Gatekeeper vs Tradogram
Three of fourteen models named Gatekeeper first on the direct prompt; zero named Tradogram. Gatekeeper was named by twelve of the fourteen models and Tradogram by eight and Gatekeeper carries 34 labels and Tradogram 11, so the shares are not directly comparable.
Named in two 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 supplier management page.
By framing
GatekeeperFirst choices, of fourteen modelsTradogram
Across every category in the November 2026 Edition, Gatekeeper and Tradogram were named in the same answer twenty-six times, of the 86 answers naming Gatekeeper and the 125 naming Tradogram. In those answers Tradogram took the first choice eight times and Gatekeeper one.
Every model, every framing
| 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 | | | |
Gatekeeper Tradogram first choice named as an alternative argued againstblank: not namedEach cell is one answer, Gatekeeper on the left and Tradogram on the right.The direct prompt
Gatekeeper first, Tradogram not the choice
3 of 14 modelsTradogram was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
8 of 14 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
3 of 14 modelsThe answer made no first choice from these two in this category.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.
By buyer segment
Tradogram21%#2 of 9Gatekeeper0%#6 of 9Gatekeeper14%#2 of 11Tradogram10%#3 of 11Gatekeeper0%#9 of 9Tradogram0%#– of 9What the models said about Gatekeeper
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“Avoid enterprise solutions (SAP Ariba, Coupa, Jaggaer, Gatekeeper)” DeepSeek V4 Flash · budget prompt · hard negative
“Gatekeeper starts around $10,500 a year, which makes it the benchmark to quote back at vendors who will not give you a number.” Muse Glimmer 30B · budget prompt · soft negative
“About $875/month[1] | More expensive, but stronger contract and vendor governance features” Perplexity Sonar · budget prompt · soft negative
“The best supplier management software for a company with a limited budget is Gatekeeper, which starts at around $10,500 a year.” Llama 4 Maverick · budget prompt · first choice
“Mid-market platforms like Gatekeeper (~$10,500/year starting) or Precoro (~$499/month) might be appropriate” DeepSeek V4 Flash · scale prompt · first choice
“Gatekeeper or Kodiak Hub represent the best balance of functionality, ease of use, and value” MiniMax M2.5 · direct prompt · first choice
What the models said about Tradogram
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Three of four in this category shown.
“If you need full procurement (POs, contracts, approvals) → Tradogram (free tier first, then ~$99–225/month)” DeepSeek V4 Flash · budget prompt · first choice
“Start with Tradogram ($99/month) if you need a professional solution with room to grow” Kimi K2 · budget prompt · first choice
“Best Overall for Budget Procurement & Supplier Management: Tradogram” Gemini 3.5 Flash · 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.