Meta’s Llama 4 Models Spark Momentum — and Scrutiny — for Open-Source AI

Meta’s Llama 4 aims to close the gap with GPT-4 and Claude — but benchmark questions and transparency concerns could complicate adoption. Meta has announced its latest open-weight language models — Llama 4 Behemoth, Maverick and Scout — each designed to push the boundaries of what open AI systems can achieve across different scales and use cases. How do these models stack up against competing offerings from OpenAI, Google, Anthropic and others? And will their performance live up to the hype amid scrutiny over benchmark transparency? Let’s dive into what Meta’s release strategy reveals about the future of open foundation models and the industries they aim to serve.  Meta’s latest lineup of large language models (LLMs), referred to as the Llama 4 family, builds on earlier iterations with improvements in performance, scale and deployment flexibility. The series includes three primary variants — Llama 4 Behemoth, Llama 4 Maverick and Llama 4 Scout — each aimed at different enterprise and research needs.

While Meta has not revealed exact parameter counts for Behemoth, independent reports suggest a substantial increase over Llama 2’s top-end 70 billion parameters. Architectural refinements reportedly include updated attention mechanisms, longer context windows and finer-grained token control, although specific technical details remain limited. Llama 4 models are offered under an open-weight licensing model that allows researchers and developers to access and deploy them under certain restrictions, predictably prohibiting large-scale commercial competition with Meta itself. Early impressions emphasize just how massive Llama 4’s models are compared to earlier open-weight LLMs. Independent experts are already excited about the sheer scale of Behemoth’s size and Scout’s unprecedented context length.

Ilia Badeev, head of data science at Trevolution Group, told VKTR that for the Behemoth model, it’s the parameters. “The name of the model does justice to it — it is a massive behemoth in terms of parameters. Scout’s context window is great for large prompts. Let’s say you want to summarize a book — it’s perfect for that.” Continue reading here.