Meta Muse Wonder Is HERE – Is THIS Really a Frontier Model?

0:00 Look, someone else is skating. Get out of here. This is how dogs feel when they start barking, when they see other dogs out the window. I understand. Meta has come out with their Fable competitor,

0:11 which I think now would be an Opus competitor cuz I think Opus beats Fable in a lot of things. However, this is Meta's Muse Wonder. Also camed, I believe, Watermelon based off of some of

0:21 the teasing that was being posted on X with photos of Watermelon and then references to this model. This is supposedly just based off of what we're seeing right here, a true and proper

0:31 Frontier state-of-the-art model. Now, I'm extremely interested in testing this because in some of our previous testing of models like Muse Spark 1.3, it did not necessarily seem like the benchmarks

0:43 reflected its capability. At least in our suite of tests, it was actually the sole defining point in which I decided Deepsw S. 1.1 was no longer necessarily um something I could trust properly for

0:56 coding capability. So, let's see how this model really is. Now, I do have to say, of course, we're going to go over this a little bit more in depth. Don't forget to subscribe so we can hit that

1:05 80k number and then crawl up on to the 100K. And additionally to that, I subscribed. So, as we're going to see, there are two variants of this model that have been dropped. One being

1:15 slightly less powerful and one being slightly more powerful. In order to access the more powerful of the two, which is this Muse Wonderful, you need to subscribe to the Muse Code plan. So,

1:26 I just subscribed to the $50 a month plan, and I have two simultaneous sessions going right here. The browser OS test v2.7 and then the C++ skate game. This is all

1:38 I've done. This model's been out for like 25 minutes and it's already used 52% of my 5h hour usage on that $50 a month plan. and my weekly usage is already at 17. So, the two models that

1:52 have been released are Muse Wonder, which is the slightly less capable variant of this, and then Muse Wonder Full, which is the highly capable variant of this, slightly more capable.

2:03 They start out here by just showcasing the leaps in capability that have happened over the recent releases. And Meta really has been doing a lot of impressive work lately from Musepark to

2:13 Musepark 1.1, 1.2, 2 1.3 and now we have Muse Wonder right here. We can see the leap between this and what I would assume to be its predecessor being that it is um the leap before it. Here this

2:25 is a pretty significant jump in capability. And then even for the full version of Muse Wonder, an even larger one only second to Opus 55. We have Claude Fable 51 here as well as Argon

2:37 which is still currently not yet released. So folks may I mean you can only judge this based off of this chart right here. No one's actually really used it yet. Now, of course, in this

2:47 blog post, we see a lot of the traditional things we're used to seeing, such as the benchmark charts here. And I do like that both of these are shown here benchmarked as well. Both run on

2:56 the highest potential reasoning effort for these models. And they really do seem to be performing very, very well. I mean, this is, I think, the highest deep SWE score that we've seen. Now, they

3:07 mentioned two new things right here, which I've not really seen mentioned as often in model releases and things like this. the concept of active memory and active focus. And I think what this ties

3:17 back into is if we scroll all the way to the top right here, they mention this is soon going to power experiences in Muse. Now, Meta has been going very heavily in the personal agent space. I think a lot

3:27 of folks very much like the agent. So, this is something that seems to have intertwined capabilities, empowering better, more capable agents that know more about you and things like this. I

3:38 do have to wonder though if the usage that we're seeing right here, I mean, that's ridiculous. I don't even think we're going to get a complete result out of this just from a browser OS. And this

3:48 has only been working for 14 minutes and we're going to hit our usage limits. So, off the bat, I am kind of ticked off and that seems like a waste of $50. So, the pricing for Muse Wonder, which is the

3:58 slightly less capable one that we had seen in those benchmark charts. It is $4 per million in and $20 per million out. This is expensive. And what is the comparable pricing for this? I feel like

4:09 it's Opus 55 price. Yes. So, this is exactly the same price as Claude Opus 55. Additionally, what is the comparable GPT model? I don't think there is a GPT model currently at this

4:22 price. Yeah, so there's no comparable GPT model for this price. So, GPT61 Soul is half the price, at least for Million Output tokens. And then if you would like to use the full version of this

4:32 model, you can only do so currently through the Muse code subscription, which you can see it does seem like we have two results now to take a peek at though. So let's just jump in and take a

4:44 look at our browser OS. I am really expecting to see fantastic high-end results here, especially considering that it's benchmarking that way and it also uses a lot of usage. Okay, we have

4:56 Nova OS. All right, we do have some movement. A relatively dense bit of welcome, but not bad. So, this is probably like a $25 browser OS

5:14 that I like. These are our wallpapers, the dynamic ones. Probably stick with that actually. No, because that's giving me like optical illusion like vibes. Maybe we'll stick with this. Let's check

5:26 the big one. All right. There's no right click. No command center. So, this is our special feature. Okay. Good. I can speak to it at least.

5:42 Allow visiting. Give me a refund. Oh, okay. Refund Muse code subscription now. Okay. So, it's, you know, why are you

5:56 special? I'm Nova wallpaper plasma. All right, let's just check our Okay. Okay, not bad, but not opus 55 level. I know

6:14 this is one specific test, but you know, it's not Opus 55 level. It's not Sonnet 55 level. It is around haiku 55 level. All right, crash. Watch your health. I think we're stuck in like a mesh

6:29 collider corner there. Can we steal one of the police vehicles? No. We lost the police. I don't know how, but I'll take it. All right. Overall,

6:44 let's keep going. Next up, we have our Starfall Defender 3D. Okay, the traditional. This one's actually It's not bad. I do like the effects when these asteroids

6:55 blow up. That's actually kind of cool. It's competent enough. Okay, ship destroyed. Next up, we have our mail application. Acceptable. It's got meta references to

7:06 apps on here. I'm still just stuck on the fact that our usage is burnt out totally. Notes and files. Notes and files. Is this a unified? Can we save this as a txt? Good. Okay. I

7:18 have not actually seen that in a bit of time, so that's good to see. terminal. All right. There may be a redeeming application down there. 76* 6

7:33 472. Uhoh. Paint. That was there already. I didn't do that. Is this like a muse looking? It's like their logo, which is interesting.

7:48 Cool. And then we can also set our custom picture as a desktop background. Let's change that back though too. Okay, good. Settings and wallpaper studio. Okay, I

8:01 do like the We have our painting there as well. Particle nebula. And we can set the animation speed as well. Good. I like the star warp one. And if it's slower, it's a little No, it's still

8:11 kind of like weird. Dusk waves accent color. Okay. And it changes some elements on here. Now, something I'm quite eager to see. Okay. Well, less so now after seeing it. Generative synth.

8:29 Calm out. It's designed to calm you down after it blows through your usage limits. And then finally, we have welcome. And our special feature was just this one

8:47 that we could speak to and like so open GTA good. Overall, I'm going to refrain from judging anything too heavily based on a first result and we'll move on to the

9:03 other result we did manage to get which was the self-contained C++ skate game. So here, okay, good. This is definitely better than I had worried.

9:16 This is actually there are elements here that I'm pleased with such as the fact that the players actually pumping. We can interact with pedestrians. There is some, you know, immersion here and the

9:28 board moves independently from the player. Now, unfortunately, this is still like not even in the same galaxy as the GPT. This is GPT6 Soul. Um, actually 61 Soul did a significantly

9:42 better job than this. Good. And we do even have a balance feature. And I will say the human model for the skater is not really that bad. We do have a puddle and we have a fountain. Let's see if we

9:55 have the typical fire hydrant spraying water onto the street. Oh, okay. And there's also like a a road. And that was actually kind of a sick trick.

10:10 However, I I can't help but be honest that these results are completely tainted by the fact that this was two of these chewed up, forget the 5h hour window. Two of these chewed up 36%

10:24 of a weekly limit on a $50 a month subscription. That's ridiculous. That's just That's not That's crap. That is crap. All right, so I'm going to upgrade this

10:38 plan. Currently, we're on the $50 a month one, which gives you 20 times more usage than the everyday usage plan. This gives us 80 times. So, we should get four times as much usage. I'm We're

10:50 basically going to just test whatever we can till this usage runs out and then I'm probably done with this video. All right. So, on X High, we'll be doing the subway FPS test. This is the one

11:04 where the train comes when we clear a wave and then we take the train to the next station. And it's on X high here. It is not on max. We'll keep an eye on our usage. All right. I don't know that

11:13 the subway FPS is fully done, but it's been like play testing it for quite a while. And because we really do need to be somewhat cautious of the usage, we need to kind of

11:26 like um you know, be careful. All right, I'm going to say this is better so far than what I noticed with some of our previous SK game results. Let's check out how the enemies look. Okay, they are

11:40 a bit simple. Let's just Oh, wow. All right. That's how I feel having paid $200 for this subscription. All right, there are no bullet holes. However, this is the one

11:54 where the train needs to come get us. So, all right, let's take a look. Good. Not bad. Not bad. Well, not bad. All right, mind the gap. I'm not actually going to give it flack for that

12:10 issue where like the stripe of the train creepy. I like that where the stripe of the train covers the doors because that I've seen with a lot of models. I think some of the clawed ones and stuff did

12:21 that as well. So, all right, let's turn this volume up and let's just get ready. Oh, okay. Don't ask me what that is, by the way, cuz I don't know. Okay, the clock is

12:39 Was that like a cool an ammo pack? We'll we'll go to one more station and we'll see if it makes the enemies larger when we get there. Good train shake is a sentence I ever thought I'd speak.

12:53 All right. Stacks of papers. Not bad. Okay. Too easy. Oh, there's a big one. Okay, good. So that's just sometimes what I like to see

13:11 is and this one's tougher. All right, let's see what happens if we lose. Okay, end of line. Overall, it was not horrible.

13:24 Pretty simple, but again, I kind of am just more focused on preserving usage. Normally, I'd like to be like, okay, like decent start, make it way better. Maybe we will for this one, but I also

13:36 wanted to do that for the skate game as well. So, we may come back to some of these. >> Hey, Perry, could you turn your volume up all the way?

13:44 >> Okay, I'll raise it to the maximum for you. >> So, this is Perry. Now, you may be thinking, Bjan, what are you trying to sell me? Nothing. Because I do want to

13:54 point this out. This is a robot I made a few years ago, and we recently had some clawed model overhaul the UI in the software. This is on GitHub if anyone wants to build it. It is 3D printable.

14:04 All the parts list is there and basically you can print it in whatever color you want and then speak with it. Currently it uses the GPT real-time voice AI but you can fork it to work

14:13 with local or whatever you want to do. So I just wanted to showcase this as I think it's pretty cool. What do you think Perry? >> That sounded great. It's

14:26 friendly. It sounded great. Clear, friendly, and confident. There's a nice come hang out with this vibe. If you want to tighten it, you can try.

14:35 >> No, I don't want to tighten it. Thanks. Okay, so back to the test, but the link for this will be in the description of the video if anyone wants to build this. Next up, I want to try the non full

14:45 model. So, this is Muse Wonder 1.0. We're running it on the maximum potential effort, and this is the one that is available just like through API and stuff like that. It scored a little

14:55 lower than the full version on benchmarks and stuff, but I am interested in seeing how this does because you never know which one may be better for certain tasks and practice

15:04 hopefully. This is the Blender and GDAU backyard pool party game test where it needs to create a backyard pool party where you jump off a diving board in Blender and GDAU. One just a little

15:14 newer that we've run in some recent models with some varying levels of success. All right, so in 9 and 1/2 minutes, we've received our pool party result using Blender and GDAU. Again,

15:24 this is just the Muse Wonder 1.0. This is not the full version, but they were pretty close on benchmarks for better, for worse. So, with that, let's now take a look. Okay, decent little SVG icon.

15:41 Good. Um, okay. It's just so simple in terms of like the maybe we have to click. Okay. Big Bubba, Luna, Captain Cannon, or Kid Rocket. I think we're going to

15:58 pick Big Bubba. Okay. Um, this is I'm actually not as infuriated with this as you may think. It's got a decent aesthetic to it. I

16:20 just wish it was a little more like shift is to spread eagle. Okay. Oh, and we can actually Okay, let's just we can move as well.

16:37 Oh no. Okay. I mean, there are elements like what we've wanted, like the dog running around, the bounce and things like this. I'm wondering if this is a model where

16:49 you just not every model's a oneshot stunner. So, it's possible that we need to push it a little more. Let's just see. Okay. So, all right. So, I'm telling it, give me like a full visual

17:00 overhaul, AAA quality, which, you know, we'll see what we get from this. It does seem pretty fast, though. And this is our first test with the just the Muse Wonder one, not the full version. All

17:10 right. In just over 16 minutes, we received our updated version for the backyard pool party game. Okay. The annual Suburban Splash Off.

17:25 Okay. I must admit I'm having a bit of trouble differentiating some of the improvements so far. Okay, we get an instant replay now. Not bad. Um, I think

17:41 it made some of the background NPCs maybe a little more noticeable. Big Bubba definitely has a stomach that coincides with his name. Um, oh,

17:54 okay. There birds in the sky. The dog is running around. A primitive dog. Oh, wait. You know what? Let's get creative. Don't ask me where that came from.

18:09 But we'll see what we get. And this is again not using the full model. This is using the um non full model, the one that's a little lower on benchmarks, but perhaps less usage. All right. In about

18:20 10 minutes after just a random thought came to mind, we turned our backyard pool party into an like a beat him up. Let's just see what happens. I think we're going to go.

18:33 All right. Unfortunately, it's it's lagging pretty bad, but definitely kind of kind of what I had hoped to see. Just in kind of a molding of two of our highly

18:54 regarded tests being Street Eat and then Backyard Pool Party. We won good. The big issue I had with this one was just the lag. We fell in the pool. That's actually

19:14 kind of cool. I like that it left that in. I'm just trying the special. I think we're going to fall in the pool. All right.

19:28 I don't know what to say about this. Overall, it works very quickly. It seems like the Muse Wonder model, so not the full one, but the one under it seems very token

19:40 efficient and fast. That was on its highest potential reasoning effort. So interesting, and I don't quite know what to make of that. All right, cool. It seems like the um usage reset

19:55 thanks to me. Next up, we did the robot arm test where it needs to move the little orange toy car from one side of the mat to the other. This is purposely designed to be tough because they only

20:05 get one static camera angle and depth perception is basically not there at all. So overall, I will say good things are it moved cautiously. It was not trying to rip itself off the table or

20:17 thrash around like we saw with the clawed results which for some reason are just like odd. They try to escape. It did a nice job of just kind of slowly trying to do things. But as we can see

20:27 right here, even if we just quickly cycle through some of the images that it itself took and was looking at, it unfortunately never really understood like, wait, this is a depth perception

20:37 issue. The vehicle's not getting moved. Maybe I'll try to search around a little. And ultimately, after about an hour and 15 minutes, I opted to stop this. Now, this was not the full

20:47 capability model. This was just the Muse Wonder on max effort mode. I find that sometimes like full-on power and a higher reasoning effort does not necessarily help this task. So overall,

20:59 it did not successfully complete it. However, it didn't make any destructive actions with the ARM. So for that, I will say its cautious approach was at least nice to see. All right. So, one of

21:09 the things that I wanted to go back to and now that the usage issues were identified as a bug, so we're actually able to do this. The full version of this model on Max, I basically gave it a

21:19 follow-up to the Skate Game saying this is pretty poorly done. I'd like you to redo it. Now, additionally to that, this was just given a single static reference photo. I believe this one right here.

21:29 And I told it to make it look more like this and just make it overall way better. This was started from an entirely new session. So, I opened this folder and I created a new session. So,

21:39 it didn't have the history of the actual creation of this game. And I said, make it look more like this. Now, we see a ton of reference shots right here of how this has been doing. And this is really

21:50 coming on to like 2 hours basically. Let's take a look at this. Okay, look at that. Look at the recording camera that it's actually showing us in the bottom right. I'm going to say this right here

22:01 is a significant significant significant improvement and I am rather pleased with what I'm seeing. Now, yes, we would have liked to have maybe seen this. Maybe not to this degree of insaness off the bat.

22:16 However, this shows a pretty promising ability to improve itself based on a reference photo right here. This is really a very, very faithful replication of the source image I gave it. Look,

22:28 someone else is skating. Get out of here. This is how dogs feel when they start barking when they see other dogs out the window. I understand.

22:39 Well, this model just gave me an understanding of something that I don't think I otherwise ever would have received on my own. So, for that, I am thankful.

22:47 This is definitely much better. Can we? Good. We can hit the car. Oh, and there's still the replay feature. So, let's try to do something sick and then we'll This is definitely more in line

22:58 with what I would have wanted to have seen. All right. All right. All right. Don't bail. Yeah. Let's replay it anyway. How How long is this replay going for?

23:13 Oh, okay. This is a long replay. And it actually has a bar on the bottom there, the yellow one that's moving around that actually shows us like the progression

23:24 of this clip. Does it show the bales in the replay? We're going to find out inevitably. Okay. No, but that's all right. That VFX cam, I have to say that is absolutely

23:37 cool. I've not seen any other model do that before. And that does add something to this that I do like. Somebody's walking a dog. Here we have the fire hydrant spraying.

23:47 Oh, okay. You would think we would have run over the dog, but nonetheless. So, this is definitely better. Now, we did have to give it a reference

23:58 photo from Sonnet, but I mean, those results are absolutely monstrous. I'm very pleased with this. It did take quite a while, but just the ability to replicate the image and then also

24:09 putting in the other skater there, the the camera in the bottom right, the way the pigeons fly away, really that camera in the bottom right is kind of making me quite pleased with this.

24:20 Overall, a significant significant improvement when given a decent source. Next up, we're trying the front-end website test where it has to create the beautiful watch website. This isn't the

24:30 one where it does the special edition watch dubbed the BJN. I'm just more focused on how it creates the watch, I suppose, with the model and how it does in some front end like this. This was

24:40 run with the full version of this model on Mac. So, absolute highest potential here. Let's take a look and see how it did. at least judging this based off of a front end result if nothing more.

24:49 Cool. It is in a single file. And this isn't the one with the special edition BJN watch. So, it's just like a normal watch. The only problem I'm noticing here,

25:00 well, aside from the strap not being like the sideways um watch face is letting it down, but it's interesting because the numeral markers are properly oriented. So, the 12 does have two of

25:13 them. So, it just made the face sideways. Overall though, aesthetically, this definitely is not bad. Interestingly, I also noticed a crooked watch face just in the subway FPS result

25:24 kept not chased. Okay, we have some pretty bold front end. This is a decent looking exploded view as well, I will say. No movement on the emulated movement, however, I would like to see

25:37 that, but acceptable. And it does have a few individual pieces here. Let's see if we have our other models as well. Good. Much better. I would personally rather have the 39

25:50 than Slappice. Okay, acceptable. Next up, I gave it a follow-up prompt for the subway FPS, just asking it to overall improve the entirety of it massively. I did not give it any reference photos, so

26:02 that's where this will differ from the skate game improvement. Additionally to that, this was run from within the same session as when the result was created from scratch. So, that was also a way

26:12 that this differs from the skate game improvement. But let's just take a look at this. Is that better? I see some difference in like Okay, it's

26:25 definitely better in that regard. All right. And I didn't tell it to do that. I just said make it better. Let's see if our zombie enemies have changed at all. Okay. One of them has like a hard hat,

26:35 but unfortunately they still have It just added like some light bloom and things like that. If I turn the flashlight off, it's not as bad. It just kind of restyled their outfits. It

26:44 didn't necessarily change their design. Oh, I think that's supposed to be a fan. I think it's just kind of sideways. Okay. I I didn't think that would

26:59 happen, but that's good attention to detail. Let's just Let's just do that one more time. All right. Now, let's stand back from the

27:09 train. We'll see if the interior of the train is at all like Is that a NPC? It is. Okay.

27:25 Look, these handle things are actually swaying when the train moves. That's something I purposely pointed out almost like kind of as a joke, just to be a jerk. That was something I pointed out

27:35 that other models did not take the care to do. So, that is an interesting little attention to detail that I'm surprised to see here and happy to see. We also have some arrival boards for the trains.

27:46 Delayed disturbance. Okay. All right. And this is pretty much like the same overall. It did add some graphical improvements and it didn't take super long for this to be done. So, an

27:56 improvement. Did it happen again? Ah, okay. So, they did fix the usage glitch. It's just you don't you don't get a ton of usage. So overall, that is going to conclude our first look and test of Muse

28:10 Wonder. Interestingly, it seems like the usage just reset even from when we looked at it a second ago and saw it was tapped out. So, if there are some issues with this, I'm then going to refrain

28:21 from commenting on the usage for the price of the subscription because it's not an accurate reflection of what it will end up being. That's fine. I would say overall these are definitely showing

28:31 some strength. The result that sticks with me the most is the second iteration of the skate game where we gave it a reference photo and it really did a fantastic job replicating that. Even

28:40 adding in that secondary camera of I suppose I should just show what I'm talking about right here. So like this second camera right there from the like thing that's recording us for the

28:52 replays. That was absolutely awesome. I very much liked this and this did a significantly better job when given this reference photo even having other skaters. something I've never actually

29:01 seen before. So, it definitely has some capability because yes, I gave it a reference photo that I think Sonnet had done. However, if this model was not good, it would not have been able to do

29:11 a nice job in replicating what was seen in that photo. So, I'm happy to see that. And then this was pretty cool, the replay as well. And overall, this made me bullish for this model. Additionally,

29:22 I do have to say in the case of the I won't say smaller of the two because I don't know, but in the case of the more accessible and less performant of the two, this Muse Wonder model seems to be

29:33 pretty token efficient. Just as a first impression, it was finishing its results pretty quickly. Even on the highest reasoning effort that it has, I do have to say though, again, the benchmarks

29:43 right here are putting both of these, so these are the two new Muse Wonder models. These are putting these both leagues ahead of some of the other ones that are listed right here. And I

29:54 understand that it may just not be as good as doing aesthetically pleasing 3D stuff. I mean, Anthropic is planning to potentially IPO. They probably want to make sure those models look the best in

30:05 the oneshot generations that get shared on social media and get probably some of the widest reach leading to first impressions of the model. I understand that even if I'm not showing this doing

30:15 like intricate bug fixing on camera and things like that, I understand there are differences in terms of what the models are trained to excel at. However, personally, as someone who sees a lot of

30:26 models and tests a lot of models, when something comes out with benchmark scores listed right here, it gives the impression that this is going to pretty much mop the floor with everything else

30:35 right here, including those oneshot results. And this is not the case here. I find a lot of folks online were mentioning that they felt some disappointment based on first results

30:45 for this. There were even some mentions of this by folks working at Meta saying, "Well, we didn't really train it to excel at 3D." I get that. But I will say in that announcement blog post that we

30:55 first looked at, one of the prominently listed things was a game where that little Muse agent was cooking in a bakery or something like that. So, you know, I mean, make of that what you

31:05 will. And that's why I like running these tests because we have a ton of reference point for how other models have performed in the same exact tests and then that allows us to basically

31:15 objectively view the results and see what we think. Overall, I love seeing Meta actually competitive. I think these models are actually pretty good and I will be honest, I think they come off

31:24 worse than they are. Specifically in the case of this one right here where it was token efficient and it seemed pretty smart. I will say though at $4 per million in and $20 per million out, I

31:36 think it's too expensive when comparing to both this and okay, neither of these, but that is the same price as this model right here. And GPT61 Soul is cheaper than that and is a powerful model even

31:49 if it's kind of like not good company. However, positives about Meta, they are the only ones who have like decent openweight models. Well, at least out of themselves, XAI,

32:01 Anthropic, and OpenAI. OpenAI had GPTO OSS, and those were very good when they came out, but that was quite a long time ago. Meta has Muse Glimmer, and they do also talk about potentially open

32:12 sourcing some of the Spark models. So, they're still in very good standing, at least in terms of that, and I do very much appreciate that. So, these models are polarizing and I will be interested

32:22 to see some of the results that get showcased with these when a little additional time has passed. So, overall, I think that is going to conclude our first look and test of Meta's Muse

32:34 Wonder. If you have any questions, please feel free to leave them in the comments. Hey, that's kind of cool. And thanks for watching.