Meta has officially raised the bar in the generative AI arms race with the launch of Llama 4, its most advanced suite of open models to date. Unveiled recently, the Llama 4 family includes two models now publicly available – the Llama 4 Scout and Llama 4 Maverick and the upcoming Llama 4 Behemoth, a massive model in development that signals Meta’s commitment to AI leadership. The new series builds on Meta’s open-source tradition while introducing breakthrough features in efficiency, scalability, and performance.
While the world has grown accustomed to high-profile releases from OpenAI, Anthropic, and Google, Meta’s Llama 4 enters the arena with a particular focus to create models that are not only powerful and capable across a range of tasks but also optimised for real-world deployment at scale.
Llama 4: Core features and models
The Llama 4 series is engineered with one key architectural innovation in mind – the mixture-of-experts (MoE) design. Instead of activating all parameters for every request, the MoE approach enables the model to selectively engage only the parts it needs. This keeps performance high while reducing the hardware and cost burden for users.
Llama 4 Scout
Scout is the lightest of the new models and is designed for efficiency. It can run on a single Nvidia H100 GPU and supports a 10 million-token context window, making it ideal for long-document processing, codebases, and enterprise tasks. Despite its compact footprint, Scout is claimed to beat major contenders like Google’s Gemma 3 and Mistral 3.1 in multiple benchmarks, offering a strong option for developers looking to balance performance with affordability.
Llama 4 Maverick
Maverick is Meta’s answer to high-performance needs. Built with 64 experts (only 2 are active at a time), it delivers results on par with OpenAI’s GPT-4o and DeepSeek-V3, especially in areas like code generation, logical reasoning, and math problem-solving. Yet Maverick does this while keeping inference costs lower due to its efficient parameter usage. It’s a prime candidate for enterprise deployment where speed and cost are critical factors.
Llama 4 Behemoth
Still under wraps but already in training, Llama 4 Behemoth is shaping up to be a category-defining model. With 2 trillion parameters, 288 billion of which are activated during inference, it’s Meta’s boldest step yet toward surpassing OpenAI’s GPT-4.5 and Anthropic’s Claude 3. It reportedly leads in STEM benchmarks, making it especially promising for science, engineering, and complex data analysis applications.
Multimodal Capability
Llama 4 models are also built for the multimodal era. The new architecture supports not only text but also vision and audio inputs, positioning Meta’s assistant to better understand real-world environments. This capability will roll out across WhatsApp, Instagram, Messenger, and the web, making Meta AI far more context-aware and interactive than before.
The comparison: DeepSeek’s rise and the new rivalry
With Llama 4, Meta isn’t just chasing OpenAI or Google. It’s also positioning itself against a rising star in the AI world, DeepSeek, the Chinese research lab that’s made waves with its impressively capable and cost-efficient open-source models.
DeepSeek’s most recent release, DeepSeek-V3, has demonstrated performance on par with GPT-4 in mathematical reasoning and coding. What sets DeepSeek apart isn’t just raw capability, but how little it costs to train. Reports suggest V3 was trained for just $6 to $10 million, a fraction of what Western companies spend. That level of efficiency is forcing the AI establishment to rethink their strategies.
Unlike Meta, which relies on massive compute clusters (over 100,000 Nvidia H100s and an expected $65 billion AI investment in 2025), DeepSeek takes a leaner approach, using architectural innovations to stretch limited resources to the max. And it’s working.
The open-source balancing act
Meta has long championed open-source AI, and Llama 4 continues that tradition, but with guardrails. The models are available for research and commercial use, except by companies with more than 700 million users. This clause is likely aimed at protecting Meta’s competitive edge against rivals like Google or Microsoft, while still fostering a thriving developer and research ecosystem.
The Llama 4 models are expected to power a wide array of AI tools, assistants, and applications. With Meta hosting its first LlamaCon developer conference on April 29, more tools, APIs, and roadmap details are on the way, potentially including open fine-tuning recipes or model distillations for smaller devices.
Final thoughts
The Llama 4 launch marks a milestone not only for Meta, but for the broader AI industry. With modular architecture, expanded context windows, and multimodal input, Meta is signaling that it wants to lead in both research and real-world deployment. The models are strong, flexible, and ready to scale.
But it’s also clear that Meta’s dominance is no longer guaranteed. DeepSeek’s rise has proven that a nimble, cost-efficient challenger can compete with trillion-parameter giants. The era of big AI is becoming more distributed, more open, and more competitive.
As both Meta and DeepSeek push their models forward, the coming year will determine whether innovation at scale or innovation under constraint wins out. Either way, users, developers, and enterprises stand to benefit from the most exciting wave of AI advancements yet.
