One of fourteen models named Prophix first on the direct prompt; zero named Jirav. Prophix was named by twelve of the fourteen models and Jirav by ten and Prophix carries 34 labels and Jirav 16, so the shares are not directly comparable.
Named in eight categories this edition.
Named in six 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 FP&A 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. One of one in this category shown.
“Prophix often receives significantly fewer positive mentions and lower "positive-to-negative" ratios” Qwen 3.7 Flash · negative prompt · soft negative
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Two of two in this category shown.
“Be careful with "from $50/month" Jirav pricing: the public plans are described as being for FP&A advisory practices” GPT-6 Luna · budget prompt · soft negative
“Jirav is an affordable entry point for small businesses ... making it a relatively cost-effective choice” Claude Haiku 4.5 · budget 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.