Mention vs Google Alerts
Zero of fourteen models named Mention first on the direct prompt; zero named Google Alerts. Mention was named by thirteen of the fourteen models and Google Alerts by ten and Mention carries 33 labels and Google Alerts 18, so the shares are not directly comparable.
Named in five categories this edition.
By Google, Mountain View, California, United States, founded 1998. Named in six 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 PR and media monitoring page.
By framing
MentionFirst choices, of fourteen modelsGoogle Alerts
Across every category in the October 2026 Edition, Mention and Google Alerts were named in the same answer fifty times, of the 124 answers naming Mention and the 127 naming Google Alerts. In those answers Google Alerts took the first choice eight times and Mention ten.
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 | | | |
Mention Google Alerts first choice named as an alternative argued againstblank: not namedEach cell is one answer, Mention on the left and Google Alerts on the right.The direct prompt
Neither was the first choice, one was named
3 of 14 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
11 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
Mention17%#3 of 11Google Alerts0%#6 of 11Mention11%#4 of 13Google Alerts4%#8 of 13Mention0%#– of 8Google Alerts0%#– of 8What the models said about Mention
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of seven in this category shown.
“it now starts at ~$599/month — it's no longer a viable option for small budgets.” DeepSeek V4 Flash · budget prompt · hard negative
“Mention is now enterprise-only, so it is no longer a limited-budget choice.” Muse Glimmer 30B · budget prompt · hard negative
“Now ~$599/month (discontinued cheaper plans in 2026)” Kimi K2 · budget prompt · hard negative
“Respona (for outreach) and Mention or Awario (for monitoring) are excellent choices” Mistral Small · paraphrase prompt · first choice
“I'd pick Mention for most small teams, because it is explicitly positioned as affordable” Perplexity Sonar · budget prompt · first choice
“Add Mention ($29/month) if you want real-time social + news monitoring in one place” GLM 4.7 FlashX · budget prompt · first choice
What the models said about Google Alerts
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Four of four in this category shown.
“Avoid free/low-tier options like Google Alerts for anything beyond casual use—they miss depth” Grok 4.1 Fast · negative prompt · hard negative
“While free, Google Alerts is generally considered too basic for professional PR monitoring.” GLM 4.7 FlashX · negative prompt · hard negative
“basic tools (like Google Alerts and Excel sheets) aren't robust enough” Gemini 3.5 Flash · paraphrase prompt · soft negative
“the best starting point is often free or very low-cost tools like Google Alerts... Test Google Alerts first.” Grok 4.1 Fast · budget 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.