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Skills intelligence · September 2026 Edition

iMocha vs TechWolf

Three of twelve models named iMocha first on the direct prompt; zero named TechWolf. iMocha was named by eleven of the twelve models and TechWolf by eight and iMocha carries 18 labels and TechWolf 11, so the shares are not directly comparable.

iMocha

accepted challenger

Named in six categories this edition.

TechWolf

accepted challenger

Named in two categories this edition.

First-choice share9%9%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate0%18%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#3#5A position in a field of 16; printed, not drawn.
Labels1811A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, iMocha reading right to left. Rank and label count are printed, not drawn.TalentGuard was named alongside these two in nine of the twelve direct answers. TalentGuard vs iMocha · TalentGuard vs TechWolf · Skills Base vs iMocha

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 skills intelligence page.

By framing

How many of the twelve models made each the first choice, per way of asking, and how many argued against it.
iMochaFirst choices, of twelve modelsTechWolf
Direct30
Paraphrase132 against TechWolf
Comparative00
Budget-constrained00
Scale-constrained01
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.

Every model, every framing

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

The direct prompt

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

iMocha first, TechWolf not the choice

3 of 12 modelsTechWolf was named in the answer but not as the choice, or not at all.
Grok 4.1 FastTalentGuard, iMocha alternatives: Fuel50
Mistral SmallTalentGuard, iMocha alternatives: Bryq, INOP
GLM 4.7 FlashXiMocha alternatives: MuchSkills, Skills Base, TalentGuard

Neither was the first choice, one was named

4 of 12 modelsThe answer put something else first and named one of the two as an alternative.
Claude Haiku 4.5TalentGuard alternatives: Amplemarket, iMocha
Perplexity SonarTalentGuard alternatives: INOP, SkillPanel, Skills Base, iMocha
Llama 4 MaverickTalentGuard alternatives: Criteria Corp, Engagedly, iMocha
Kimi K2TalentGuard alternatives: 365Talents, Engagedly, Fuel50, TechWolf

Neither was named

5 of 12 modelsThe answer made no first choice from these two in this category.
GPT-5.4 miniEightfold AI, SkillPanel alternatives: Fuel50, Retrain.ai, Workday Skills Cloud
Gemini 3.5 FlashCareer Bird alternatives: Docebo, Fuel50, Workera
DeepSeek V4 FlashFuel50, TalentGuard alternatives: MuchSkills, Skills Base
Qwen 3.7 FlashEightfold AI alternatives: Clad.io, DeepSkill, Gloat, Lightcast Open Skills
MiniMax M2.5TalentGuard alternatives: 365Talents, Gloat

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.
iMocha0%#7 of 10
TechWolf0%#– of 10
The full small business standing →
Mid-marketThe figures above
Level: the same share of first choices.
iMocha9%#3 of 16
TechWolf9%#5 of 16
The full mid-market standing →
Enterprise
TechWolf leads by twelve points.
TechWolf12%#3 of 10
iMocha0%#– of 10
The full enterprise standing →

What the models said about iMocha

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

“Start with iMocha for its mid-market focus and comprehensive feature set” GLM 4.7 FlashX · direct prompt · first choice
“Start with TalentGuard or iMocha for most mid-market B2B” Grok 4.1 Fast · direct prompt · first choice
“Option A: iMocha (Best overall for B2B Tech & Skills Validation)” Gemini 3.5 Flash · paraphrase prompt · first choice

What the models said about TechWolf

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

“or *TechWolf* (built for complex global structures)” Gemini 3.5 Flash · paraphrase prompt · soft negative
“Strong but can be overkill/heavier integration” DeepSeek V4 Flash · paraphrase prompt · soft negative
“at 500 employees, specialized mid-market platforms like TechWolf, 365Talents, or SkillPanel may be a better fit for speed and cost” DeepSeek V4 Flash · scale prompt · first choice
“Primary Recommendation: TechWolf + Lightcast (Open Skills) Combination” MiniMax M2.5 · paraphrase prompt · first choice
“My Top Recommendation: TechWolf or 365Talents” Kimi K2 · paraphrase prompt · first choice
Also compared
TalentGuard vs iMochaTalentGuard vs TechWolfSkills Base vs iMochaSkills Base vs TechWolfiMocha vs Lightcast Open SkillsiMocha vs 365TalentsiMocha vs Eightfold AIiMocha vs Fuel50Lightcast Open Skills vs TechWolfTechWolf vs 365TalentsTechWolf vs Eightfold AITechWolf vs Fuel50All twenty-eight head-to-head pages and the full skills intelligence standing

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.