Meta Launches Llama 4 – New AI Model Supports 10 Million Tokens
Meta has unveiled its new Llama 4 model series, including the Scout and Maverick models. These new open AI models support multimodality and a context window of up to 10 million tokens.
What happened?
Meta has launched its new Llama 4 AI model family, which includes the Scout and Maverick versions. The models are based on a Mixture-of-Experts (MoE) architecture, and the Scout model features 17 billion active parameters across 16 experts. One of the most prominent specifications is support for a context window of up to 10 million tokens, along with native multimodal processing.
Key facts
| Modellserie | Llama 4 (Scout och Maverick) |
|---|---|
| Kontextfönster | Upp till 10 miljoner tokens |
| Arkitektur | Mixture-of-Experts (16 experter) |
| Aktiva parametrar (Scout) | 17 miljarder |
Why it matters
A context window of 10 million tokens is among the largest in the AI industry, enabling the processing of entire books, extensive codebases, or thousands of documents in a single query. By utilizing the MoE architecture, Meta can offer high performance and multimodality while maintaining computational efficiency.
Who is affected?
The launch primarily concerns AI developers, data analysts, and enterprises building large-scale applications for the analysis of long documents, source code, or complex log files. End users of personal AI assistants based on the Llama architecture will also benefit from an improved capacity to handle large volumes of data.
Impact on the EU
Llama 4 is being released with open weights, allowing European developers to download and run the models on their own infrastructure. This facilitates compliance with the EU's General Data Protection Regulation (GDPR) and the AI Act, as no sensitive data needs to be transmitted to servers outside the EU.
What else you should know
Meta has not yet published full technical graphs for all benchmark tests, meaning independent verification of performance claims compared to GPT-4o and Mistral Large 4 is awaited from third-party evaluators.
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