AI responses, an independent measurement
Do AI assistants recommend themselves?
Only 2 of 6 AIs put their own brand strictly first.
Claude (49%) · ChatGPT (48%) each cite themselves more than any other assistant in their own answers. Across the rest of the panel: 3 put a rival first and 1 ties with a rival. Grok is almost absent outside its own answers: only 1 of 233 full answers from the other models mention it.
The panelThe 8 compared tools View panel
ChatGPT
Claude
DeepSeek
Gemini
Grok
Microsoft Copilot
Mistral
Perplexity
Who recommends whom? The self-preference matrix ¶
Share of each model's full answers (rows) citing each assistant (columns). The outlined cell is the asked model's own brand: its self-recommendation.
The matrix uses 281 full answers, 7 fewer than the 288 collected responses. Conditional answers and answers without a usable recommendation are excluded; each row's n makes those exclusions visible.
↔ Swipe the matrix to see everything.
| Model asked | ||||||||
|---|---|---|---|---|---|---|---|---|
| 34% | 49% its brand | 2% | 29% | 0% | 5% | 12% | 7% | |
| 35% | 33% | 2% its brand | 35% | 2% | 6% | 4% | 10% | |
| 35% | 40% | 2% | 27% its brand | 0% | 6% | 8% | 6% | |
| 15% | 10% | 0% | 12% | 0% | 8% | 15% its brand | 12% | |
| 48% its brand | 23% | 4% | 40% | 0% | 17% | 12% | 6% | |
| 35% | 40% | 0% | 40% | 19% its brand | 19% | 6% | 6% |
Which tools do AIs recommend most often? ¶
The average recommendation rate is calculated across all tested situations, weighted equally. One assistant may recommend several tools, so these percentages are not expected to add up to 100%.
| # | Tool | Average recommendation rate | |
|---|---|---|---|
| 1 | 34% | ||
| 2 | 33% | ||
| 3 | 29% | ||
| 4 | 14% | ||
| 5 | 9% | ||
| 6 | 8% | ||
| 7 | 3% | ||
| 8 | 2% | ||
The tool each model spontaneously surfaces ¶
Use case by use case, the tool each model most often puts first (every use case counts the same, chatty or not).
This view counts use-case wins; the matrix above counts mentions in full answers. A model can therefore mention its own brand often while a rival wins more use cases.
Tool most often highlighted by each assistant: ChatGPT (6)
For the same use case, a different dominant tool depending on the model ¶
Which use cases AIs associate with which tool ¶
For each cell, the percentage shows the share of responses recommending the tool. Select a dot for details. ‘n’ is the number of responses analyzed; results based on few responses are indicative.
↔ Swipe the matrix to explore use cases.
| Tool | First AI assistant for a beginner | Coding and debugging help | Writing and editing | Research and current information | Data analysis | Studying and learning | Everyday work productivity | Privacy-sensitive tasks |
|---|---|---|---|---|---|---|---|---|
Click a dot to show the detail of an association.
Why do AIs recommend each tool? ¶
The main argument models give for each tool, the price they quote, and the caveat they attach. These claims are reported as model output and have not been independently verified as facts.
Which assistants give a clear recommendation? ¶
Share of answers where the model commits to one tool, vs “it depends…”, vs doesn't commit.
The same need, a different profile, a different tool ¶
For each buyer profile, the tools the models surface most (existing answers sliced by profile).
ChatGPT67%
Claude56%
Microsoft Copilot39%
Gemini61%
Microsoft Copilot28%
Claude22%
Claude92%
Gemini67%
ChatGPT50%
Gemini92%
Microsoft Copilot8%
ChatGPT8%
Perplexity100%
Claude58%
ChatGPT58%
Mistral58%
ChatGPT25%
Claude25%
Other tools that appeared spontaneously ¶
Comparable tools the models cited spontaneously, beyond the tracked panel: worth watching (or adding to the comparison).
How do AIs present each tool? ¶
Breakdown of favorable / neutral / critical verified mentions. Click a tool for a favorable and a critical verbatim.
How it is measured, and what it is not ¶
What we measure
288 responses · 6 models · 8 use cases · 8 tools · query cost $3.95
Each question asks for advice without offering a list of tools. We measure the tools the assistant chooses to mention spontaneously, then how it presents them.
Protocol: 48 questions × 6 models, one response per question-model pair. Models are queried through APIs: this report measures their behavior, not the interface or hidden system prompt of consumer apps.
What these results do not prove
- Not a ranking of the tools' actual quality.
- Not a feature or pricing comparison.
- We measure model behavior, not the truth. Low-n rates are indicative.
Raw data and independent verification
The public dataset contains one row per response, the prompts, exact model IDs, and the 0/1 mentions used to recompute the results.
Download the data and recomputation code →To cite this page
“According to the Opinion Radar observatory dated July 22, 2026, ChatGPT has the highest average recommendation rate in the tested situations (34%), ahead of Claude (33%).”
Reference link: this page (public methodology included). Attribute the claims to the AI models, never to the tools themselves.
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