Posted by bigwheels 4 hours ago
https://huggingface.co/meta-models/Muse-Spark-Open-816B-A42B
https://9to5mac.com/2026/10/15/meta-launches-new-muse-spark-...
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.
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.
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.
- 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.
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.
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.
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.
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)
> 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.
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)
Although to be fair, Kimi contains an identical carve out.
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.
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.
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.
People love doing heavy math stuff with them (https://destevez.net/2026/05/getting-peak-tops-on-a-ryzen-ai... - not my blog)
NPUs are not inherently useless, but definitely so the way they are forced onto users machines.
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.
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.
How has Meta responded? With a failed phone and speculative headset.
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
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.
Yeah because its gone a bit viral. It'll settle down over time as people try it and dispose of it.
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?
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.
>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.
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.
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.
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.
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?
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.
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.
Are you defining consumer AI as just AI as it relates to a narrow subset of social networking?
Theyve had a few million downloads on what, DAU of billions?
Lol get a reality check.
That zucks.
Donald, is that you?
its not. ppl downloaded it in hype arent using anymore
That's a bold statement lol.
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.
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".
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...
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.
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...
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?
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 :)
You are correct but at my age my brain is never going to stop being surprised by this statement.
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!
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
[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.)
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) 39