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Meta’s new Llama 4 lineup: Scout, Maverick, and Behemoth, represents a major leap in open-source AI. These models are part of the Llama ai family, designed for high performance across text, image, and video inputs. While competitors focus on closed, large-scale models, Llama 4 meta takes the open-weight route, giving developers direct access to cutting-edge multi-modal capabilities.
Notably, Llama 4 Maverick surpassed 1400 on the LMarena benchmark, outperforming models like GPT-4o, Gemini 2.0 Flash, and DeepSeek V3. The models also support an unprecedented 10 million token context length, the longest among open-weight LLMs, making them ideal for large-context applications.
In this post, we’ll break down each model and explore llama 4 features, benchmarks, and how Llama 4 vs ChatGPT compares for real-world developer use cases.
Among the Llama 4 models, Scout stands out as a compact, high-efficiency model purpose-built for developers who need performance without the computational burden. In an era dominated by billion-parameter giants demanding clusters of H100s, Llama 4 Scout delivers an unexpected sweet spot: multimodal capability, massive context length, and practical deployability, all from a single GPU.
Unlike prior iterations, Llama 4 Scout is built on Meta’s new Mixture of Experts (MoE) architecture, a key leap in Llama 4 features. Instead of running a monolithic transformer, Scout activates a subset of “expert” networks per input. This sparse activation paradigm reduces compute cost while allowing the model to generalize across diverse tasks. For developers, this translates into faster inference, lower memory overhead, and scalability across more modest infrastructure setups.
At its core, Llama 4 Scout is optimized for real-world engineering use cases:
Scout represents a significant shift in what developers can do with open-weight models. It bridges the gap between experimental research and deployable AI, offering Llama 4 capabilities without infrastructure lock-in. Whether you're building a context-aware code assistant, running in-editor summarization tools, or parsing thousands of support tickets, Scout brings industrial-scale intelligence to accessible environments.
Now openly available via Llama.com and Hugging Face, Scout, and its sibling Maverick, marks the beginning of a more usable, open-source AI frontier. Meanwhile, Llama 4 Behemoth continues training in the background, promising even more power to come.
Llama 4 Maverick stands at the core of Meta’s open-source LLM initiative, a model that doesn’t just scale in size but scales in purpose. Designed for advanced reasoning, multilingual capabilities, multimodal tasks, and code generation, Maverick is the model you reach for when Scout isn’t enough.
Like Scout, Maverick leverages the Mixture of Experts (MoE) architecture, activating only 17B parameters out of 400B during inference. What makes it special is its 128 expert pathways, allowing highly specialized internal routing depending on the nature of the prompt, be it coding, image-to-text understanding, or long-context dialogue.
According to Meta’s LMarena benchmarks, Maverick outperforms GPT-4o, DeepSeek V3, and Gemini 2.0 Flash in multiple domains like code generation, reasoning, and context tracking. It doesn’t top the charts against closed models like GPT-4.5 or Claude 3.5, but for an open-weight LLM, its performance-to-cost ratio is unmatched.
Maverick is the go-to choice for building production-grade assistants, whether it's an AI writing tool with tonal control, a multilingual chatbot, or a visual question-answering system. The architecture is tuned to improve refusal handling, instruction following, and creative writing with far better context-awareness and nuance.
Maverick embodies what open-weight models are becoming: not just research artifacts, but production-grade engines, with the freedom to adapt, optimize, and self-host.
Llama 4 Behemoth is Meta’s largest and most powerful model to date, a research-only “teacher model” designed not for deployment, but to distill intelligence into its smaller siblings, Scout and Maverick.
While Behemoth remains unreleased, its influence is foundational. Acting as a distillation base during training, it provided high-quality synthetic data and knowledge transfer, improving the alignment, reasoning, and generalization capabilities of the deployable Llama 4 models. Think of it as the brain behind the scenes, responsible for fine-tuning what the world can access.
Behemoth isn’t something you’ll run on your infrastructure anytime soon, but its existence signals where open-weight LLMs are headed. It also highlights Meta’s strategy: leverage ultra-large models for training, and then ship efficient, high-performing variants that can actually be used in the wild.
The Llama 4 models, Scout, Maverick, and Behemoth, were developed through a rigorous two-phase training pipeline: pre-training to build foundational multimodal intelligence, and post-training to align performance with specific use cases. Meta’s approach emphasizes architectural efficiency, scalability, and safety, distinguishing Llama 4 from prior generations.
Llama 4 models were trained on over 30 trillion tokens of diverse text, image, and video data, making them inherently multimodal.
The post-training stage enhances reasoning, safety, and alignment through a series of carefully sequenced optimizations:
This end-to-end pipeline makes the Llama 4 models not only large in scale but deeply optimized for real-world tasks, including long-context comprehension, multimodal understanding, and efficient deployment across varying hardware configurations.
Llama 4 Maverick, built on a Mixture-of-Experts (MoE) architecture with 17B active parameters and 400B total parameters, brings top-tier performance at a fraction of the cost. It consistently outperforms or matches much larger models like GPT-4o, Gemini 2.0 Flash, and DeepSeek v3.1 across core benchmarks, all while being highly efficient.
Why it matters: Llama 4’s MoE structure activates only a subset of experts per query, driving down costs while maintaining quality.
Takeaway: Excels at interpreting visuals, diagrams, and math-heavy prompts, ideal for educational, analytics, and tutoring applications.
Takeaway: Highly capable at structured image comprehension, from forms to scanned documents.
Takeaway: Efficient code reasoning and generation with fewer active parameters, well-suited for dev tools and coding agents.
Takeaway: Robust generalization across STEM, language, and logic, reliable for decision support and domain-specific assistants.
Takeaway: Ready for global use cases without requiring fine-tuning.
Takeaway: Maintains coherence in long documents, useful for RAG, legal, and research-heavy applications.
Llama 4 Maverick is more than a cost-effective model, it’s a high-performing, multimodal, multilingual generalist that competes head-to-head with flagship models at a fraction of the size and price. For AI agents, coders, or context-heavy tasks, it delivers where it counts, with both speed and depth.
Llama 4 Scout is a general-purpose model with 17B active parameters, 109B total, and 16 experts, delivering top-tier performance in its class. It extends context length to an impressive 10M tokens (up from 128K in Llama 3), enabling use cases like multi-document summarization, personalized tasking over long user histories, and full-repo code reasoning.
The NiH benchmark tests whether a model can retrieve a “needle” from a long input (“haystack”):
Scout is one of the few open models to accurately retrieve across 10M tokens, across both text and video — a massive leap for real-world long-context tasks.
Scout’s performance is driven by:
With iRoPE, Scout doesn’t just scale — it remembers precisely across vast, multi-modal contexts.
Llama 4 Scout is trained on sequences of up to 48 images, including video frame stills. This gives it a strong grasp of temporal coherence and evolving visual states—critical for:
While inference performs best with up to 8 images, the model’s architecture is optimized to handle complex visual narratives with minimal loss of context.
Scout excels at aligning textual prompts with specific visual regions, making its answers both grounded and accurate. This is especially useful for:
This grounding precision reduces hallucinations and ensures responses are directly tied to observable visual input. It’s particularly valuable in domains like medical imaging, robotics, and UI analysis.
Llama 4 Behemoth is Meta’s most powerful LLM to date, a 2 trillion parameter multimodal mixture-of-experts model with 288B active parameters per inference. Designed as a teacher model, it delivers state-of-the-art performance in math, multilingual tasks, and image understanding, despite not being optimized for reasoning.
Behemoth was used to codistill Llama 4 Maverick, leveraging a novel dynamic loss function that balances soft and hard targets. This approach improved quality across benchmarks while keeping training efficient. Most forward passes were amortized during pretraining, with Behemoth only activated on new data to generate targets.
Llama 4 Scout and Maverick are Meta’s first models built on a Mixture-of-Experts (MoE) architecture, where each token activates only a subset of the total model parameters. This allows for massive scale (e.g., Maverick’s 400B parameters) while keeping inference efficient, only 17B parameters are used per token.
Maverick employs 128 experts with FP8 weights, making it possible to run on a single 8xH100 node. This reduces memory footprint and latency significantly. Additionally, Meta applies channelwise weight quantization and dynamic per-token activation quantization to maximize accuracy without increasing compute cost.
Further optimization is enabled by the CUTLASS-based GroupedGEMM kernel in vLLM, co-developed with Red Hat, leveraging advanced GPU kernel techniques introduced in Meta’s Machete and FP8 inference initiatives.
Unlike previous models, Llama 4 uses early fusion multimodality, integrating text, image, and video tokens into a shared backbone during pretraining. There’s no freezing of text parameters or use of separate multimodal heads. This unlocks joint learning from large-scale unlabeled multimodal data, enhancing the model’s ability to reason across modalities with minimal task-specific tuning.
Llama 4 embeds AI safety mechanisms directly into the model pipeline, from pretraining through to system-level deployment. Meta's Developer Use Guide: AI Protections underpins this work, incorporating adversarial robustness and tunable safeguards. The goal is to empower developers with models that are safe, secure, and adaptable out-of-the-box.
Meta partnered with Red Hat to ensure day-one support for Llama 4 models in vLLM, the open-source inference engine that’s become standard in the LLM community (44k+ GitHub stars, nearly 1M weekly PyPI installs). Through close pre-release collaboration, developers can now deploy Llama 4 models immediately using a stack optimized for latency, throughput, and memory efficiency.
Llama 4 marks a pivotal moment in the evolution of open-source AI, bringing together scale, efficiency, and native multimodality in a way that opens new frontiers for real-world applications. Whether it’s Scout’s unprecedented 10M-token context window, Maverick’s MoE-powered performance, or Behemoth’s role as a teacher model, the Llama 4 suite demonstrates what’s possible when model design meets practical deployment.
At GoCodeo, we’re particularly excited by these breakthroughs. As a platform focused on building AI-native developer tools that assist with full-stack application development, we see long-context understanding, image reasoning, and code-centric training as foundational to the next generation of autonomous developer agents. These aren’t just benchmarks, they’re real capabilities that will power context-aware coding, seamless UI generation, and multi-modal developer workflows.
With models like Llama 4 being openly available and vLLM-ready from day one, the future is closer than ever, and we’re here for it.
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