prompt · first choice
“*Algolia:* Known for blazing-fast speed, excellent developer documentation, and growing AI features. Excellent for headless setups.” Gemini 3.5 Flash · scale prompt · first choice
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“enterprise-focused, custom pricing almost always $1,000+/month” DeepSeek V4 Flash · budget prompt · hard negative
“Avoid enterprise tools like Bloomreach (custom, pricey).” Grok 4.1 Fast · budget prompt · hard negative
“enterprise-grade solutions like Bloomreach, Constructor.io, or Coveo are highly capable, they typically require custom contracts running several thousands of dollars per month” Gemini 3.5 Flash · budget prompt · soft negative
“Best for: Enterprise brands needing a full commerce experience with search, merchandising, content personalization, and a CDP all in one platform.” Mistral Small · comparative prompt · first choice
“Algolia or Bloomreach typically offer the best balance of B2B-specific capabilities” Kimi K2 · direct prompt · first choice
“Bloomreach offers a broader commerce experience approach that includes intelligent search as part of its offerings” Claude Haiku 4.5 · comparative prompt · alternative
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.