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Skills intelligence · November 2026 Edition

Skills Base vs TalentGuard

One of fourteen models named Skills Base first on the direct prompt; six named TalentGuard. Skills Base was named by twelve of the fourteen models and TalentGuard by eight and Skills Base carries 23 labels and TalentGuard 12, so the shares are not directly comparable.

Skills Base

accepted challenger

Named in two categories this edition.

TalentGuard

accepted challenger

Named in three categories this edition.

First-choice share28%15%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#1#2A position in a field of 20; printed, not drawn.
Labels2312A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Skills Base reading right to left. Rank and label count are printed, not drawn.MuchSkills was named alongside these two in ten of the fourteen direct answers. Skills Base vs 365Talents · Skills Base vs MuchSkills · Skills Base vs Lightcast Open Skills

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.
Skills BaseFirst choices, of fourteen modelsTalentGuard
Direct16
Paraphrase31
Comparative00
Budget-constrained90
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 Skills Base and TalentGuard 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
Skills Base TalentGuard first choice named as an alternative argued againstblank: not namedEach cell is one answer, Skills Base on the left and TalentGuard on the right.

The direct prompt

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

Skills Base first, TalentGuard not the choice

1 of 14 modelsTalentGuard was named in the answer but not as the choice, or not at all.
GLM 4.7 FlashXSkills Base alternatives: 365Talents, AG5, MuchSkills, iMocha

TalentGuard first, Skills Base an alternative

6 of 14 modelsSkills Base was named in the answer but not as the choice, or not at all.
Claude Haiku 4.5TalentGuard alternatives: Fuel50, MuchSkills, Workday Skills Cloud, iMocha
Perplexity SonarTalentGuard alternatives: Skills Base, iMocha
DeepSeek V4 FlashMuchSkills, TalentGuard alternatives: 365Talents, Disprz, Skills Base
Kimi K2MuchSkills, TalentGuard alternatives: 365Talents, Skills Base
MiniMax M2.5TalentGuard alternatives: 365Talents, MuchSkills, iMocha
Muse Glimmer 30BTalentGuard alternatives: 365Talents, Cognisess, Disprz, Fuel50, MuchSkills

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.
Grok 4.20MuchSkills alternatives: 365Talents, AG5, Skills Base
Qwen 3.7 Flash365Talents alternatives: Cognisess, Disprz, Skills Base
GPT-6 LunaSkillPanel alternatives: Fuel50, Skills Base

Neither was named

4 of 14 modelsThe answer made no first choice from these two in this category.
GPT-5.4 mini365Talents alternatives: Fuel50, MuchSkills
Gemini 3.5 FlashRetrain.ai alternatives: 365Talents, DevSkiller, Gloat, MuchSkills, iMocha
Mistral SmallBeamery, MuchSkills alternatives: AG5, Degreed, Workday Skills Cloud
Llama 4 Maverick365Talents

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
Skills Base leads by fifty-five points.
Skills Base55%#1 of 13
TalentGuard0%#– of 13
The full small business standing →
Mid-marketThe figures above
Skills Base leads by thirteen points.
Skills Base28%#1 of 20
TalentGuard15%#2 of 20
The full mid-market standing →
Enterprise
TalentGuard is not named for this buyer.
Skills Base10%#– of 16
TalentGuard—not named
The full enterprise standing →

What the models said about Skills Base

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

“the consensus pricing comparison points to Skills Base as the lowest-cost skills intelligence / skills-matrix option that still functions as a full platform” Muse Glimmer 30B · budget prompt · first choice
“For most budget-conscious companies, Skills Base offers the best balance of features, genuinely usable free tier, and low per-user cost.” DeepSeek V4 Flash · budget prompt · first choice
“Skills Base stands out as the best skills intelligence platform for companies with a limited budget.” Grok 4.20 · budget prompt · first choice

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 of Eightfold or Gloat.” Claude Haiku 4.5 · direct prompt · first choice
“TalentGuard appears to be purpose-built for your segment, offering structured frameworks without the complexity typically seen in enterprise solutions” MiniMax M2.5 · 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.