Seven of fourteen models named Rippling first on the direct prompt; zero named Toggl Track. Rippling was named by thirteen of the fourteen models and Toggl Track by nine and Rippling carries 27 labels and Toggl Track 10, so the shares are not directly comparable.
Named in thirty-three 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 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 time and attendance 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.
No label in this category carried a quote.
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Four of five in this category shown.
“Clockify / Toggl (more basic, free tier available)” MiniMax M2.5 · scale prompt · soft negative
“Toggl is widely considered the industry leader for mid-sized consultancies, dev shops, and agencies.” Qwen 3.7 Flash · paraphrase prompt · first choice
“I'd suggest starting with ClickTime or Toggl Track as your primary options” Kimi K2 · paraphrase prompt · first choice
“For most mid-sized B2B companies ... Toggl Track offers the best balance” DeepSeek V4 Flash · 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.