Hyperproof vs LogicGate
Five of fourteen models named Hyperproof first on the direct prompt; two named LogicGate. Hyperproof was named by thirteen of the fourteen models and LogicGate by ten and Hyperproof carries 30 labels and LogicGate 22, so the shares are not directly comparable.
Named in five 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 GRC platforms page.
By framing
HyperproofFirst choices, of fourteen modelsLogicGate
Across every category in the October 2026 Edition, Hyperproof and LogicGate were named in the same answer thirty times, of the 149 answers naming Hyperproof and the 99 naming LogicGate. In those answers LogicGate took the first choice two times and Hyperproof three.
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 4.1 Fast | | | | | | |
| 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 | | |
Hyperproof LogicGate first choice named as an alternative argued againstblank: not namedEach cell is one answer, Hyperproof on the left and LogicGate on the right.The direct prompt
Hyperproof first, LogicGate an alternative
5 of 14 modelsLogicGate was named in the answer but not as the choice, or not at all.LogicGate first, Hyperproof an alternative
2 of 14 modelsHyperproof was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
4 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
Hyperproof0%#7 of 12LogicGate0%#8 of 12Hyperproof19%#1 of 14LogicGate3%#6 of 14Hyperproof2%#10 of 13LogicGate2%#9 of 13What the models said about Hyperproof
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Four of five in this category shown.
“good platforms, but typically positioned for larger programs and higher price points” GPT-5.4 mini · budget prompt · soft negative
“If I had to pick one default recommendation for a typical mid-sized B2B firm, I'd choose Hyperproof” Perplexity Sonar · paraphrase prompt · first choice
“For most mid-market B2B companies, the practical sweet spot is Hyperproof (if you manage 3+ frameworks)” DeepSeek V4 Flash · direct prompt · first choice
“I'd recommend Hyperproof as a strong starting point for a governance, risk, and compliance (GRC) tool.” Grok 4.1 Fast · paraphrase prompt · first choice
What the models said about LogicGate
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Four of four in this category shown.
“good platforms, but typically positioned for larger programs and higher price points” GPT-5.4 mini · budget prompt · soft negative
“If you want the best balance of capability + usability + mid-market fit, start with LogicGate.” GPT-5.4 mini · direct prompt · first choice
“For broader GRC (risk + audit + governance): LogicGate or ZenGRC—configurable without coding.” Grok 4.1 Fast · direct prompt · first choice
“Consider modern alternatives — Newer platforms (LogicGate, Onspring, Drata, Vanta) trade some depth for faster time-to-value” Kimi K2 · negative prompt · alternative
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.