What Is Llama 4? Meta's Open-Source AI Powerhouse in Three Sizes
Meta's Llama 4 represents the most ambitious open-source AI release of 2025, introducing three distinct models — Behemoth, Maverick, and Scout — each designed for different scales of deployment. Released on April 5, 2025, Llama 4 marks Meta's first model family to use a Mixture-of-Experts (MoE) architecture, delivering dramatic efficiency gains while pushing the boundaries of what open-weight models can achieve.
For developers, researchers, and enterprises weighing their options between closed-source giants like Claude Opus 4.6, GPT-5.2, and DeepSeek V3.2, Llama 4 offers a compelling alternative: frontier-class performance with the freedom to self-host, fine-tune, and deploy without per-token API fees. Here's everything you need to know about Meta's latest AI models and how they stack up against the competition.
The Three Llama 4 Models: Scout, Maverick, and Behemoth
Unlike previous Llama releases that offered models in roughly similar architectures at different sizes, Llama 4 takes a fundamentally different approach. Each model serves a distinct purpose, from lightweight efficiency to research-grade power.
Llama 4 Scout: The Efficiency Champion
Llama 4 Scout is built for speed, efficiency, and ultra-long-context tasks. With 109 billion total parameters but only 17 billion active parameters spread across 16 experts, Scout delivers surprisingly strong performance while running on a single NVIDIA H100 GPU. That's remarkable for a model of this caliber.
Scout's headline feature is its 10 million token context window — the largest of any openly available model at launch. To put that in perspective, 10 million tokens translates to roughly 7.5 million words, or about 15 full-length novels processed simultaneously. This makes Scout ideal for:
- Massive document analysis — Legal contracts, research papers, entire codebases
- Long-form summarization — Condensing hundreds of pages into actionable insights
- RAG pipelines — Retrieval-augmented generation with enormous knowledge bases
- Enterprise search — Querying across millions of internal documents
For teams building applications that need to reason over large volumes of text without expensive infrastructure, Scout is the clear choice. Its single-GPU requirement means deployment costs are a fraction of what Maverick or competing closed-source models demand.
Llama 4 Maverick: The Flagship Workhorse
Maverick is Meta's general-purpose powerhouse and the model most directly competing with Claude Opus 4.6 and GPT-5.2. With 400 billion total parameters and 17 billion active parameters across 128 experts, Maverick combines broad capability with efficient inference thanks to MoE.
Maverick supports a 1 million token context window and excels across multiple domains:
- Creative writing and chat — Meta positions Maverick as its best conversational model
- Complex coding tasks — Multi-file code generation, debugging, and refactoring
- Multilingual applications — Strong performance across dozens of languages
- Multimodal understanding — Native text and image comprehension
According to Meta's benchmarks, Maverick outperforms OpenAI's GPT-4o and Google's Gemini 2.0 on coding, reasoning, multilingual, and image benchmarks. However, it falls short of newer models like Gemini 2.5 Pro and Claude 3.7 Sonnet on some evaluations — a gap that more recent model updates from competitors have likely widened further.
Maverick requires an NVIDIA H100 DGX system or equivalent multi-GPU setup, putting it in enterprise territory for self-hosted deployments. However, it's available through cloud providers and API services at competitive per-token rates.
Llama 4 Behemoth: The Research Giant
Behemoth is the crown jewel of the Llama 4 family — and the most mysterious. With 288 billion active parameters, 16 experts, and nearly 2 trillion total parameters, Behemoth is designed as a "teacher model" that powers the smaller Scout and Maverick through a process called codistillation.
At the time of the April 2025 announcement, Behemoth was still in training and has not been publicly released as open weights. Meta's internal benchmarks show Behemoth outperforming GPT-4.5, Claude 3.7 Sonnet, and Gemini 2.0 Pro on STEM evaluations including math problem-solving. Notably, it did not surpass Gemini 2.5 Pro on all benchmarks.
Behemoth's primary role is advancing the state of the art and distilling its knowledge into more practical models. When (or if) it becomes publicly available, it will require significant compute infrastructure — likely multi-node GPU clusters — making it suitable primarily for research labs and large enterprises.
Architecture Deep Dive: Why Mixture-of-Experts Matters
The Llama 4 family's adoption of MoE architecture is a strategic inflection point. Traditional dense models activate all parameters for every token, making larger models exponentially more expensive to run. MoE models, by contrast, route each token to only a subset of specialized "expert" sub-networks.
This means Maverick's 400 billion parameters don't all fire simultaneously — only 17 billion are active per inference step. The result: Maverick delivers performance comparable to much larger dense models at a fraction of the compute cost. DeepSeek V3 and V3.2 pioneered this approach in the open-source space, and Meta has clearly taken notes.
All three models are also natively multimodal, trained on text, image, and video data from the ground up rather than having vision capabilities bolted on after the fact. This gives Llama 4 models more natural visual understanding compared to models that added multimodality as an afterthought.
Benchmarks and Performance Comparison
How does Llama 4 stack up against the current generation of frontier models? Here's a comparison based on available benchmark data and reported performance:
| Model | Total Params | Active Params | Context Window | Architecture |
|---|---|---|---|---|
| Llama 4 Scout | 109B | 17B | 10M tokens | MoE (16 experts) |
| Llama 4 Maverick | 400B | 17B | 1M tokens | MoE (128 experts) |
| Llama 4 Behemoth | ~2T | 288B | TBD | MoE (16 experts) |
| Claude Opus 4.6 | Undisclosed | Undisclosed | 1M tokens | Dense (proprietary) |
| GPT-5.2 | Undisclosed | Undisclosed | 128K tokens | Dense/MoE (proprietary) |
| DeepSeek V3.2 | 671B | 37B | 128K tokens | MoE (open-source) |
Performance Highlights
| Benchmark Category | Llama 4 Maverick | Claude Opus 4.6 | GPT-5.2 | DeepSeek V3.2 |
|---|---|---|---|---|
| Coding | Strong (beats GPT-4o) | Excellent | Excellent | Very Strong |
| Reasoning | Strong | Excellent | Excellent | Strong |
| Multilingual | Excellent | Very Strong | Strong | Excellent |
| Long Context | Excellent (1M) | Excellent (1M) | Good (128K) | Good (128K) |
| Multimodal | Native (text+image) | Native (text+image) | Native (text+image) | Text-focused |
| Open Source | ✅ Yes | ❌ No | ❌ No | ✅ Yes |
It's worth noting that Maverick's early benchmarks generated some controversy. Reports indicated the version tested against competitors was an "experimental" variant, not the standard open-weights release — raising questions about whether the published numbers reflect real-world performance for self-hosted deployments.
The Open-Source Advantage: Why Llama 4 Matters
The significance of Llama 4 extends far beyond raw benchmarks. As the open-source AI movement continues to gain momentum, Meta's models represent a critical counterweight to the closed-source dominance of OpenAI and Anthropic. As we've covered in our analysis of open-source AI challenging proprietary LLMs, the gap between open and closed models is shrinking rapidly.
Key advantages of Llama 4's open-weight approach include:
- No per-token API costs — Self-host and pay only for compute
- Full fine-tuning freedom — Customize models for your specific domain
- Data privacy — Keep sensitive data on your own infrastructure
- No vendor lock-in — Switch providers or deploy anywhere
- Community innovation — Thousands of fine-tuned variants on Hugging Face
Licensing Considerations
However, Llama 4's openness comes with caveats. The license includes notable restrictions:
- EU restriction — Users and companies domiciled in the EU or with principal place of business there are prohibited from using or distributing the models, likely due to the EU AI Act and data privacy regulations
- Large company clause — Companies with more than 700 million monthly active users must request a special license from Meta
- Not truly "open source" — The Open Source Initiative (OSI) has argued that Llama's license doesn't meet the definition of open source due to these restrictions
These limitations contrast with DeepSeek V3.2's more permissive MIT license, which places virtually no restrictions on commercial use. For global enterprises, especially those with EU operations, this licensing gap is a significant consideration.
Use Cases: Which Llama 4 Model Should You Choose?
| Use Case | Best Model | Why |
|---|---|---|
| Document analysis at scale | Scout | 10M context window handles massive documents on a single GPU |
| Customer-facing chatbot | Maverick | Best conversational quality and creative writing |
| Code generation and review | Maverick | Strongest coding benchmarks in the family |
| Budget-conscious deployment | Scout | Single H100 GPU requirement minimizes infrastructure costs |
| Multilingual applications | Maverick | Excellent cross-language performance with 128 experts |
| Research and distillation | Behemoth | 2T parameter teacher model for training smaller models |
| RAG with large knowledge bases | Scout | Long context window ideal for retrieval-augmented generation |
Llama 4 vs. Claude Opus 4.6 vs. GPT-5.2: The Real Trade-offs
Choosing between Llama 4 and proprietary alternatives isn't just about benchmarks — it's about your deployment model, budget, and technical requirements.
Choose Llama 4 Maverick if: You need a self-hosted solution with no per-token costs, want to fine-tune for your domain, or require data to stay on your infrastructure. Maverick delivers competitive performance for coding, chat, and multilingual tasks without vendor lock-in.
Choose Claude Opus 4.6 if: You need the absolute best reasoning and coding performance via API and don't mind per-token pricing. Claude's extended thinking capabilities and agent-focused features make it the top choice for complex autonomous workflows. See our detailed comparison of Claude Opus 4.6 vs. GPT-5.3 Codex for more on this front.
Choose GPT-5.2 if: You're already embedded in OpenAI's ecosystem with existing integrations, or you need strong general-purpose performance with the broadest third-party tool support.
Choose DeepSeek V3.2 if: You want open-source with the most permissive license (MIT), especially for EU-based deployments where Llama 4's restrictions are a dealbreaker. DeepSeek's MoE architecture offers similar efficiency advantages.
What's Next for Llama 4?
Meta has signaled that the April 2025 release is "just the beginning" for the Llama 4 collection. Key developments to watch:
- Behemoth public release — When Meta makes the 2T-parameter model available, it could reshape the competitive landscape
- Reasoning capabilities — None of the current Llama 4 models are "reasoning" models like OpenAI's o1/o3; expect Meta to address this gap
- EU availability — Regulatory changes or licensing updates could open Llama 4 to EU users
- Community fine-tunes — The Hugging Face ecosystem has already produced numerous specialized variants
Meta has also integrated Llama 4 into Meta AI, its assistant across WhatsApp, Messenger, and Instagram, rolling out to 40 countries. This gives Llama 4 immediate distribution to billions of users — a scale advantage no other open-source model can match.
Frequently Asked Questions
Is Llama 4 Behemoth available to download?
As of early 2026, Llama 4 Behemoth has not been publicly released. Meta announced it as still in training during the April 2025 launch. Behemoth serves primarily as a "teacher model" used to improve Scout and Maverick through codistillation. Meta has not announced a specific release date for the public weights.
Can I use Llama 4 commercially?
Yes, with restrictions. Llama 4 Scout and Maverick are available under Meta's Llama license, which permits commercial use for most companies. However, companies with over 700 million monthly active users need a special license from Meta. Additionally, EU-domiciled users and companies are currently prohibited from using or distributing the models.
How does Llama 4 Scout's 10M context window compare to competitors?
Scout's 10 million token context window was the largest of any openly available model at launch. For comparison, Claude Opus 4.6 supports 1 million tokens, GPT-5.2 supports 128K tokens, and DeepSeek V3.2 supports 128K tokens. Gemini models from Google offer up to 2 million tokens. Scout's context length makes it uniquely suited for processing entire codebases, legal document sets, or research paper collections in a single pass.
What hardware do I need to run Llama 4?
Scout can run on a single NVIDIA H100 GPU, making it the most accessible model in the family. Maverick requires an NVIDIA H100 DGX system or equivalent multi-GPU setup. Behemoth, when released, will require even more substantial infrastructure — likely multi-node GPU clusters. For users without their own hardware, both Scout and Maverick are available through cloud providers and API services.
Is Llama 4 truly open source?
Meta releases Llama 4 as "open weights," meaning the model parameters are freely available for download. However, the Open Source Initiative (OSI) has argued that Llama's license doesn't meet the strict definition of "open source" due to restrictions on EU usage and the large-company licensing requirement. By contrast, DeepSeek V3.2 uses the MIT license with virtually no restrictions, which more closely aligns with traditional open-source principles.
The Bottom Line
Llama 4 is a landmark release for open-weight AI. Scout's 10M context window and single-GPU efficiency make it a game-changer for document-heavy applications. Maverick delivers flagship-class performance that genuinely competes with proprietary models for coding, chat, and multilingual tasks. And Behemoth, whenever it arrives, could push the entire field forward.
The trade-offs are real — licensing restrictions, the benchmark controversy, and the lack of built-in reasoning capabilities mean Llama 4 isn't a slam dunk over Claude Opus 4.6 or GPT-5.2 in every scenario. But for teams that value self-hosting, customization, and cost control, Meta's latest models are the strongest open-weight option available today.
At Serenities AI, we'll continue tracking Llama 4's evolution, community fine-tunes, and how it compares as newer models from Anthropic, OpenAI, and DeepSeek continue to raise the bar.