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Introducing Muse Spark 1.1

https://ai.meta.com/blog/introducing-muse-spark-meta-model-api/?_fb_noscript=1
65•ot•48m ago

Comments

redox99•35m ago
Very strong pricing, cheaper than Grok 4.5, particularly the cached reads. We'll have to wait to see if it's actually worth using (it's not on OpenRouter yet).
rpgbr•17m ago
That's what one does when its product and public perception is way behind competitors.
Tiberium•29m ago
The pricing is insane: $1.25/$4.5 for 1M tokens, and $0.15 for cached input!

https://dev.meta.ai/docs/getting-started/pricing-rate-limits

ignoramous•18m ago
Cheaper than Qwen 3.7 Max. Second indication, after Grok 4.5 ($2 in / $6 out), that the BigLabs are feeling the GLM 5.2 heat.
cedws•10m ago
Does anybody actually care about 'MechaHitler' Grok? For me it simply does not even register as an option.
fallingbananna•8m ago
Meta isn’t right now on the radar for most folks picking models.

If they have a really good model, it makes sense to subsidise it, to gain users, before they align prices with competitors.

kilroy123•29m ago
I personally do not like Meta, but I'll say this. The more competition, the better for regular consumers. (Enterprise too)

- Chinese models

- Grok

- Meta

- Google

- OpenAI

- Anthropic

I think this is a win. I'm building like crazy to take advantage of all these subsidized tokens while I can.

alansaber•19m ago
Meta's local llama models used to be the face of open source AI. The scene has really changed.
cpt100•16m ago
Yeah, I think it is definitely great. Having said that, I am still debating in my mind whether the volume of software engineers needed in the AI era is going to increase or decrease because of all of these advancements.

On the one hand, because it is easy to build products, more and more people will build. And more and more products and features will be built. However, a lot of people who are non-technical will also try to build, but they get stuck, and then they will need engineers. The sheer volume of product built by both experienced technical companies and non-technical novice startups and founders and wannabe founders is going to be massive. That is the bull case for having more software engineers needed in the near future.

On the other hand, in a year or so, people will build all these products, and most of them won't be able to market them, sell them and make money. Eventually, there won't really be a need for that many software engineers.

I think overall the bull case is probably going to win net net.

BugsJustFindMe•10m ago
> On the one hand, because it is easy to build products, more and more people will build.

And those people won't need to be software engineers.

> but they get stuck, and then they will need engineers

You've implicitly assumed here that the AI systems will always be worse than the average engineer. That is IMO myopic. I'm not sure that it's even true now let alone in the nebulous future.

greenavocado•27m ago
Meta is back in the game, albeit not at the top. Impressive stuff, nonetheless.
qpricjalcbeu•19m ago
Weren't they caught multiple times gaming the benchmark even more so then the rest?
alansaber•19m ago
Let me assure you, literally everybody does this
cpt100•15m ago
They are not open source anymore, right?
fau•25m ago
"We're thrilled to be releasing Muse Spark 1.1, a testament to our research momentum."

Let's see how it does on the Creative Writing bench ;)

EgregiousCube•22m ago
Their published benchmarks seem to indicate that it's pretty good at coding and multimodal, but VERY good at successful tool calls.

What kind of use case would be best for that shape?

alansaber•20m ago
This sounds... kind of useless? Really good JSON or similar constrained decoder performance is interesting, but normal decoder > tool validator loop with good error message > tool retry is almost always able to get a tool to work second try, and input is cached so it's not expensive.
aldanor•11m ago
Things are not always that simple, eg https://lucumr.pocoo.org/2026/7/4/better-models-worse-tools/
winstonp•7m ago
The avg coding session has hundreds or thousands of tool calls. Even a 5% failure rate noticeably notches up token use and cost. See Gemini.
alansaber•3m ago
Yes, but each tool call has a different failure %. The tool calls that make up the majority of volume like grep are going to have nowhere near a 5% failure. A custom user-defined skill having a 5% failure rate is probably fine.
xnorswap•
lnenad•17m ago
Considering the DeepSWE result (imho if you're gonna give value to benchmarks this is one of the best) it's not good enough.
qpricjalcbeu•15m ago
Yeah, no thanks. I cannot think of a worse company to trust with additional personal data.
niek_pas•11m ago
Me neither, though LLMs also provide services that don’t involve personal or sensitive data
frangonf•15m ago
Is this the model trained on Meta "draftees"? Are we seeing this in the jump on JobBench?
Jcampuzano2•13m ago
Competition for cheaper and efficient models is a good thing, regardless of if you don't like SpaceX, Meta, etc. Especially from US based labs

I for one am really glad to get competitive models that will push the major labs to bring prices down. While Chinese open source labs are also great, unfortunately when it comes to US/Western political pressure it won't often have as much of a bearing on labs bringing prices down, especially for enterprises.

Also if these numbers are true, this is truly breaking ground finally for Meta.

zb3•11m ago
This is not open-weights, right?
phillipcarter•9m ago
My trust factor is gone with Meta right now. Has there been any independent analysis to confirm they didn't cheat on benchmarks again?
guluarte•8m ago
A lot of these benchmarks are unfamiliar. Are labs just choosing the ones that make them look best?
arbayi•5m ago
when I try to sign up for meta.com, the only two quick options they show are instagram and facebook, or you have to go through the manual process.

what advantage does this give them? is it really that hard to add github or google login options there?

carimura•4m ago
I missed the fact that Meta was developing and releasing closed-weights models... bummer. Would be great to see some more progress with American open-weights models.
linkjuice4all•8m ago
I see some similarities to 3D printing here. It’s great that everyone can make their own toothbrush holder (or whatever) but I’m probably not going to pay for someone’s weekend project.

I’m “seeing” more devs stepping into the SendCutSend stage where they’re cleaning up/fixing/productizing vibe coded projects so maybe there will be some new demand in that space?

Lomlioto•7m ago
At least in China a lot of software developers are now struggling.

I think for a lot of type of software we have now reached peak employment.

Someone payed a few k just for a normal website.

wolttam•10m ago
To expand on Chinese models:

- DeepSeek

- GLM (Z.ai)

- Minimax

- Kimi (Moonshot)

- Hy3 (Tencent)

- Qwen (Alibaba)

(Each one of these with weights available to download and run locally)

croes•10m ago
While data centers are still using lots energy created from fossil fuels and many still evaporate water for cooling?

No wonder we still can’t get climate change under control

Lomlioto•9m ago
Its the biggest technology race we have ever seen. Richest companies, smartest people, richest countries.

I do not know if competition is good, we will see in a few years.

Looking forward having a physical job for a change :D

pa7ch•5m ago
A bit much describing our tech leadership as smartest people we've ever seen.
anematode•3m ago
Greediest, perhaps?
12m ago
Debugging and diagnosis is very tool call heavy, whether that's grepping / transforming logs, calling out to profilers/tracers, or even just writing up incident reports.

Bug diagnostics is about being okay at coding but better at tooling.

Given a good diagnostic report, it can be handed to opus for the fix.

Opus is okay at writing reports, but it still regularly gets table widths wrong in typst documents, leaving the last column full of text but only a handful of characters wide.

paytonjjones•5m ago
I wonder if we'll start to see that pattern with every new release. Tool use likely changes rapidly, so the newest, rather than most intelligent, model may always have an edge.

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