AI model by Meta
Meta Muse Spark for business
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.
- Reviewed on September 24, 2026
- Meta
Capability tiersRelative, not benchmarks
Key facts about Meta Muse Spark
- Model ID at review
- Muse Spark 1.3
- Provider
- Meta
- Main category
- General purpose
- Open weights
- No, available as a hosted service
- Last reviewed
- September 24, 2026
Fit
Where it fits and where it does not
Good at
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Mixed input: text, images, video and PDFs.
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Tool calling and structured outputs.
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Long inputs, with a 1 million token window.
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General assistant and extraction tasks.
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Teams that want a Meta model without hosting it.
Not the right choice for
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Self-hosting, since it is not open weight.
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Projects that need a long track record with a model.
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Teams that need Llama models on the same API, which it does not offer.
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Decisions made before testing on your own data.
Use cases
Business use cases we would use it for
Document and media reading
Tool-using assistants
Provider comparison
Our notes
When we would choose it
In April 2026, Meta launched Muse Spark, a proprietary model offered through the new Meta Model API. At review time the current version is Muse Spark 1.3. Meta lists a 1 million token context window, input of text, images, video and PDFs, and support for tool calling and structured outputs. Llama models are not offered on this API.
This is a change of direction for Meta, which was best known for open-weight Llama models. Meta also lists an open-weight model in the new family, Muse Glimmer, distilled from Muse Spark under the Apache 2.0 license. We have not added it to this directory yet, since we could not verify enough detail at review time.
Because the API is new, we would treat Muse Spark as a candidate in a comparison rather than a default. The meters on this page are our early view and the price level is an estimate, since we did not verify pricing. Confirm current prices and limits with Meta before planning costs.
A fair test puts Muse Spark next to Claude Sonnet, GPT-6 Sol and Gemini Flash on the same set of real tasks, with the same scoring. See AI model evaluation.
If you already use Llama models and like Meta as a provider, note that Muse Spark is a different product: hosted, not downloadable, and with different terms. For open weights from Meta, Llama 4 remains the most recent Llama generation.
Before you commit
Things to check before you commit
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Pricing
We did not verify pricing at review time. Check the Meta Model API pricing.
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Data terms
Read the API data terms and regions before sending business data.
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Maturity
The API is newer than its competitors. Check stability and support terms.
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Versions
Muse Spark has had several versions in 2026. Pin the one you test.
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.
Gemini 3.8 Flash
Google's most capable Flash model, stable since September 2026, for agents, software engineering and enterprise workflows with full multimodal input.
GPT-6 Sol
The middle model of the GPT-6 family, positioned for complex coding and agentic workflows at a mid price level.
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.
- Document automation Read invoices, forms, contracts and IDs, pull out the right fields and route them for review.
- Customer portals A secure place where your customers upload documents, check status, pay and message your team.
- AI agents AI that takes actions in your systems, such as qualifying leads or processing requests, with people checking the results.
Solutions
View all solutions- AI document processing Read forms, applications, IDs and statements, extract the fields you need and route each document for the right review.
- AI support agent An assistant that answers routine questions from your own content, checks orders and hands anything else to a person.
- 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.
- Invoice processing Read supplier invoices, match them to orders and push approved ones into accounting, with exceptions flagged for review.
Industries
View all industries- Education and elearning Course platforms, student portals, content workflows and AI tutoring assistants for schools and training companies.
- SaaS and startups MVPs, subscription billing, AI features, multi-tenant platforms and scaling support for founders and product teams.
- Construction and facilities Job tracking, maintenance requests, inspections, quotes and proof-of-work photos for builders and facility teams.
- Finance and accounting Client portals, document collection, invoice and receipt processing, and reporting for accounting firms and finance teams.
- Insurance Claims intake, document and photo processing, policy knowledge assistants and customer portals for brokers and insurers.
Case 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.
- 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.
- RAG vs fine-tuning Two ways to make AI work with your knowledge, compared by cost, accuracy and upkeep.
Glossary terms
View all glossary terms- Multimodal model A multimodal model is an AI model that can take in more than one kind of input, such as text with images, audio or video.
- 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.
- 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.
- Artificial intelligence Artificial intelligence is the broad field of building software that performs tasks that normally need human judgment, such as understanding language or images.
- 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.
- Fine-tuning Fine-tuning is further training an existing AI model on your own examples so it learns a specific style, format or task.
FAQ
Questions people ask us
Have a question that is not here? Ask us directly.
No. It is a proprietary model offered through the Meta Model API. Meta lists a separate open-weight model, Muse Glimmer.
Meta lists text, image, video and PDF input.
At review time, no Llama models were offered on that API.