Llama 4: Everything You Need to Know
Meta's Llama 4 family brings MoE architecture, native multimodality, and a 10M-token context window to open-weight AI. Here's the full breakdown.
Key Takeaways
| Takeaway | Details |
|---|---|
| MoE Architecture | Llama 4 adopts Mixture of Experts architecture, departing from Llama 3's dense transformer design. |
| 10M Context Window | Scout model offers the largest context window of any openly available model at 10 million tokens. |
| Competitive Performance | Maverick achieves 85.5% MMLU, 88.0% HumanEval, and 84.7% Math, competing with GPT-4o and Claude 3.5 Sonnet. |
| Commercial License | Llama 4 Community License permits commercial use except for companies with over 700 million monthly active users. |
| Native Multimodality | Maverick processes vision and text natively from training rather than as an add-on capability. |
| Self-Hosting Option | Quantized Maverick runs on four A100 GPUs, eliminating per-token costs for organizations with scale. |
What Is Llama 4?
Llama 4 is Meta AI's fourth generation of open-weight large language models, released in April 2025. It represents a significant architectural leap from Llama 3. Where Llama 3 used a dense transformer, Llama 4 adopts Mixture of Experts (MoE) architecture and adds native multimodal capabilities.
The Llama 4 family consists of three models: Scout (the efficient option with a 10M context window), Maverick (the flagship multimodal model), and Behemoth (the largest, most capable model, released under the Llama 4 Community License for most uses).
Llama 4 Scout: The Long Context Marvel
Scout is the most technically remarkable member of the family. Its 10 million-token context window is the largest of any openly available model. It enables processing entire large codebases, books, or years of conversation history in a single prompt. It uses a 16-expert MoE architecture that makes it surprisingly efficient for its capability level.
At $0.05/$0.17 per million tokens, Scout represents extraordinary value for long-document analysis, multi-file code understanding, and large-scale RAG applications. Its main limitation is that very long contexts can exhibit the 'lost in the middle' problem for retrieval-heavy tasks.
Llama 4 Maverick: The Open Frontier
Maverick uses 128 experts and processes up to 1M tokens. It competes credibly with GPT-4o and Claude 3.5 Sonnet across most benchmarks, a remarkable achievement for an open-weight model. MMLU: 85.5%, HumanEval: 88.0%, Math: 84.7%. It is natively multimodal, enabling vision and text understanding from training rather than as a bolt-on.
Being open-weight means Maverick can be fine-tuned, quantized, and self-hosted. For organizations that need frontier-quality performance with data sovereignty, Maverick is the strongest option currently available. It has already spawned dozens of fine-tuned variants on Hugging Face within weeks of release.
The Llama 4 License
Llama 4 is released under the Llama 4 Community License, which permits commercial use for most organizations. The key restriction: companies with over 700 million monthly active users must contact Meta directly for a license. Competing AI service providers are also restricted from using Llama 4 as a backend without permission.
For the vast majority of developers and organizations, the license is effectively permissive. You can fine-tune, deploy, and build products on top of Llama 4 without royalties. This makes Llama 4 viable for serious commercial applications.
Llama 4 vs Closed Models: How Does It Stack Up?
Maverick is genuinely competitive with GPT-4o and Claude 3.5 Sonnet on most tasks, with some meaningful gaps. Llama 4 is weaker on complex multi-step reasoning, non-English languages, and precise instruction-following at the tail. For 70 to 80 percent of real-world tasks, the difference is negligible.
The open-weight advantage is real. Self-hosted Maverick with 4-bit quantization runs on four A100 GPUs and eliminates per-token costs. Organizations with significant scale and sensitivity requirements will find this equation compelling. The tradeoff is infrastructure complexity and the absence of OpenAI and Anthropic-level safety filtering.
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