SpyFu vs Google Alerts
Zero of fourteen models named SpyFu first on the direct prompt; zero named Google Alerts. SpyFu was named by thirteen of the fourteen models and Google Alerts by eleven and SpyFu carries 19 labels and Google Alerts 14, so the shares are not directly comparable.
Named in two 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 competitive intelligence tools page.
By framing
SpyFuFirst choices, of fourteen modelsGoogle Alerts
Across every category in the October 2026 Edition, SpyFu and Google Alerts were named in the same answer thirty-five times, of the 73 answers naming SpyFu and the 127 naming Google Alerts. In those answers Google Alerts took the first choice five times and SpyFu four.
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 | |
SpyFu Google Alerts first choice named as an alternative argued againstblank: not namedEach cell is one answer, SpyFu on the left and Google Alerts on the right.The direct prompt
Neither was the first choice, one was named
1 of 14 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
13 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
SpyFu12%#3 of 15Google Alerts3%#6 of 15SpyFu0%#– of 7Google Alerts0%#– of 7What the models said about SpyFu
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“Verdict: Use only for basic PPC keyword research, not for comprehensive competitive intelligence.” GLM 4.7 FlashX · negative prompt · hard negative
“Examples to scrutinize: Tools like some SEO/spy suites (e.g., SpyFu, certain features in SEMrush/Ahrefs) or generic scrapers.” Grok 4.1 Fast · negative prompt · soft negative
“Tools like Semrush, Ahrefs, and SpyFu ... are not full competitive-intelligence platforms” Perplexity Sonar · negative prompt · soft negative
“For the tightest budget, SpyFu appears to be the most cost-effective option specifically designed for competitor research.” MiniMax M2.5 · budget prompt · first choice
“My top overall recommendation: If you can afford ~$29/month, SpyFu gives you the most actionable competitive intelligence” DeepSeek V4 Flash · budget prompt · first choice
“Best low-budget choice: SpyFu—if your competitive intelligence is mainly about SEO and Google Ads.” GPT-6 Luna · 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. Three of three in this category shown.
“some tiers of Owler, Visualping, or Google Alerts) are not full CI tools” Qwen 3.7 Flash · negative prompt · soft negative
“A combination of Google Alerts + Social Media monitoring + free versions of tools like SEMrush or Similarweb often provides the best value.” Claude Haiku 4.5 · budget prompt · first choice
“the best default choice is usually Google Alerts + one low-cost SEO/visibility tool” GPT-5.4 mini · 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.