Meta Llama 4: New Open AI Models with up to 10 Million Token Context Window

The New Llama 4 Model Family: A Quantum Leap in Open AI

Meta has achieved a significant breakthrough in the field of open AI models with the release of the Llama 4 model family. The new models not only surpass their predecessors in terms of performance and capabilities, but also set new standards in the AI landscape with a revolutionary 10-million-token context window.

Overview: The Llama 4 Model Family

With Llama 4, Meta has introduced not just a single model, but an entire family of specialized AI models:

• Llama 4 Scout: The compact 17B model with 10 million token context window
• Llama 4 Maverick: The versatile 400B model with 1 million token context window
• Llama 4 Behemoth: The powerful 2T model (currently still in training)

The Three Variants in Detail

Llama 4 Scout (17B)

Scout is the most compact model in the family and has been optimized for applications where efficiency and speed are paramount:

• 17 billion parameters
• Optimized for edge devices and resource-constrained environments
• Ideal for real-time applications and mobile devices
• Supports context windows of up to 10 million tokens

Llama 4 Scout is the first publicly available model with such an extensive context window and opens up completely new application possibilities, from analyzing entire books to processing complex scientific documents.

Llama 4 Maverick (400B)

Maverick positions itself as a versatile all-round model with impressive performance:

• 400 billion parameters
• Balanced ratio between performance and resource requirements
• Excellent capabilities in complex reasoning tasks
• Supports context windows of up to 1 million tokens

In our initial tests at Evoya AI, Maverick has proven to be extremely versatile and delivers results in many areas that are comparable to or better than proprietary models like GPT-4o.

Llama 4 Behemoth (2T)

Behemoth is the flagship of the Llama 4 family and sets new standards with its enormous context window:

• 2 trillion parameters
• Outstanding performance on complex tasks and long context
• Multimodal capabilities for text, code, and images

Not much is known yet about Llama 4 Behemoth, as the model is still in training and has not been released. One can be curious.

Technical Innovations in Llama 4

The Llama 4 model family brings several technical innovations:

• Improved Architecture: Optimized transformer architecture with more efficient attention mechanisms
• Advanced Training: New training methods that significantly reduce the hallucination rate
• Multimodal Capabilities: Integration of text, code, and image understanding in one model

The Significance of the 10-Million-Token Context Window

The 10-million-token context window of Llama 4 Scout is more than just an impressive number – it is a paradigm shift for AI applications:

• Processing entire books or scientific papers in one go
• Analysis of extensive codebases without information loss
• Long-term memory for complex conversations
• Processing and analysis of large datasets in one context

For comparison: GPT-4o offers a context window of 128K tokens, while Claude 3 Opus comes with 200K tokens. With 10 million tokens, Llama 4 Scout exceeds these values by a multiple and thus enables completely new application scenarios.

In our initial tests at Evoya AI, we could already observe how the large context window significantly improves the quality and coherence of responses for complex tasks. The ability to consider large amounts of context leads to more precise and nuanced results.

Meta's Open Source Strategy and EU Licensing

An important aspect of the Llama 4 release is Meta's continuation of its open source strategy, albeit with some notable changes:

• The models are released under a modified open source license
• Commercial use is permitted under certain conditions
• Restrictions for EU users: Due to regulatory uncertainties related to the AI Act, the models are only available with restrictions in the EU

The EU licensing restrictions pose a significant challenge for European companies. Meta is responding to the uncertainties related to the EU AI Act, which underscores the complexity of the regulatory landscape for AI in Europe.

Implications for the Open Source Community

The release of Llama 4 has far-reaching implications for the open source AI community:

• Democratization of advanced AI technologies
• Promotion of innovation through open access to state-of-the-art models – Llama 3 was already a game changer here
• Challenge for proprietary models through powerful open alternatives

Especially for smaller companies and startups, the Llama 4 models open up new possibilities to develop advanced AI applications without being dependent on proprietary services.

At Evoya AI, we have already begun integrating the Llama 4 models into our workspace and testing initial use cases. The results are promising and show the enormous potential of this new model family.

Llama 4 Soon Available at Evoya AI

With the Llama 4 model family, Meta has taken a significant step in the development of open AI models. The combination of powerful models in various sizes, the revolutionary 10-million-token context window, and open licensing (albeit with restrictions in the EU) will sustainably shape the AI landscape.

For companies and developers, the Llama 4 models offer an attractive alternative to proprietary services and open up new possibilities for innovative applications. However, the EU licensing restrictions pose a challenge that underscores the complexity of the regulatory landscape for AI in Europe.

At Evoya AI, we will continue to test the Llama 4 models and integrate them into our workspace to provide our customers with access to the latest developments in the field of open AI. The future of AI remains dynamic – and with Llama 4, Meta has made another important contribution to this future.