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Skills intelligence · October 2026 Edition

TalentGuard vs iMocha

Five of fourteen models named TalentGuard first on the direct prompt; three named iMocha. TalentGuard was named by ten of the fourteen models and iMocha by twelve and TalentGuard carries 14 labels and iMocha 19, so the shares are not directly comparable.

TalentGuard

accepted challenger

Named in three categories this edition.

iMocha

accepted challenger

Named in four categories this edition.

First-choice share13%9%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate0%5%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#2#3A position in a field of 16; printed, not drawn.
Labels1419A 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 iMocha · TalentGuard vs 365Talents

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 modelsiMocha
Direct53
Paraphrase221 against iMocha
Comparative01
Budget-constrained00
Scale-constrained00
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.

Every model, every framing

The eighty-four answers behind the chart above, one cell each: where TalentGuard and iMocha 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 iMocha first choice named as an alternative argued againstblank: not namedEach cell is one answer, TalentGuard on the left and iMocha on the right.

The direct prompt

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

Both were the first choice

1 of 14 modelsThe answer named them together, and the judge labeled each a first choice.
Grok 4.1 FastTalentGuard, iMocha alternatives: 365Talents, Engagedly, Fuel50, Gloat

TalentGuard first, iMocha an alternative

4 of 14 modelsiMocha was named in the answer but not as the choice, or not at all.
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

iMocha first, TalentGuard an alternative

2 of 14 modelsTalentGuard was named in the answer but not as the choice, or not at all.
Gemini 3.5 FlashiMocha alternatives: Fuel50, Retrain.ai, TalentGuard
GLM 4.7 FlashXiMocha alternatives: 365Talents, Fuel50

Neither was the first choice, one was named

1 of 14 modelsThe answer put something else first and named one of the two as an alternative.
Mistral SmallFuel50, Neobrain alternatives: TalentGuard, TechWolf

Neither was named

6 of 14 modelsThe answer made no first choice from these two in this category.
Claude Haiku 4.5INOP alternatives: Fuel50, Skillsoft Percipio
GPT-5.4 mini365Talents alternatives: Eightfold AI, SkillPanel
Perplexity Sonar365Talents alternatives: Docebo, Workday Skills Cloud
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
iMocha leads by seven points.
iMocha7%#3 of 11
TalentGuard0%#– of 11
The full small business standing →
Mid-marketThe figures above
The order flips: TalentGuard leads at mid-market.
TalentGuard13%#2 of 16
iMocha9%#3 of 16
The full mid-market standing →
Enterprise
The order flips: iMocha leads at enterprise.
iMocha2%#8 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 iMocha

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

“overkill and cost-prohibitive for most mid-sized B2B firms” DeepSeek V4 Flash · paraphrase prompt · soft negative
“look at Degreed, 360Learning, Cornerstone, iMocha, or Acorn; ... if you want skills verification, look at iMocha” Perplexity Sonar · comparative prompt · first choice
“combined with iMocha or Textkernel Skills Intelligence API for inference capabilities” Grok 4.1 Fast · paraphrase prompt · first choice
“I'd recommend starting with iMocha ... Best all-around choice: iMocha” GPT-5.4 mini · paraphrase 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.