Kompyte vs Visualping
Nine of fourteen models named Kompyte first on the direct prompt; zero named Visualping. Kompyte was named by fourteen of the fourteen models and Visualping by eleven and Kompyte carries 56 labels and Visualping 17, so the shares are not directly comparable.
Named in two categories this edition.
Named in three 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
KompyteFirst choices, of fourteen modelsVisualping
Across every category in the October 2026 Edition, Kompyte and Visualping were named in the same answer sixty times, of the 176 answers naming Kompyte and the 99 naming Visualping. In those answers Visualping took the first choice twelve times and Kompyte twenty-one.
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 | | | | |
Kompyte Visualping first choice named as an alternative argued againstblank: not namedEach cell is one answer, Kompyte on the left and Visualping on the right.The direct prompt
Kompyte first, Visualping not the choice
9 of 14 modelsVisualping was named in the answer but not as the choice, or not at all.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
2 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
Kompyte26%#1 of 15Visualping16%#2 of 15Kompyte38%#1 of 12Visualping4%#5 of 12What the models said about Kompyte
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“Avoid if you work in a fast-moving industry.” DeepSeek V4 Flash · negative prompt · hard negative
“Products such as Crayon, Klue, and Kompyte ... do not explain buyer decision-making or deal-level context” Perplexity Sonar · negative prompt · soft negative
“some users report it can become pricey relative to its utility if you only need basic tracking” Qwen 3.7 Flash · negative prompt · soft negative
“The best competitive intelligence tool for a mid-market B2B company is Kompyte, as it is considered the strongest mid-market option” Llama 4 Maverick · direct prompt · first choice
“Kompyte is consistently ranked as the best dedicated competitive intelligence platform for companies on a tight budget” Qwen 3.7 Flash · budget prompt · first choice
“Pick Crayon or Kompyte. These tools are built to surface competitor updates in a format sellers can use quickly.” GPT-5.4 mini · comparative 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
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.