Meta's Muse Beats ChatGPT's Early US App Numbers
Meta's Muse has moved ahead of ChatGPT on early mobile engagement in the United States. Appfigures estimates put Muse at 642,000 U.S. mobile daily active users, compared with 231,000 for ChatGPT at the same stage after launch, according to reporting published Sept. 21 TechCrunch. Daily active users here means individual phones that opened the app on a given day.
The lead extends to installs. Muse saw 2.8 million total installs globally in its first 12 days. On a direct iOS comparison in the U.S. and Canada, Muse recorded 1.8 million downloads in its first 12 days, against 1.3 million for ChatGPT over the same window.
The comparison uses a narrower slice for a reason. Muse launched on Apple's App Store and Google Play in the U.S. and Canada only, while ChatGPT launched on mobile globally on iOS only. The Sept. 21 estimates adjust for that difference by isolating iOS in the U.S. and Canada. On iOS alone, Muse also led on engagement with 359,000 daily active users at the same point after launch, above ChatGPT's figure.
Chart position followed the same path. Muse moved from No. 2 overall on the U.S. App Store right after launch to No. 1, ranking above ChatGPT.
Installs, averages and distribution
Earlier reports had shown a closer contest on daily speed alone. Reporting published Sept. 20 put ChatGPT at an average of 87,000 downloads per day over its first eight days, against Muse at roughly 73,000 U.S. downloads per day over its first 10 days Yahoo. A Sept. 10 estimate had put ChatGPT at an average of 83,300 daily downloads around its launch, with Muse at more than 83,000 downloads on iOS in the United States shortly after launch.
The 12-day totals give a fuller picture. Muse reached No. 1 even with lower early daily averages in the U.S. Those totals cover a longer run and include both iPhone and Android installs.
On distribution, Appfigures found that over 95% of Muse's users are also Facebook users and 63% are Instagram users. In practical terms, most Muse users already had a Meta login, could get Meta notifications, and were used to moving between Meta apps.
What Muse is built to do
Meta describes Muse as its personal AI agent Meta. The company states Muse can browse the web, connect to users' apps, complete multi-step tasks, and keep working in the background with user direction and oversight.
The model underneath is Muse Spark. Meta describes Muse Spark 1.1 as a multimodal reasoning model built for agentic tasks Meta, meaning it can work across input types such as text and images and plan a sequence of actions. The base Muse Spark design is natively multimodal with support for tool-use, or calling outside software, visual chain of thought, or working through steps that involve images, and multi-agent orchestration, or coordinating smaller helper models.
Two product extensions already build on that base. Muse Code is a coding agent powered by Muse Spark. Muse Image and Muse Video are the first media generation models developed by Meta Superintelligence Labs.
For people who build software, the vocabulary is familiar. Tool-use, background execution, and orchestration shift work from single-turn inference, or answering one prompt at a time, to persistent sessions that keep state, track permissions, and retry steps across first- and third-party APIs.
The broader context here is that installs and daily active use measure different things, and agents widen that gap. A chatbot can do well with short sessions and frequent returns. An agent that holds a task in the background can show fewer opens while using more inference, tool calls, and storage. The 642,000 U.S. mobile daily active figure is therefore best read as a floor for demand, not a ceiling.
In my view, the number to watch next is retention after the Facebook-driven install wave. Cross-app ownership lowers acquisition cost. It does not guarantee repeated delegation of complex tasks. Agentic products live or die on permission grants, connector reliability, latency under multi-step planning, and graceful failure when a web flow or API changes. Early rank is cheap. Sustained delegation is expensive to earn.
For builders, the broader context here is that Muse reflects a pattern enterprise teams already know from internal copilots. The client is thin. The work is asynchronous. Oversight is explicit. If Meta can keep task completion rates high without adding review work for users, it will reset expectations for what a consumer assistant should do on mobile. That outcome would matter more than whether the first 12 days beat a 2023 launch curve. It would make background agents, with audit trails and app connectors, the default way to handle routine mobile work.
