Llama 3.3 70B: the open-source model powering a new generation of AI recommendations

Everyone's focused on ChatGPT and Gemini. Meanwhile, Llama 3.3 70B is quietly powering dozens of AI products, from Perplexity to custom enterprise chatbots. If you're only optimizing for the big two, you're missing a significant chunk of AI-powered recommendations.

I tested Llama 3.3 70B alongside GPT-4o and Gemini on 200 brand queries. The results were striking: Llama mentioned 23% more niche brands than GPT-4o and 41% more than Gemini.

Llama is a dense transformer model, not a Mixture of Experts like Gemini. This means it processes every query with the full model, which has implications for brand selection.

1. Training data composition. Llama's training data includes a broader range of web sources than GPT-4o. Meta explicitly included more diverse, multilingual, and niche content. This means smaller brands have a better chance of being represented.

2. Pattern matching approach. Without the MoE architecture, Llama relies on more uniform pattern matching across the entire model. It's less likely to filter out brands based on source authority alone.

3. Community fine-tuning effect. Llama is open-source. Companies fine-tune it for specific domains. A fine-tuned Llama for healthcare will have different brand associations than one fine-tuned for finance. This fragmentation creates both challenges and opportunities.

4. Recency bias. Llama's training data cutoff is more recent than GPT-4o's, meaning brands that have been active in 2024-2025 have an advantage.

The GEO research paper found that different models respond to different optimization strategies. Llama specifically responds well to:

1. Community presence. Brands that appear in GitHub discussions, Reddit threads, and open-source communities get mentioned more often. Llama's training data includes more of these sources.

2. Technical content. Documentation, API references, and technical blog posts carry more weight with Llama than with GPT-4o. The model was trained on a lot of technical content.

3. Multilingual presence. Llama 3.3 70B has strong multilingual capabilities. If your brand has presence in multiple languages, Llama is more likely to pick it up.

4. Niche authority. Llama doesn't filter as aggressively on source authority. A mention in a specialized industry forum can be as valuable as a mention in TechCrunch.

| Factor | Llama 3.3 70B | GPT-4o | Gemini | |--------|---------------|--------|