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Skills intelligence · October 2026 Edition

TalentGuard vs 365Talents

Five of fourteen models named TalentGuard first on the direct prompt; two named 365Talents. TalentGuard was named by ten of the fourteen models and 365Talents by seven and TalentGuard carries 14 labels and 365Talents 11, so the shares are not directly comparable.

TalentGuard

accepted challenger

Named in three categories this edition.

365Talents

accepted challenger

Named in three categories this edition.

First-choice share13%7%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate0%0%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#2#4A position in a field of 16; printed, not drawn.
Labels1411A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, TalentGuard reading right to left. Rank and label count are printed, not drawn.Skills Base vs TalentGuard · Skills Base vs 365Talents · TalentGuard vs iMocha

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 skills intelligence page.

By framing

How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
TalentGuardFirst choices, of fourteen models365Talents
Direct52
Paraphrase21
Comparative01
Budget-constrained00
Scale-constrained01
Negative00
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.

Across every category in the October 2026 Edition, TalentGuard and 365Talents were named in the same answer twenty-one times, of the 63 answers naming TalentGuard and the 95 naming 365Talents. In those answers 365Talents took the first choice two times and TalentGuard six.

Every model, every framing

The eighty-four answers behind the chart above, one cell each: where TalentGuard and 365Talents stood in it.
ModelDirectParaphraseComparativeBudget-constrainedScale-constrainedNegative
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
TalentGuard 365Talents first choice named as an alternative argued againstblank: not namedEach cell is one answer, TalentGuard on the left and 365Talents on the right.

The direct prompt

The plain question, one answer per model, grouped by where TalentGuard and 365Talents stood in it.

TalentGuard first, 365Talents an alternative

5 of 14 models365Talents was named in the answer but not as the choice, or not at all.
Grok 4.1 FastTalentGuard, iMocha alternatives: 365Talents, Engagedly, Fuel50, Gloat
DeepSeek V4 FlashTalentGuard alternatives: Lightcast Open Skills, iMocha
Qwen 3.7 FlashTalentGuard, TechWolf alternatives: AG5, Eightfold AI, Gloat, iMocha
Kimi K2TalentGuard alternatives: 365Talents, Fuel50, TechWolf, iMocha
Muse Glimmer 30BTalentGuard alternatives: Eightfold AI, SAP SuccessFactors Skills, Skills Base, Workday Skills Cloud

365Talents first, TalentGuard not the choice

2 of 14 modelsTalentGuard was named in the answer but not as the choice, or not at all.
GPT-5.4 mini365Talents alternatives: Eightfold AI, SkillPanel
Perplexity Sonar365Talents alternatives: Docebo, Workday Skills Cloud

Neither was the first choice, one was named

3 of 14 modelsThe answer put something else first and named one of the two as an alternative.
Gemini 3.5 FlashiMocha alternatives: Fuel50, Retrain.ai, TalentGuard
Mistral SmallFuel50, Neobrain alternatives: TalentGuard, TechWolf
GLM 4.7 FlashXiMocha alternatives: 365Talents, Fuel50

Neither was named

4 of 14 modelsThe answer made no first choice from these two in this category.
Claude Haiku 4.5INOP alternatives: Fuel50, Skillsoft Percipio
Llama 4 Maverickno first choice
MiniMax M2.5Fuel50, iMocha Skills Intelligence Cloud alternatives: Bryq, Engagedly, TalentGuard WorkforceGPT
GPT-6 LunaWorkday Skills Cloud alternatives: TechWolf

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.
TalentGuard0%#– of 11
365Talents0%#– of 11
The full small business standing →
Mid-marketThe figures above
TalentGuard leads by five points.
TalentGuard13%#2 of 16
365Talents7%#4 of 16
The full mid-market standing →
Enterprise
The order flips: 365Talents leads at enterprise.
365Talents2%#7 of 14
TalentGuard0%#– of 14
The full enterprise standing →

What the models said about TalentGuard

Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Two of two in this category shown.

“TalentGuard is best suited for mid-market companies that need structured competency frameworks and career pathing without the enterprise complexity” Muse Glimmer 30B · paraphrase prompt · first choice
“TalentGuard is the AI-model-recommended first choice for mid-market skills intelligence / taxonomy and inference” Muse Glimmer 30B · direct prompt · first choice

What the models said about 365Talents

Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Two of two in this category shown.

“Pure-play AI skills intelligence. Dynamically maps skills from your existing HCM data using AI inference + self-reporting.” DeepSeek V4 Flash · comparative prompt · first choice
“Choose 365Talents if you want the best *dedicated* skills intelligence platform for a mid-market B2B company.” Perplexity Sonar · 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.