Meta’s new ‘Muse Wonder’ model: good for AI agents, bad for design

Claire Vo (0:00)

Welcome back to How I AI. I'm Claire Vo, Product Leader and AI Obsessive, here on a mission to help you build better with these new tools. Today, I'm reviewing Meta's new Frontier Intelligence Model, Muse Wonder, which comes in two versions, Wonder and Wonderful. I am going to put this model through the How I AI benchmark to see if it can enter the race for Frontier Intelligence, especially around coding, knowledge work, and agentic workflows. Let's get to it. This episode is brought to you by DX. In a recent study across more than 500 engineering organizations, DX found that spend on AI tools has grown 28x over the last year. The share of AI authored code is climbing, but overall innovation has remained flat. As teams generate code faster, new friction in code review and validation is offsetting those early velocity gains. DX tracks speed, quality, and cost together across the software development lifecycle, giving engineering leaders clear visibility into how AI impacts delivery and whether those investments are translating into real value. Download the full report at getdx.com/howiai.

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Let's talk about what Muse is and then we'll get into how it performed on the How I AI bench. So as Zuck announced on X and I guess probably Threads, Muse Wonder is their new family of frontier models with state of the art intelligence and performance on personal and professional agentic work. And if you look at the benchmarks and how they're comparing themselves, they're really putting it in the class of Fable, GPT-6, Astra and Claude Opus 5.5. Both of those are now my daily drivers. Now, if you look at this benchmark, where they're really grading themselves is a gen-take personal and professional work. They're saying that this is the best model for driving a personal agent. Now, this makes a lot of sense given their push into Muse, which in my opinion is a very great personal AI agent, and the model behind it has been zippy and cheerful to be with. So I'm not surprised they're looking at it, but they're not just looking at personal agents, they are looking at professional knowledge work as well. So you're seeing a lot of things around specific domain expertise, like legal work, financial work, business workflows, professional work, et cetera. So you can see they put a lot of emphasis in their benchmark table on those personal and domain expert benches. But they also care about coding. So they are saying that they outperformed every other model on their bench, including Opus 5.5, Fable, and Astra on DeepSwee and Swee Marathon. So they are really putting themselves in the big leagues when it comes to coding. We are going to see if that holds true, and if I would really move some of my agentic coding workflows onto Muse Wonder. But in addition to the benchmarks, they're calling out three capabilities, what they're calling frontier capabilities that make Muse Wonder a differentiated set of models in the market. The first is domain expertise. And so they are looking at applying specialized knowledge to problems that people encounter at work. And again, this is where you see them really aiming to perform on legal analysis, finance benchmarks. So they are saying, we have specialized expertise in knowledge work beyond coding that we do really well in. The other two are these ideas of what they call active memory. And so they are saying that they outperform all these other models on use and creation of memory. This is very important in personal agents. I'm not surprised to see that they have come up with something kind of fancy here, but they are trying to carry important context about you across conversations and make sure that across conversations, you're getting the right context for you in the task. The other thing that they're really focused on is sustained effort, and they call this active focus. And so when Muse Wonder goes into active focus, that means that it's working towards a challenging goal, and it's trying different things. And so really surprising, we'll give you a little spoiler alert on the bench. We did see active focus actually come into play and outperform on some of our long running tasks in the benchmark. So our bench is showing that this active focus investment on our longer running tasks is actually outperforming some of the other models. So that might be a place that I double down later and go into more deeply. Okay, now let's talk about the most annoying part of Muse Wonder.

It is that you have to download Muse Code. I mean, I guess you don't technically have to download Muse Code, but this did force me to subscribe to the Muse subscription, get access to the Meta model APIs, and download Muse Code, which I will say in my very light experience was deeply unimpressive, but did the trick and I didn't actually have to pay attention to it. So if you want to know how I actually tested this, I did sign up for Muse Code, which you can sign up for at developer.meta.com. I copied this install script in Terminal, which I have not opened Terminal, I don't know, in three months. So that was a real vibe. And then I gave myself access to Muse Code. And then I had Claude Code run it headlessly. So I did not want to have anything to do with Muse Code. It's an agentic coding harness. I'm sure it's fine. I don't want to pay attention to it. I just love my desktop apps of Codex and Claude Code. So I lived there. But do you want to try Muse Code? That is completely available to you. And it looks like this.