Exploring Meta’s Llama 4 Herd: A New Generation of AI Models

The artificial intelligence landscape continues its rapid evolution, and Meta has just made a significant contribution with the release of the Llama 4 Herd. This new family of large language models (LLMs) includes three distinct variants: Llama 4 Scout, Llama 4 Maverick, and the highly anticipated Llama 4 Behemoth. Designed to push the boundaries of AI capabilities, these models aim to challenge established players like GPT-4o, Claude 3.7 Sonnet, and Gemini 2.x Pro.

Let’s dive into the details of the Llama 4 Herd, compare their specifications and performance against competitors, and consider their potential impact, particularly in areas like software development.

Meet the Llama 4 Herd: Models and Specifications

The Llama 4 models are built using a Mixture-of-Experts (MoE) architecture. This sophisticated design allows the models to activate only specific subsets of their parameters for each piece of data processed, leading to greater computational efficiency without compromising performance. Here’s a closer look at each member of the herd:

Llama 4 Scout

• Parameters: 17 billion active (out of 109 billion total)
• Experts: 16
• Context Window: 10 million tokens
• Hardware: Operable on a single NVIDIA H100 GPU (80GB)
• Training: Pre-trained on 30 trillion tokens (text, images, video, 200+ languages)
• Refinement: Utilizes Lightweight Supervised Fine-Tuning (SFT), online Reinforcement Learning (RL), and Direct Preference Optimization (DPO)
• Purpose: Scout is optimized for efficiency and accessibility. Its massive context window makes it ideal for tasks involving long-form content, such as summarizing entire books, analyzing extensive codebases, or processing multimodal inputs like diagrams alongside text.

Llama 4 Maverick

• Parameters: 17 billion active (out of 400 billion total)
• Experts: 128
• Context Window: 10 million tokens
• Hardware: Requires multiple GPUs (e.g., 2-4 H100s)
• Training: Uses the same 30 trillion token dataset as Scout but with greater emphasis on multimodal data.
• Refinement: Employs enhanced SFT, RL, and DPO for superior all-around performance.
• Purpose: Positioned as the versatile workhorse, Maverick excels in general-purpose applications, including sophisticated image and text understanding, complex reasoning, and creative generation tasks.

Llama 4 Behemoth (Currently in Training)

• Parameters: Estimated 288 billion active (out of nearly 2 trillion total)
• Experts: Undisclosed (expected to be numerous)
• Context Window: Anticipated to exceed 10 million tokens
• Hardware: Will require significant large-scale computing clusters.
• Training: Likely trained on over 50 trillion tokens, with a strong focus on Science, Technology, Engineering, and Mathematics (STEM) datasets.
• Projected Performance: Meta forecasts Behemoth will set new benchmarks, particularly in STEM fields, potentially outperforming models like GPT-4.5 and Claude 3.7 Sonnet upon release.

A key factor across the herd is the significantly expanded training dataset (double Llama 3’s 15 trillion tokens) and native multimodal capabilities. Post-training techniques like DPO are crucial for improving response accuracy and reducing unwanted outputs.

Llama 4 vs. The Competition: A Comparative Look

How does the Llama 4 family stack up against other leading proprietary models?

• Against Gemini 2.5 Pro: Google’s Gemini 2.5 Pro demonstrates strong reasoning and coding benchmark scores. While it currently outperforms Scout and Maverick in certain raw metrics, Llama 4 models boast a much larger 10 million token context window compared to Gemini’s 1 million. The upcoming Behemoth model could potentially bridge the performance gap in reasoning and coding.
• Against Claude 3.7 Sonnet: Anthropic’s Claude 3.7 Sonnet is highly competitive in coding and safety, featuring advanced reasoning capabilities. It rivals Maverick in coding benchmarks but has a significantly smaller context window (200K tokens), limiting its use in very long-context scenarios where Llama 4 excels. Behemoth is anticipated to surpass Sonnet in several areas.
• Against GPT-4.5/4o: OpenAI’s models are known for strong multimodal capabilities and conversational fluency, often achieving top-tier benchmark scores (like MMLU). While GPT-4.5 likely surpasses Scout and Maverick in general performance metrics based on available data, Llama 4’s open-source nature and Behemoth’s sheer scale present a compelling challenge.

Benchmark Insights

Meta’s published benchmarks provide valuable insights:

• Pre-trained Models: Larger Llama 3.1 models remain competitive with Llama 4 on reasoning and knowledge tasks, though Llama 4 Maverick shows a distinct lead in code generation. Multilingual performance is comparable. Llama 4 models demonstrate strong native multimodal understanding (charts, documents).
• Instruction-Tuned Models: While Llama 4 models show promise in image reasoning, competitors like Gemini 2.5 Pro and Claude 3.7 Sonnet currently lead, with GPT-4.5 expected to be very strong. In coding, Gemini and Claude edge out the currently available Llama 4 models. For reasoning and knowledge tasks, Gemini and Claude show significant advantages. Llama 4 Maverick improves long-context understanding over Scout, but competitors are expected to perform strongly here as well.

Llama 4 vs. Llama 3: Key Improvements

Comparing Llama 4 directly to its predecessor, Llama 3.1, highlights significant advancements:

• Efficiency: Llama 4 Scout (17B active parameters) slightly outperforms Llama 3.1 70B on benchmarks like MMLU and significantly surpasses it in MATH and MBPP (coding). Llama 4 Maverick (17B active parameters) outperforms the much larger Llama 3.1 405B model across MMLU, MATH, and MBPP. This demonstrates the power of the MoE architecture.
• Training Data: The training dataset was doubled from 15 trillion to 30 trillion tokens, enriching the models’ knowledge base.
• Context Window: The leap from Llama 3’s 128K token window to Llama 4’s 10 million tokens unlocks fundamentally new applications requiring understanding of vast amounts of information.
• Multimodality: Llama 4 models possess native image processing capabilities not present in Llama 3.1.

Spotlight on Llama 4’s Coding Capabilities

Llama 4 shows particular strength in coding tasks:

• Benchmark Performance: Maverick achieves a strong 77.6 pass@1 on MBPP and significantly outperforms Llama 3.1 405B on LiveCodeBench (43.4 vs 27.7), indicating robust real-world coding ability.
• Long Context Advantage: The 10M token context window allows models like Scout and Maverick to analyze entire code repositories, assist in debugging large-scale projects, or generate code that is highly aware of the surrounding context.
• Multimodal Coding: Maverick can interpret visual inputs like software diagrams (e.g., UML charts or flowcharts) and generate corresponding code, offering a powerful tool for developers.
• Accessibility: Llama 4 Scout’s ability to run on a single high-end GPU makes advanced AI more accessible to individual developers and researchers. Maverick, while requiring more resources, is still feasible for smaller teams.

Putting Llama 4 to the Test

While benchmarks provide guidance, hands-on testing reveals true capabilities. Consider exploring Llama 4 Scout and Maverick (available via platforms like Hugging Face and llama.com) with prompts like these:

1. Write a Python function that takes a list of numbers and returns the list sorted in descending order without using built-in sorting functions.
2. Summarize the key events and themes of George Orwell’s 1984 in under 150 words.
3. Explain the concept of quantum entanglement in simple terms for a high school student.
4. Describe the major differences between renewable and non-renewable energy sources, highlighting their environmental impact.
5. Implement a simple algorithm in JavaScript that checks if a string is a palindrome.

Compare their responses to those generated by other leading models to gauge their strengths and weaknesses across different task types.

Conclusion

Meta’s Llama 4 Herd marks a significant step forward for open-source AI. Scout and Maverick offer compelling performance, massive context windows, and multimodal features, making them powerful tools accessible to a wider range of developers and researchers. Their strong coding abilities are particularly noteworthy. While the true potential of the powerhouse Behemoth model remains to be seen upon its release, the Llama 4 family is poised to be highly competitive, challenging proprietary models and driving innovation across various fields, especially STEM and software development.

Leveraging cutting-edge AI like Meta’s Llama 4 requires expertise. At Innovative Software Technology, we specialize in integrating advanced large language models into your business operations. Whether you need custom AI solutions, fine-tuning assistance for models like Llama 4 Maverick, or strategies to harness Llama 4’s exceptional coding and long-context capabilities for software development and data analysis, our team can help. Partner with Innovative Software Technology to unlock the potential of generative AI and multimodal understanding, driving efficiency and innovation within your organization by implementing state-of-the-art Llama 4-based applications.