GPTunneL has expanded its lineup of available neural networks by adding two cutting-edge models from Meta's Llama 4 family. Users can now try out the flagship Llama 4 Maverick and the fast Llama 4 Scout directly in the platform's interface.
Key features of Llama 4
Both new models are built on a Mixture of Experts (MoE) architecture. In an MoE architecture, there are many specialized "expert" subnetworks, each of which excels at a particular type of data or task.
A special mechanism called a router (gating network) analyzes the incoming request and dynamically decides which experts to engage to generate the most relevant and accurate response.
Llama 4 Maverick
Llama 4 Maverick (400B) is the flagship model of the new generation.
- It has a total of 400 billion parameters and draws on 128 different expert subnetworks, with 17 billion parameters activated to process any given request.
- The model stands out with a 1 million token context window, letting it work with huge volumes of information.
- Generation speed is 123 tokens per second — slightly less than Scout, but higher than many other models.
Maverick is specifically optimized for complex intellectual tasks that require deep contextual understanding and precision, including mathematical computation, programming (writing, debugging, and analyzing code), and demanding text work in the humanities and sciences.
Llama 4 Scout
Llama 4 Scout (107B) is a more compact, faster, yet still spacious alternative:
- This model has 107 billion parameters distributed across 16 experts, and likewise uses 17 billion active parameters to process a request.
- Scout's key advantage is its massive context window — 10 million tokens. You can feed the model dozens of books in a single chat and still get coherent answers. That's more than any other model offers.
- Generation speed is 130 tokens per second, making it one of the fastest models on the market.
This huge context capacity makes the model exceptionally effective for tasks involving analysis of very long texts, multi-volume documents, extensive databases, or maintaining ultra-long conversations without losing the thread. Speed and efficiency make Scout a great choice for applications where fast processing of large volumes of text matters.
It's also worth mentioning that Meta has announced an even larger model, Llama 4 Behemoth, with a total of 2 trillion parameters, though it's still in training and not yet available for wide use.
Where to try Llama 4
You can evaluate the performance, accuracy, and unique context-handling capabilities of the new Llama 4 Maverick and Llama 4 Scout models today. GPTunneL provides convenient access to these cutting-edge neural networks without needing a VPN or complicated API setup. Try Llama 4 Maverick and Scout on GPTunneL right now.