Two of fourteen models named BlueConic first on the direct prompt; one named Census. BlueConic was named by eleven of the fourteen models and Census by five and BlueConic carries 22 labels and Census 10, so the shares are not directly comparable.
Named in six categories this edition.
By Fivetran, United States. Named in five categories 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 customer data platforms page.
| Model | Direct | Paraphrase | Comparative | Budget-constrained | Scale-constrained | Negative |
|---|---|---|---|---|---|---|
| Claude Haiku 4.5 | ||||||
| GPT-5.4 mini | ||||||
| Gemini 3.5 Flash | ||||||
| Perplexity Sonar | ||||||
| Grok 4.20 | ||||||
| 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. Three of three in this category shown.
“Users flag the highest complaints around support and unfinished features (≈6.2/10). Features arrive half-baked” GLM 4.7 FlashX · negative prompt · hard negative
“You generally cannot afford enterprise-grade tools like Tealium, Segment (at scale), or BlueConic.” GLM 4.7 FlashX · budget prompt · hard negative
“BlueConic is frequently cited as the top choice for mid-market companies that need sophisticated B2B capabilities” Qwen 3.7 Flash · direct prompt · first choice
No label in this category carried a quote.
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.