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Mentoring software · September 2026 Edition

Mentorgain vs Ten Thousand Coffees

Three of twelve models named Mentorgain first on the direct prompt; zero named Ten Thousand Coffees. Mentorgain was named by eight of the twelve models and Ten Thousand Coffees by eleven and Mentorgain carries 22 labels and Ten Thousand Coffees 15, so the shares are not directly comparable.

Mentorgain

accepted challenger

Named in one category this edition.

Ten Thousand Coffees

accepted challenger

Named in one category this edition.

First-choice share12%2%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#3#8A position in a field of 11; printed, not drawn.
Labels2215A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Mentorgain reading right to left. Rank and label count are printed, not drawn.Together was named alongside these two in eleven of the twelve direct answers. Together vs Mentorgain · Together vs Ten Thousand Coffees · Mentorloop vs Mentorgain

Share is the count of first choices across the direct, paraphrase, budget and scale prompts over all twelve 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 twelve models made each the first choice, per way of asking, and how many argued against it.
MentorgainFirst choices, of twelve modelsTen Thousand Coffees
Direct30
Paraphrase20
Comparative00
Budget-constrained00
Scale-constrained11
Negative00
Bars are first choices, 0 to 12 each sideModels that argued againstA model can name both, so the two sides of a row do not sum to twelve.

Across every category in the September 2026 Edition, Mentorgain and Ten Thousand Coffees were named in the same answer eleven times, of the 49 answers naming Mentorgain and the 43 naming Ten Thousand Coffees. In those answers Ten Thousand Coffees took the first choice zero times and Mentorgain zero.

Every model, every framing

The seventy-two answers behind the chart above, one cell each: where Mentorgain and Ten Thousand Coffees 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
Mentorgain Ten Thousand Coffees first choice named as an alternative argued againstblank: not namedEach cell is one answer, Mentorgain on the left and Ten Thousand Coffees on the right.

The direct prompt

The plain question, one answer per model, grouped by where Mentorgain and Ten Thousand Coffees stood in it.

Mentorgain first, Ten Thousand Coffees not the choice

3 of 12 modelsTen Thousand Coffees was named in the answer but not as the choice, or not at all.
Mistral SmallMentorgain, Together alternatives: Guider, Mentorloop
Kimi K2Mentorgain, Together alternatives: Mentorloop, Qooper
GLM 4.7 FlashXMentorgain alternatives: MentorcliQ, Mentorloop, Qooper, Together

Neither was the first choice, one was named

4 of 12 modelsThe answer put something else first and named one of the two as an alternative.
Perplexity SonarTogether alternatives: MentorCloud, Mentorgain
DeepSeek V4 FlashTogether alternatives: MentorCloud, MentorcliQ, Mentorloop, Qooper, Ten Thousand Coffees
Llama 4 MaverickTogether alternatives: Guider, Mentorgain
MiniMax M2.5Together alternatives: Mentorgain, Mentorloop

Neither was named

5 of 12 modelsThe answer made no first choice from these two in this category.
Claude Haiku 4.5Mentorloop alternatives: Chronus, Together
GPT-5.4 miniChronus alternatives: MentorcliQ, Mentorloop, Qooper
Gemini 3.5 FlashTogether alternatives: Mentorloop, Qooper
Grok 4.1 FastTogether alternatives: MentorcliQ, Mentorloop
Qwen 3.7 FlashChronus alternatives: Culture Amp, Glint, MentorciSe, Together

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
Mentorgain leads by two points.
Mentorgain2%#7 of 11
Ten Thousand Coffees0%#– of 11
The full small business standing →
Mid-marketThe figures above
Mentorgain leads by ten points.
Mentorgain12%#3 of 11
Ten Thousand Coffees2%#8 of 11
The full mid-market standing →
Enterprise
Mentorgain leads by two points.
Mentorgain2%#– of 7
Ten Thousand Coffees0%#5 of 7
The full enterprise standing →

What the models said about Mentorgain

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

“I'd start with Together Platform or Mentorgain for a demo. They hit the sweet spot of functionality without enterprise complexity.” Kimi K2 · paraphrase prompt · first choice
“the best employee mentoring platform for a mid-sized B2B company would be MentorGain, Together Platform, or PushFar” Llama 4 Maverick · paraphrase prompt · first choice
“Best overall value for a mid‑market B2B company (fast go‑live, low cost, strong features): Mentorgain” GLM 4.7 FlashX · direct prompt · first choice

What the models said about Ten Thousand Coffees

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

“Mid-market / fast deployment: Together, Mentorloop, Ten Thousand Coffees, Mentorgain” DeepSeek V4 Flash · scale prompt · first choice
“specializes in automated, smart introductions to break down organizational silos” Gemini 3.5 Flash · comparative prompt · alternative
“Best For: Networking, peer-to-peer learning, and organic connection.” GLM 4.7 FlashX · comparative prompt · alternative
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.