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

Episode Date: October 14, 2026

I put Meta’s new Muse Wonder (and Wonderful) frontier models through the How I AI bench, which covers nearly 20 tasks across prototyping, agentic workflows, coding, writing, and creative work. I scored everything with an LLM-as-a-judge pass and my own voice notes, then pulled together the first-look results you see here.What you’ll learn:Why Meta’s “personal and professional agent” framing mostly holds up, and where it doesn’tWhat active focus mode actually does on long-running tasks, and whether it’s realThe three agentic tasks where Muse ranked first, and why those categories matterWhat Muse’s design output actually looks like (it’s not good)How it compares to Sonnet on voice assistant tasksWhere Muse lands on SVG and short-form video versus Opus 5.5, Astra, and FableWhether I’m actually putting Muse into any of my agent workflows—Brought to you by:DX—Engineering intelligence for the AI eraJira AI SDLC—Get your tokens’ worth with Jira—In this episode, we cover:(00:00) Intro and what Muse Wonder is(01:19) Meta's frontier capability claims(04:36) Muse code(06:06) Benchmark overview and how the How I AI bench works(07:51) Overall results and TL;DR verdict(11:53) Agentic tasks(13:36) Email writing and voice assistant performance(14:35) Where Muse falls short(18:28) SVG illustration results(19:33) Short-form video editing(21:18) Fast on one-shot work, slow on agent tasks(22:15) Final verdict and where I'll use Muse Wonder next—Tools referenced:• Meta Muse Wonder / Muse Wonderful: https://research.meta.ai/blog/muse-wonder• Muse Code: https://developer.meta.com/ai/lp/muse-code—Where to find Claire Vo:ChatPRD: https://www.chatprd.ai/Website: https://clairevo.com/LinkedIn: https://www.linkedin.com/in/clairevo/X: https://x.com/clairevo—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [e-mail].

Welcome back to HowIAI. I'm Claire Vaux, 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 agenic 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 life cycle, 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 slash how IAI. That's GETDX.com slash how IAI. Let's talk about what Mews 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, GPT6 Astra, and

Claude Opus 55. Both of those are now my daily drivers. Now, if you look at this benchmark, where they're really grading themselves is agentic 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 55, Fable, and Astra on DeepSuite and Sweet 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 agenetic 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. 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. So 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. It's trying different things. And so really surprising, I will 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.meda.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 Claudecode run it headlessly. So I did not want to have anything to do with Muse Code. It's a nagentic 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. It's not that thrilling. I legitimately have not opened a terminal app in a very, very, very long time. So just know that I did not really test Muse code. I signed up. I gave Cloud Code access to Me's Code and ran it headlessly. But let's get into how the model performed. And just a reminder on how I AI Bench works, we run against almost 20 different tasks.

That's everything from writing a PRD. to doing a set of prototypes, to redesigning the chat PRD site, to running long running computer use, agentic research, code repair tasks, and then some creative tasks. And then a judge, a LLM as a judge, judge them, and then I judge them. For sake of speed, I'm just going to like give you all my hot take feedback on these. So this is really like a first look kind of review. This isn't a benchmark review because I just don't want to run. We just did Opus 55. and Soul 6-1 and the whole thing. So I don't want to do the whole thing again.

I just want to look at Muse and say, do I think it's done a good job? And I really do think it has. So I'm excited to show you this bench and let's get into what it returned for us. So this is what it looks like when the bench runs. I group things by prototype, agentic work, coding work, writing work, and creative work. And then we have an LLM as a judge, judge them. Then I go through and actually just do voice notes now.

I used to click things and say things. I was like, why am I doing that? So you use voice notes to Claude. Claude takes my scores. The L.M. As a judge, ranks them, and then puts them together in a presentation, which is working on right now. So you just see, again, like we do different types of prototypes and wireframes. I do different agentic workflows, which is very interesting.

We code a back end feature. We do a code audit for some issues and some real code. We write emails. We do PRDs. and we even do creative work like drawing. So it's a pretty comprehensive bench. So I'm just going to tell you kind of like the TLDR of what my first impressions are of Muse and then we will go into where it was really strong and where it wasn't strong. So here are the results. It is the best at agentic work. So I tested four tasks. You can see what those are here. We did like billing ticket

management, customer apply, inbox triage, research, long term research. And and the voice of an assistant. And it was my favorite across the board. So it's not surprising to me that Muse, which is a very good agent, underneath it is going to be a very good agenic model with the right sort of like coworker voice ability to do knowledge work and the ability to do long running research tasks well. And then, you know, like it sits up with the big guys in terms of coding. And so, you know, we only have two tasks here.

I really think one, it's like kind of boring for you all to think does it do coding? well or not. We can look at the benchmarks. I don't think you all want to watch like bug fixes and do like agenetic verification. But like all of them are good and all of them did well on my coding tasks. It's actually kind of interesting that Astra is a little bit down here at the bottom. But like it's in the ballpark. So it's totally a viable option for backend coding and architecture review. So no complaints on agents or coding. Where it really started to fall off the the cliff are on creative tasks. And I think I've just been spoiled lately by Opus 55. I know. I know. Your GPD girl here has been using Opus 55 because for creative tasks for design,

for video, for art, it is just the best on the market right now. I'll do an episode in the future about how I'm using Opus 55 right now. I think it's really cool. But writing, it's fine. It's not great. I would not love a muse writer. Prototypes. The the design sensibility is just like 18 months behind. Maybe a year behind. I don't know. It's just not good. It's a lot of like blurple, rounded serif fat fonts. Don't love it. And then the creative work that it did is like fine, but the worst of all the options. And so I wouldn't, you know, yet rely on it for those things. But it's going to be really interesting as like an orchestrator model or a voice model or an agent model in the loop. And you can definitely tell

that it's done that well. This episode is brought to you by Jira Product Discovery. With AI, a single PM can summarize research, draft a PRD, and build a working prototype. But multiplayer mode, that's broken. You can't get everyone aligned on what should actually ship with just a markdown file. Jira Product Discovery gives your team one place to capture insights,

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So you can see here it did really well. It ranked first on backend feature implementation, inbox triage, computer use, email, batching, and the voice of assistant. So I actually liked it almost as much as I liked Sonnet for voice. And then as you get into prototypes and creative work, it really just starts to fall off a cliff. I judged those designs. much more harshly than our LLM as a judge did, but this is definitely not good. We can look at a couple examples, but I just don't really love it. And so let's talk about what a good agentic task for benchmarking looks like and why

Muse did really well. So we run this task where you use computer use and you have to resolve a billing ticket in an admin app. And there's a couple things that the agent has to navigate. It's linked to the wrong account. So it has to like identify and find the right. account. The refund amount is weird and there's a duplicate so you have to kind of like work around that. And there's a prompt injection task in the instructions. And it did a really good

job. It investigated. It fixed the link for the right customer. It issued the correct refund because it was a duplicate. It made the plan change and then it replied and mark solve. It was the best of all of them. It like worked through that in a clear way. It, you know, like hit all of. And our planted kind of things that could go wrong and it did really well. So if you're thinking about how to test like what is a good agent, I think figuring out how to navigate a complex SaaS app and resolve a complex kind of like customer problem is a great one, which is why we use this billing ticket as a test case for this bench. Now, the other two things that I thought I did really well, and then I ranked at five on both of them, is I would let Muse write emails for me. Now, my emails are two

sentences long. So I am like, yep, sounds good, whatever, see. Like, that's how I send emails. And Muse did a really nice job of writing emails for me. So I will trust Mews, agent Mews to write emails for me. And I will trust Model Mews to write emails for me. It just really followed my instructions quite well. Again, you can see this like conversational basic voice in my voice assistant test, which is basically like, if I plug this model into an open club, would I like talking to it? And you can see here, it's got like a nice, uh, consumery human voice to it. So it's like a little slop. There are m dashes in here. But again, it's not annoying. It's not cloying. It's just kind of a nice, straightforward, personable voice assistant. So this one is the one that Sonnet, um,

has been dominating in the bench. And I think Muse is like right up there with Sonnet performance. And so again, like if you are building a, especially a personal agent, maybe a knowledge agent, and you're looking for that like central orchestrator voice, Muse Wonder might be the thing for you. So I think it's done really well at that. You can tell it's focused on that. And it's just demonstrated those capabilities end to end. Now, where did it do more poorly? It does not write PRDs well.

It was a short PRD. which I love. It followed a template, which I love. But you can see here we had some like formatting issues. It's a little hard to scan and it was very, very short. And I just didn't think it had the detail compared to the other pherty's that were generated by the other agents. So wasn't in love with pherty's. And then where it really fell down was design. And so if you see here, I'm just going to click through. Actually, I'm going to open up the full outputs. I'm click through the designs. This is supposed to be an editorial assignment desk. We've seen this a couple times. It's kind of like middle of the line design, but a lot of the things didn't work.

One of the things that I really test in terms of prototypes is how much of the stuff works. And a lot of things just didn't click or do anything interesting. And so it's hard to understand what this prototype is trying to do. Same with kind of like these more complicated UI. design so we do a dock scheduler we do like an incident room and both of these what's super frustrating is it like got it got it gets like 80% of it right and then it just totally falls down and so this i pulled it up at first this doc scheduler app i was like oh it's using color right it's doing all these things but then as i clicked through some of the stuff like this xbox doesn't doesn't look good there's just this polish to it that wasn't there that was even worse in this incident management platform

where I told it specifically to be dark mode because it was a dev tool and all developers need dark mode. It did in light mode and had this like horrible overlay. So again, I just thought a lot more errors in front end coding with meta muse wonder than I have with these other models in a really long time. And then I had this like sort of consistent slop style. Again, I think I can probably at this point tell slop per model, like what kind of slop I'm going to get from what model. And Metamuse is going to give you fat serif fonts, this kind of font, and rounded corner slop. It's just going to give you like basic template boilerplate SaaS slop across the app.

So you'll see in this B2B renewals app and this plant care app, you just are going to get the same kind of like design components. they're fine but they're not beautiful and I don't think they're what like an astra or an opus 55 would generate and the worst thing it did was the chat PRD website which I have to show off just got a beautiful reef fresh it's so stunning I did an opus 55 redesign it's so pretty it's got these like beautiful little interactive components to it it's just lovely we love the chat Purity website and Muse obliterated it. Like it is so ugly. The images are broken. It isn't even in our kind of like brand design guidelines. It has access to our guidelines. It just didn't do it. This was so disappointing. And again, like using emojis, all sorts of stuff that's just like old timey slop we don't like. And so I don't, I would not reach for Muse Wonder on front end. web design. It's just not going to be the model I reach for. I just do not think it's very good.

Now, I haven't tested it with games, for example. I need to press on it more from a back-end coding experience. But I'll just say for a lot of you, you're saying like, can I vibe code something? Can I build an app for my kids? Can I build something for work? And I just, I don't love the front and sensibility of a Meta-Mews. I just don't enjoy it. Finally, we can get to the creative aspects of Metamuse. I do two tasks here. I have it make some SVGs and then I have it cut a short form video. Let's look at those. The SVGs, first of all, I just have to say like a year ago, if you would have told me that you could get coded SVGs at this quality, it would have blown my mind. So again, we're spoiled for choice here. I cannot believe how good these new frontier

models are at creating little illustrations and SVGs. This did a totally fine job. This is totally fine. Totally fine. They're so cute. It's a little PRD. It's a little microphone. It's a little bug. That being said, it just was the worst of the bunch or seventh out of eight. Soul and Astra were so good at this. Opus 5-5 was so good at this. And I would just say like Muse was okay, but not great at it. And I just think that this is one of those things where all the scores were compressed towards the top. And so I'm not saying Muse is bad. I'm just saying I wouldn't reach forward. it for that specific use case. And then on to our very last creative task.

I have these models cut a short form video of the how I AI content. Video editing is one of those things that I think these models are getting a lot better at. And so I'd like to test how it does. And I think when you watch this, which I will let you watch, you will see that the design sensibility is not quite there yet. Although I do love this layout. I'm going to think about I usually put my head up top in the image at the bottom. I think about the image at the top of my head at the bottom.

That's not bad. But if you watch this video, you can just see, like, it's pretty good at cutting the cuts, which a lot of models struggle with. So it gets the timing almost 100% right. But the overlays and the design are just really crummy. And something, it did something with the audio, because the audio is also very strange. So let's watch it really quickly. So no complaints on agents or coding.

Where it really started to fall off the cliff are, on creative tasks. I think I've just been spoiled lately by Opus 55. I know. I know. Your GPT girl here has been using Opus 55 because for creative tasks for design, for video, for art, it is just the best on the market right now. So you can see like the captions are on my face. They're actually kind of ugly. This like bar at the top I don't like. Like this is not a short form video. I will run this though through a couple of my other skills. We'll see. I'm not calling it a no. I'm just not calling it a yes. And then it did something very strange to the audio. So I have to double down there and figure out what's going on. So I gave this a two out of five. It found the right

40 seconds. I think the track was great, but the video was not great. And so again, for creative tasks, it just really didn't do the best. Now overall performance, I will say one of the things that I noticed about MetaMuse is it is very fast. And with like sort of one shot fast tasks, it is fast. And so note superiority, very fast, drawing characters, doing prototypes, very fast. It's a zippy model. And I think you're going to like that again for that agentic voice, that back and forth feedback. But when it does, agentic tasks, it does take a long time. This might be that, you know, focused effort mode that they say that they have. And so it is spending a lot of time. And so it is spending a lot of time on things like the videos, inbox triage, backend features. And so it'll be interesting to see

you know, over short, kind of like in the loop tasks versus long tasks, how the performance notes out. I've just started to test this and keep track of performance. So I will do that in the next bench. So I will just say little tasks fast, long tasks a little bit longer. In conclusion, it was really strong at agenetic work. It was serviceable at coding. back end work. I'm going to test a lot more there. It would definitely let it right on my behalf and I think it has a nice voice. It definitely just falls short on design sensibility, which is so much of my work, so much of my work is creative front-end design. So it really fell down there. And so we're going to have to see if putting skills into it, if giving it more direction,

we can get that creativity up. But honestly, I'll just rely on Opus 5 for that. But I am going to take Muse Wonder and see if I can put it in the loop of a couple of agents I'm building and see if I like both the performance and the user experience. So that is my first look at the meta muse wonderful model. I've showed you what it's good at. I've showed you where it's not doing well and where I'm going to take it next. Again, we try to do these benchmarks regularly so you have a sense of what models you should pay attention to, which ins you can ignore.

I think meta's in the game. So it'll be really interesting to watch as they evolve their model strategy. as they release new things, if this becomes my preferred model in some use cases. It's not there yet, but never say never. It certainly is trending in the right direction, and it's something that I'm going to keep playing with and I think you should too. Thanks for joining How I AI. Thanks so much for watching.

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