One of twelve models named Concord first on the direct prompt; zero named GetAccept. Concord was named by ten of the twelve models and GetAccept by seven and Concord carries 19 labels and GetAccept 13, so the shares are not directly comparable.
Austin, founded 2014. Named in one category this edition.
founded 2015. Named in four 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 contract lifecycle management and e-signature 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. Three of four in this category shown.
“My top recommendation: Concord for its flat pricing and comprehensive features” Mistral Small · budget prompt · first choice
“Juro or Concord would be the best starting points” GLM 4.7 FlashX · direct prompt · first choice
“I'd usually start with Concord” GPT-5.4 mini · 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.
“The best e-signature and contract management tools for mid-sized B2B companies are: 1. GetAccept” Llama 4 Maverick · 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.