Llama 3.3vsLlama 4 Scout

Llama 3.3 is Meta's most efficient high-performance model, delivering capability comparable to the much larger Llama 3.1 405B while using only 70 billion parameters. This dramatic efficiency gain means organizations can deploy near-frontier AI capabilities on significantly less hardware. The model supports a 128K context window, strong multilingual performance across dozens of languages, and excellent coding and reasoning abilities. As a fully open-source model, it can be self-hosted, fine-tuned for specific domains, and deployed without API costs. Llama 3.3 has become the de facto standard for organizations that need powerful AI but want to maintain control over their infrastructure and data. It's widely available through cloud providers and can run on consumer GPUs.

Llama 4 Scout is Meta's efficient mixture-of-experts model featuring an industry-leading 10 million token context window — the largest of any production model. Despite its enormous context capacity, Scout uses an efficient MoE architecture that keeps inference costs manageable. The model excels at processing extremely large documents, entire codebases, and long conversation histories. As a fully open-source model, it can be self-hosted and fine-tuned. Scout's combination of massive context and open weights makes it uniquely suited for enterprise document processing, legal review, and research applications requiring analysis of very large text corpora.