Is Meta’s Muse Wonder model any good?

1. 0:00 – 1:19

   Intro and what Muse Wonder is

   1. CVClaire Vo0:00

      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 Wonder Full. 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 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/howiai. That's G-E-T-D-X.com/howiai.

2. 1:19 – 4:36

   Meta's frontier capability claims

   1. CVClaire Vo1:20

      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 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, um, 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 DeepSWE and SWE Marathon. So they are really putting themselves in the big leagues when it comes to coding. We are gonna 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. 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 and 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.

3. 4:36 – 6:06

   Muse code

   1. CVClaire Vo4:36

      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 wanna know how I actually tested this, I did sign up for Muse Code, um, which you can sign up, uh, 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 ha-headlessly. So I did not wanna have anything to do with Muse Code. It's an agentic coding harness. I'm sure it's fine. I don't wanna pay attention to it. I just love my desktop apps of Codex and Claude Code, so I lived there. But if you wanna try Muse Code, that is, uh, completely available to you, and it looks like this. It's, it's not that thrilling. Um, I, 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 Claude Code access to Muse Code and ran it

4. 6:06 – 7:51

   Benchmark overview and how the How I AI bench works

   1. CVClaire Vo6:06

      headlessly. But let's get into how the model performed. And just a reminder on how the 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 ChatPRD site to running long-running, um, computer use, agentic research, code repair tasks, and then some creative tasks. And then a judge, a, um, LLM as a judge judge them, and then I judge them. For sake of speed, I'm just gonna, 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 wanna run... We just did Opus 5.5 and Sol 6.1 and the whole thing, so I don't wanna do the whole thing again. I just wanna 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 I use voice notes to Claude. Claude takes my scores, the LLM as a judge ranks them, and then puts them together in a presentation, which it's 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, um, 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

5. 7:51 – 11:53

   Overall results and TL;DR verdict

   1. CVClaire Vo7:51

      bench. So I'm just gonna tell you kinda like the TL;DR 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. Um, we did like billing ticket management, customer reply, inbox triage, research, long-term research in 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 agentic model with the right sort of like coworker voice ability to, to do knowledge work and the ability to do long-running research tasks well. And then, you know, like it's in the... 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 w- you all wanna watch like bug fixes and do like agentic verification. But, like, all of them are good, and all of them did well on the 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 back-end coding and architecture review. So no complaints on a- 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 5.5. I know, I know. Your GPT girl here has been using Opus 5.5 because for creative tasks, for design, for video, for art, it is just the best on the market right now. I'll do a m-- uh, I'll do an episode in the future about how I'm using Opus 5.5 right now. I think it's really cool. But writing, it's fine. It's not great. I would not love a Muse writer. Um, 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 gonna 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, prioritize with evidence, and build living roadmaps everyone can rally around. And because it's powered by Atlassian's teamwork graph, it has the context you need to decide what to build. Those decisions flow straight into Jira so your developers or coding agents can start building with the context behind every decision. More than 25,000 companies already use Jira Product Discovery. Try it free at atlassian.com/howiai. That's atlassian.com/howiai. Jira Product Discovery, better product decisions in the AI era. So you can see here it did really well. It ranked first on back-end 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 it's definitely not good. We can look at a couple examples, but I just don't really love it.

6. 11:53 – 13:36

   Agentic tasks

   1. CVClaire Vo11:53

      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- And there's a couple things that the agent has to navigate. Um, it's linked to the wrong account, so it has to, like, identify and find the right account. Um, the refund amount is weird, and there's a duplicate, so you have to kinda, 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 pla-cha- plan change, and then it replied and marked 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 our, 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, um, test case for this bench. Now, the other two things that I thought it did really well, and then I ranked it 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, C." Like, that's how I send emails, and Muse did a really nice job of writing emails for me. So I will trust Muse, Agent Muse to write, uh, emails for me, and I will trust Model Muse to write emails for me. It just really followed my instructions quite

7. 13:36 – 14:35

   Email writing and voice assistant performance

   1. CVClaire Vo13:36

      well. Again, you can see this, like, conversational basic voice in, uh, my voice assistant test, which is basically like if I plug this model into an open claw, 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 em dashes in here. But again, it's not annoying, it's not cloying. It's just kind of a nice, straightforward, personable, um, 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 per- uh, 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.

8. 14:35 – 18:28

   Where Muse falls short

   1. CVClaire Vo14:35

      Now, where did it do more poorly? It does not write PRDs well. Um, 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 PRDs that were generated by the other agents. So wasn't in love with PRDs. And then where it really fell down was design. And so if you see here, I'm just gonna click through. Actually, I'm gonna open up the full outputs. I'm gonna 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 designs. So we do a doc 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, 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, 'cause it was a dev tool, and all developers need dark mode. It did it in light mode and had this, like, horrible overlay. So again, I just got 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. Um, again, I think I can probably, at this point, tell slop per model, like what kind of slop I'm gonna get from what model. And Meta Muse is gonna give you fat serif fonts, this kind of font, and rounded corner slop. It's just gonna give you, like, basic template, boilerplate, SaaS slop a- across the app. So you'll see in this B2B renewals app and this plant care app, you just are gonna 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, a Astra or a Opus 5.5 would generate. And the worst thing it did was the ChatPRD website, which I have to show off just got a beautiful refresh. It's so stunning. I did an Opus 5.5 redesign. It's so pretty. It's got these, like, beautiful little interactive components to it. It's just lovely. We love the ChatPRD 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 gonna 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-end sensibility of- and Meta Muse. I just don't enjoy it.

9. 18:28 – 19:33

   SVG illustration results

   1. CVClaire Vo18:28

      Finally, we can get to the creative aspects of Meta Muse. 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've told me that you could get coded SVGs at this quality, it would've 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. Sol 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 for it for that specific use case. And then on to our

10. 19:33 – 21:18

    Short-form video editing

    1. CVClaire Vo19:33

       very last creative task. I have, um, these models cut a short-form video of the How I AI content. Um, 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, um, how it does. And I think when you watch this, which I will let you watch, um, you will see that the design sensibility is not quite there yet, although I do love this layout. I'm gonna think about-- I usually put my head up top and the image at the bottom, but think about the image at the top and 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, um, 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 a- agents or coding. Where it really started to fall off the, the cliff are on creative tasks. I think I've just been spoiled lately by Opus 5.5. I know, I know. Your GPT girl here has been using Opus 5.5 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, 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 give this a two out of five. Um, 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.

11. 21:18 – 22:15

    Fast on one-shot work, slow on agent tasks

    1. CVClaire Vo21:18

       Now, overall performance, I will say one of the things that I noticed about Meta Muse is it is very fast, and with, like, sort of one-shot fast tasks, it is fast. And so notes to PRD, very fast. Drawing characters, doing prototypes, very fast. It's a zippy model, and I think you're gonna 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. Um, and so it is spending a lot of time on things like the videos, inbox triage, back-end 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 nets out. I've just started to test this and keep track of performance, so I will do that in the next benchmark. So I will just say little tasks, fast. Long tasks, a little bit longer.

12. 22:15 – 24:02

    Final verdict and where I'll use Muse Wonder next

    1. CVClaire Vo22:15

       In conclusion, it was really strong at agentic work. It was serviceable at coding and back-end work. I'm gonna test a lot more there. I would definitely let it write 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. Um, and so we're gonna 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 5.5 for that. But I am gonna take Muse Wonder and see if I can put it in the loop of a couple 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 Wonder Full model. I've showed you what it's good at, I've showed you where it's not doing well, and where I'm gonna 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 ones 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 gonna keep playing with, and I think you should too. Thanks for joining How I AI. Thanks so much for watching. If you enjoyed the show, please like and subscribe here on YouTube, or even better, leave us a comment with your thoughts. You can also find this podcast on Apple Podcasts, Spotify, or your favorite podcast app. Please consider leaving us a rating and review, which will help others find the show. You can see all our episodes and learn more about the show at howiai pod.com. See you next time.

Episode duration: 24:04