365Talents vs TechWolf
Three of fourteen models named 365Talents first on the direct prompt; zero named TechWolf. 365Talents was named by eleven of the fourteen models and TechWolf by eleven and 365Talents carries 17 labels and TechWolf 23, so the shares are not directly comparable.
Named in three categories this edition.
Named in two categories this edition.
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
365TalentsFirst choices, of fourteen modelsTechWolf
Across every category in the November 2026 Edition, 365Talents and TechWolf were named in the same answer twenty-eight times, of the 101 answers naming 365Talents and the 81 naming TechWolf. In those answers TechWolf took the first choice four times and 365Talents three.
Every model, every framing
| Model | Direct | Paraphrase | Comparative | Budget-constrained | Scale-constrained | Negative |
|---|---|---|---|---|---|---|
| Claude Haiku 4.5 | ||||||
| GPT-5.4 mini | | | ||||
| Gemini 3.5 Flash | | | | |||
| Perplexity Sonar | | | ||||
| Grok 4.20 | | | ||||
| 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 | | |
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
365Talents first, TechWolf not the choice
3 of 14 modelsTechWolf was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
7 of 14 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
4 of 14 modelsThe answer made no first choice from these two in this category.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
365Talents0%#– of 13TechWolf0%#12 of 13365Talents11%#3 of 20TechWolf9%#6 of 20TechWolf16%#2 of 16365Talents0%#8 of 16What the models said about 365Talents
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Three of four in this category shown.
“Shortlist 3–4 vendors suited to your size (suggest: 365Talents, MuchSkills, Degreed, Fuel50).” DeepSeek V4 Flash · scale prompt · first choice
“Often cited as the top *dedicated* skills intelligence platform for mid-market buyers” Qwen 3.7 Flash · direct prompt · first choice
“The best skills intelligence platform for a mid-market B2B company is 365Talents” Llama 4 Maverick · 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. Six of eight in this category shown.
“Avoid These if Budget is a Constraint ... TechWolf ... They're overkill (and over-budget) for smaller companies.” DeepSeek V4 Flash · budget prompt · hard negative
“Avoid for mid-market: Pure enterprise plays like TechWolf” Grok 4.20 · direct prompt · hard negative
“Platforms to Approach with Caution (For Mid-Market) * Eightfold AI / Workday Skills Cloud / TechWolf: ... mid-market companies often find them over-engineered.” Gemini 3.5 Flash · direct prompt · soft negative
“Inference tool: TechWolf if the priority is automatically discovering skills from employees’ actual work” Perplexity Sonar · paraphrase prompt · first choice
“Top Pick: TechWolf – Excels at AI-powered skills inference from unstructured data” Grok 4.20 · paraphrase prompt · first choice
“Skills Base for taxonomy management + TechWolf for AI-driven skills inference” GLM 4.7 FlashX · paraphrase prompt · first choice
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.