One of twelve models named Customer.io first on the direct prompt; two named Product Fruits. Customer.io was named by ten of the twelve models and Product Fruits by seven and Customer.io carries 16 labels and Product Fruits 12, so the shares are not directly comparable.
Named in seven categories this edition.
Named in two categories this edition.
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 in-app messaging and customer communications 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 4.1 Fast | ||||||
| Mistral Small | ||||||
| DeepSeek V4 Flash | ||||||
| Llama 4 Maverick | ||||||
| Qwen 3.7 Flash | ||||||
| Kimi K2 | ||||||
| GLM 4.7 FlashX | ||||||
| MiniMax M2.5 |
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. One of one in this category shown.
“Can be overkill compared with PLG-focused tools” Perplexity Sonar · negative prompt · soft negative
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Three of three in this category shown.
“Product Fruits is the top pick for web-based SaaS, combining no-code in-app announcements with Flows” Claude Haiku 4.5 · comparative prompt · first choice
“Top Pick: Product Fruits ... is currently the top-rated specialist.” Qwen 3.7 Flash · direct prompt · first choice
“Product Fruits (Top Pick for B2B SaaS)” MiniMax M2.5 · direct 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.