Llama 4 Scout: Meta’s Most Ambitious Open-Weight Model Yet
Meta’s newest release, Llama 4 Scout, is making waves in the AI community. While the Llama 4 suite includes Maverick and the upcoming Behemoth, Scout has quickly stolen the spotlight as one of the most innovative open-weight models available today. With a 10 million-token context window, a mixture-of-experts (MoE) design, and strong performance across reasoning, multimodal, and coding benchmarks, Scout is redefining what’s possible for publicly available AI systems.
A Model Built for Scale
At first glance, Scout might appear to be the “lighter” member of the Llama 4 lineup, but its design makes it one of the most intriguing models in Meta’s history. Unlike dense models where all parameters activate at once, Scout uses an MoE architecture—only a small fraction of its parameters activate per token.
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Active Parameters: 17 billion
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Total Parameters: 109 billion
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Experts: 16 (with only 2 active at a time)
This approach makes Scout compute-efficient while still highly scalable. It can run on a single NVIDIA H100 GPU, making it accessible to smaller labs, developers, and startups that may not have massive compute budgets.
The 10 Million-Token Context Window
Scout’s defining feature is its unprecedented context length. With support for 10 million tokens, it goes far beyond anything we’ve seen in open-weight models. For comparison, most cutting-edge LLMs today top out between 200K and 1M tokens.
Why does this matter? Because longer context windows enable entirely new workflows:
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Multi-document summarization – Summarize entire archives, research papers, or legal cases without chunking.
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Codebase reasoning – Feed the model full repositories to debug, document, or generate improvements.
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Extended conversations – Maintain continuity over long sessions without losing track of earlier details.
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Knowledge-intensive tasks – Process textbooks, technical manuals, or even whole books in one pass.
Meta pre-trained and post-trained Scout on a 256K token window, but early reports suggest it generalizes well to much longer contexts. If true, Scout could open the door to workflows that were simply impractical before.
Multimodal Capabilities
Another area where Scout shines is multimodal reasoning. Unlike earlier Llama models, Scout was trained not just on text but also images and video. This makes it natively capable of handling tasks that blend modalities:
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Visual grounding – Aligning text with specific parts of an image.
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VQA (Visual Question Answering) – Answering detailed questions about images or diagrams.
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Video understanding – Parsing and reasoning about short clips paired with text.
Benchmarks show Scout outperforming other open-weight models in multimodal tasks, and even rivaling much larger closed models from Google and OpenAI.
Benchmark Performance
Despite its efficiency-first design, Scout holds its own across coding, reasoning, and multimodal evaluations:
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Reasoning: 74.3 on MMLU Pro, 57.2 on GPQA Diamond – higher than any other open-weight model at release.
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Coding: 32.8 on LiveCodeBench – competitive with larger Llama models and ahead of Gemini Flash-Lite.
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Multimodal: 88.8 on ChartQA and 94.4 on DocVQA – outperforming Gemini 2.0 Flash-Lite and Mistral 3.1.
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Long-Context: Outperforms Gemini in full-book tests on the Massive Textual Overlap Benchmark.
These results are particularly impressive given Scout’s modest active parameter size and ability to run on a single GPU.
Why Scout Stands Out
Several features make Scout uniquely important in today’s AI landscape:
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Accessibility – Unlike giant models that require specialized clusters, Scout can be deployed by smaller teams.
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Efficiency – The MoE design makes it less resource-hungry without sacrificing performance.
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Flexibility – Its multimodal training allows it to work naturally with text, images, and video.
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Scalability – With its 10M-token context window, Scout is better positioned for real-world use cases that demand large-scale input.
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Open Weights – Meta continues to release Scout openly, allowing developers and researchers to build on it.
Of course, Meta has added one licensing caveat: if your product or service has more than 700 million monthly active users, you’ll need separate approval to use Scout. But for the vast majority of teams, this won’t be an obstacle.
Use Cases on the Horizon
Scout could transform multiple industries:
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Legal Tech: Entire case files, contracts, and rulings processed in one pass.
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Healthcare Research: Medical studies and patient records analyzed holistically.
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Software Engineering: Automated reasoning across massive repositories, CI/CD pipelines, and bug tracking systems.
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Education: Summarizing textbooks, creating adaptive study guides, or generating domain-specific learning materials.
Its mix of scale, efficiency, and multimodal intelligence makes Scout a compelling option for anyone looking to push the boundaries of what AI can handle.
Access Llama 4 Scout
Meta has released Scout under its open-weight license, available for download on the official Llama channels and Hugging Face. It’s also accessible through Meta AI integrations on WhatsApp, Messenger, Instagram, and Facebook.
But if you want a faster way to try it out, Llama 4 Scout is already available on our all-in-one AI platform UltraGPT. You can experiment with its long-context reasoning, multimodal understanding, and coding abilities—all without worrying about infrastructure setup.
Final Thoughts
While Llama 4 Maverick and Behemoth have their strengths, Scout is the true game-changer of this release. It combines unprecedented context length, multimodal fluency, and benchmark-topping reasoning into a package that is efficient and deployable by nearly anyone.
In a competitive landscape where AI models are growing bigger but also harder to access, Scout strikes the right balance: powerful enough to compete, efficient enough to use, and open enough to innovate with.
And for those eager to experience what a 10M-token context window feels like in action—you can start experimenting today at UltraGPT.