Semrush vs AlphaSense
Zero of fourteen models named Semrush first on the direct prompt; zero named AlphaSense. Semrush was named by thirteen of the fourteen models and AlphaSense by fourteen and Semrush carries 27 labels and AlphaSense 36, so the shares are not directly comparable.
Boston, United States, founded 2008. Named in ten 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 market intelligence platforms page.
By framing
SemrushFirst choices, of fourteen modelsAlphaSense
Across every category in the October 2026 Edition, Semrush and AlphaSense were named in the same answer eighty-five times, of the 407 answers naming Semrush and the 176 naming AlphaSense. In those answers AlphaSense took the first choice eight times and Semrush 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 4.1 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 | | | |
Semrush AlphaSense first choice named as an alternative argued againstblank: not namedEach cell is one answer, Semrush on the left and AlphaSense on the right.The direct prompt
Neither was the first choice, one was named
7 of 14 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
7 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
Semrush9%#2 of 16AlphaSense0%#15 of 16AlphaSense17%#2 of 14Semrush0%#14 of 14What the models said about Semrush
No label in this category carried a quote.
What the models said about AlphaSense
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of seven in this category shown.
“Avoid enterprise-oriented platforms like Crayon, Klue, or AlphaSense” Perplexity Sonar · budget prompt · hard negative
“Avoid enterprise platforms — Klue, Crayon, AlphaSense, and ZoomInfo” Kimi K2 · budget prompt · hard negative
“Avoid These if Money is Tight: ... AlphaSense (~$12K–$50K+/year)” DeepSeek V4 Flash · budget prompt · hard negative
“Start with established players like AlphaSense, Euromonitor (Passport), or CB Insights that have institutional validation” Kimi K2 · negative prompt · first choice
“My default recommendation is AlphaSense if your team needs to understand markets, competitors, and companies” GPT-6 Luna · paraphrase prompt · first choice
“Choose AlphaSense if your team spends lots of time searching documents and synthesizing research.” GPT-5.4 mini · 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.