Meta’s Llama series has revolutionized open-source AI, and Llama 4 represents a major step forward in multimodal intelligence. Released on April 5, 2025, Llama 4 introduces natively multimodal models that process text, images, and video, built on a mixture-of-experts architecture for efficiency and performance. This guide covers everything from features and access to comparisons and real-world applications, addressing popular searches like “Llama 4 Meta features” and “Llama 4 release date.” Whether you’re a beginner experimenting with AI or an advanced developer building custom solutions, we’ll explain concepts step by step.
What is Llama 4 from Meta?
Llama 4 is Meta’s latest open-source large language model family, succeeding Llama 3.1 from July 2024. It includes three main variants: Scout, Maverick, and a preview of Behemoth. Unlike previous text-focused models, Llama 4 is natively multimodal, meaning it handles diverse inputs like images and text without needing separate processing.
For beginners: Imagine Llama 4 as a versatile AI assistant that not only chats but also “sees” and understands visuals, making it useful for tasks like describing photos or analyzing charts.
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Key Features of Llama 4
Llama 4 stands out with its multimodal capabilities and efficiency, making it a go-to for developers.
Beginner-Level Features
- Multimodal Support: Processes text, images, and video inputs, enabling tasks like image captioning or visual question answering.
- Long Context Windows: Handles up to 10 million tokens in Scout, ideal for summarizing long documents.
- Open-Source Accessibility: Free to download and customize, with safety tools included.
Advanced-Level Features
- Mixture-of-Experts Architecture: Scout uses 16 experts, Maverick 128, reducing compute needs while maintaining high performance.
- Early Fusion Pre-Training: Integrates text and vision tokens for better understanding.
- Benchmark Performance: Maverick excels in image reasoning (MMMU: 73.4%), coding (LiveCodeBench: 43.4%), and multilingual tasks (MMLU: 84.6%).
| Feature | Scout | Maverick | Behemoth |
|---|---|---|---|
| Active Parameters | 17B | 17B | Preview (high intelligence) |
| Total Parameters | 109B | 400B | Not specified |
| Context Window | Up to 10M tokens | Industry-leading | Advanced |
| Multimodality | Text + Image | Text + Image | Teacher model |
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Llama 4 Release Date and Updates
Llama 4 Scout and Maverick were released on April 5, 2025, earlier than anticipated, possibly pulled forward from LlamaCon on April 29, 2025. Behemoth’s full release was delayed to late 2025 or potentially 2026. As of January 2026, updates include expanded availability on AWS Bedrock and SageMaker JumpStart, supporting up to 3.5 million context windows for Scout.
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How to Access Llama 4
- Downloads: Available on llama.meta.com for free, with weights and code on GitHub.
- API and Cloud: Use the Llama API for integration, or deploy on AWS Bedrock (serverless) or SageMaker.
- Integrations: Power Meta AI in WhatsApp, Messenger, and Instagram; also on platforms like Hugging Face.
- Pricing: Inference costs $0.19-$0.49 per million tokens for Maverick.
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Llama 4 vs Other Models: Comparisons
Llama 4 competes with closed models while being open-source.
- vs Llama 3.1: Adds native multimodality and MoE for better efficiency and vision tasks.
- vs GPT-4.5: Behemoth preview outperforms in STEM benchmarks.
- vs Claude 3.7: Stronger in multilingual and long-context scenarios.
| Model | Strengths | Llama 4 Edge |
|---|---|---|
| GPT-4.5 | General reasoning | Open-source, multimodal efficiency |
| Claude 3.7 | Coding | Longer context, lower cost |
| Gemini 2.0 | Multimodality | MoE architecture for speed |
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Real-World Use Cases for Llama 4
Llama 4 powers diverse applications.
- Beginner: Use in Meta AI for chat-based image analysis or content creation in apps like Instagram.
- Intermediate: Build chatbots for customer service, summarizing visuals in Messenger.
- Advanced: Engineering efficiency at banks like ANZ, or custom multimodal agents for research.
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Latest Updates and Future Developments
As of January 2026, Llama 4 is integrated into Meta’s ecosystem, with ongoing Behemoth training. Future plans include advancements in speech and reasoning for 2026 releases. Programs like Llama Startup support builders.
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Beginner to Advanced: Tips for Using Llama 4
- Beginners: Start with Meta AI demos on meta.ai.
- Intermediate: Fine-tune on Hugging Face for specific tasks.
- Advanced: Deploy on AWS for scalable apps, leveraging MoE for cost savings.
FAQ
When was Llama 4 released?
Scout and Maverick launched on April 5, 2025; Behemoth is previewed and expected later.
What are the features of Llama 4?
Native multimodality, MoE architecture, long context (up to 10M tokens), and strong benchmarks in reasoning and vision.
How does Llama 4 compare to Llama 3.1?
It adds multimodal support and efficiency via MoE, outperforming in vision and context handling.
How to access Llama 4 models?
Download from llama.meta.com or use via AWS Bedrock/SageMaker.
What are Llama 4 benchmarks?
Maverick scores 73.4% on MMMU, 43.4% on LiveCodeBench, and 84.6% on multilingual MMLU.
Can Llama 4 handle images and video?
Yes, it’s natively multimodal for text, image, and video processing.