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Mentoring software · September 2026 Edition

ADPList vs Ten Thousand Coffees

Zero of twelve models named ADPList first on the direct prompt; zero named Ten Thousand Coffees. ADPList was named by twelve of the twelve models and Ten Thousand Coffees by eleven and ADPList carries 20 labels and Ten Thousand Coffees 15, so the shares are not directly comparable.

ADPList

accepted challenger

Named in one category this edition.

Ten Thousand Coffees

accepted challenger

Named in one category this edition.

First-choice share2%2%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate15%0%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#7#8A position in a field of 11; printed, not drawn.
Labels2015A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, ADPList 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 ADPList · Together vs Ten Thousand Coffees · Mentorloop vs ADPList

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.
ADPListFirst choices, of twelve modelsTen Thousand Coffees
Direct00
Paraphrase00
Comparative00
Budget-constrained10
Scale-constrained01
Negative003 against ADPList
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.

Every model, every framing

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

The direct prompt

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

Neither was the first choice, one was named

1 of 12 modelsThe answer put something else first and named one of the two as an alternative.
DeepSeek V4 FlashTogether alternatives: MentorCloud, MentorcliQ, Mentorloop, Qooper, Ten Thousand Coffees

Neither was named

11 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
Perplexity SonarTogether alternatives: MentorCloud, Mentorgain
Grok 4.1 FastTogether alternatives: MentorcliQ, Mentorloop
Mistral SmallMentorgain, Together alternatives: Guider, Mentorloop
Llama 4 MaverickTogether alternatives: Guider, Mentorgain
Qwen 3.7 FlashChronus alternatives: Culture Amp, Glint, MentorciSe, Together
Kimi K2Mentorgain, Together alternatives: Mentorloop, Qooper
GLM 4.7 FlashXMentorgain alternatives: MentorcliQ, Mentorloop, Qooper, Together
MiniMax M2.5Together alternatives: Mentorgain, Mentorloop

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
Level: the same share of first choices.
ADPList0%#– of 11
Ten Thousand Coffees0%#– of 11
The full small business standing →
Mid-marketThe figures above
Level: the same share of first choices.
ADPList2%#7 of 11
Ten Thousand Coffees2%#8 of 11
The full mid-market standing →
Enterprise
Level: the same share of first choices.
ADPList0%#– of 7
Ten Thousand Coffees0%#5 of 7
The full enterprise standing →

What the models said about ADPList

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

“ADPList: A free platform with a large pool of mentors, but limited slots and no accountability built-in.” Llama 4 Maverick · negative prompt · soft negative
“Platforms with Ethical Controversies or "Ghosting" Issues (Exercise Caution) Example: ADPList” Gemini 3.5 Flash · negative prompt · soft negative
“ADPList — ⚠️ Moderate Caution” Kimi K2 · negative prompt · soft negative
“start with a free plan from a reputable platform like ADPList, MicroMentor, or SCORE” Mistral Small · budget prompt · first choice
“Free or community-based platforms (like ADPList, MicroMentor, SCORE, and UStrive) are often safer choices for individuals” Mistral Small · negative prompt · alternative
“Prefer platforms with verified mentors, refunds, and positive volume reviews (e.g., ADPList for free tech mentoring” Grok 4.1 Fast · negative prompt · alternative

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.