FaceUp vs AllVoices
One of fourteen models named FaceUp first on the direct prompt; one named AllVoices. FaceUp was named by eleven of the fourteen models and AllVoices by eight and FaceUp carries 28 labels and AllVoices 15, 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 whistleblowing and investigations page.
By framing
FaceUpFirst choices, of fourteen modelsAllVoices
Across every category in the November 2026 Edition, FaceUp and AllVoices were named in the same answer twenty-three times, of the 83 answers naming FaceUp and the 41 naming AllVoices. In those answers AllVoices took the first choice zero times and FaceUp seven.
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 | |
FaceUp AllVoices first choice named as an alternative argued againstblank: not namedEach cell is one answer, FaceUp on the left and AllVoices on the right.The direct prompt
FaceUp first, AllVoices not the choice
1 of 14 modelsAllVoices was named in the answer but not as the choice, or not at all.AllVoices first, FaceUp not the choice
1 of 14 modelsFaceUp was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
5 of 14 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
7 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
FaceUp18%#3 of 10AllVoices0%#6 of 10FaceUp0%#13 of 13AllVoices0%#– of 13What the models said about FaceUp
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 a platform that is easy to set up and use, FaceUp is a strong choice.” Mistral Small · budget prompt · first choice
“FaceUp is called out as the best practical fit for mid-size teams.” Muse Glimmer 30B · paraphrase prompt · first choice
“request demos from Whispli and FaceUp” Gemini 3.5 Flash · direct prompt · first choice
What the models said about AllVoices
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Four of five in this category shown.
“Less budget-friendly and primarily US-oriented” Perplexity Sonar · budget prompt · soft negative
“such as WhistleHub, AllVoices, Resolver, or EthicsPoint/Navex” Qwen 3.7 Flash · negative prompt · first choice
“### 1. AllVoices (Best for User Adoption & Modern UI)” Qwen 3.7 Flash · paraphrase prompt · first choice
“1. AllVoices ⭐ Best Overall for Mid-Market” GLM 4.7 FlashX · direct 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.