AI Indexes
HR AI Index
Index › Learning and development › Mentoring software › PushFar vs MentorCity
Mentoring software · October 2026 Edition

PushFar vs MentorCity

Zero of fourteen models named PushFar first on the direct prompt; one named MentorCity. PushFar was named by twelve of the fourteen models and MentorCity by seven and PushFar carries 22 labels and MentorCity 10, so the shares are not directly comparable.

PushFar

accepted challenger

Named in two categories this edition.

MentorCity

accepted challenger

Named in one category this edition.

First-choice share9%6%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#7A position in a field of 12; printed, not drawn.
Labels2210A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, PushFar reading right to left. Rank and label count are printed, not drawn.Together was named alongside these two in ten of the fourteen direct answers. Together vs PushFar · Together vs MentorCity · Mentorloop vs PushFar

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 mentoring software page.

By framing

How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
PushFarFirst choices, of fourteen modelsMentorCity
Direct01
Paraphrase10
Comparative00
Budget-constrained42
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.

Across every category in the October 2026 Edition, PushFar and MentorCity were named in the same answer thirteen times, of the 70 answers naming PushFar and the 26 naming MentorCity. In those answers MentorCity took the first choice four times and PushFar one.

Every model, every framing

The eighty-four answers behind the chart above, one cell each: where PushFar and MentorCity 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
GPT-6 Luna
Muse Glimmer 30B
PushFar MentorCity first choice named as an alternative argued againstblank: not namedEach cell is one answer, PushFar on the left and MentorCity on the right.

The direct prompt

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

MentorCity first, PushFar not the choice

1 of 14 modelsPushFar was named in the answer but not as the choice, or not at all.
Llama 4 MaverickGuider, MentorCity, Qooper, Together Corporate Mentorship

Neither was the first choice, one was named

2 of 14 modelsThe answer put something else first and named one of the two as an alternative.
MiniMax M2.5Mentorloop, Together alternatives: Chronus, MentorcliQ, Mentorgain, PushFar
Muse Glimmer 30BMentorloop alternatives: Guider, MentorCity

Neither was named

11 of 14 modelsThe answer made no first choice from these two in this category.
Claude Haiku 4.5Mentorloop, Together alternatives: Guider, Qooper
GPT-5.4 miniChronus alternatives: MentorcliQ, Together
Gemini 3.5 FlashTogether alternatives: Guider, Mentorloop, Qooper
Perplexity SonarMentorcliQ alternatives: Chronus, Together
Grok 4.1 FastMentorcliQ alternatives: Chronus, Qooper, Together
Mistral SmallTogether alternatives: Mentorgain, Qooper
DeepSeek V4 FlashMentorcliQ alternatives: Chronus, Qooper, Together
Qwen 3.7 FlashChronus, MentorFlow alternatives: CoachHub, Lighthouse, Talmundo
Kimi K2Together alternatives: MentorcliQ, Qooper
GLM 4.7 FlashXMentorcliQ, Mentorloop alternatives: Chronus, Qooper
GPT-6 LunaTogether alternatives: Chronus, MentorcliQ, Qooper

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
PushFar leads by twelve points.
PushFar19%#2 of 12
MentorCity8%#3 of 12
The full small business standing →
Mid-marketThe figures above
PushFar leads by four points.
PushFar9%#4 of 12
MentorCity6%#7 of 12
The full mid-market standing →
Enterprise
Level: the same share of first choices.
PushFar0%#8 of 8
MentorCity0%#– of 8
The full enterprise standing →

What the models said about PushFar

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

“Best Free Option: PushFar ... widely recognized as the best free option for internal mentoring programs” GLM 4.7 FlashX · budget prompt · first choice
“For a limited budget, the strongest recommendation from the results is PushFar.” Perplexity Sonar · budget prompt · first choice
“would be MentorGain, Together Platform, or PushFar” Llama 4 Maverick · paraphrase prompt · first choice

What the models said about MentorCity

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 best mentoring program software for a mid-market B2B company includes MentorCity, Together Corporate Mentorship, Qooper, and Guider.” Llama 4 Maverick · direct prompt · first choice
“The best mentoring program software for a company with a limited budget is MentorCity, which excels for smaller teams” Llama 4 Maverick · budget prompt · first choice
“MentorCity (Best Overall Budget Pick)” Grok 4.1 Fast · budget 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.