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Competitive intelligence tools · October 2026 Edition

SpyFu vs Visualping

Zero of fourteen models named SpyFu first on the direct prompt; zero named Visualping. SpyFu was named by thirteen of the fourteen models and Visualping by eleven and SpyFu carries 19 labels and Visualping 17, so the shares are not directly comparable.

SpyFu

accepted challenger

Named in two categories this edition.

Visualping

accepted challenger

Named in three categories this edition.

First-choice share6%4%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate21%6%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#4#5A position in a field of 12; printed, not drawn.
Labels1917A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, SpyFu reading right to left. Rank and label count are printed, not drawn.Kompyte was named alongside these two in twelve of the fourteen direct answers. Kompyte vs SpyFu · Kompyte vs Visualping · Klue vs SpyFu

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

How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
SpyFuFirst choices, of fourteen modelsVisualping
Direct00
Paraphrase00
Comparative001 against SpyFu
Budget-constrained32
Scale-constrained00
Negative003 against SpyFu · 1 against Visualping
Bars are first choices, 0 to 14 each sideModels that argued againstA model can name both, so the two sides of a row do not sum to fourteen.

Across every category in the October 2026 Edition, SpyFu and Visualping were named in the same answer forty-one times, of the 73 answers naming SpyFu and the 99 naming Visualping. In those answers Visualping took the first choice nine times and SpyFu seven.

Every model, every framing

The eighty-four answers behind the chart above, one cell each: where SpyFu and Visualping stood in it.
ModelDirectParaphraseComparativeBudget-constrainedScale-constrainedNegative
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 Visualping first choice named as an alternative argued againstblank: not namedEach cell is one answer, SpyFu on the left and Visualping on the right.

The direct prompt

The plain question, one answer per model, grouped by where SpyFu and Visualping stood in it.

Neither was named

14 of 14 modelsThe answer made no first choice from these two in this category.
Claude Haiku 4.5Klue alternatives: Crayon, Kompyte
GPT-5.4 miniKompyte alternatives: Crayon, Klue, Similarweb
Gemini 3.5 FlashKlue alternatives: BattleReady, Crayon, Kompyte
Perplexity SonarKompyte alternatives: Crayon, Similarweb, ZoomInfo
Grok 4.1 FastKompyte alternatives: Semrush
Mistral SmallKlue, Kompyte
DeepSeek V4 FlashKompyte alternatives: Klue
Llama 4 MaverickKompyte
Qwen 3.7 FlashKlue alternatives: Ahrefs, Clutch, Crayon, G2 Buyer Intent, Kompyte, Semrush
Kimi K2Kompyte alternatives: Klue, Semrush
GLM 4.7 FlashXKompyte alternatives: Crayon, Klue
MiniMax M2.5ZoomInfo alternatives: Google Alerts, Mention, Semrush
GPT-6 LunaKlue alternatives: Crayon
Muse Glimmer 30BKompyte alternatives: Crayon, Klue

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

The same question asked on behalf of a different buyer. Each standing is computed within its segment and they are never added together. The figures above are the mid-market standing, which is the one the category orders by.
Small business
Visualping leads by three points.
Visualping16%#2 of 15
SpyFu12%#3 of 15
The full small business standing →
Mid-marketThe figures above
The order flips: SpyFu leads at mid-market.
SpyFu6%#4 of 12
Visualping4%#5 of 12
The full mid-market standing →
Enterprise
Level: the same share of first choices.
SpyFu0%#– of 7
Visualping0%#– of 7
The full enterprise standing →

What 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 Visualping

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
“the best single starting tool is usually Visualping if your main need is tracking changes on competitors’ websites” Perplexity Sonar · budget prompt · first choice
“Top Recommendation: Visualping (Free tier; paid from ~$10-14/month)” Grok 4.1 Fast · budget prompt · first choice
Also compared

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.