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Whistleblowing and investigations · November 2026 Edition

Ethico vs AllVoices

One of fourteen models named Ethico first on the direct prompt; one named AllVoices. Ethico was named by nine of the fourteen models and AllVoices by eight and Ethico carries 12 labels and AllVoices 15, so the shares are not directly comparable.

Ethico

accepted challenger

Named in one category this edition.

AllVoices

accepted challenger

Named in two categories this edition.

First-choice share8%4%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate0%7%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#4#6A position in a field of 13; printed, not drawn.
Labels1215A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Ethico reading right to left. Rank and label count are printed, not drawn.Whistlelink was named alongside these two in twelve of the fourteen direct answers. Whistlelink vs Ethico · Whistlelink vs AllVoices · Whistleblower Software vs Ethico

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

How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
EthicoFirst choices, of fourteen modelsAllVoices
Direct11
Paraphrase31
Comparative00
Budget-constrained001 against AllVoices
Scale-constrained00
Negative01
Bars are first choices, 0 to 14 each sideModels that argued againstA model can name both, so the two sides of a row do not sum to fourteen.

Every model, every framing

The eighty-four answers behind the chart above, one cell each: where Ethico and AllVoices stood in it.
ModelDirectParaphraseComparativeBudget-constrainedScale-constrainedNegative
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
Ethico AllVoices first choice named as an alternative argued againstblank: not namedEach cell is one answer, Ethico on the left and AllVoices on the right.

The direct prompt

The plain question, one answer per model, grouped by where Ethico and AllVoices stood in it.

Ethico first, AllVoices not the choice

1 of 14 modelsAllVoices was named in the answer but not as the choice, or not at all.
GPT-5.4 miniEthico alternatives: Case IQ, FaceUp, Whispli

AllVoices first, Ethico not the choice

1 of 14 modelsEthico was named in the answer but not as the choice, or not at all.
GLM 4.7 FlashXAllVoices alternatives: Case IQ, NAVEX EthicsPoint, WeMoral, Whistlelink

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.
Claude Haiku 4.5Whistleblower Software alternatives: AllVoices, Ethico, FaceUp, VoxWel, Whispli, Whistlelink
Grok 4.20Whistleblower Software alternatives: AllVoices, FaceUp, Whistlelink
DeepSeek V4 FlashWhistleblower Software alternatives: AllVoices, FaceUp Whistleblowing System, Whistlelink
Kimi K2Whistlelink alternatives: AllVoices, Case IQ, NAVEX WhistleB
GPT-6 LunaCase IQ alternatives: EQS Integrity Line, Ethico, NAVEX One

Neither was named

7 of 14 modelsThe answer made no first choice from these two in this category.
Gemini 3.5 FlashFaceUp, Whispli alternatives: NAVEX One, Vault Platform, Whistlelink
Perplexity SonarWhistlelink alternatives: Formalize, Whispli, Whistleblower Software
Mistral SmallSpeakUp, Whistlelink alternatives: EQS Integrity Line, Mitratech Syntrio
Llama 4 MaverickWhistlelink alternatives: NAVEX EthicsPoint, Red Flag Reporting, SpeakUp
Qwen 3.7 FlashWhistlelink alternatives: Case IQ
MiniMax M2.5Whistlelink alternatives: Case IQ, NAVEX One, WhistleB
Muse Glimmer 30BWhistlelink alternatives: NAVEX EthicsPoint, Syntrio / Mitratech

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

The same question asked on behalf of a different buyer. Each standing is computed within its segment and they are never added together. The figures above are the mid-market standing, which is the one the category orders by.
Small business
Level: the same share of first choices.
Ethico0%#– of 10
AllVoices0%#6 of 10
The full small business standing →
Mid-marketThe figures above
Ethico leads by four points.
Ethico8%#4 of 13
AllVoices4%#6 of 13
The full mid-market standing →
Enterprise
Ethico leads by two points.
Ethico2%#9 of 13
AllVoices0%#– of 13
The full enterprise standing →

What the models said about Ethico

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 practical pick would be Ethico if you want the best balance of whistleblowing hotline + case management + human support” GPT-5.4 mini · direct prompt · first choice
“my default recommendation would be Ethico if you want a purpose-built ethics hotline plus case management in one platform” GPT-5.4 mini · paraphrase prompt · first choice
“I recommend Ethico as the top choice for an ethics hotline combined with case management.” Grok 4.20 · paraphrase 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
Also compared

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.