What is Meta Llama 4?
Meta Llama 4 is the fourth generation of Meta's open-source large language model family, released in April 2026. It represents a significant architectural leap over Llama 3: all models in the family use a Mixture of Experts (MoE) design, are natively multimodal (text and image input), and feature dramatically larger context windows โ up to 10 million tokens for the Scout variant.
The Llama 4 family consists of three models: Scout (17B active parameters, optimized for efficiency and speed on consumer hardware), Maverick (17B active / 400B total parameters, designed for high-quality reasoning with strong benchmark performance), and Behemoth (still-training 2T parameter model, expected to be the most capable open-source model ever released when it drops).
What makes Llama 4 strategically significant is the combination of frontier-level capability with open weights and a permissive commercial license. Developers can fine-tune Llama 4 on proprietary data, deploy it on their own infrastructure, and build commercial products without per-token costs โ something impossible with GPT-4o or Claude.
Key Features
Mixture of Experts (MoE) Architecture
All Llama 4 models use MoE architecture โ only a subset of parameters (the "active" parameters) are activated for each token, making large models run efficiently. Maverick has 400B total parameters but only 17B active at any time, giving it the speed of a 17B model with reasoning quality approaching much larger dense models. This efficiency makes Llama 4 viable on a wider range of hardware than equivalent dense models.
Native Multimodality
Llama 4 models process both text and images natively from the ground up โ not bolted on as an afterthought. You can analyze charts, diagrams, screenshots, documents, and photos alongside text queries. The vision understanding is strong enough for practical applications: reading complex tables, describing UI layouts, analyzing scientific figures, and understanding product images in e-commerce contexts.
10-Million Token Context (Scout)
Llama 4 Scout supports a 10-million token context window โ the largest of any public model. In practice this means you can load entire codebases, large document collections, or long conversation histories without truncation. For research, legal document analysis, or codebase-level AI applications, this is transformative. Maverick offers a more practical 1-million token context for most deployment scenarios.
Open Weights & Commercial License
Llama 4 is available under a custom Meta commercial license that permits fine-tuning and deployment for most commercial use cases (with restrictions on very large deployments). Weights are downloadable from Hugging Face and Meta's website, making Llama 4 accessible to any developer or company without API dependencies, rate limits, or usage costs beyond compute.
โ Pros
- Open weights โ run locally, fine-tune, deploy anywhere
- 10M token context on Scout โ unprecedented for open-source
- MoE efficiency โ frontier capability at lower compute cost
- Native multimodal โ text and image from the ground up
- Strong benchmark performance vs. GPT-4o and Claude 3
- Free for most commercial use cases
- Large community and ecosystem (fine-tunes, tools)
โ Cons
- Behemoth (flagship) not yet publicly available
- Deployment requires significant GPU infrastructure
- License restricts use cases above 700M MAU threshold
- No built-in safety layer for production deployments
- Vision quality slightly below GPT-4o Vision
- Meta.ai chat interface is basic compared to Claude/ChatGPT
Pricing
- Free (self-hosted): Download weights from Hugging Face or Meta's website. Run on your own GPU infrastructure at cost of compute only.
- Meta.ai: Free web interface for Llama 4 Scout โ no account required for basic use.
- Cloud APIs: Llama 4 is available via Groq, Together AI, Replicate, and AWS Bedrock at competitive per-token rates (typically $0.10โ$0.40 per million input tokens).
- Meta Llama API (Beta): Meta's own hosted API, currently in limited beta.
Try Meta Llama 4 โ Free, Open Source
Access Llama 4 Scout for free at Meta.ai, or download the weights to run and fine-tune on your own infrastructure with no per-token costs.
Try Llama 4 at Meta.aiMeta Llama 4 vs Competitors
| Model | Type | Context | Multimodal | Best For |
|---|---|---|---|---|
| Llama 4 Maverick | Open (MoE) | 1M tokens | Yes | Open-source frontier model |
| Google Gemma 4 | Open (dense) | 128K | Yes | Efficient on-device models |
| Mistral Large | Open (dense) | 128K | Partial | European AI, code |
| GPT-4o | Closed API | 128K | Yes | General best-in-class |
| Claude 3.5 Sonnet | Closed API | 200K | Yes | Reasoning & coding |
Final Verdict
Meta Llama 4 is the most important open-source AI release of 2026. The MoE architecture, native multimodality, and 10M token context on Scout give it capabilities that were proprietary-only six months ago, now available for free to any developer. For teams that need to fine-tune on proprietary data, deploy on-premise, or build AI products without per-token costs, Llama 4 is the clear foundation.
As a consumer chatbot, Meta.ai remains less polished than ChatGPT or Claude. But as a model for developers and enterprises building AI applications, Llama 4 Maverick is competitive with closed frontier models in most benchmarks. When Behemoth releases publicly, Meta will have the most powerful freely-available model ever โ watch this space.
Best for: AI developers, researchers, enterprises needing on-premise deployment, and any team that values data privacy and control over paying per-token to a closed API provider.
