What is Meta Muse? Will they pay creators to use their work in their AI? How can I earn with Meta Muse?
Meta Muse is a personal AI agent, not a creator revenue platform, and it does not pay creators for use of their work in its AI training.
Here's the answer from Mary's digital twin. The gray check mark turns green once Mary has confirmed it.
Meta Muse, launched by Meta Platforms on September 8, 2026, is a personal AI agent, meaning it is software that acts on your behalf rather than simply answering your questions. Think of it as a digital assistant that can open your email, fill out forms, make bookings, complete purchases, and keep working on tasks even after you put your phone down. It connects to apps you already use, things like Gmail, Google Calendar, WhatsApp, and OpenTable, and operates on its own dedicated, private computer running inside Meta's cloud infrastructure.
It is available in the US on iOS, Android, and at muse.ai, with support for Meta's AI glasses arriving soon. Meta offers a free tier of Muse, as well as two subscription options, at $20 per month and $100 per month.
Does it pay creators for their work?
No, and this is where you need to read the fine print carefully. Users must opt out of letting Meta use their interactions with Muse to train the company's AI models. If they do not, Meta will scrub "critical personally identifying information" before using those conversations and interactions to improve AI models. So by default, your usage is feeding the machine unless you say otherwise. That is the opposite of getting paid.
There is a separate arrangement aimed at developers and companies building on top of Muse, not everyday creators. Developers using the contributor tier for Muse Spark 1.3, the AI model powering Muse, can pay about 95% less than those using the standard version. In exchange, they give Meta permission to use their prompts and the model's responses to improve future AI products. Meta is not sending anyone a check. It compensates developers through cheaper access to the model. That is a discount on a bill, not income.
What about earning as a creator through Muse?
The company is exploring potential monetary initiatives, such as taking a cut of AI agent-related shopping transactions, but has not settled on any concrete plan. Mark Zuckerberg has described a future where Meta could eventually take "a very small cut" when Muse helps users make money, save money, or complete purchases, with the fee potentially coming from businesses involved in those transactions rather than directly from users.
There is also Meta One, a new subscription tier aimed at creators and small business owners. Meta One gives subscribers more self-expression features and AI usage, and is designed to help creators spend more time creating and less time guessing. But this is a tier you pay for, not one that pays you.
The honest answer right now: Muse is a productivity tool, not a creator monetization platform. Your data is the price of entry unless you opt out, and any revenue-sharing model for creators is still on paper. If you are building something around AI, the opportunity is in using the agent capability to work faster and deliver more, not in waiting for Meta to cut you a check for your content.
Follow-ups
What kind of tasks can Muse Spark 1.3 actually handle compared to other AI models?
Muse Spark 1.3, the underlying AI model powering Meta Muse and available to developers through the Meta Model API, is built for long, multi-step work rather than quick one-off questions, and it pulls ahead of most competitors specifically in coding and agentic tasks.
Quick reminder of what "agentic" means, since it keeps coming up: an agentic model does not just answer. It plans a sequence of steps, uses tools, checks the results, corrects mistakes, and keeps going until the job is done. That is the category Muse Spark 1.3 is designed to lead.
What it actually handles well
Muse Spark 1.3 can understand an objective, plan steps, use tools, evaluate results, and adjust its approach when required. In plain terms: give it a goal, not just a prompt, and it works toward it. It is designed to sustain longer-horizon work by juggling multiple workflows in a single thread, and when given an open-ended objective it uses tools to generate its own context across messy and conflicting sources, proactively corrects gaps in its plan, and keeps track of what it has learned to produce a final deliverable.
Coding is where it is most clearly differentiated. Coding is one of the biggest areas of focus for Muse Spark 1.3. Meta trained the model on additional long-horizon coding tasks, improving its ability to handle software engineering workflows that involve multiple steps, including understanding an existing codebase, making changes, testing those changes, and fixing problems that emerge during the process.
On benchmarks, instruction following and coding rank in the 96th and 97th percentiles respectively, and reasoning and mathematics benchmarks score in the 84th and 81st percentiles.
Where it is smarter than its predecessor
The upgrade from Muse Spark 1.2 is less about raw intelligence and more about discipline. It is better calibrated on its own limits instead of hallucinating outcomes, and uses roughly 20% fewer tool calls and 25% fewer tokens versus Muse Spark 1.2 in internal comparisons. Fewer tool calls means faster results and lower cost, which matters a lot when a task runs across dozens of steps.
It can ask for clarification when a request is unclear, seek help when stuck, and request confirmation before taking actions with significant consequences. That last point is important: it will pause and check with you before doing something irreversible, rather than charging ahead.
How it compares to other models
Muse Spark 1.3 ranks in the top 13 of 435 AI models tracked for overall intelligence, top 9 of 208 for coding, and top 10 of 190 for agentic tasks. It accepts text, images, files, audio, and video. Its context window, the amount of information it can hold in memory at once, is larger than 94% of listed models, which is why it can work through a long software project or a sprawling document without losing the thread.
Meta One honest limitation: unlike earlier open releases from Meta, Muse Spark 1.3 keeps its model weights closed and gates its highest reasoning mode behind partner preview access. Weights, in this context, are the internal parameters of the model, the part that defines how it thinks. Keeping them closed means you cannot run it on your own machine the way you could with earlier Meta models. You reach it through Meta's API or through Muse Code, their terminal-based coding agent.
For everyday users of the Meta Muse personal assistant app, Muse Spark 1.3 is the engine underneath. The tasks it handles there, booking, email, scheduling, multi-app automation, are where its long-horizon discipline shows up most visibly in daily life.
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