Meta Llama 4 Scout & Maverick: Open-Source AI Explained
🦙 Open Source AI
Meta Llama 4 Scout & Maverick: Open-Source AI Explained
The first natively multimodal open-weight models from Meta – with a 10 million-token context window, competitive benchmarks, and free weights anyone can download and deploy.
By Free AI News June 1, 2026 9 min read
Quick Answer: Llama 4 Scout and Maverick are Meta’s open-weight multimodal AI models, released April 2025 via Hugging Face. Scout holds the longest context window of any open-source model – 10 million tokens – and runs on a single high-end GPU. Maverick benchmarks above many closed models. Both are free to download and deploy under Meta’s Llama 4 Community License.
Imagine sending a 15,000-page document to an AI model and getting a coherent answer back. That is not a marketing claim – it is the actual capability of Llama 4 Scout’s 10 million-token context window. When Meta released Llama 4 Scout and Llama 4 Maverick in April 2025, the open-source AI landscape shifted. These are not incremental updates; they are the first natively multimodal, Mixture-of-Experts models Meta has released as open weights. Developers, researchers, and self-hosters now have access to models that match or exceed many closed proprietary systems – for free. This guide explains what both models do, what hardware you actually need to run them, and where they stand against the competition in mid-2026.
What Are Llama 4 Scout and Llama 4 Maverick?
Llama 4 is the fourth generation of Meta’s open-weight large language model series. Unlike previous Llama generations, which were dense transformer models focused on text, Llama 4 introduces two major architectural changes: a Mixture-of-Experts (MoE) architecture and native multimodality through early fusion. The result is a family of models that can see images, process extraordinarily long documents, and respond in over 12 languages – all while keeping inference costs low by activating only a fraction of their total parameters per token.
The Llama 4 Scout model has 109 billion total parameters with 17 billion active parameters and 16 experts. Its headline feature is a 10 million-token context window, the longest of any openly available model as of April 2026. Scout is designed with accessibility in mind: it fits on a single server-grade GPU via on-the-fly quantization, and can even run on consumer-grade hardware with more aggressive quantization settings.
The Llama 4 Maverick model has 400 billion total parameters with 17 billion active parameters and 128 experts. Despite sharing the same 17B active parameter count as Scout, Maverick’s larger pool of experts allows it to develop far more specialized knowledge. Its context window is 1 million tokens – shorter than Scout but still vastly longer than most closed models. Maverick is the performance-first option, achieving an ELO rating of 1,417 on LMArena at launch, outscoring many proprietary closed-weight models.
Both models were trained on up to 40 trillion tokens spanning 200 languages, with dedicated fine-tuning for 12 languages including Arabic, Spanish, German, and Hindi. They are available as base and instruction-tuned variants on the Hugging Face Hub and via the official Llama website.
How Does the Mixture-of-Experts Architecture Make Llama 4 So Efficient?
Mixture-of-Experts is one of the most consequential architectural shifts in modern AI. In a traditional dense model like Llama 3, every parameter participates in every forward pass. A 70B dense model uses all 70 billion parameters to process each token. That is computationally expensive and creates a hard ceiling on how large you can scale before inference becomes impractical.
MoE breaks this constraint. Instead of one monolithic feed-forward network, MoE models have many parallel expert networks. For each token, a learned router selects which experts to activate – typically just a small subset. This means you can have a model with hundreds of billions of total parameters while only “running” a fraction of them per token. Llama 4 Scout’s 109B total parameters slim down to 17B active per token. Llama 4 Maverick’s 400B total parameters also resolve to just 17B active per token.
The practical effect is dramatic. Both Scout and Maverick have the latency and memory footprint of a 17B dense model during inference, while drawing on the breadth of knowledge encoded in a much larger parameter space during training. According to Meta’s official benchmarks, Maverick achieves results comparable to the original DeepSeek V3 on reasoning and coding tasks – at less than half the active parameters. This is why MoE has become the dominant architecture for frontier open-source models in 2026: it lets labs pack more capability into each inference dollar.
The tradeoff is storage. Even though you only use 17B parameters at inference time, you still need to store all 109B (Scout) or 400B (Maverick) parameters on disk and in memory. This is why running Maverick locally remains out of reach for most users despite its reasonable inference speed.
What Are the Context Window and Multimodal Capabilities of Llama 4?
Context window is the amount of text – or combined text and image content – a model can process in a single conversation turn. Most closed models max out at 128K to 200K tokens. GPT-4o launched with a 128K context window. Claude Sonnet offers up to 200K. Llama 4 Scout’s 10 million-token window is roughly 50x larger than GPT-4o’s limit and equivalent to approximately 15,000 pages of text.
In practice, this enables use cases that were previously impossible without chunking: summarizing entire code repositories in one pass, analyzing a year of corporate meeting transcripts together, or comparing thousands of legal documents simultaneously. The caveat is that running the full 10M context requires substantial hardware – in local deployments, most users will set a more practical context size of 32K to 128K based on available VRAM, reserving the headline figure for cloud or API deployments.
Both models use early fusion for native multimodality. This means text and image understanding is baked into the core architecture from the start of training, rather than bolted on through an adapter module. The result is smoother reasoning about images in context – the model genuinely integrates visual and textual information rather than treating them as separate streams. Both Scout and Maverick can accept interleaved text and image inputs, describe images, reason about charts, and answer questions grounded in visual content.
The training dataset is another strength: 40 trillion tokens across 200 languages. Most open-source models train on significantly less data. Meta’s data scale, combined with the MoE architecture, is a key reason these models compete so effectively with much more expensive closed alternatives. You can read the full announcement on the Meta AI blog.
How Much Hardware Do You Need to Run Llama 4 Locally?
Running Llama 4 locally is realistic for Scout and near-impossible for Maverick on home hardware. Here is a practical breakdown:
Model Total Params Active Params Min VRAM (Q4) Practical Setup
Llama 4 Scout 109B 17B ~24GB Single RTX 3090 / RTX 4090 / Mac M3 Ultra (192GB)
Llama 4 Maverick 400B 17B ~200GB+ Multi-GPU server or cloud (4x H100 SXM)
For Scout, 4-bit quantization (Q4_K_M via llama.cpp or Ollama) brings memory down to around 55GB for the full context capability. Unsloth’s 1.78-bit dynamic quantization goes further, fitting Scout into approximately 24GB VRAM at around 20 tokens per second – usable on a single RTX 3090 or RTX 4090. With 32GB+ Apple Silicon unified memory (M3 Pro or later), Scout runs well with Ollama at practical context sizes. CPU-only inference on 32GB+ system RAM is technically possible but very slow.
For Maverick, Q4 quantization still needs around 200GB of VRAM. That puts it in the range of a 4x H100 SXM server at roughly $10.00 per hour on cloud GPU providers, or 8x H100 SXM at around $20.00 per hour for full precision. For most developers and researchers, Maverick is best accessed through third-party API providers that host it – several cloud inference platforms offer it at Meta’s estimated cost of approximately $0.19 per million tokens (3:1 blended).
Both models are supported by Ollama, Hugging Face Transformers (v4.51.0+), and Text Generation Inference (TGI). Tensor-parallel inference and automatic device mapping are supported in transformers, making multi-GPU Scout setups straightforward to configure. Check the running Llama 3 locally guide for general self-hosting setup tips that apply to Llama 4 as well.
How Does Llama 4 Compare to Other Open-Source Models in 2026?
The open-source model landscape has changed significantly since Llama 4’s April 2025 release. DeepSeek V4, Qwen 3.6, Kimi K2.6, and Mistral Small 4 have all arrived since then. Where does Llama 4 fit now?
Llama 4 Maverick vs DeepSeek V4: DeepSeek V4 wins on coding tasks (LiveCodeBench, SWE-Bench Verified), hard reasoning (GPQA Diamond, MMLU-Pro), and licensing – it ships under an MIT license, which is more permissive than Llama’s Community License. Maverick holds the edge on long-context understanding thanks to its 1 million-token window and native multimodality. For pure text generation and reasoning, DeepSeek V4 is the stronger choice in 2026; for document-heavy or visual tasks, Maverick competes effectively. See the DeepSeek V4 open-source breakdown for a detailed comparison.
Llama 4 Scout vs Qwen 3.6: Qwen 3.6 (Apache 2.0 license) and Scout are both strong general-purpose models in the accessible hardware tier. Qwen 3.6 tends to outperform Scout on coding-specific benchmarks. Scout dominates on context length – its 10 million tokens vs Qwen 3.6’s 128K is not a close comparison. If your use case involves very long documents, Scout wins decisively. Read the Qwen 3.6 Apache open-source guide for more detail.
License considerations: The Llama 4 Community License allows commercial use for most applications. The key restriction is a 700 million monthly active user threshold – companies above that size need a separate agreement with Meta. For the vast majority of developers, startups, and researchers, this is not a practical limitation. However, developers who need a fully permissive open license for redistribution or derivative works may prefer Apache 2.0 models like Qwen 3.6 or Gemma 4.
The broader picture: Llama 4 remains the best open-source option for long-context workloads and multimodal applications. For coding or hard reasoning workloads, newer 2026 releases have pushed ahead. Llama 4 Scout’s local hardware accessibility – particularly the ability to run on a single consumer GPU – continues to make it a practical first choice for developers who want to self-host rather than pay API fees. Browse the top 5 open-source LLMs for self-hosting for a full ranked comparison across all the leading models.
🔑 Key Takeaways
Llama 4 Scout uses a Mixture-of-Experts architecture with 109B total parameters but only 17B active per token, giving it the performance footprint of a much smaller model at inference time.
Scout holds the longest context window of any open-source model as of mid-2026 at 10 million tokens, equivalent to roughly 15,000 pages – enabling document processing tasks that are impossible with most closed models. Download weights via Hugging Face or run locally with Ollama.
Maverick achieved an ELO rating of 1,417 on LMArena at launch, outscoring many closed proprietary models and demonstrating that open-weight models no longer lag the frontier by a meaningful margin.
Scout can run locally on a single RTX 3090 or RTX 4090 using 1.78-bit Unsloth quantization at approximately 20 tokens per second, making local self-hosting genuinely accessible to individual developers.
Both models are free to download and deploy commercially under Meta’s Llama 4 Community License, though the license is less permissive than Apache 2.0 or MIT and restricts very large-scale deployments.
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Frequently Asked Questions
What is the difference between Llama 4 Scout and Llama 4 Maverick?
Llama 4 Scout has 109 billion total parameters (17B active) and 16 experts, with a 10 million-token context window optimized for accessibility on lower hardware. Maverick has 400 billion total parameters (17B active) and 128 experts, a 1 million-token context window, and higher benchmark scores – but requires significantly more GPU memory. Scout is for self-hosters; Maverick is for cloud deployments and API users.
Can I run Llama 4 locally on my own computer?
Llama 4 Scout runs on a single RTX 3090 or RTX 4090 (24GB VRAM) with Unsloth’s 1.78-bit quantization at around 20 tokens per second. A Mac with 32GB+ Apple Silicon unified memory also works well via Ollama. Maverick requires approximately 200GB VRAM at Q4 quantization – a multi-GPU server setup – which puts it out of reach for home hardware for most users.
Is Llama 4 free to use commercially?
Yes, for most use cases. The Llama 4 Community License permits commercial deployment without a fee. The main restriction applies to services with over 700 million monthly active users, which must seek a separate license from Meta. Individual developers, startups, and mid-size companies can freely download, fine-tune, and deploy Llama 4 models for commercial products.
How does Llama 4 Maverick compare to GPT-4o?
At launch in April 2025, Maverick scored an ELO of 1,417 on LMArena, which placed it competitively with or above several closed models at the time. Meta’s benchmarks showed it matching DeepSeek V3 on reasoning and coding with fewer active parameters. By mid-2026, newer closed models have raised the bar further, but Maverick’s 1 million-token context window and free weights mean it remains highly competitive for long-context and multimodal applications.
What is Llama 4 Behemoth and when will it be released?
Llama 4 Behemoth is the third planned Llama 4 model with approximately 2 trillion total parameters. Meta previewed it alongside Scout and Maverick in April 2025, describing it as a teacher model used to distill and improve Scout and Maverick during training. As of June 2026, Behemoth has not been publicly released. Meta has not announced a firm release date, and it remains an internal research model for now.