Mentorloop vs PushFar
One of twelve models named Mentorloop first on the direct prompt; zero named PushFar. Mentorloop was named by twelve of the twelve models and PushFar by eleven and Mentorloop carries 36 labels and PushFar 21, so the shares are not directly comparable.
Named in one category this edition.
Named in one category this edition.
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
MentorloopFirst choices, of twelve modelsPushFar
Across every category in the September 2026 Edition, Mentorloop and PushFar were named in the same answer forty-seven times, of the 98 answers naming Mentorloop and the 66 naming PushFar. In those answers PushFar took the first choice thirteen times and Mentorloop eight.
Every model, every framing
| Model | Direct | Paraphrase | Comparative | Budget-constrained | Scale-constrained | Negative |
|---|---|---|---|---|---|---|
| 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 | | | |
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
Mentorloop first, PushFar not the choice
1 of 12 modelsPushFar was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
8 of 12 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
3 of 12 modelsThe answer made no first choice from these two in this category.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
PushFar39%#1 of 11Mentorloop20%#2 of 11Mentorloop12%#2 of 11PushFar10%#4 of 11Mentorloop0%#7 of 7PushFar0%#– of 7What the models said about Mentorloop
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of seven in this category shown.
“lower entry price, but note that Smart Match and HRIS are locked behind the Enterprise tier” Perplexity Sonar · direct prompt · soft negative
“Mentorloop offers a lower entry price but locks some features behind its Enterprise plan” Llama 4 Maverick · direct prompt · soft negative
“mid-sized fit over niche/SMB tools (e.g., Mentorloop)” Grok 4.1 Fast · paraphrase prompt · soft negative
“Mentorloop is the well-known mid-market option ... a sensible pick when you want a real program but are not a Fortune 500.” Claude Haiku 4.5 · direct prompt · first choice
“Together Software or Mentorloop are likely your best bets due to their ease of use and mid-market focus.” Claude Haiku 4.5 · paraphrase prompt · first choice
“Mentorloop is the most practical starting point due to its low administrative barrier and high ROI.” Gemini 3.5 Flash · paraphrase 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. 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
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.