AI model by Meta
Llama 4 Scout for business
An open-weight Llama 4 model with image input and a very long context window, for teams that want to host a capable model themselves.
- Open weights
- General purpose
- Vision and multimodal
- Open weights
- Reviewed on September 24, 2026
- Meta
Capability tiersRelative, not benchmarks
Key facts about Llama 4 Scout
- Model ID at review
- Llama-4-Scout-17B-16E-Instruct
- Provider
- Meta
- Main category
- Open weights
- Open weights
- Yes, Llama 4 Community License
- Last reviewed
- September 24, 2026
Fit
Where it fits and where it does not
Good at
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Self-hosting with full control over data.
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Very long inputs, with a context window Meta lists at 10 million tokens.
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Reading images together with text.
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Assistant chat and summaries.
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Running on a range of third-party hosts as well as your own hardware.
Not the right choice for
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Teams without the skills to run and monitor models.
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The hardest reasoning tasks, where current hosted leaders do better.
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Uses that the Llama license does not allow.
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Projects that want the newest Meta model, which is now Muse.
Use cases
Business use cases we would use it for
Private document assistants
Long-context analysis
Private extraction
Our notes
When we would choose it
Llama 4 Scout was released by Meta in April 2025 as an open-weight model with text and image input. Its model card lists a context window of 10 million tokens, far longer than most models. It is published under the Llama 4 Community License, which allows commercial use with conditions that you should read before building on it.
Scout is a practical choice when a business wants a capable model it can host itself, whether on its own hardware or through a hosting provider of its choice. Data stays where you put it, and the model version never changes unless you change it.
In 2026, Meta moved its newest work to the Muse family, starting with the proprietary Muse Spark. At review time, no newer Llama had been published in Meta's official model collection. That makes Llama 4 the previous generation, still useful and widely supported, but not where Meta is putting new effort.
For self-hosted projects, we would compare Scout with Gemma 4, Mistral Small and Qwen models on the same tasks. Licenses differ, and so do hardware needs. See open weights models.
A very long context window is only useful if your hardware can hold it and if the model still finds the right detail at that length. We would test retrieval inside long inputs before relying on it, and use a retrieval step when the documents are larger than the budget allows. See RAG knowledge assistants.
Before you commit
Things to check before you commit
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License
The Llama 4 Community License has conditions. Read it before building a product on the model.
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Hardware
Long contexts need a lot of memory. Size the hardware for your real inputs.
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Tool calling
Tool calling and structured output depend on the host or framework you use. Test them.
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Generation
Meta has moved its newest models to the Muse family. Llama 4 is the previous generation.
Alternatives
Models to compare it with
Llama 4 Maverick
The larger Llama 4 open-weight model, with image input and a 1 million token context window, for self-hosted assistants and analysis.
Gemma 4
Google's open-weight model family under Apache 2.0, in sizes from phone-friendly to 31B, with image input and function calling.
Meta Muse Spark
Meta's newer proprietary model family, offered through the Meta Model API, with text, image, video and PDF input, tool calling and a 1 million token window.
Keep exploring
Solutions, services and guides
Related services
View all related services- LLM integration Add a large language model to software you already have, with the guardrails, costs and logging handled.
- AI model evaluation Test candidate AI models on your real data and tasks, then pick the one that balances quality, speed and cost.
- AI Product Development AI agents, knowledge assistants, copilots and document automation built into the way your team already works.
- AI copilots AI helpers built into your product or internal tools that draft, summarize and suggest while people decide.
- Document automation Read invoices, forms, contracts and IDs, pull out the right fields and route them for review.
Solutions
View all solutions- Knowledge assistant (RAG) Ask a question in plain words and get an answer from your own documents, with links to the sources.
- AI document processing Read forms, applications, IDs and statements, extract the fields you need and route each document for the right review.
- AI-assisted content workflows Briefs, first drafts, edits and repurposing in a workflow where people set the angle and approve every word.
- AI translation workflows Translate websites, products and support content quickly with AI, a shared glossary and native-speaker review where it matters.
- Feedback analysis Read every review, survey and ticket, group them by theme and sentiment, and show what customers keep asking for.
- Screening assistant Summarize each application against your criteria, flag strong matches and missing information, and leave every decision to a person.
Industries
View all industriesCase studies
View all case studiesGuides and articles
View all guides and articles- How to choose an AI model for your business A step-by-step way to pick an AI model by testing candidates on your own tasks, data rules and budget.
- RAG vs fine-tuning Two ways to make AI work with your knowledge, compared by cost, accuracy and upkeep.
- What is RAG and when do you need it? How retrieval-augmented generation lets AI answer from your documents, when it fits and how to build it well.
- How to keep customer data safe when using AI Practical steps to protect customer data when you use AI services, from data terms to access and logging.
MCP servers
View all mcp servers- Files and storage Servers that connect AI to cloud drives and object storage, so it can find, read and organize documents.
- AWS S3 Amazon S3 is handled through the managed AWS MCP Server, which lets AI run scripted AWS actions, create presigned links and search AWS docs.
- Box Box's official MCP server lets AI search and read files, ask questions across documents with Box AI, extract data and upload files.
- Dropbox Dropbox's own MCP server lets AI list, search and read files, convert them to markdown and create, move, share and delete files. In beta.
- Google Drive Google's own Drive MCP server lets AI search, read and create files in Google Drive, with your Google Workspace sign-in.
- OneDrive Microsoft's OneDrive MCP server, part of its Work IQ preview, lets AI find, read, create, move, share and delete files in OneDrive.
Glossary terms
View all glossary terms- Large language model A large language model, or LLM, is an AI model trained on vast amounts of text that can understand and generate language, and often images and code.
- Context window A context window is the maximum amount of text, measured in tokens, that an AI model can consider at once, including the question, documents and its answer.
- Open-weights model An open-weights model is an AI model whose trained parameters are published, so anyone can download and run it on their own hardware under its license.
- Token A token is a small piece of text, often part of a word, that AI models read and write, and that providers use to measure limits and pricing.
- Inference Inference is the step where a trained AI model is used to produce an output, such as an answer, a label or a prediction, from new input.
- LLM integration LLM integration is connecting a large language model to your software and data, so AI features work inside your own products and processes.
FAQ
Questions people ask us
Have a question that is not here? Ask us directly.
It is released under the Llama 4 Community License, which allows commercial use with conditions. Read the license for your case.
The model card lists 10 million tokens. Real limits depend on your hardware and host.
At review time Meta had not published a newer Llama. Its newest models are in the Muse family.