AI model by Meta
Llama 4 Maverick for business
The larger Llama 4 open-weight model, with image input and a 1 million token context window, for self-hosted assistants and analysis.
- Open weights
- General purpose
- Vision and multimodal
- Open weights
- Reviewed on September 24, 2026
- Meta
Capability tiersRelative, not benchmarks
Key facts about Llama 4 Maverick
- Model ID at review
- Llama-4-Maverick-17B-128E-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-hosted assistants that need more capability than small models.
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Image and text input together.
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Long inputs, with a 1 million token context window.
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General chat, drafting and summarizing.
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Availability on many third-party hosts.
Not the right choice for
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Small servers, since it is a large model.
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The hardest reasoning or coding tasks.
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Uses outside the Llama license terms.
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Teams that would rather pay per request than run hardware.
Use cases
Business use cases we would use it for
Private assistants
Document summaries
Image and text tasks
Our notes
When we would choose it
Llama 4 Maverick is the larger of the two Llama 4 models Meta released in April 2025. It accepts text and images, and its model card lists a context window of 1 million tokens. Like Scout, it is published under the Llama 4 Community License.
Maverick suits self-hosted projects that need more capability than a small model can give, such as a private assistant for a large team or summaries of long, sensitive documents. It is also widely offered by third-party hosting providers, which gives you open-weight control without running the hardware yourself.
The trade-off is size. A larger model needs more expensive hardware and more care to run well. For many teams, a smaller open model such as Gemma 4 or Mistral Small gives most of the quality at a fraction of the cost. We would test both before deciding.
Meta's newest models are now in the Muse family, and at review time no newer Llama had been published in its official collection. Llama 4 remains a solid, well-supported option for open-weight projects, but it is the previous generation.
Whatever the model, we would build the integration so it can be swapped. Open-weight models improve quickly, and a new release from Google, Mistral, Qwen or others may beat today's choice within months. See LLM integration.
Before you commit
Things to check before you commit
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License
Read the Llama 4 Community License before building a product on the model.
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Hardware
Maverick is large. Price the hardware or hosted endpoint for your traffic.
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Tool calling
Support depends on the host and framework. Test tool calls and JSON output.
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Quality gap
Compare with a hosted model on the same test set before you commit.
Alternatives
Models to compare it with
Llama 4 Scout
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.
Mistral Large 3
Mistral's open-weight general-purpose flagship under Apache 2.0, with image input, tool calling, structured outputs and a 256K token window.
DeepSeek V4.1 Flash
DeepSeek's default, best-value model: open weights under MIT, image input, tool calling, JSON output, optional thinking 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.
- RAG knowledge assistants Assistants that answer questions from your own documents and show where each answer came from.
- AI copilots AI helpers built into your product or internal tools that draft, summarize and suggest while people decide.
- MCP integration Connect AI assistants to your CRM, files, databases and tools through Model Context Protocol servers, with safe permissions.
Solutions
View all solutions- Internal help desk assistant An assistant in Slack or Teams that answers policy and how-to questions from your handbooks and opens tickets when needed.
- Knowledge assistant (RAG) Ask a question in plain words and get an answer from your own documents, with links to the sources.
- 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- Knowledge and memory Servers that give AI long-term memory or access to knowledge bases, wikis and personal notes.
- Confluence Atlassian's official Rovo MCP server lets AI search and read Confluence spaces and pages, create and edit content and add comments and labels.
- Memory The MCP project's Memory reference server gives AI a local knowledge graph to store people, things and facts and recall them later.
- Obsidian A community MCP server that lets AI list, read, search, edit, append to and delete notes in an Obsidian vault through the Local REST API plugin.
- Readwise Readwise's official hosted MCP server lets AI search and manage your reading highlights and Reader documents.
- Sequential Thinking The MCP project's Sequential Thinking reference server gives AI a structured way to break a problem into steps and revise them.
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.
- AI copilot An AI copilot is an assistant built into a tool people already use, which suggests, drafts and explains while the person stays in control.
- Embeddings Embeddings are lists of numbers that represent the meaning of text or images, so software can find items that are similar in meaning.
FAQ
Questions people ask us
Have a question that is not here? Ask us directly.
Maverick is the larger model with a 1 million token context window. Scout is smaller and lists a 10 million token window.
Yes. Both Llama 4 models accept text and images.
Not necessarily. Many hosting providers offer Llama 4 models, so you can use open weights without running hardware.