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Index › Learning and development › Mentoring software › Mentorloop vs PushFar
Mentoring software · October 2026 Edition

Mentorloop vs PushFar

Four of fourteen models named Mentorloop first on the direct prompt; zero named PushFar. Mentorloop was named by eleven of the fourteen models and PushFar by twelve and Mentorloop carries 27 labels and PushFar 22, so the shares are not directly comparable.

Mentorloop

accepted challenger

Named in two categories this edition.

PushFar

accepted challenger

Named in two categories this edition.

First-choice share15%9%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#2#4A position in a field of 12; printed, not drawn.
Labels2722A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Mentorloop 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 Mentorloop · Together vs PushFar · Mentorloop vs MentorcliQ

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.
MentorloopFirst choices, of fourteen modelsPushFar
Direct40
Paraphrase21
Comparative00
Budget-constrained24
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, Mentorloop and PushFar were named in the same answer fifty-one times, of the 95 answers naming Mentorloop and the 70 naming PushFar. In those answers PushFar took the first choice ten times and Mentorloop sixteen.

Every model, every framing

The eighty-four answers behind the chart above, one cell each: where Mentorloop and PushFar 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
Mentorloop PushFar first choice named as an alternative argued againstblank: not namedEach cell is one answer, Mentorloop on the left and PushFar on the right.

The direct prompt

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

Mentorloop first, PushFar an alternative

4 of 14 modelsPushFar was named in the answer but not as the choice, or not at all.
Claude Haiku 4.5Mentorloop, Together alternatives: Guider, Qooper
GLM 4.7 FlashXMentorcliQ, Mentorloop alternatives: Chronus, Qooper
MiniMax M2.5Mentorloop, Together alternatives: Chronus, MentorcliQ, Mentorgain, PushFar
Muse Glimmer 30BMentorloop alternatives: Guider, MentorCity

Neither was the first choice, one was named

1 of 14 modelsThe answer put something else first and named one of the two as an alternative.
Gemini 3.5 FlashTogether alternatives: Guider, Mentorloop, Qooper

Neither was named

9 of 14 modelsThe answer made no first choice from these two in this category.
GPT-5.4 miniChronus alternatives: MentorcliQ, Together
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
Llama 4 MaverickGuider, MentorCity, Qooper, Together Corporate Mentorship
Qwen 3.7 FlashChronus, MentorFlow alternatives: CoachHub, Lighthouse, Talmundo
Kimi K2Together alternatives: MentorcliQ, 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
Mentorloop leads by seventeen points.
Mentorloop37%#1 of 12
PushFar19%#2 of 12
The full small business standing →
Mid-marketThe figures above
Mentorloop leads by six points.
Mentorloop15%#2 of 12
PushFar9%#4 of 12
The full mid-market standing →
Enterprise
Mentorloop leads by two points.
Mentorloop2%#5 of 8
PushFar0%#8 of 8
The full enterprise standing →

What the models said about Mentorloop

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

“Start with Mentorloop Pro ($299/mo). It's the only major platform that combines commercial-grade features with small-team pricing.” Kimi K2 · budget prompt · first choice
“I'd recommend evaluating Mentorloop or Together first, as they're most frequently cited as purpose-built for your company size.” Claude Haiku 4.5 · direct prompt · first choice
“If you want low admin overhead and organic adoption, Mentorloop is the most referenced mid-market pick.” Muse Glimmer 30B · direct prompt · first choice

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
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.