Zero of twelve models named Tradogram first on the direct prompt; zero named Coupa. Tradogram was named by nine of the twelve models and Coupa by twelve and Tradogram carries 19 labels and Coupa 33, so the shares are not directly comparable.
Named in four 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 twelve 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 strategic sourcing 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 |
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. Two of two in this category shown.
“I'd recommend starting with Tradogram or Prokuria as they offer strong e-sourcing and RFP functionality without enterprise-level pricing” MiniMax M2.5 · paraphrase prompt · first choice
“Recommended as the best for SMB (small and medium-sized business) sourcing, offering features tailored to smaller budgets” Mistral Small · budget prompt · first choice
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. One of one in this category shown.
“I'd recommend Coupa if you want a broadly adopted, fast-implementing procurement platform with sourcing capabilities” Perplexity Sonar · 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.