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Mentoring software · September 2026 Edition

PushFar vs ADPList

Zero of twelve models named PushFar first on the direct prompt; zero named ADPList. PushFar was named by eleven of the twelve models and ADPList by twelve and PushFar carries 21 labels and ADPList 20, so the shares are not directly comparable.

PushFar

accepted challenger

Named in one category this edition.

ADPList

accepted challenger

Named in one category this edition.

First-choice share10%2%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate5%15%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#4#7A position in a field of 11; printed, not drawn.
Labels2120A 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 eleven of the twelve direct answers. Together vs PushFar · Together vs ADPList · Mentorloop vs PushFar

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.
PushFarFirst choices, of twelve modelsADPList
Direct00
Paraphrase10
Comparative001 against PushFar
Budget-constrained41
Scale-constrained00
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.

Across every category in the September 2026 Edition, PushFar and ADPList were named in the same answer seventeen times, of the 66 answers naming PushFar and the 32 naming ADPList. In those answers ADPList took the first choice zero times and PushFar four.

Every model, every framing

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

The direct prompt

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

Neither was named

12 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
DeepSeek V4 FlashTogether alternatives: MentorCloud, MentorcliQ, Mentorloop, Qooper, Ten Thousand Coffees
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
PushFar leads by thirty-nine points.
PushFar39%#1 of 11
ADPList0%#– of 11
The full small business standing →
Mid-marketThe figures above
PushFar leads by eight points.
PushFar10%#4 of 11
ADPList2%#7 of 11
The full mid-market standing →
Enterprise
Level: the same share of first choices.
PushFar0%#– of 7
ADPList0%#– of 7
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. Four of five in this category shown.

“PushFar is more affordable but less feature-rich” Kimi K2 · comparative prompt · soft negative
“If you need to scale beyond ~10 people: PushFar (~$200/month flat) is the cheapest way to run a proper company-wide program” DeepSeek V4 Flash · budget prompt · first choice
“PushFar stands out as the top recommendation for mentoring program software” Grok 4.1 Fast · budget prompt · first choice
“start with Mentornity (if under 10 users) or PushFar (~$200/month)” MiniMax M2.5 · budget prompt · first choice

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
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.