Ninar AI At Ninar AI, we're constantly sifting through the evolving landscape of AI-generated answers to bring brands clear, actionable insights. Today, we're sharing a particularly striking finding from our recent scans: Mistral AI mentioned brands in 52.3% of its responses. This makes Mistral the most brand-forward engine we observed in this dataset, significantly outperforming others like Gemini and even Claude.
We've analyzed 44,336 total AI answers across 9 different AI engines. Our mission at Ninar AI is to measure how brands appear in these AI-generated conversations. For this specific report, we focused on brand mention rates across a subset of engines where we had sufficient data points for comparison. The results highlight crucial differences in how various AI models integrate, or omit, brand information. This isn't just a technical curiosity; it has direct implications for brand visibility and strategic planning in the age of AI.
Understanding Brand Visibility with Ninar AI
Our core offering at Ninar AI is about giving brands visibility into the AI dimension. We know that traditional SEO and SEM metrics don't fully capture how AI engines are talking about, or failing to talk about, your brand. This is why Ninar AI tracks brand mentions, sentiment, and other critical data points across engines like ChatGPT, Gemini, Perplexity, Claude, AI Overviews, AI Mode, Meta AI, Grok, and DeepSeek. For this particular analysis, we pulled data from engines where we had a sufficient number of 'probes' (queries designed to elicit brand mentions) to draw meaningful conclusions.
When we talk about 'mention rate' at Ninar AI, we're referring to the percentage of times an AI engine included a specific brand name in its response to a relevant query. A higher mention rate indicates that the engine is more likely to bring up a brand when a user asks about a related product, service, or industry. This is gold for brands looking to stay top-of-mind. Conversely, a low mention rate means your brand might be invisible in these critical AI interactions, even if you rank well on traditional search engines.
The Surprising Leaders: Mistral and Llama
One of the most compelling insights from our Ninar AI scans is the strong performance of Mistral and Llama in terms of brand mention rates. Mistral, with 65 total probes, generated 34 brand mentions, achieving an impressive 52.3% mention rate. Hot on its heels was Llama, also with 65 total probes, which produced 32 mentions for a 49.2% mention rate. These numbers suggest that these particular engines, perhaps due to their training data, architecture, or prompt interpretation, are more inclined to surface brand information.
What does this mean for brands using Ninar AI to track their performance? It suggests that if your target audience is interacting with Mistral or Llama, there's a higher probability your brand could be mentioned. This isn't just about direct queries; it's about contextual mentions. If a user asks, "What's the best way to [solve a problem your brand addresses]?", a brand-forward AI might name specific brands, while others might offer generic advice. Ninar AI's platform helps us pinpoint these differences, allowing our clients to understand where their AI visibility is strongest.
Claude and Gemini: A Mid-Tier Performance
Moving down the list, we see Claude and Gemini in the mid-tier for brand mention rates. Claude, with a more substantial 160 total probes, had 69 mentions, resulting in a 43.1% mention rate. Gemini, with 95 total probes, yielded 27 mentions, giving it a 28.4% mention rate. While these aren't as high as Mistral or Llama, they still represent a significant opportunity for brands. Claude's higher probe count gives us a bit more confidence in its consistent behavior, showing it's a valuable platform for brand visibility that shouldn't be overlooked.
For brands monitoring their AI presence with Ninar AI, these figures indicate that while Claude and Gemini are mentioning brands, there's likely more variability or a higher bar for inclusion compared to Mistral and Llama. This could be due to stricter content guidelines, a preference for broad informational answers, or different approaches to integrating commercial entities. Understanding these nuances through Ninar AI's data allows brands to tailor their strategies. Perhaps certain types of content or specific product details resonate better with these engines, leading to higher mention rates.
The Silence from ChatGPT and Perplexity
Perhaps the most unexpected finding in this particular dataset was the complete absence of brand mentions from ChatGPT and Perplexity. Both engines registered 5 total probes, and in both cases, they delivered 0 mentions, resulting in a 0.0% mention rate. While the number of probes for these two engines in this specific dataset is relatively low compared to others, this zero-mention rate is still a striking observation. It suggests that, for the types of queries we ran in this particular test, these engines did not surface any brand information.
This "AI silence" is a critical data point for brands. It doesn't necessarily mean these engines will never mention brands, but it does highlight that their propensity to do so can be highly contextual and potentially lower than other models. Ninar AI helps brands understand these blind spots. If your brand isn't appearing in responses from widely used engines like ChatGPT, you have a significant visibility gap. This zero-mention rate could be due to a variety of factors: the specific nature of the queries, strict commercial content policies, or a preference for highly generalized information. Ninar AI continuously monitors these trends to provide a complete picture.
Actionable Insights for Brands from Ninar AI's Data
Based on these findings from Ninar AI's platform, we've identified a few key takeaways for brands navigating the AI landscape:
1. Prioritize Visibility on Brand-Forward Engines: Our Ninar AI data clearly shows that some engines, like Mistral (52.3% mention rate) and Llama (49.2% mention rate), are significantly more likely to mention brands. This means if you're developing content or optimizing your presence, it's crucial to understand how these specific engines are trained and how they interpret information. Brands should investigate if there are specific content formats, data structures, or semantic signals that these engines favor, increasing the likelihood of a mention. Ninar AI's ongoing monitoring can help pinpoint these patterns.
2. Don't Overlook the Nuances of Each AI Engine: The vast difference between Mistral's 52.3% and ChatGPT's 0.0% (in this specific dataset) isn't just a number; it's an indicator of diverse AI personalities. Brands can't assume a one-size-fits-all AI strategy. What works for one engine might not work for another. Ninar AI tracks this across 9 engines precisely because we understand these differences are vital. Brands need to understand which engines their audience uses and tailor their strategies accordingly, perhaps focusing on broad informational content for less brand-forward AIs and specific product details for more brand-friendly ones.
3. Identify and Address "AI Silence" Gaps: The 0.0% mention rate from ChatGPT and Perplexity, even with limited probes, serves as a stark warning. If your brand isn't being mentioned by certain popular AI engines, it represents a significant gap in your visibility. Ninar AI helps identify these gaps. Brands need to actively investigate why this "AI silence" is occurring. Is it a lack of structured data? Are there specific content gaps? Are there opportunities to partner or integrate with these platforms in ways that encourage brand mentions? This isn't about gaming the system, but ensuring your brand is present when relevant. Ninar AI's insights can guide this diagnostic process.
The world of AI search is dynamic, and brand visibility within it is a moving target. What Ninar AI's data consistently shows is that active monitoring and a nuanced understanding of each AI engine's behavior are not just nice-to-haves, but essentials for any brand seeking to maintain relevance and reach in this new era.
This analysis is based on 44,336 AI engine responses processed through Ninar AI's scanning infrastructure in May 2024.