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

PushFar vs Guider

Zero of fourteen models named PushFar first on the direct prompt; one named Guider. PushFar was named by twelve of the fourteen models and Guider by nine and PushFar carries 22 labels and Guider 14, so the shares are not directly comparable.

PushFar

accepted challenger

Named in two categories this edition.

Guider

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#6A position in a field of 12; printed, not drawn.
Labels2214A 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 Guider · 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 modelsGuider
Direct01
Paraphrase12
Comparative00
Budget-constrained40
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.

Every model, every framing

The eighty-four answers behind the chart above, one cell each: where PushFar and Guider stood in it.
ModelDirectGUParaphraseGUComparativeGUBudget-constrainedGUScale-constrainedGUNegativeGU
Claude Haiku 4.5GUGU
GPT-5.4 miniGU
Gemini 3.5 FlashGU
Perplexity SonarGUGU
Grok 4.1 Fast
Mistral SmallGU
DeepSeek V4 FlashGU
Llama 4 MaverickGU
Qwen 3.7 Flash
Kimi K2GU
GLM 4.7 FlashX
MiniMax M2.5
GPT-6 Luna
Muse Glimmer 30BGUGU
PushFarGU Guider first choice named as an alternative argued againstblank: not namedEach cell is one answer, PushFar on the left and Guider on the right.

The direct prompt

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

Guider 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

4 of 14 modelsThe answer put something else first and named one of the two as an alternative.
Claude Haiku 4.5Mentorloop, Together alternatives: Guider, Qooper
Gemini 3.5 FlashTogether alternatives: Guider, Mentorloop, Qooper
MiniMax M2.5Mentorloop, Together alternatives: Chronus, MentorcliQ, Mentorgain, PushFar
Muse Glimmer 30BMentorloop alternatives: Guider, MentorCity

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
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 nineteen points.
PushFar19%#2 of 12
Guider0%#– of 12
The full small business standing →
Mid-marketThe figures above
PushFar leads by four points.
PushFar9%#4 of 12
Guider6%#6 of 12
The full mid-market standing →
Enterprise
Level: the same share of first choices.
PushFar0%#8 of 8
Guider0%#6 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 Guider

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
“I would recommend Guider if you want the best balance of ease of launch, affordability, and enough structure” Perplexity Sonar · paraphrase prompt · first choice
“Together and Guider are frequently highlighted as top choices” Mistral Small · paraphrase prompt · first choice
Also compared
Together vs PushFarTogether vs GuiderMentorloop vs PushFarMentorloop vs GuiderMentorcliQ vs PushFarMentorcliQ vs GuiderPushFar vs ChronusPushFar vs MentorCityPushFar vs QooperChronus vs GuiderGuider vs MentorCityGuider vs QooperAll twenty-eight head-to-head pages and the full mentoring software 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.