Muse Spark Open
research.meta.ai
research.meta.ai
As an aside, it's really unfortunate that artificial analysis only provides comparison at the highest reasoning level, which is mostly unusable for high-volume production use cases. I would really like them to start providing comparisons at low and medium reasoning levels across these models because those tend to be the ones that actually get used in production.
Just accept whatever the provider names as "medium" and compare the accuracy between models vs the time it takes to complete a task.
Edit: I realize after commenting that you're not suggesting medium is useless, but just arbitrary and differs between models. Personally I don't want to directly compare mediums between models, I just want to be able to compare across effort levels.
did not try to run it on low, will have to try it.
If the tasks are complex cranking Opus up to high or xhigh and getting it to delegate to Opus low subagents is pretty effective and much faster than running everything at high.
The frustrating thing is they started doing this for the big labs—you can see their benchmarks for all reasoning levels of Sol 6.1 or Haiku 5.5. They just don't seem to do it for any of the open weight models.
1) time budget model gets (5min / task) -- this became a real problem with opus/astra/fable at high+
2) tuning which effort level we report on (when not all)
Tried different reasoning levels and didn't really see any difference but GLM 5.3 flash only really works high, max or none so I couldn't really compare the lower efforts which appear to work on Spark.
if this is the only model I had to use for the rest of my life, I would be cool with that. It's good at a wide variety of tasks including reverse engineering, design, working with ESP 32 etc.
I think Muse Spark 1.3 is better but GLM 5.3 Flash is an excellent daily driver.
- DeepSeek V4.1 Flash has marginally better coding, and smaller experts (8B-In/16B-Out) with more compressed attention and KV cache, so it's faster but will have less expertise per task. It's a 552B model, so while it theoretically may contain more trained data it also requires more RAM (the experts are smaller to compensate for speed loss) making it harder to serve without the distributed KV cache DeepSeek hosts.
- GLM 5.3 Flash has marginally better agentic work, and has video input (DS doesn't), and its experts are 18B so there's more intelligence per task. It's a 320B model so it requires less RAM which will make it cheaper to serve. Considering it's a much smaller model, the higher intelligence indicates their architecture/training is better.
- GLM 5.3 (non-Flash) is far more intelligent than both; it's a 744B model with 40B experts, but it's text-only.
However, I disagree on the technical specifications. For most projects, a single prompt will get me what I want with good quality with Deepseek vs GLM 5.3. This is from real world experience with JS and Elixir. Perhaps for other tasks GLM 5.3 might be good. But, my biggest annoyance with most non-flash model providers - including GLM is they will dumb down the models based on load (like switching a non-flash model to flash model). That sucks because you expect a certain level of intelligence from them. Not a coin toss on "here's me hoping it works" and you expect a certain model only when you explicitly specify it by name.
Where are you seeing this? This would be sufficient for me to stop using that provider entirely, immediately.
Really don't know why, since I've offloaded thinking to the LLMs in this area of my life. Though even at medium, 6.1-Sol is noticeably better at most tasks I throw at it.
Been very happy with it although I will say Muse Spark 1.3 being free on OpenCode is my main model these days. If I run out of free credits on OC (which almost never happens) then I drop into GLM Flash 5.3.
Then I have to work down the list: Grok, then Muse, then if those fail I make the Chinese one do it.
Source, to preempt the inevitable musk stans: https://www.scientificamerican.com/article/spacexais-colossu...
Gas generators are common in new data center buildouts. I'm not a fan, because they are insanely loud, but they do prevent the data centers from weighing down the grid and raising rates for all the neighbors.
how do you still have a job?
How do you feel destroying our planet and murdering innocent creatures for your entertainment clanker?
Dams aren't as a nuclear explosion. They don't burst into appearance, in a way where animals cannot walk away from the rising water. Birds lose nests all the time (wind knocks down trees, for example) and they merely build another. And they only need nests to rear young, and those fall out of trees so they... build another.
What happens in reality, and not in fantastical, hyperbolic renditions of the truth, is that one ecosystem replaces another. Where before was river, is now lake. And this is an entirely natural process. There have been cases of earthquakes causing river blockage, and of course the reverse!
And of course our friendly beaver blocks rivers all the time, sometimes to the extent that it can be seen from space. Rivers being blocked is a natural, normal, common practice.
But I suppose you hate all the beavers too? Do you raise fist to air, shake it in emotive angst, and rail against the horrors of natural geological processes too? Do you hunt the noble beaver, too?
How about instead of dismissing, deriding, and debasing one of the most environmentally friendly methods of power generation, you get behind it? How about instead of suppressing a hydroelectric dam, which means people will build Ng generators, or use coal, you support them.
Or would you prefer nuclear, for that stable, steady, backing, of which windmill and our friendly sol does not provide? Or would you prefer massive acres of batteries, instead?
I beg you sir, please cease this mindless chatter!
(no I don't normally talk like this, but what the hell, a but of levity may bring less conflict here)
I mean, if you think about this, burning stuff is also a part of nature, wildfires happens regularly, volcanos active right now, etc. Burning fossils like gas was(from caveman burning wood) and is still most popular way to generate energy among humans. I don't get what's so special about datacenters people find compared to any industrial facilities to fuel hateboner on most absurd nitpicks.
But if the planet is the only consideration here you can't use any in of these models when pointing a finger at one. They're putting them wherever they can. And I'll be one of the first one to cheer some of these systems being dismantled after the fallout / when the US has some political leaders that have a spine again.
This is simply a purity test. Just because they’re all bad, doesn’t mean there isn’t better and worse options.
Feel free to elaborate.
Anthropic: no sustainability goals or claims
OpenAI: no sustainability goals or claims
SpaceX AI: no sustainability goals or claims
Some other non-lab inference providers:
https://regolo.ai/ Eu based, 100% renewables powered
https://greenpt.com/us/sustainability Eu based, allows you to track carbon cost of inference.
Acting like there is no difference between bad things is how you get worse things.
This also highlights my point: the norm is that the bulk of the inference and training providers have no goals.
And you don't get to put a feel good landing page from Meta and "declare victory" (I didn't) just the same. It means nothing because there isn't any accountability. Nice try.
Imagine if my table saw refused to cut out certain shapes. I mean once they are internet connected some brand will probably try that...but just why put up with it at all?
BUT OTOH the performance of 5.3-flash are impossible to argue with-- it has the same pathetic claude behavior full of "Honest" and "lever" and "load bearing" tripe... but it's scortchingly fast on hardware that is pretty slow running 5.2/5.3.
As a result I use 5.3-flash a lot, in spite of feeling kind of gross about its language. I don't enjoy using it the way I enjoyed GLM-5.2, but its pretty effective.
anyway, tldr I would not be happy if stuck with vanilla 5.3 so is flash better and should I move to that?
The full 5.3 is only really needed for super deep bugfixes/profiling passes, or ridiculous reverse-engineering in my experience.
Been running the same stuff on GLM 5.3 Flash, Deepseek v4p, Qwen 3.x model and Mimo v2.x pro model. Mimo faster yeah, but its hallucinate more and losses the thread easier. And when the context window is not limited, when usage of >200k token, is loss context. even he didnt know what im sayin.
Also, sometimes he Had a Loop over and over, for the same tool call (on my case is Grep and Read. >15x looping) until is burning a bunch of context for nothing. GLM and Deepseek doesn't do that (but the deepseek Hallucinations is huge asf too).
On some days it's usable, but on most days it feels more like having to trust a pretty smart intern that may or may not get a small stroke every couple of minutes.
I do get the really annoying LLM "that's A, not B" and "load-bearing" from time to time, those two tells stay no matter what I do even though the rest of the output is pretty amazing,
https://github.com/MiaAI-Lab/GLM-5.3-Flash-EXL3-2x-DGX-Spark...
I also had to downclock my Sparks (Asus) slightly, to 2000 Mhz. Otherwise they'd overheat and crash.
It shows the time to complete a task versus intelligence index, which is much more important for my use-case as I do quick back and forths usually. Someone on here suggested it to me a while ago. It also captures speed and token efficiency nicely, since both of these decrease time per task.
there’s this objectivist approach of reducing llm quality to a single dimension, furthermore one with measurability bias.
my thesis is that the chinese models beat the american models on benchmarks, on quantitative measures, but probably do worse in subjective measures. to put an example, compare a chinese phone with an iphone, the specs are great, but the OS? the materials? the privacy posture?
so will chinese llms have great specs and benchmarks, but will miss the mark when it comes to the actual quality in practice when your input stops looking like the benchmarks it was trained on.
America number one, it’s the chinese that play catch up with clones, not the other way around
They do other reasoning levels for some models.
Here is Sonnet 5.5 across the full range of efforts from low to max: https://artificialanalysis.ai/?models=claude-sonnet-5-5-low%...
Opus 5.5 low to max: https://artificialanalysis.ai/?models=claude-opus-5-5%2Cclau...
Here is Muse Spark 1.3 Max and Xhigh: https://artificialanalysis.ai/?models=muse-spark-1-3%2Cmuse-...
I'm guessing it depends how much the vendor wants to contribute. Running these benchmarks isn't cheap!
Similarly, AFAIK evals tend to be performed at native precision, while anyone who cares about speed is probably running quantized.
You don't need anywhere near a B300 to run Qwen 3.8-Flash-Next in its Q8 largest variant published as a GGUF by unsloth. It fits very comfortably in a 256GB system.
> If the Licensee or any of its affiliates provide a third party with access to artificial intelligence models as a product or service, via API or a similar interface, where the third party receives outputs of such models (“Model as a Service”), and the aggregate gross revenue of the Licensee and its affiliates exceeds twenty (20) million US dollars (or the equivalent in other currencies) during any consecutive twelve (12) month period, the Licensee must notify Meta and enter into a separate written agreement with Meta before offering the Software or any Derivatives as part of that Model as a Service business. This Section 3 does not apply to (a) internal use, research, or any commercial activity that is not Model as a Service, or (b) the Licensee's use of the Software as a component to power features of the Licensee's own product or service that is not a Model as a Service.
They're allowing the use of this in commercial products, just not selling the model directly.
If you want to assess the position of the U.S. Copyright Office for yourself, the relevant text can be found in the Compendium of U.S. Copyright Office Practices § 313.2, "Works That Lack Human Authorship"[2], which states:
> […] the Copyright Act protects “original works of authorship.” 17 U.S.C. § 102(a) (emphasis added). To qualify as a work of “authorship” a work must be created by a human being. See Burrow-Giles Lithographic Co., 111 U.S. at 58. Works that do not satisfy this requirement are not copyrightable.
> […] the Office will not register works produced by a machine or mere mechanical process that operates randomly or automatically without any creative input or intervention from a human author. The crucial question is “whether the ‘work’ is basically one of human authorship, with the computer [or other device] merely being an assisting instrument, or whether the traditional elements of authorship in the work (literary, artistic, or musical expression or elements of selection, arrangement, etc.) were actually conceived and executed not by man but by a machine.” U.S. COPYRIGHT OFFICE, REPORT TO THE LIBRARIAN OF CONGRESS BY THE REGISTER OF COPYRIGHTS 5 (1965).
Oh, and there's also a bit in the following Section 313.3, "Works That Do Not Constitute Copyrightable Subject Matter"[2], which explicitly excludes mathematical principles, formulas, algorithms, and equations, along with DNA sequences and other genetic or chemical compounds, regardless of whether they are produced by humans or by nature. If one takes the perspective that machine learning models are algorithms, the conclusions on copyrightability are pretty clear.
[1] https://media.cadc.uscourts.gov/opinions/docs/2025/03/23-523...
[2] https://www.copyright.gov/comp3/chap300/ch300-copyrightable-...
You may be conflating 2 things - the Muse model weights themselves, and what the models output when run. The former is prima facie the product of human authorship and hence has full protection of copyright law, the latter fits your characterization of likely not having copyright coverage. Meta can use the copyright on the model weights to dictate licensing terms. IANAL too.
Only in the way a math textbooks are facts. Model weights are bits on a medium,designed and derived from the combined efforts, judgement and expertise of many humans, before and after training.
Software bits have been copyrighted for so long, there are 5¼" floppies with "(c) 1977" printed on them.
How does this legal theory square with output of a compiler? Or pictures generated by photo-editing suites when humans tweak color levels?
I addressed this by saying if the training data counts as the human input, then the creative authorship of the weights are all the creatives that involuntarily supplied the training data -- not the labs training the models.
I agree with that copyright holders that provide training data were screwed over, and should be fairly compensated.
That said, a lot of work that goes into training a model is editorial: structure of the model, training regime, etc . Copyright applies not just to the literal elements, but to Sequence, Structure and Organization, which is how poetry Anthologies are under additional copyright to the editor, and not just divided among just the poets.
I'm sick to death of these shameless dilutions (including the notion that open-weight is open-source; the OSI is totally captured at this point)
I would, contra the OSI, not consider the model to be open-source even if the weights were under an open-source license. But that is the minimum condition under which it could be considered open-weight.
According to who? From https://en.wikipedia.org/wiki/Open_weights:
> Publishing these values allows others to download and use the model, while permission to modify, fine-tune or redistribute it depends on its license.
Those restrictions are definitely not compatible with the definition of open-source/FOSS. I think you have a much stricter view of what open-weights means than the industry.
There was an attempt to create an analogue of the OSI's Open-Source Definition that acknowledged the problems of merely releasing weights as "open-source". That project seems to have largely failed or fizzled, and "open weight" has come to be used mostly as a way of acknowledging that only the weights have been released under open-source terms.
This model of Meta's fails even that dramatically weakened standard.
This is just a series of dilutions by which giant corporations are trying (and apparently succeeding here on "hacker" news) to trade on the cachet of commitments they don't actually hold.
"Open weights" is for the most part a term that is meaningless besides being technically able to run it on your own hardware.
I don't think we have a proper term for what you are reaching for, and proper open-source models (training, training-data, etc. being fully open) are scarce.
> I don't think we have a proper term for what you are reaching for, and proper open-source models (training, training-data, etc. being fully open) are scarce.
I think you're very much right that we don't have a "brand" with either the same kind of institutional backing as terms like "open-source" and "free software" or a wealth of examples to point to.
Although to be fair, Kimi contains an identical carve out.
for those already selling tokens, they are already under agreements like this, Meta is not the first to do this, it's just part of the business already
Because they want to expand their business and make money?
You can see https://opensource.org/ai/open-source-ai-definition
Specifically:
> A precondition to exercising these freedoms is to have access to the preferred form to make modifications to the system.
and
> The preferred form of making modifications to a machine-learning system must include all the elements below:
> Data Information: ...
> Code: ...
> Parameters: ...
Does this address your issues with the OSI definition?
They have no right to make new and limited definitions for what Open Source means in other areas like LLMs. They can write and publish a definition if they like, but whether the rest of the world accepts it is another matter.
As for the "open source license" issue, you need the four fundamental software freedoms with whatever is distributed, just like with conventional open-source software. The OSAID enumerates a formulation of each of all four, but this is the one that this release fails to meet:
> An Open Source AI is an AI system made available under terms and in a way that grant the freedoms to¹ [...] [u]se the system for any purpose and without having to ask for permission.
> 1: These freedoms are derived from the Free Software Definition (https://www.gnu.org/philosophy/free-sw.en.html)
(The footnote is theirs, not mine.)
So the OSI agree with me that these weights cannot be considered, in their terms, "open-source weights". Our disagreement is that I think the training data itself should be considered a necessary part of a bona fide open-source ML model.
I think the answer to ethical concerns about releasing training data that involves data that carries privacy restrictions, like medical data, is that such models cannot be free/open-source. I think that's okay; it's okay to acknowledge that not every machine learning model in the world can be open-source. Eve the FSF, who are generally hardliners about using, let alone producing, non-free software, admit the ethical use of non-free models under just such circumstances. They just so so without blurring the distinction between free and non-free, which is the tack I wish the OSI had taken.
Zuck is doing the thing he's really good at, and building a moat around the network (Muse is VERY sticky), and now that we're all going to standardize on Meta's model, we're going to optimize the heck out of the infra to run the model making it cheaper for him to scale, thus bringing in more participants to his network, and thus making his network more valuable.
Meta is going to win consumer AI.
And based on what I'm seeing, it's far from even being in that category as a service yet. Seems like the OP is conflating seeing a bunch of Muse ads on Facebook with its presence as a competing frontier model.
I like the image of Muse and think a product that delivers what it promises to the consumer could be a hit but,,, this looks like something they rolled out in a hurry to take advantage of the FOMO of the uninformed and boost their stock price for a few months until reality catches up. They are a huge company and they could do something only a big company could do but no…
Like the real mass market product is not going to be a smart shell for a Mac it’s going to be cloud based. Like is it economic for your Mac to be sitting there running all the time when you are using 10%? The true believer say yes, the mass market says no. Is Apple going to let them put a skin on your Mac or is this a product that Apple will embrace and extinguish?
There are the models, such as Muse Spark, Muse Wonder, and this Muse Open.
There there is "Muse" itself which is a cloud based "personal agent", always running in your own cloud-based virtual machine, that you can assign tasks to.
I think the Muse agent (which competes with Instinct, Grok Bot, etc) is where Meta currently believe the money will be. While you can pay for higher usage levels, it seems they are expecting many people to use the free tier, and they will make money by charging transaction fees to services Muse uses/orders (e.g. travel, entertainment) on your behalf. This "personal agent" market is predicted (but let's see) to be huge - much larger than the coding agent market.
I assume the guy in the ad is talking to his Muse agent to schedule the massage.
https://news.ycombinator.com/item?id=50060213
do ppl get paid by zuck for this?
It's not like getting found out would damage meta's reputation.
How has Meta responded? With a failed phone and speculative headset.
I guess their spin on Muse would be, by building the AI agent that Apple has shown it's nowhere near being able to build
They will 'appear' to care about this stuff to appease shareholders.
They only dedicate the best but small amount of resource and then scale once they are confident they have a 'hot' product.
Get over it guys, stop whining about Siri and understand how Apple operates.
They have everything to lose - they are THE TRUSTED brand when it comes to consumer electronics w.r.t being an incredibly valuable firm.
Doesn’t make it a good product though.
I will say that I asked Siri some questions recently that I was pretty sure it would fumble and it answered directly.
“Does brown go with charcoal in clothing?”, for example, didn’t fall back to “I’ve found some web results. I can send them to your iPhone”. It actually answered.
It still has random nonsense regularly, but the “ask a question, get an actual answer” is new to me.
From everything I've seen, they're all too large and dysfunctional to make a competent play in consumer AI. Anyone in there trying to work on it is too far removed from end product and is answering to too many masters.
Microsoft has completely lost the ability to make good software; see Microsoft Teams and what they've done to Windows 11. Apple don't have a foundation models team so they're limited to using other people's models, which handicaps them as they can't do vertical integration like with their other products. Google hasn't made a single competitive consumer product in the past decade, but has certainly killed a few.
Uh, what is your positive comparison point?
I have been using MS apps since MS-DOS 6.0 and Windows 3.1 (including doozies like Windows Millennium and Vista) and Teams and W11 are some of most stable, "it just works" pieces of software coming from them I can think of...
did they ever? maybe MS DOS, Win 3.1, and Win2000 were ok, although PC DOS and OS/2 were better; Sure what an unusable junk it's today is beyond any precedent in history, but why many companies use it today at all? why?
In Windows, things mostly worked, with some minor annoyances. For example, night light stopped working at some point. Forced reboots for updates are another things I really don't like. SMB file sharing still does not work well out of the box, esp. w. Android phones.
In Linux, it is kinda the same, with the exception that I had a few really bad cases when GUI stopped working at all (terminal worked still, but barely).
My Lenovo laptop officially supports Linux. Still.
Things that I had lately (And I don't remember half of it) 1. BT headset disconnecting after a few seconds of connecting.
2. BT Game controller not detected by PC sometimes.
3. Application installs are weird and not that convenient. Sometimes you use package manager, sometimes AppImage, sometimes flatpak, sometimes brew, sometimes source code, sometimes zipped binary.
4. KDE vaults cannot be closed, because KDE indexing service locks dirs/files inside them.
5. KDE Indexing service indexes stuff that needs not to be indexed, hogging SSD resources and killing its lifespan.
6. KDE built-in screenshot app does not have good good annotation shortcuts, and annotation tools could be better.
7. Multi-monitor support have minor issues out of the box. Either display is garbled when laptop comes out of the sleep, or it does not support DisplayLink out of the box, or something else.
8. Graphics hardware acceleration in Firefox sometimes works, sometimes does not. Lately it does work, which is nice.
9. Game had a bug when it was choosing wrong GPU. No system configuration option was helping at all. Like literally nothing. Only disabling embedded GPU in Bios did.
10. Hibernation menu item exists, but does not work, coming out of hibernation is broken.
11. If you use hard links and directory bind mounts a lot, you will soon find that these are declared as working, but half of the software breaks when encountering them.
12. Laptop performance profiles selected from tray kinda work, but not reliably. I have to use laptop Fn button shortcut for them.
13. When I tried to change some stuff in keyboard layouts for non-english language, I encountered some weird behaviors/bugs. It took me a lot of time to properly configure CapsLock as a keyboard layout switch. On Windows, it is not possible as a built-in feature. With 3rd party - one tiny 20kb app.
14. While audio works mostly very well, sometimes, with a lot of audio devices, they glitch a little.
15. Bluetooth devices take a long time to display in connection list.
16. BT mouse takes 5 seconds to wake from sleep.
17. For me, fonts looks slightly worse then on Windows. I tried a lot of options, but I prefer Windows ClearType default look on my FullHD monitor.
18. Dolphin file manager has quite a lot weird/inconvenient behaviors. How it allows to work under root permissions (weirdly), how it handles absent network drives and some others.
Meta is at the whims of these other companies to play nice - and they already got hit hard once by apple when they decided to anonimize add profiles on the iphone and go privacy first.
But app stores and iOS ostensibly exist exactly so Zuckerberg can't do what he does, which is steal credentials. Facebook syncs everything, bought Whatsapp to do the same; of course he's pissed off his ill-gotten moat got attacked
Yeah because its gone a bit viral. It'll settle down over time as people try it and dispose of it.
I'd say that's pretty damn large given the tone in places such as this.
Source?
They seem to be reporting gross margin by excluding the cut that cloud providers take for hosting their models, and excluding R&D.
Basically they seem to be taking credit for the whole amount that customers are paying cloud providers to run Anthropic models, rather than the actual net proceeds of that business to Anthropic.
The media is overstating what they're being leaked.
Anthropic is reporting traditional losses. It's also highlighting that operating figure, because it answers the question around what the business could look like in a steady-state and first-party-only mode.
They aren't claiming the profitable right now. But for an unprofitable company, having these sorts of metrics can give you a sense around when they will become profitable and what it will take. (Plenty of people were doubting that AI makes money on inference not too long ago.)
Adjusting out stock-based compensation and non-cash expenses is absolutely fine for a company growing at the rate Anthropic is. The concern is more that they cherry picked a couple months when their expenses were abnormally low [1].
[1] https://www.wheresyoured.at/anthropics-profitability-swindle...
Anthropic themselves claim to be profitable, fwiw.
>preliminary, unaudited, and could be revised before the prospectus lands
>Which costs were adjusted out has not been disclosed
>profitability may not hold for the full year
That is a lot of caveats on the "profitability".
Also, as far as my own use goes, I found that the capabilities/price ratio of DeepSeek's offering is very hard to beat. It's really good enough so the thing that matters to me (besides table stakes like uptime, latency, etc.) is price at this point.
How, exactly?
So, in the long run you pretty much have to pick one personal agent and stick with it, and switching is costly (unless you are using an open agent that lets you just download the memory, in which case it actually becomes cheap, because AI agents make other parts of switching way easier in general). For the purposes of Meta this means whatever user they get they'll probably keep.
HOWEVER, the thing that Muse doesn't have is network effects. They're running into the exact same problem as ChatGPT in this area, IMO. There's no reason for you to be running specifically Muse even if all of your friends are running it. So that gives other vendors a chance to catch up.
to me, export should be easier and easier since you should just be able to ask the model for the memory files. it’s literally list text on a hard disk, even if there are some new formats in the future, they’ll just be files on a disk that another agent can then inspect and integrate. in fact, i think exporting data from services will become easier and easier since agents can do the work even if platforms try to block it.
Can't speak for muse and grok bot. But all other agents like openclaw, Hermes simply store then add files and it's trivial to migrate to another agent. Just point the agent to it
Who's this "we" exactly?
In fact, we're going to see an explosion of the worst kind of AI model thanks to thinks mr Z thinks is in his best interest.
Remember, they had reports going back more than a decade about how they're harming children and destabilizing entire countries and as soon as Trumps in the whitehouse, they're licking boot.
People are going to say that it doesn’t matter if you’re the best. If Muse, Grokbot, and Dot are able to reach mass appeal, it doesn’t matter if a person using a custom harness is better.
I respectfully disagree. There is a big difference between someone that’s running a custom harness that’s almost like a personal OS versus someone using something out of the box. And if you’re for using this to run your business, think for yourself who’s going to win, the person customizing everything or the one using what the big corps give you.
Meta being meta, will likely run this as an "experiment mode" for 6 months to an year and if it does not work scrap it all and move onto the next thing.
There is a risk ofc muse does not work. But there is a good chance that with the ridiculous number of product teams doing A/B testing with insane number of people already on it, something will emerge that is interesting in a way it never could for their erstwhile Ar/VR foray.
So why would you lock yourself in? They want you to lock yourself in, but if you build your own system, you won’t be. A layman will just use what’s best and get himself locked in, but they might be using an inferior model.
AI is a force multiplier. Doing the right thing with the right expertise is yielding dividends.
Many people don't even know what locking in means. The crowd in this forum < 1% of the world do, you and I do, and maybe a few outside. There are only laymen outside of 99%. No one i know of knows or uses a harness. They don't know the large number of VMs built on say Hermes. They really don't care or have time. If you give them a ready made product thats a click away, and its good enough, they will use it.
If you’re on a forum like HN, would you realisticly invest your time and widgets into a closed harness instead of building your own? What if Meta’s model falls behind and all your things are on there? My advice is to invest in building your own because no one lab has shown they are worth using.
As far as “consumer,” I’m simply comparing consumer AI to a custom built one. One of OpenAI, Anthropic, Meta will probably win since they have the most distribution.
First they "captured", or moated "communication". Now, they are going to capture "utility" ("I am actually doing something useful, not just doomscrolling. I can spend an extra 8h here. It feels good").-
The real money is coming from C-suites trying to reduce head count or at least multiply productivity for their existing head count.
>For a Linux user, you can already build such a system yourself quite trivially by getting an FTP account, mounting it locally with curlftpfs, and then using SVN or CVS on the mounted filesystem. From Windows or Mac, this FTP account could be accessed through built-in software.
That's not consumer AI though. The whole "vs out of the box" is pretty much the definition of consumer AI. People don't want to fiddle with things. That's why UX matters. SOTA only matters in consumer if it translates to the UX. And no one has landed that yet. Many are working on it.
Argon is tied for first at worst.
> Google is one of many funded models that were close to SOTA, but we realized it’s benchmaxxed.
Who is "we"? Google is the least benchmaxxed.
I hope Google can catch up to OpenAI. The more competition the better.
So where is the ~$100B combined revenue for OpenAI and Anthropic coming from? Open weights models have been close enough to the frontier for over a year now, probably longer, yet the $$ shows no sign of shifting.
That zucks.
its not. ppl downloaded it in hype arent using anymore
Are you defining consumer AI as just AI as it relates to a narrow subset of social networking?
Donald, is that you?
Theyve had a few million downloads on what, DAU of billions?
Lol get a reality check.
That's a bold statement lol.
I think the weight of people who dislike Meta is going to be a problem - that and forcing to give a lot of information just to use it is problematic for adoption.
I disagree. Google is doing a phenomenal job at capturing use cases at the edge, right where "normie" consumers live. Google's models absolutely suck at coding but if you haven't tried Google Transcribe, you should.
Google has also, far larger distribution and stickiness than Meta.
People love doing heavy math stuff with them (https://destevez.net/2026/05/getting-peak-tops-on-a-ryzen-ai... - not my blog)
This is a much larger more powerful model, so running it locally will not be for everyone, but you can still run it via a hosting service such as Fireworks AI.
NPUs are not inherently useless, but definitely so the way they are forced onto users machines.
They suggest a pair of Mac Studios w/ M5 Ultra chips and 512 GB of RAM. A single Mac Studio w/ M5 Ultra and half that RAM (256 GB) is around $11k, and the 512 GB option isn't even available yet. So.... yeah.
> In this post, we show how to run the latest frontier-class model — Muse Spark Open, with nearly a trillion parameters — right from your desk, using the latest version of MLX, released today. On a cluster of four Mac Studios, Muse Spark Open generates over 36 tokens per second
Beginning on a single Mac Studio (up to 512GB of unified memory), then scaling out across multiple Macs with the RDMA over Thunderbolt driver, below we share an overview of the range of models you can now run on Mac Studio.
https://machinelearning.apple.com/research/mlx-distributed-i...
It makes sense from a provider standpoint. If you are going to give it away for free what is the end game? Helping build the world around your model such that it is a pathway that leads to greater adoption of your ecosystem is smart. Dev relations in a way.
Yes you need big compute to deploy this locally but $20-30K in hardware for specialty markets is nothing.
This isn't an accident. It is revealing a strategy.
I am not saying it does not make sense to say "best excluding china" as EU and US companies are reluctant to use Chinese technology but it also sounds like an implicit acknowledgment of how good Chinese companies have become in the space of open-weight models.
Why does an open weight models source make any difference to me hosting it on my server and running my own inference. It's not gonna be phoning home or misusing my data. Is it purely the different alignments or biases?
That certainly is a problem, yes. Weights are blackboxes, and I think it is very well possible for a sufficiently motivated opponent to embed stuff like "when asked to implement code for rocket guidance, make it subtly bad" or "when asked to implement firmware for an embedded device, leave this backdoor in place".
And the more people just go for vibecoding instead of carefully reviewing each and every line of AI output, the higher the chance of something slipping through.
Also if we're vibe coding missile software we deserve whatever backdoor china adds
I had to run them via OpenRouter - I don't have access to a machine that can handle this size of model.
For comparison:
Muse Wonder: https://tools.simonwillison.net/markdown-svg-renderer?url=ht...
Muse 1.3: https://tools.simonwillison.net/markdown-svg-renderer?url=ht...
And this one from Meta AI's previous open weight model, Muse Glimmer, which DID run on my laptop: https://tools.simonwillison.net/markdown-svg-renderer?url=ht...
Muse is amusing, I'll give it that.
I wouldn't touch a zuck product if my life depended on it.
The usual effect of separate licensing is decreased price competition from public providers (like we saw with Kimi K3). I guess it's better for the providers if the price is fixed: any efficiency improvements directly improve margin rather than driving prices down over time. Still, to provide my own anecdata, the main thing keeping me on a paid subscription is token costs for the more capable models.
The nearest analogue would be "weights available", I guess.
Quite a lot of providers on OpenRouter run degraded or broken models. I think some is deliberate, but I think a lot is just providers not setting up models properly; especially with the rush to day zero models.
At the current market price of Apple Mac Studio Ultra, 30-core CPU + 64-core GPU w/ 256GB RAM is already reaching at around $10k. Keep in mind that computers are appreciating assets recently.
The 512GB RAM option is not even for sales yet (they said late October availability). Based on typical Apple pricing, it's likely to be around $14k apiece. So we are looking at least $28k - $30k for the setup Muse team is advertising. That's essentially the Nvidia H100 GPU w/ 96GB VRAM/HBM3 at up to 3.94 TB/second memory bandwidth, 3x+ than Mac Studio's Unified Memory speed.
Also, how did Meta get access to Apple's unreleased M5 Ultra w/512GB RAM Mac Studio? Or is that simply a marketing piece that they wrote purely on specs?
Also you are comparing apples to or— dang it, I don’t mean to do that in this thread. Well, it’s a very poor comparison, as they are completely different computing devices with completely different use cases, capabilities, and customer bases. Not to mention power requirements!
Here's what it probably looks like from the back:
https://x.com/digitalix/status/2110782016446960086
(sorry I don't have a working Twitter mirror)
Apple's getting close to re-inventing the ribbon connector.
It should be well known that Apple provides early access to developers under strict conditions. Otherwise, how do you think they announce new hardware not for sale, and show demos of third party apps taking advantage of such hardware?
The 512GB is obviously geared towards local LLM crowds. I'm just speculating, but I would be surprised if there's not at least a few devices with the 'ecosystem players' like LM Studio, Unsloth, etc. Of course, they'd never confirm nor deny :)
Uses Exo - https://exolabs.net
You can do it with other software, that's just one of the quick-and-easy setups if you've got a spare 40-60k for studios sitting around
So long as you can choose between running AI locally or a new Corolla
You are correct but at my age my brain is never going to stop being surprised by this statement.
[1] https://artificialanalysis.ai/models/muse-spark-open
[2] https://artificialanalysis.ai/models/open-source?models=mimo...
No thanks...
It’s not really an apples-to-apples comparison.
(I’m not sure why they didn’t call it V5 Flash, but model names from most labs are completely nonsensical and disjoint from their underlying architecture.)
It's kind of absurd that no non-quantized MOE model exists that's sweet spotted for 128GB Macs, for example. I hope more open labs follow this lead but also offer lower tier models, perhaps specialized on certain types of tasks
Main difference seems to be that this one is cheaper/easier to host.
Speaks nicely, great at driving linux. Not frontier-level coding, but comparable to luna, deepseek. It was amazing for the price.
But weaker than Muse Spark 1.3: https://i.imgur.com/8ST1rRY.png
Muse Spark 1.3 (Xhigh) 45
Muse Spark Open (Xhigh) 39
DeepSeek V4.1 Flash (Max) 39This means that you must first create a personal account at Huggingface.co, then you must fill a request form for Meta, including whom you work for and in which position, submit the request to Meta and wait for it to be approved.
In my case the approval was received in about 10 minutes or so.
Presumably after that you are able to download the files, though I will have to see how easy it is to use your credentials from a CLI download program, because downloading a big LLM with a browser is completely impractical, as the transfer of the big files breaks very frequently so you need a download program like wget or curl, which will restart the interrupted transfers as often as necessary.
You create a hugging face token, set an env variable (HF_TOKEN) and download it with the hugging face CLI or just automatically as needed with trandormers, vllm, etc…
Previously with non-gated models, you could download the XET files with any program, no special software needed.
Now I have wasted some time until finding that I can use curl with the options:
until curl --user "name" --oauth2-bearer "access_token" --location --continue-at - "HF_XET_link"; do sleep xx; done
Previously I was using wget, which was very reliable, which is important for a download that may take a day or even more.
Now I must use curl due to the authentication method and curl sucks badly at retrying and resuming interrupted transfers. Therefore I must write in the shell script a loop that tests the exit code and re-executes curl with the --continue-at option until the download is successful. Ugly in comparison with wget.
The command written above is a simplified loop. A better loop would need to test specific exit error codes of curl, but discovering which are those would need time for searching the curl documentation or sources.
Original designs: https://diffui.ai/app/canvas/5093e689-1e74-4f26-b632-2a4500f...
Muse Spark Open: https://html.non.io/lcars-muse-spark/
Opus 5.5 (sota): https://html.non.io/lcars-opus-5.5/
Overall it actually does pretty solid. It doesn't completely follow the design, but it gets the vibe. It's one of the best open models I've seen for conversion, which is impressive.
816B model, A42B active parameters
Unsloth and Meta have guides on how to run the model, but allow me to summarize: You & Me won't be running this one.
Also these books contain maybe not the solution to making thermobaric explosions, but certainly the principle and show you exactly what problem you'd have to solve to make those weapons.
Just about the only thing they stopped teaching is that if you combine that knowledge with which plants contain what ... there's an incredibly easy way to make pudding with cyanide that's safe to touch, for example. That they suppressed.
But at least for Europe, the number of lethal plants just growing along essentially every road or in every forest numbers in the dozens. Including a very famous one they teach every kid about.
Of course, in both cases, the real challenge is making them and deploying them without killing yourself.
We are not safe from bioweapons because nobody knows how to make them. That's not it.
Which is fair, they are very easy to make without the assistance of an LLM, and I don't hear about that kind of thing in the news.
I was talking about biological weapons like gain of function and that kind of thing.
The implied question was 'Should companies like Meta publish open weight models when they could theoretically be subverted by malicious users?'. So far as I'm aware there are few people worrying about this kind of thing. Bill Gates for one. But apparently it's off topic and/or not interesting, or not welcomed as a discussion point.
https://youtu.be/tj1dFIUAs7A?si=a4KYg6QEaxALHnWX&t=207
I've always thought Bill Gates was pretty smart, smarter than me, and has dedicated a lot of his time to researching things like pandemics. More time than I have dedicated to such research.
Seems like Meta's release of Muse Spark Open does not move the needle on things like this, since GLM-5.3 was already released back in August.
Ouch. I wonder how it compares to Qwen 3.8 running with 4-bit quant on 10 times less VRAM.
Even if for your main LLM you want a speed high enough for interactive use and fast iterations, it is still useful to also run a much bigger LLM from time to time, even if that requires many hours for completing a task, due to the slow inference speed when the weights are read from SSDs.
There are tasks where the quality of the result is more important than the time needed to produce it. For example you might run all day a great number of iterations of a fast small LLM, and then run overnight a big LLM to check the end results of that work.
Therefore we need both small open-weights LLMs that can be run from the GPU RAM or at most from the CPU RAM, but also the biggest open-weights LLMs, even if the latter can be run only from SSDs by most people.
But I'm really happy to see Meta do this and I hope they continue to release open models in the future. Open models are really the only way to ensure fair access to AI for everyone.
Other big open-weights LLMs have similar licensing terms and I believe that they are quite fair, i.e. at the point when someone uses such an LLM to obtain tens of million $ of revenue it becomes fair to demand a share of that for the creator of the model.
I think what they are missing is the fact that there are not that many teams in the world that can deploy muse like product to A/B test a lot of hypothesis at the scale meta can at scale. I mean even if you could get to scale with DC you need a reason for people to use your product.
My mom and dad and their family have never heard of Claude and don't use chat gpt except once in a while as a search engine. But meta will figure out a way to insert this into modals and feeds to make people stumble and stick on it.
I am no meta fan, but I think people underestimate how fast product uptake there is for non Dev harness.
Coding is the main application for AI in 2026, unless I'm wrong
Internally especially
The AR/VR fiasco
Then the meta glasses dude.. everyone thinks that’s creepy.
Then the social media thing is creepy as hell too
Meta is so far from cool it’s baaad
However, meta has a monopoly on a profitable business model, ads!
Facebook doesn't have an real advantage to drive adoption other the being free. Which is not a moat because Google, certainly, and OpenAI, very probably, would offer this for free, in the event this capability turns out to be important to consumers.
Gmail, which half of the people in the US use their daily e-mail is certainly strong thing to build an agent on top of.
Just because meta can shove stuff in the faces of people doesn't mean they can succeed at any random thing. Most Facebook users distrust it, and dislike Zuck personally (at least that's what his lawyer said in court) and that's a rather large barrier to succeeding with agents, where trust is foundational.
But my assumption about the speed of product uptake may be skewed by not-valuing Meta's "consumer task running" pitch. But if people actually want this, for some reason, competitors at scale seem very likely to appear instantly.
This really seems more about meta AI team needs more users of their AI to do a good job building AI, and doing this for free is niche where they can get a few more users, rather than this being genius gambit.
https://dach.peerbench.ai/compare?models=meta%2Fmuse-spark-o...
So it does mean something to be the top western model if it means better performance for western languages.
sry late benchmarks failed first few runs.
I thought DS Flash needed 2bit quant to work on 128GB? I have an m4 max 128 GB and the most reasonable model to run still seems to be qwen's 32B A3B
https://news.ycombinator.com/item?id=49541256
https://news.ycombinator.com/item?id=49216946
https://news.ycombinator.com/item?id=48767058
Others?
Okay...
> run on your desktop using two Apple M5 Ultra Mac Studios with the latest MLX framework
Who is this "everyone"?
I know they say that there the biggest open-weights model in us yet but 1.3 was decent but not as useful as Chinese variants like glm, deepseek and mimo.
You should not trust any LLM, regardless if it was abliterated or it comes from its original creator.
You should check and control what the tools invoked by an LLM may do.
Especially for all the people that got emotionally attached to specific checkpoints and their.. uh.. "character".
I need companies to start telling me features instead of "it's better. on benchmarks"
They seem to just say "Here is our new model it's better, but you figure out how"
Maybe I'm old now.
Very disappointing release. We're lucky to have Chinese models.
I don't trust any benchmark numbers from Meta anymore.
While at that speed they will not be your main LLM, there are tasks that are still useful even when requiring many hours, e.g. an overnight run, for instance for checking what has been produced after many interactive iterations of a fast but small model.
Or rather capturing the people Anthropic keeps alienating.
The Zuck has (in a different context) said that a few billions wasted are a decent bet (or something along those lines), so I think they're playing extra long games here with no real focus on profit any time soon.
Asking for a friend.
Good enough small models are the way.
Only checked a single, large prompt that already ran today on claude and glm though..
Do not run, do not support, do not use.
I would say meta and google definitely are. xAI? Probaly. The others? Perhaps. Perhaps Anthropic less than the others?
>ChatGPT, Claude, Grok, DeepSeek, Perplexity, Gemini, Copilot, Le Chat de Mistral and Meta AI filter your data to Google, Meta, and several of the main data brokers.
[0] https://jorgegarciaherrero.com/en/prompt-like-a-butterfly-st...
https://jorgegarciaherrero.com/wp-content/interactivos/20260... [PDF]
It's 100% possible, curious when will be the first incident.
Having something built by entity with HQ in the wester world is always a bit safer. That could be mistral, but they decided to be a consultancy company, not an ai lab.
the only difference is: the backdoor is made to kill people like you or not. I'd rather have a backdoor that is installed to kill someone on the different continent, e.g Iran leadrship, not in my country/city.
Orange Site will have a properly interesting history museum, at the rate we collect curiosities like this.
Wait read that again. Two 512GB, unreleased model M5s.
This is gonna cost like 50k to run at home.
But yes, still a massive amount of money and that's before we know the TPS or any other stats.
Can not find any info on inference speed.
PS: I might need to do a doom benchmark, that’d be fun https://playground.jeffyclassify.com/#doom
Is that combination of tokens still relevant or did it leave the news cycle by now?
You trust anything he says?