Zero of fourteen models named Joiin first on the direct prompt; zero named Datarails. Joiin was named by twelve of the fourteen models and Datarails by ten and Joiin carries 12 labels and Datarails 19, so the shares are not directly comparable.
Named in two categories this edition.
Named in ten 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 financial consolidation 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. Three of four in this category shown.
“The best financial consolidation software for a company with a limited budget is Joiin, which is considered the best budget option for SMEs.” Llama 4 Maverick · budget prompt · first choice
“My Top Recommendation: Start with Joiin ($23/month) for the best combination of features, integrations, and reasonable pricing” GLM 4.7 FlashX · budget prompt · first choice
“Best low-cost multi-entity consolidation: Joiin for small businesses and advisors, with plans from $23 per month.” Muse Glimmer 30B · 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.