Amplemarket vs Cognism
One of twelve models named Amplemarket first on the direct prompt; one named Cognism. Amplemarket was named by ten of the twelve models and Cognism by twelve and Amplemarket carries 21 labels and Cognism 50, so the shares are not directly comparable.
United States. Named in five categories this edition.
Named in eight categories 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 data enrichment page.
By framing
AmplemarketFirst choices, of twelve modelsCognism
Across every category in the September 2026 Edition, Amplemarket and Cognism were named in the same answer sixty-seven times, of the 134 answers naming Amplemarket and the 361 naming Cognism. In those answers Cognism took the first choice four times and Amplemarket twenty-two.
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 | | | | |
Amplemarket Cognism first choice named as an alternative argued againstblank: not namedEach cell is one answer, Amplemarket on the left and Cognism on the right.The direct prompt
Both were the first choice
1 of 12 modelsThe answer named them together, and the judge labeled each a first choice.Neither was the first choice, one was named
7 of 12 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
4 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
Amplemarket2%#– of 10Cognism0%#9 of 10Amplemarket7%#3 of 10Cognism4%#4 of 10Amplemarket12%#3 of 9Cognism12%#2 of 9What the models said about Amplemarket
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Five of five in this category shown.
“Pros: Top scores (29/30 data). Cons: Higher cost.” Grok 4.1 Fast · direct prompt · soft negative
“High entry (~$7K/year min, annual only)” Grok 4.1 Fast · paraphrase prompt · soft negative
“Amplemarket is the best overall tool, offering high-accuracy enrichment with AI, engagement, deliverability, and intent signals in one platform.” Llama 4 Maverick · comparative prompt · first choice
“For mid-market companies, Apollo.io, Amplemarket, or Cognism offer a good balance of features and price depending on geography.” Claude Haiku 4.5 · direct prompt · first choice
“some recommended tools include: Amplemarket: Offers curated multi-source enrichment, high accuracy, and engagement data.” Llama 4 Maverick · negative prompt · first choice
What the models said about Cognism
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Four of four in this category shown.
“Data quality gaps outside the "Diamond Data" verified subset; Limited US coverage” MiniMax M2.5 · negative prompt · hard negative
“Avoid enterprise tools like ZoomInfo/Cognism ($15K+/yr)—not budget-friendly” Grok 4.1 Fast · budget prompt · hard negative
“I'd generally recommend Cognism if your priority is high-quality contact data, CRM enrichment, and compliance-conscious prospecting” GPT-5.4 mini · paraphrase prompt · first choice
“For mid-market companies, Apollo.io, Amplemarket, or Cognism offer a good balance of features and price depending on geography.” Claude Haiku 4.5 · direct 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.