HR AI Index
Index Performance and talent management Internal talent marketpl › Gloat vs Phenom
Internal talent marketplace · September 2026 Edition

Gloat vs Phenom

Two of twelve models named Gloat first on the direct prompt; zero named Phenom. Gloat was named by eleven of the twelve models and Phenom by nine and Gloat carries 37 labels and Phenom 14, so the shares are not directly comparable.

Gloat

criticized challenger

Named in four categories this edition.

Phenom

accepted challenger

Named in seven categories this edition.

First-choice share9%2%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate49%14%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#4#6A position in a field of 12; printed, not drawn.
Labels3714A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Gloat reading right to left. Rank and label count are printed, not drawn.Fuel50 was named alongside these two in ten of the twelve direct answers. Fuel50 vs Gloat · Fuel50 vs Phenom · 365Talents vs Gloat

Share is the count of first choices across the direct, paraphrase, budget and scale prompts over all twelve 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 internal talent marketplace page.

By framing

How many of the twelve models made each the first choice, per way of asking, and how many argued against it.
GloatFirst choices, of twelve modelsPhenom
Direct206 against Gloat
Paraphrase114 against Gloat
Comparative40
Budget-constrained007 against Gloat · 2 against Phenom
Scale-constrained101 against Gloat
Negative00
Bars are first choices, 0 to 12 each sideModels that argued againstA model can name both, so the two sides of a row do not sum to twelve.

Across every category in the September 2026 Edition, Gloat and Phenom were named in the same answer ninety-seven times, of the 279 answers naming Gloat and the 282 naming Phenom. In those answers Phenom took the first choice two times and Gloat nineteen.

Every model, every framing

The seventy-two answers behind the chart above, one cell each: where Gloat and Phenom 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
Gloat Phenom first choice named as an alternative argued againstblank: not namedEach cell is one answer, Gloat on the left and Phenom on the right.

The direct prompt

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

Gloat first, Phenom not the choice

2 of 12 modelsPhenom was named in the answer but not as the choice, or not at all.
Kimi K2365Talents, Gloat alternatives: Fuel50
GLM 4.7 FlashXFuel50, Gloat alternatives: Beamery, Sprad, TalentGuard

Neither was the first choice, one was named

3 of 12 modelsThe answer put something else first and named one of the two as an alternative.
Claude Haiku 4.5Fuel50 alternatives: Phenom
GPT-5.4 miniFuel50 alternatives: 365Talents, Gloat, Workday
Llama 4 MaverickSprad alternatives: Gloat, Phenom

Neither was named

7 of 12 modelsThe answer made no first choice from these two in this category.
Gemini 3.5 FlashNestor alternatives: 365Talents, Docebo, Engagedly, Fuel50
Perplexity SonarFuel50 alternatives: 365Talents, Engagedly
Grok 4.1 Fast365Talents alternatives: Fuel50, Sprad
Mistral Small365Talents alternatives: Engagedly, Sprad
DeepSeek V4 Flash365Talents alternatives: Fuel50, SkyHive
Qwen 3.7 FlashTalentGuard alternatives: Eightfold AI, Fuel50
MiniMax M2.5365Talents alternatives: Cornerstone OnDemand, Fuel50, TalentGuard, Workday

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
Gloat leads by four points.
Gloat4%#3 of 8
Phenom0%#– of 8
The full small business standing →
Mid-marketThe figures above
Gloat leads by seven points.
Gloat9%#4 of 12
Phenom2%#6 of 12
The full mid-market standing →
Enterprise
Gloat leads by forty-nine points.
Gloat49%#1 of 9
Phenom0%#7 of 9
The full enterprise standing →

What the models said about Gloat

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

“Gloat & Eightfold: Unless you have more than 2,000 employees... these enterprise solutions can result in low adoption and high implementation fatigue.” Gemini 3.5 Flash · direct prompt · hard negative
“avoid these for now. They are expensive to purchase and require substantial, ongoing internal resources to manage effectively.” Gemini 3.5 Flash · paraphrase prompt · hard negative
“Avoid pure enterprises like Gloat/Eightfold: High cost, complex setup, data-hungry AI underperforms at mid-scale.” Grok 4.1 Fast · paraphrase prompt · hard negative
“Gloat is a leading *internal mobility* platform, especially for enterprise workforce agility and project matching.” Perplexity Sonar · comparative prompt · first choice
“Best overall for mid‑market B2B: Fuel50 ... or Gloat (mature gig/project marketplace)” GLM 4.7 FlashX · direct prompt · first choice
“If your priority is unlocking internal mobility → Gloat (pure-play) or suite-native options.” DeepSeek V4 Flash · comparative prompt · first choice

What the models said about Phenom

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

“Fuel50 or Phenom - ... These are more approachable than the enterprise-grade options for mid-sized teams.” Claude Haiku 4.5 · 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.