Chili Piper vs Default
Two of fourteen models named Chili Piper first on the direct prompt; one named Default. Chili Piper was named by fourteen of the fourteen models and Default by ten and Chili Piper carries 61 labels and Default 25, 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 lead routing and management page.
By framing
Chili PiperFirst choices, of fourteen modelsDefault
Across every category in the November 2026 Edition, Chili Piper and Default were named in the same answer sixty times, of the 313 answers naming Chili Piper and the 66 naming Default. In those answers Default took the first choice zero times and Chili Piper six.
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 | | | | | |
Chili Piper Default first choice named as an alternative argued againstblank: not namedEach cell is one answer, Chili Piper on the left and Default on the right.The direct prompt
Both were the first choice
1 of 14 modelsThe answer named them together, and the judge labeled each a first choice.Chili Piper first, Default an alternative
1 of 14 modelsDefault was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
12 of 14 modelsThe answer put something else first and named one of the two as an alternative.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
Chili Piper5%#7 of 16Default0%#14 of 16Chili Piper7%#4 of 15Default2%#7 of 15Chili Piper11%#2 of 10Default0%#9 of 10What the models said about Chili Piper
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Three of three in this category shown.
“If your primary goal is instantly booking inbound website leads onto rep calendars: Choose Chili Piper or RevenueHero.” Gemini 3.5 Flash · comparative prompt · first choice
“Chili Piper or Default are the strongest picks if your priority is fast, high-converting inbound lead handling” DeepSeek V4 Flash · direct prompt · first choice
“If your main pain point is slow response times to inbound leads: Choose Chili Piper for instant calendar booking.” Qwen 3.7 Flash · comparative prompt · first choice
What the models said about Default
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“Avoid Chili Piper / LeanData / Default unless you have enterprise budget” Muse Glimmer 30B · budget prompt · hard negative
“Avoid: ... Default ($15,000/year)” Kimi K2 · budget prompt · hard negative
“can easily cost thousands of dollars per year, making them impractical for smaller budgets” Gemini 3.5 Flash · budget prompt · soft negative
“Chili Piper or Default are the strongest picks” DeepSeek V4 Flash · direct prompt · first choice
“Default / Qualified: Modern, all-in-one inbound routing, scheduling, and enrichment platforms built for agile RevOps teams.” Gemini 3.5 Flash · scale prompt · alternative
“Modern mid-market teams wanting routing + scheduling in one clean UX | Quote-based | Newer platform; less proven at scale” DeepSeek V4 Flash · paraphrase 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.