PushFar vs Chronus
Zero of fourteen models named PushFar first on the direct prompt; two named Chronus. PushFar was named by twelve of the fourteen models and Chronus by fourteen and PushFar carries 22 labels and Chronus 42, so the shares are not directly comparable.
Named in two categories this edition.
Named in two categories this edition.
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
PushFarFirst choices, of fourteen modelsChronus
Across every category in the October 2026 Edition, PushFar and Chronus were named in the same answer thirty-eight times, of the 70 answers naming PushFar and the 145 naming Chronus. In those answers Chronus took the first choice seven times and PushFar 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 | | | | | ||
| GPT-6 Luna | | | | |||
| Muse Glimmer 30B | |
PushFar Chronus first choice named as an alternative argued againstblank: not namedEach cell is one answer, PushFar on the left and Chronus on the right.The direct prompt
Chronus first, PushFar not the choice
2 of 14 modelsPushFar was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
6 of 14 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
6 of 14 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
PushFar19%#2 of 12Chronus0%#12 of 12Chronus15%#4 of 8PushFar0%#8 of 8What 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 Chronus
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“Legacy enterprise tools (like Chronus or MentorcliQ) are often too complex, highly manual to configure, and cost-prohibitive for mid-market budgets.” Gemini 3.5 Flash · paraphrase prompt · soft negative
“Chronus is strong, but its pricing is quote-based and aimed more at enterprise use, so it's usually not the most budget-friendly starting point.” GPT-5.4 mini · budget prompt · soft negative
“typically start at $10,000+ per year and require you to go through a lengthy "contact sales" process” Gemini 3.5 Flash · budget prompt · soft negative
“Enterprise platforms like MentorcliQ and Chronus are best for organizations that need to run, scale, and measure structured mentoring programs internally” GLM 4.7 FlashX · comparative prompt · first choice
“Chronus is currently one of the most popular platforms for growing companies.” Qwen 3.7 Flash · direct prompt · first choice
“Chronus is currently the gold standard for large-scale corporate programs.” Qwen 3.7 Flash · comparative 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.