Artificial analysis shows this: underperforming Qwen3.8 Flash Next and GLM 5.3 Flash, both of which you can run on a single B300 . I think Meta has a bit of catching up to do in the open-weight space.
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.
Been using GLM 5.3 Flash via Ollama Cloud basic subscription:
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.
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.
this is properly bad as you can run glm on 6000pro, at (in my eyes) pretty good quant, and a q2 on a 5090. im suprised at how good 5.3 flash is at some parts. i still think ds4.1 flash is better but it is more demanding. i hope meta pushes something in a range of 300-400b with a 15-20b active parameters. i know its a crowded space. but it would be fast and probably mostly good enough.
Yep, just benchmarked it against GLM5.3 Flash on my own local bench and it's slower in TTFT, tok/s and concurrency. It also scored lower.
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.
> 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.
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.
My immediate reaction is that it’s weaker on coding than other open-weight models and that coding is a primary use case for running locally (not the only one, but surely a major one). That said, it takes a lot to get full Kimi and GLM running locally.
it’s quite suspect to claim that a model is ‘underperforming’ so resoundingly, especially after so little time since release.
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
very true. when configuring custom agents for enterprise, we almost always default to none or low reasoning knobs. The added latency from medium or higher reasoning levels compared to the improvement in response quality is a diminished return.
Since there are many comments here arguing that this is not open weight, I looked at the license. Basically, you're free to use this except this clause:
> 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.
Still not an open-source license. Means no restrictions on "fields of endeavor", a.k.a. the free software definition's "freedom 0": the freedom to run the software for any purpose.
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)
Also, IANAL, but under U.S. copyright law, the weights of machine learning models are excluded from copyright as they are the product of an automated optimization process (e.g., stochastic gradient descent, expectation maximization, genetic algorithms) rather than human authorship. Granted, this has yet to be fully tested in court and going to court is expensive, so it's likely that your employer would prefer to err on the side of caution and respect such attempts at model licensing anyway, similar to how one might treat a ToS agreement. Nonetheless, this was partially tested last year in Thaler v. Perlmutter which affirmed that copyright requires human authorship, reading the Copyright Act's provisions on ownership, duration, and transferability as presupposing a human author[1].
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.
Yes, but still, what about users looking for NPU support. What happened to Microsoft's copilot+ PC campaign? (the NPU hardware, not the logo key and ad campaign)
AMD's XDNA2 NPU is best supported by ONNX, I think Qualcomm Hexagon is too. The best consumer unified architectures are Apple, AMD ai+ and Qualcomm Snapdragon X Elite, even intel has an NPU offering.
Like I said last week. I think zucks original llama thesis is proving more correct by the week. The rent market on intelligence never materialized, if you have THE frontier model, you can charge rent (for a few weeks, maybe month). But after a certain capability level it's price that drives the decision.
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.
Google, Apple, and Microsoft are each in a stronger position to win consumer AI because they're guardians over the foundations of the user's experience. Meta always has to wonder whether they're going to be edged out, like when Apple decided to limit permissions or set new rules on user data handling.
How has Meta responded? With a failed phone and speculative headset.
If you’re falling for “sticky” interfaces, you’re not going make it. Your competitor won’t have the stickiness and the ability to use any harness and that will overcome Muse in the long-run. Let’s not forget, Muse is a close to SOTA model right now, but so was Google, and now they’re irrelevant. Google is one of many funded models that were close to SOTA, but we realized it’s benchmaxxed.
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.
Also honestly, the models don't have to get much smarter to handle the consumer side of things. They could innovate a ton on the harness to farm out requests and manage the state of the agent. They have an incentive to do this to reduce the infra usage so they can go wider with the agent.
I'm looking forward to seeing Muse devices like Amazon's devices but with superintelligence:
- Muse-equivalent of Alexa
- Muse-equivalent of Ring security devices: doorbells, cameras, alarms, etc
- Muse home automation devices (lights on/off, smart roller, smoke detector, water detecto, etc)
- Muse ring
...
last year was google winning AI, this year is meta winning AI. I think what is true is Meta, Google will all find their place in the AI world and continue to make huge amount of money. A consumer will use many different AI products, each with a different use case. Everyone will make enough money to live well.
> The rent market on intelligence never materialized
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.
The model license requires large inference providers to contact Meta separately to obtain a license to serve the model. I know we've seen this before with other large models, but it's still a bit irritating.
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.
Then it's not open-weight. Open-weight means "weights available under an open-source license", and licenses prohibiting commercial use are not open-source.
The nearest analogue would be "weights available", I guess.
There's obvs decreased price competition, but it also does help with ensuring model quality.
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.
re: "Apple’s unified memory architecture lets the M5 Ultra's integrated GPU use up to 512 GB per Mac Studio, so two machines can hold an entire model. And the chip’s 1.2 TB/s of memory bandwidth is fast enough to read the small slice each token needs. The result is two Mac Studios behave like one supercomputer with a terabyte of unified memory."
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, 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 :)
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!
It seems like meta and mistral are both hedging their "best" claims geographically because they've realized they don't have to even beat deep seek anymore, they can just win customers by being "the best one I'm allowed to use".
How does this compare to Muse Spark 1.3 contribute? I've used Spark 1.3 a lot lately , since its currently free to use via OpenCode. I haven't had the best experience in the past with models from Meta. But Spark 1.3 is has been surprisingly great. Especially for tool calling.
I partly agree with the sentiment, but this is also the first time I've seen an actual lab announce they optimized for consumer hardware (very expensive, but still consumer).
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
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.
And disclaimer since this comes up with these - objective with this test is to be a good image->html test, not a good web design. Also it's meant to use 3js to render the planets, which if you don't have GPU acceleration enabled for your browser will lower the FPS significantly.
I think a lot of people in this thread are openly dismissive: "oh its just open claw, anyone can and many have build that".
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.
It's not just openclaw, there's OpenAI's Dots and Gemini Spark.
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.
The exciting thing about this is in them optimizing for inference on Macs. This, and optimization for other consumer hardware, is where local AI needs to go
Given the size of Macs (2 or 4 of them) required to run it, they're just paying lip service to that option. Other models, e.g. deepseek flash, are much more practical size. Even that takes a 96 or 128GB ram Mac.
This is great progress for "open-weight" models. Quotes due to the restrictions, but still progress none-the-less. Looking forward to seeing any quants that all mere mortals to run this at home without early access Apple hardware.
In terms of performance it seems like this has somewhat worse benchmarks than Muse Spark 1.3, closer to 1.2 but not the same. Of course significantly worse than Muse Wonder Full.
Well, this is much better than nothing -- we should be grateful that whatever the market conditions and corporate incentives, a major lab is releasing a capable open-weight model. But still very far behind DeepSeek and GLM. Even on consumer hardware, Qwen 3.8 27B is going to compare favorably to this.
It's really nice that all AI pages are using canvas animations. A few days ago, Gemma 4's animation wasn't that impressive. Same with Muse. So far, the best animations I've seen are from OpenAI and Anthropic.
Ehm.. I have a model that runs on my older gaming desktop that scores as well (or better) as this model does on DeepSWE. This model does better than Flash Next in many areas and I am sure is much stronger overall, but hmm.
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.
Who would have thought that one of the top American tech companies would boast about how their latest product is the best in class in the US, not the world, because they're behind China.
It performs worse than Muse Spark 1.3 and 1.2. And it has an outdated attention mechanism which at 1M tokens will need over 200GB just for the KV cache.
Very disappointing release. We're lucky to have Chinese models.
Not at an appreciable token rate.
I wonder if these large open weight models are considered safer to release for that reason?
I'm thinking no because any small to medium size criminal organization or state funded group will be able to cobble together the resources to achieve some kind of runtime.
Even at 10 tok/sec, making bioweapons at 10 tok/sec is concerning.
I don't think this is going to change anything for most people, but it certainly fits a niche for US-based companies that refuse to run Chinese models and only want to run their own inference.
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.
What is the point of releasing "The Most Capable US Open‑Weight Model?" I can understand why I might care about the location of a model serving provider, but why should I as a model user care about the location that an open weights model was trained?
”Serve the model on either two Mac Studios, with M5 Ultra and 512GB of unified memory; or four Mac Studios with 256GB of unified memory, for a total of 1TB of unified memory, enough to hold a NVFP4 quantized version of Muse Spark Open”
I tried Muse Spark 1.3 for a while because it's so cheap, but it's definitely in a lower league than Astra, Opus 5.5 and Argon. Still produces slop if you try and vibe code with it, whereas the more capable models write good code.
Tech people really are drug addicts. Rant about Zuck and Meta for 364 days a year and then jump over each other to be first in line the moment he shows something shiny.
Nemotron, Inkling, Gemma, Trinity Large, Snowflake Arctic were all released/updated in the last 6 months, and there are countless other smaller models out there. US open weight is very far from "dead", and certainly doesn't need Zuck as a savior.
great, I've been reading series of papers related to open source/open weight LLM Backdoors.
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.
Artificial analysis shows this: underperforming Qwen3.8 Flash Next and GLM 5.3 Flash, both of which you can run on a single B300 . I think Meta has a bit of catching up to do in the open-weight space.
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.
Been using GLM 5.3 Flash via Ollama Cloud basic subscription:
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.
I agree, however It's too easy to game arbitrary limits like "medium". There is no olympics for "just rolled out of bed and slightly hungover"
The most important graph for me on Artificial Analysis is this: https://artificialanalysis.ai/?agentic-speed=intelligence-vs...
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.
this is properly bad as you can run glm on 6000pro, at (in my eyes) pretty good quant, and a q2 on a 5090. im suprised at how good 5.3 flash is at some parts. i still think ds4.1 flash is better but it is more demanding. i hope meta pushes something in a range of 300-400b with a 15-20b active parameters. i know its a crowded space. but it would be fast and probably mostly good enough.
Yep, just benchmarked it against GLM5.3 Flash on my own local bench and it's slower in TTFT, tok/s and concurrency. It also scored lower.
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.
> 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.
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.
There’s a reason they put the “US” in “the most capable US open weight model”.
My immediate reaction is that it’s weaker on coding than other open-weight models and that coding is a primary use case for running locally (not the only one, but surely a major one). That said, it takes a lot to get full Kimi and GLM running locally.
Artificial analysis puts it next to Mistral's new model.
it’s quite suspect to claim that a model is ‘underperforming’ so resoundingly, especially after so little time since release.
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
Which is crazy bc they promised to open source muse 1.2 which is good. And they give us this bait and switch
yeah for botsbench.com side where we do cybersecurity investigation evals of models + harnesses, we've been having to play with:
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)
very true. when configuring custom agents for enterprise, we almost always default to none or low reasoning knobs. The added latency from medium or higher reasoning levels compared to the improvement in response quality is a diminished return.
Why didnt thry include OpenAI or Anthropic models? I get they were comparing open weights.
AA is a joke.
Since there are many comments here arguing that this is not open weight, I looked at the license. Basically, you're free to use this except this clause:
> 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.
Still not an open-source license. Means no restrictions on "fields of endeavor", a.k.a. the free software definition's "freedom 0": the freedom to run the software for any purpose.
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)
This is close to GLM terms
Also, IANAL, but under U.S. copyright law, the weights of machine learning models are excluded from copyright as they are the product of an automated optimization process (e.g., stochastic gradient descent, expectation maximization, genetic algorithms) rather than human authorship. Granted, this has yet to be fully tested in court and going to court is expensive, so it's likely that your employer would prefer to err on the side of caution and respect such attempts at model licensing anyway, similar to how one might treat a ToS agreement. Nonetheless, this was partially tested last year in Thaler v. Perlmutter which affirmed that copyright requires human authorship, reading the Copyright Act's provisions on ownership, duration, and transferability as presupposing a human author[1].
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-...
Unless the total (!) revenue of the provider is under 20 MM USD, eh? Not exactly fully open, but not _that_ harsh.
They've done considerable effort in making sure this model can run on basically every model runner, from Unsloth to MLX, https://dev.meta.ai/docs/muse-spark-open/deploy
As someone not in the loop, can you explain more of what you mean.
Anyone who has tried a smaller/quantized version locally on apple silicon? Would love to know your experience, also tk/s on what hardware specs?
Yes, but still, what about users looking for NPU support. What happened to Microsoft's copilot+ PC campaign? (the NPU hardware, not the logo key and ad campaign) AMD's XDNA2 NPU is best supported by ONNX, I think Qualcomm Hexagon is too. The best consumer unified architectures are Apple, AMD ai+ and Qualcomm Snapdragon X Elite, even intel has an NPU offering.
Like I said last week. I think zucks original llama thesis is proving more correct by the week. The rent market on intelligence never materialized, if you have THE frontier model, you can charge rent (for a few weeks, maybe month). But after a certain capability level it's price that drives the decision.
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.
Google, Apple, and Microsoft are each in a stronger position to win consumer AI because they're guardians over the foundations of the user's experience. Meta always has to wonder whether they're going to be edged out, like when Apple decided to limit permissions or set new rules on user data handling.
How has Meta responded? With a failed phone and speculative headset.
If you’re falling for “sticky” interfaces, you’re not going make it. Your competitor won’t have the stickiness and the ability to use any harness and that will overcome Muse in the long-run. Let’s not forget, Muse is a close to SOTA model right now, but so was Google, and now they’re irrelevant. Google is one of many funded models that were close to SOTA, but we realized it’s benchmaxxed.
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.
So what makes Muse "very sticky"?
> now that we're all going to standardize on Meta's model
Who's this "we" exactly?
But why are we all going to standardize on Meta's model, and how is it sticky? What does it have on the big Chinese models?
> Muse is VERY sticky
How, exactly?
I think the jury's still out on whether Muse will be sticky. My guess is that it won't be that good relative to the field.
> Meta is going to win consumer AI.
Are you defining consumer AI as just AI as it relates to a narrow subset of social networking?
There is no winner in a race to the bottom.
> Meta is going to win consumer AI.
That zucks.
Also honestly, the models don't have to get much smarter to handle the consumer side of things. They could innovate a ton on the harness to farm out requests and manage the state of the agent. They have an incentive to do this to reduce the infra usage so they can go wider with the agent.
Very sticky?
Theyve had a few million downloads on what, DAU of billions?
Lol get a reality check.
>Muse is VERY sticky
its not. ppl downloaded it in hype arent using anymore
Paid bot or Meta employee, please disclose
I'm looking forward to seeing Muse devices like Amazon's devices but with superintelligence: - Muse-equivalent of Alexa - Muse-equivalent of Ring security devices: doorbells, cameras, alarms, etc - Muse home automation devices (lights on/off, smart roller, smoke detector, water detecto, etc) - Muse ring ...
isn't it a bit early to call Muse sticky?
> Meta is going to win consumer AI.
That's a bold statement lol.
Sounds like cope. You can't claim the 'rent market on intelligence' never materialized when revenue is absolutely booming for Anthropic/etc.
last year was google winning AI, this year is meta winning AI. I think what is true is Meta, Google will all find their place in the AI world and continue to make huge amount of money. A consumer will use many different AI products, each with a different use case. Everyone will make enough money to live well.
> The rent market on intelligence never materialized
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.
Pelicans for low, medium, high, and xhigh: https://tools.simonwillison.net/markdown-svg-renderer?url=ht...
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...
I really would love a browser plugin that blocks having to read about fucking pelicans on every model release.
It's interesting the high reasoning pelican is animated. I don't think I've seen another model do that before.
A question I have about the pelican benchmark : why are the pelicans always cycling to the right?
I think it is safe to say that Muse models are not pelican-benchmaxxed.
The model license requires large inference providers to contact Meta separately to obtain a license to serve the model. I know we've seen this before with other large models, but it's still a bit irritating.
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.
Then it's not open-weight. Open-weight means "weights available under an open-source license", and licenses prohibiting commercial use are not open-source.
The nearest analogue would be "weights available", I guess.
Meta did this from the very beginning with the original Llama's. Open weights, but you have to use an account with them and agree to an EULA.
There's obvs decreased price competition, but it also does help with ensuring model quality.
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.
ya especially because it's huge, you aren't running this on your desktop
re: "Apple’s unified memory architecture lets the M5 Ultra's integrated GPU use up to 512 GB per Mac Studio, so two machines can hold an entire model. And the chip’s 1.2 TB/s of memory bandwidth is fast enough to read the small slice each token needs. The result is two Mac Studios behave like one supercomputer with a terabyte of unified memory."
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, 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 :)
> Keep in mind that computers are appreciating assets recently.
You are correct but at my age my brain is never going to stop being surprised by this statement.
1024GB of ram is bigger than 96GB of ram.
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!
how are the Macs connected to each other?
> Future is for everyone
So long as you can choose between running AI locally or a new Corolla
https://x.com/johnternus/status/2110789142326165518
39 on AA[1], about the same as Deepseek v4.1 flash and Mimo 2.5 Flash. Behind Glm 5.3 flash and Qwen3.8-Flash-Next [2]
[1] https://artificialanalysis.ai/models/muse-spark-open
[2] https://artificialanalysis.ai/models/open-source?models=mimo...
Crucially, it's about the same as DeepSeek v4.1 Flash, and about 10x more expensive to run...
No thanks...
It seems like meta and mistral are both hedging their "best" claims geographically because they've realized they don't have to even beat deep seek anymore, they can just win customers by being "the best one I'm allowed to use".
If you can’t win everything, at least win a niche.
How does this compare to Muse Spark 1.3 contribute? I've used Spark 1.3 a lot lately , since its currently free to use via OpenCode. I haven't had the best experience in the past with models from Meta. But Spark 1.3 is has been surprisingly great. Especially for tool calling.
Please give it a try and let us know ;) judging by the benchmark scores I would not expect too much of a difference.
Main difference seems to be that this one is cheaper/easier to host.
I used spark 1.3 extensively in prime-agent (a pi derivative) and I thought it was great.
Speaks nicely, great at driving linux. Not frontier-level coding, but comparable to luna, deepseek. It was amazing for the price.
Artifical Analysis says is this comparable to DeepSeek 4.1.
But weaker than Muse Spark 1.3: https://i.imgur.com/8ST1rRY.png
Finally democratizing AI access to, checks notes, people with no less than two Apple M5 Ultra Studios on their desktop.
I partly agree with the sentiment, but this is also the first time I've seen an actual lab announce they optimized for consumer hardware (very expensive, but still consumer).
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
Previous discussion of Meta models / open weight / llama:
https://news.ycombinator.com/item?id=49541256
https://news.ycombinator.com/item?id=49216946
https://news.ycombinator.com/item?id=48767058
Others?
This seems like advertising, which breaks HN rules?
Make sure that Meta reads the policies on self-promotion:
https://news.ycombinator.com/showhn.html
HN should impose a 2-post-per-month rule on all Meta posts as they do to their users!
If Meta would like more posts per month, they should donate 80 billion to an approved charity, instead of flushing it down the drain.
Image->html test for muse spark open.
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.
And disclaimer since this comes up with these - objective with this test is to be a good image->html test, not a good web design. Also it's meant to use 3js to render the planets, which if you don't have GPU acceleration enabled for your browser will lower the FPS significantly.
Besides including the sota could you include a link to the best previous open-weights (and if possible fully open-source) model?
I think a lot of people in this thread are openly dismissive: "oh its just open claw, anyone can and many have build that".
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.
It's not just openclaw, there's OpenAI's Dots and Gemini Spark.
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.
Hmm.. i don't know.
Coding is the main application for AI in 2026, unless I'm wrong
The exciting thing about this is in them optimizing for inference on Macs. This, and optimization for other consumer hardware, is where local AI needs to go
Given the size of Macs (2 or 4 of them) required to run it, they're just paying lip service to that option. Other models, e.g. deepseek flash, are much more practical size. Even that takes a 96 or 128GB ram Mac.
This is great progress for "open-weight" models. Quotes due to the restrictions, but still progress none-the-less. Looking forward to seeing any quants that all mere mortals to run this at home without early access Apple hardware.
In terms of performance it seems like this has somewhat worse benchmarks than Muse Spark 1.3, closer to 1.2 but not the same. Of course significantly worse than Muse Wonder Full.
It would be nice if the other players in the market would do the same and release yesteryears models as something to run at home.
Especially for all the people that got emotionally attached to specific checkpoints and their.. uh.. "character".
> We believe the future is for everyone
Okay...
> run on your desktop using two Apple M5 Ultra Mac Studios with the latest MLX framework
Who is this "everyone"?
Well, this is much better than nothing -- we should be grateful that whatever the market conditions and corporate incentives, a major lab is releasing a capable open-weight model. But still very far behind DeepSeek and GLM. Even on consumer hardware, Qwen 3.8 27B is going to compare favorably to this.
It's not open, like the Llama. There is a licensing agreement by which they allow you to use it for free on conditions.
It's really nice that all AI pages are using canvas animations. A few days ago, Gemma 4's animation wasn't that impressive. Same with Muse. So far, the best animations I've seen are from OpenAI and Anthropic.
Ehm.. I have a model that runs on my older gaming desktop that scores as well (or better) as this model does on DeepSWE. This model does better than Flash Next in many areas and I am sure is much stronger overall, but hmm.
Is it easy to use Muse to remove all the kinds of crap I don’t want in my Facebook feed?
Asking for a friend.
It's not open!!!
How so?
I'm super impressed by how meta has gotten back in the race. They weathered* the storm and actually came out with really good models and products.
Weathered the storm
“We believe AI is for everyone” *who can afford two M5 Studios with a total of 1 terabyte of memory.
What is the main driver for anyone to give away their hard earned weights? What is the catch?
Pissing off Dario, probably?
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.
To try to prevent as many people as possible from using one model, that gets all the data, and winner takes all.
spoiling the leaders' profit margins
UD-IQ1_S is 180Gb. UD-Q4_K_XL is 510Gb. Still beyond the average prosumer.
i love seeing major ai labs release open-weight models. really hope we'll see more of this from openai and eventually anthropic too.
This is not an open model. License is not open license, and only weights are provided.
What kind of hardware does one need to run this model? Like a small datacenter node?
Nice a Mastodon shoutout.
Glad that Meta is back on the open weights train in force.
How is this open weights? Open weights means anyone can use the weights. The license forbids certain categories.
Who would have thought that one of the top American tech companies would boast about how their latest product is the best in class in the US, not the world, because they're behind China.
Feels dumber than GLM 5.3 (only open model I use extensively). But is really fast (fireworks.ai).
Only checked a single, large prompt that already ran today on claude and glm though..
It performs worse than Muse Spark 1.3 and 1.2. And it has an outdated attention mechanism which at 1M tokens will need over 200GB just for the KV cache.
Very disappointing release. We're lucky to have Chinese models.
Why is Gemma not in the comparison table?
Probably too small, the comparisons are all larger models.
from what i understand its somewhere between muse spark 1.3 and 1.2 in some coding benches.
Given exposed benchmaxxing of several past models from Meta,
I don't trust any benchmark numbers from Meta anymore.
They completely overhauled their team since then.
https://huggingface.co/meta-models/Muse-Spark-Open-816B-A42B...
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.
https://unsloth.ai/docs/models/muse-spark-open
Not at an appreciable token rate. I wonder if these large open weight models are considered safer to release for that reason? I'm thinking no because any small to medium size criminal organization or state funded group will be able to cobble together the resources to achieve some kind of runtime. Even at 10 tok/sec, making bioweapons at 10 tok/sec is concerning.
> The 2-bit quant fits on devices with 256GB of RAM, VRAM or unified memory.
Ouch. I wonder how it compares to Qwen 3.8 running with 4-bit quant on 10 times less VRAM.
I don't think this is going to change anything for most people, but it certainly fits a niche for US-based companies that refuse to run Chinese models and only want to run their own inference.
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.
What is the point of releasing "The Most Capable US Open‑Weight Model?" I can understand why I might care about the location of a model serving provider, but why should I as a model user care about the location that an open weights model was trained?
Good luck running that on your home infra lol, requires 25k in mac minis
So: WAR!
Never meta.
Do not run, do not support, do not use.
I wonder if all the AI companies are making dossiers on their users.
I would say meta and google definitely are. xAI? Probaly. The others? Perhaps. Perhaps Anthropic less than the others?
"Muse Spark Open was optimized to run on Mac Studio with M5 Ultra, and together they’re the best way to run a powerful model on your own desktop."
”Serve the model on either two Mac Studios, with M5 Ultra and 512GB of unified memory; or four Mac Studios with 256GB of unified memory, for a total of 1TB of unified memory, enough to hold a NVFP4 quantized version of Muse Spark Open”
Can not find any info on inference speed.
Read it again. Two M5s.
Wait read that again. Two 512GB, unreleased model M5s.
This is gonna cost like 50k to run at home.
okay that's gonna be interesting to try
I tried Muse Spark 1.3 for a while because it's so cheap, but it's definitely in a lower league than Astra, Opus 5.5 and Argon. Still produces slop if you try and vibe code with it, whereas the more capable models write good code.
Try Muse Wonder Full, it's comparable.
can we stop these safety risk.... Feels like gpt 2/3 days, and now we know there wasn't any safety issue with gpt 2/3....
But does it play doom? /s
PS: I might need to do a doom benchmark, that’d be fun https://playground.jeffyclassify.com/#doom
Best I can do is p(doom)
Is that combination of tokens still relevant or did it leave the news cycle by now?
Tech people really are drug addicts. Rant about Zuck and Meta for 364 days a year and then jump over each other to be first in line the moment he shows something shiny.
Zuck is the good guy here. Damn
You don't know what is hiding in the model ...
By finding another way to siphon user data?
You trust anything he says?
US OPEN WEIGHT IS NOT DEAD THANKS ZUCK
Nemotron, Inkling, Gemma, Trinity Large, Snowflake Arctic were all released/updated in the last 6 months, and there are countless other smaller models out there. US open weight is very far from "dead", and certainly doesn't need Zuck as a savior.
Is Google not gonna make another Gemma or something?
great, I've been reading series of papers related to open source/open weight LLM Backdoors.
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.
Right. The US has famously never installed government-mandated backdoors in security-critical technology like Dual_EC_DRBG.
Mistral just released Mistral Large 4?
Link to papers?
As an aside I really wish people would stop naming their new software projects “Spark” given how many things already use that name.
The sad thing is I think it's like Elara, it's just a favorite LLM name for things and there's likely not a human behind it.