Five of fourteen models named Credo AI first on the direct prompt; zero named IBM watsonx.governance. Both were named by all fourteen models and Credo AI carries 49 labels and IBM watsonx.governance 41, so the shares are not directly comparable.
Named in two categories this edition.
Named in two 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 AI governance platforms page.
Across every category in the November 2026 Edition, Credo AI and IBM watsonx.governance were named in the same answer 113 times, of the 160 answers naming Credo AI and the 145 naming IBM watsonx.governance. In those answers IBM watsonx.governance took the first choice ten times and Credo AI thirty-four.
| Model | Direct | Paraphrase | Comparative | Budget-constrained | Scale-constrained | Negative |
|---|---|---|---|---|---|---|
| Claude Haiku 4.5 | IW | |||||
| GPT-5.4 mini | IW | IW | ||||
| Gemini 3.5 Flash | ||||||
| Perplexity Sonar | ||||||
| Grok 4.20 | IW | |||||
| Mistral Small | IW | |||||
| DeepSeek V4 Flash | IW | |||||
| Llama 4 Maverick | IW | |||||
| Qwen 3.7 Flash | ||||||
| Kimi K2 | ||||||
| GLM 4.7 FlashX | IW | |||||
| MiniMax M2.5 | IW | |||||
| 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. Six of eight in this category shown.
“$25K–$45K+/year | Enterprise-focused” DeepSeek V4 Flash · budget prompt · hard negative
“Most traditional GRC ... platforms—like Credo AI, OneTrust, Vanta, Drata, or Holistic AI—are designed more for documentation and reporting, which may not be sufficient” Mistral Small · paraphrase prompt · soft negative
“Tools such as Credo AI, OneTrust, and Holistic AI can be strong for inventories, assessments, policy mapping, and audit evidence, but may not stop unsafe prompts” Perplexity Sonar · negative prompt · soft negative
“Credo AI is widely considered a pioneer in the dedicated AI governance space. Unlike general GRC tools that added AI features later, Credo was built specifically for AI.” Qwen 3.7 Flash · comparative prompt · first choice
“Credo AI pioneered the AI governance category... Best for: Organizations needing a governance-first platform with mature policy automation and audit-ready evidence.” DeepSeek V4 Flash · comparative prompt · first choice
“Credo AI is the most-deployed control plane, a Forrester Wave Leader and Gartner Visionary, making it the best AI governance platform for most enterprises.” Claude Haiku 4.5 · comparative prompt · first choice
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 for mid-market: Heavy enterprise tools like IBM watsonx.governance, BigID, or ModelOp” Grok 4.20 · direct prompt · hard negative
“What to Avoid on a Limited Budget | IBM watsonx.governance | $3,500/month | Enterprise-priced” DeepSeek V4 Flash · budget prompt · hard negative
“avoiding for mid-market budgets: IBM watsonx.governance — starts around $75K+” DeepSeek V4 Flash · direct prompt · hard negative
“Connects AI inventories, risks, controls, and regulatory obligations in a unified system. It's designed for large enterprises needing comprehensive model lifecycle management” Mistral Small · comparative prompt · first choice
“A comprehensive enterprise platform from IBM... Credo AI and IBM shine for audits, policies, and regulatory mapping” Grok 4.20 · comparative prompt · first choice
“Choose IBM if you want a governance product that is more explicitly packaged for AI governance.” GPT-5.4 mini · 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.