Zero of fourteen models named Sling first on the direct prompt; two named Rippling. Sling was named by fourteen of the fourteen models and Rippling by seven and Sling carries 19 labels and Rippling 10, so the shares are not directly comparable.
Named in five categories this edition.
Named in twenty-nine 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 workforce scheduling 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 | ||||||
| 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 four in this category shown.
“Sling – best small-budget free tier for scheduling … The author's preference is Sling "because it lets you create schedules for up to 30 users."” Muse Glimmer 30B · budget prompt · first choice
“The best employee scheduling software for a company with a limited budget is Sling, which is free and has below-average costs on paid plans.” Llama 4 Maverick · budget prompt · first choice
“Sling is the strongest overall pick because it is repeatedly identified as the best for small budgets” Perplexity Sonar · budget 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.