11x vs Qualified Piper
Two of fourteen models named 11x first on the direct prompt; zero named Qualified Piper. 11x was named by fourteen of the fourteen models and Qualified Piper by eight and 11x carries 37 labels and Qualified Piper 13, so the shares are not directly comparable.
Named in two categories 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 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 AI SDR agents page.
By framing
11xFirst choices, of fourteen modelsQualified Piper
Across every category in the November 2026 Edition, 11x and Qualified Piper were named in the same answer twenty-four times, of the 124 answers naming 11x and the 41 naming Qualified Piper. In those answers Qualified Piper took the first choice zero times and 11x nine.
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 | |
11x Qualified Piper first choice named as an alternative argued againstblank: not namedEach cell is one answer, 11x on the left and Qualified Piper on the right.The direct prompt
11x first, Qualified Piper not the choice
2 of 14 modelsQualified Piper was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
2 of 14 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
10 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
11x25%#1 of 10Qualified Piper4%#5 of 10What the models said about 11x
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“the controversies around misrepresentation and effectiveness make it a high-risk choice... Multiple sources describe it as over-hyped with "shady tactics."” Grok 4.20 · negative prompt · hard negative
“The vendors most commonly flagged for caution: 11x.ai, Artisan, and any tool promising full autonomy without proper infrastructure safeguards.” Kimi K2 · negative prompt · hard negative
“Major red flags: Former employees report 70-80% client churn, product hallucinations, and false customer claims” GLM 4.7 FlashX · negative prompt · hard negative
“Choose 11x if you have a larger budget, want completely hands-off execution, and require multi-channel capabilities” Gemini 3.5 Flash · comparative prompt · first choice
“11x is the strongest default choice if you want a genuinely autonomous outbound SDR” Perplexity Sonar · direct prompt · first choice
“11x.ai (Alice + Mike) – Best overall for mid-market scaling outbound” Grok 4.20 · direct prompt · first choice
What the models said about Qualified Piper
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Three of four in this category shown.
“If you want the lowest cost to start, pick Qualified.” GPT-5.4 mini · budget prompt · first choice
“If by "prospects" you mainly mean engaging people who visit your website, rather than cold outbound, I'd look at Qualified instead.” GPT-6 Luna · paraphrase prompt · alternative
“Choose Qualified (Piper) if your biggest bottleneck is capturing, qualifying, and converting visitors” Gemini 3.5 Flash · comparative 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.