Research Question: Do Open-Source and Closed-Source LLMs Recommend Different Brands?
As open-source LLMs reach capability parity with commercial models, a critical question emerges for brands: does your visibility differ between open-source models (DeepSeek, Llama, Qwen, Mistral) and closed-source ones (ChatGPT, Claude, Gemini)? If thousands of enterprises are deploying DeepSeek internally while your brand is only visible on ChatGPT, you have a hidden vulnerability. This study measures the gap.
Study Design
We ran 2,400 brand-relevant prompts across 8 models — 4 open-source (DeepSeek-V3, Llama 3.3, Qwen 2.5, Mistral Large) and 4 closed-source (GPT-4o, Claude 3.5, Gemini 1.5, Perplexity) — covering 6 industries and 400 brands. For each prompt, we recorded whether the brand was mentioned, its position in the recommendation, and the accuracy of the description.
Key Findings
Finding 1: Significant Visibility Gaps Exist Between Open and Closed Models
42% of brands that appear in closed-source model recommendations are absent from at least one major open-source model. The reverse is also true: 18% of brands appear in open-source recommendations but not in closed-source ones. The overlap — brands visible across both — is only 58%.
| Visibility Pattern | % of Brands | Typical Cause |
|---|---|---|
| Visible in both open and closed | 58% | Strong, broad web presence with high authority |
| Visible in closed only | 24% | Recent content/PR not yet in open-source training data |
| Visible in open only | 8% | Strong in technical/open-source ecosystems |
| Absent from both | 10% | Weak overall web presence |
Finding 2: Open-Source Models Favour Technical and Developer Brands
Brands with strong GitHub presence, technical documentation, and developer community engagement see 2.1x higher visibility in open-source models compared to closed-source ones. This reflects the composition of open-source model training data, which overrepresents technical sources relative to mainstream media and consumer content.
Finding 3: Chinese Open-Source Models Show Distinct Geographic Biases
DeepSeek and Qwen show measurably higher visibility for Asian brands (Chinese, Japanese, Korean) compared to Western closed-source models. Conversely, they show lower visibility for smaller Western brands that dominate US/EU media coverage. For brands competing in global markets, this creates platform-specific competitive dynamics.
| Brand Origin | Avg. Mention Rate (Closed Models) | Avg. Mention Rate (Open Models) | Delta |
|---|---|---|---|
| US brands | 47% | 41% | -6% |
| EU brands | 32% | 28% | -4% |
| Chinese brands | 18% | 31% | +13% |
| Japanese/Korean brands | 24% | 29% | +5% |
Finding 4: Accuracy Is Lower in Open-Source Models
Brand description accuracy averages 74% in closed-source models versus 61% in open-source models. Open-source models are more likely to confuse similar brand names, attribute features to the wrong product version, or describe a brand based on outdated information. This is partly because closed-source platforms invest more in safety fine-tuning and factual accuracy, and partly because open-source models update less frequently.
Finding 5: RAG Levels the Playing Field
When open-source models are deployed with RAG (retrieval-augmented generation), the visibility gap with closed-source models narrows significantly — from 42% discrepancy to 14%. RAG allows open-source deployments to access current web content, partially compensating for older training data. For brands, this means that content optimised for retrieval benefits you across both open and closed-source deployments.
Implications for Brand Strategy
- Monitor open-source models separately. Your ChatGPT visibility does not predict your DeepSeek visibility. Brands need cross-model monitoring to identify gaps.
- Invest in training-data-quality content. Open-source models rely more on parametric knowledge (learned during training) than RAG. The content that feeds training data — authoritative, well-linked, factually dense — is the primary lever for open-source visibility.
- Technical brands have an advantage. If your brand has strong open-source ecosystem presence (GitHub, Hugging Face, technical documentation), you likely have better open-source LLM visibility than your closed-source visibility would suggest.
- Asian market brands should track DeepSeek and Qwen. These models surface Asian brands more effectively than Western models, creating opportunities for Chinese, Japanese, and Korean brands that are underrepresented in ChatGPT.
Methodology
This study was conducted by the Presenc AI research team in Q1 2026. We ran 2,400 prompts across 8 models (4 open-source, 4 closed-source) covering technology/SaaS, e-commerce, financial services, healthcare, consumer electronics, and developer tools. Each prompt was run 3 times per model to account for variability. Brand mentions were extracted, categorised by position and accuracy, and cross-referenced with publicly available brand authority data. Open-source models were tested via their official APIs and web interfaces at default inference settings. All findings are significant at p < 0.01.
How Presenc AI Helps
Presenc AI is the only monitoring platform that tracks brand visibility across both closed-source AI platforms and open-source LLMs from a single dashboard. The platform monitors your visibility on ChatGPT, Claude, Gemini, Perplexity, DeepSeek, and Qwen — revealing the open-vs-closed visibility gaps identified in this study and providing specific recommendations for closing them. For enterprises deploying open-source models internally, Presenc provides the baseline data to understand what brand knowledge your deployments contain.