Custify vs Planhat
Zero of fourteen models named Custify first on the direct prompt; one named Planhat. Custify was named by thirteen of the fourteen models and Planhat by fourteen and Custify carries 34 labels and Planhat 49, so the shares are not directly comparable.
Named in three categories this edition.
Named in three 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 customer success platforms page.
By framing
CustifyFirst choices, of fourteen modelsPlanhat
Across every category in the November 2026 Edition, Custify and Planhat were named in the same answer sixty-five times, of the 114 answers naming Custify and the 187 naming Planhat. In those answers Planhat took the first choice one time and Custify fifteen.
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 4.20 | | | | | ||
| 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 | | | |
Custify Planhat first choice named as an alternative argued againstblank: not namedEach cell is one answer, Custify on the left and Planhat on the right.The direct prompt
Planhat first, Custify not the choice
1 of 14 modelsCustify was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
11 of 14 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
2 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
Custify31%#1 of 10Planhat0%#9 of 10Planhat10%#3 of 9Custify0%#9 of 9What the models said about Custify
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Four of five in this category shown.
“Gainsight and Custify can feel restrictive if you need highly tailored workflows or UI behavior” GPT-5.4 mini · negative prompt · soft negative
“Custify: A customer success platform that offers a free plan and is designed for B2B and SaaS customer success teams.” Llama 4 Maverick · budget prompt · first choice
“Custify is the best overall value for a budget-conscious company with a real (if small) CS operation.” DeepSeek V4 Flash · budget prompt · first choice
“Custify is widely recommended as the best option for budget-conscious teams.” Claude Haiku 4.5 · budget prompt · first choice
What the models said about Planhat
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Five of five in this category shown.
“Called "useless" in some threads, possibly due to rollout or fit, with complaints about it not delivering intended value.” Grok 4.20 · negative prompt · soft negative
“Steep learning curve for customization... Some users report it as "useless"” DeepSeek V4 Flash · negative prompt · soft negative
“ChurnZero or Planhat are generally considered the best balance of functionality and usability” MiniMax M2.5 · direct prompt · first choice
“Mid-market companies wanting a balance of power and ease of use | 3–4 months | Strong admin experience, clean UX, and fast implementation” GLM 4.7 FlashX · scale prompt · alternative
“Flexible for mid-market, with strong analytics, segmentation, and growth tools. Scales to enterprise but starts simpler.” Grok 4.20 · scale prompt · alternative
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.