Skills intelligence · October 2026 Edition
365Talents vs TechWolf
Two of fourteen models named 365Talents first on the direct prompt; one named TechWolf. 365Talents was named by seven of the fourteen models and TechWolf by eight and both carry 11 labels, so the shares below are directly comparable.
Named in three categories this edition.
Named in one category this edition.
First-choice share7%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#4#5A position in a field of 16; printed, not drawn.
Labels1111Equal, which is what makes the shares comparable.
The two percentage rows are drawn on one 0 to 100 track, 365Talents reading right to left. Rank and label count are printed, not drawn.Skills Base vs 365Talents · Skills Base vs TechWolf · 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.
365TalentsFirst choices, of fourteen modelsTechWolf
Direct21
Paraphrase12
Comparative10
Budget-constrained00
Scale-constrained11
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 365Talents and TechWolf stood in it.
| Model | Direct | Paraphrase | Comparative | Budget-constrained | Scale-constrained | Negative |
|---|---|---|---|---|---|---|
| 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 |
365Talents TechWolf first choice named as an alternative argued againstblank: not namedEach cell is one answer, 365Talents on the left and TechWolf on the right.The direct prompt
The plain question, one answer per model, grouped by where 365Talents and TechWolf stood in it.
365Talents first, TechWolf not the choice
2 of 14 modelsTechWolf 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
TechWolf first, 365Talents not the choice
1 of 14 models365Talents was named in the answer but not as the choice, or not at all.Qwen 3.7 FlashTalentGuard, TechWolf alternatives: AG5, Eightfold AI, Gloat, iMocha
Neither was the first choice, one was named
5 of 14 modelsThe answer put something else first and named one of the two as an alternative.Grok 4.1 FastTalentGuard, iMocha alternatives: 365Talents, Engagedly, Fuel50, Gloat
Mistral SmallFuel50, Neobrain alternatives: TalentGuard, TechWolf
Kimi K2TalentGuard alternatives: 365Talents, Fuel50, TechWolf, iMocha
GLM 4.7 FlashXiMocha alternatives: 365Talents, Fuel50
GPT-6 LunaWorkday Skills Cloud alternatives: 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
Gemini 3.5 FlashiMocha alternatives: Fuel50, Retrain.ai, TalentGuard
DeepSeek V4 FlashTalentGuard alternatives: Lightcast Open Skills, iMocha
Llama 4 Maverickno first choice
MiniMax M2.5Fuel50, iMocha Skills Intelligence Cloud alternatives: Bryq, Engagedly, TalentGuard WorkforceGPT
Muse Glimmer 30BTalentGuard alternatives: Eightfold AI, SAP SuccessFactors Skills, Skills Base, Workday Skills Cloud
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.
365Talents0%#– of 11TechWolf0%#– of 11Mid-marketThe figures above
Level: the same share of first choices.
365Talents7%#4 of 16TechWolf7%#5 of 16Enterprise
TechWolf leads by eight points.
TechWolf10%#2 of 14365Talents2%#7 of 14What 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
What the models said about TechWolf
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Three of four in this category shown.
“TalentGuard or TechWolf offer the best balance of functionality, ease of use, and cost.” Qwen 3.7 Flash · direct prompt · first choice
“TechWolf or 365Talents: Highly regarded for "skills inference"” Gemini 3.5 Flash · scale prompt · first choice
“## Recommended Inference Tool: TechWolf” GLM 4.7 FlashX · paraphrase prompt · first choice
Also compared
Skills Base vs 365TalentsSkills Base vs TechWolfTalentGuard vs 365TalentsTalentGuard vs TechWolfiMocha vs 365TalentsiMocha vs TechWolf365Talents vs MuchSkills365Talents vs Fuel50365Talents vs GloatTechWolf vs MuchSkillsTechWolf vs Fuel50TechWolf vs GloatAll 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.