Three of fourteen models named iMocha first on the direct prompt; zero named Gloat. iMocha was named by twelve of the fourteen models and Gloat by ten and iMocha carries 19 labels and Gloat 23, so the shares are not directly comparable.
Named in four categories this edition.
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 skills intelligence 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. Four of five in this category shown.
“overkill and cost-prohibitive for most mid-sized B2B firms” DeepSeek V4 Flash · paraphrase prompt · soft negative
“look at Degreed, 360Learning, Cornerstone, iMocha, or Acorn; ... if you want skills verification, look at iMocha” Perplexity Sonar · comparative prompt · first choice
“combined with iMocha or Textkernel Skills Intelligence API for inference capabilities” Grok 4.1 Fast · paraphrase prompt · first choice
“I'd recommend starting with iMocha ... Best all-around choice: iMocha” GPT-5.4 mini · paraphrase 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.