Meta’s Llama 4 continues to drive enterprise adoption in 2026 — proof that the open-weight strategy works commercially. Enterprises increasingly choose Llama for a simple reason: control.
The Family
Llama 4 Maverick (405B) leads the family; Scout (17B) offers a 10M-token context window — the largest of any model at its size class. Both are free to use under the Llama license and self-hostable.
Why Enterprises Choose Llama
Data sovereignty, predictable cost, and customization: self-hosted Llama keeps data on-premises, avoids per-token pricing entirely, and allows full fine-tuning. For regulated industries, that trumps raw benchmark leadership.
When NOT to Choose Llama
If your workload demands the absolute frontier of reasoning or coding capability, closed models like GPT-5.6 or Claude Opus 5 still lead. Llama is the right choice when capability is sufficient and control matters more.
Bottom Line
The open-versus-closed debate is now a workload decision, not a philosophy. Build your evaluation around what each deployment actually needs.
Related: AI Model Comparison 2026 · AI Models in 2026: Complete Guide
