Discussion summary
Muse Spark 1.1 is not open-weight and is trained on proprietary data, with some skepticism about benchmark selection and transparency. Meta's AI efforts are viewed as improving, though concerns about benchmarks leaking and gaming persist.
What the discussion says
- Some users question the transparency and openness of the model.
- Concerns about benchmarks being manipulated or leaked.
- Optimism about Meta's recent AI progress.
- Skepticism regarding the selection of benchmarks.
“This is not open-weights, right?”
“Meta is back in the game, albeit not at the top.”
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- Hacker News
- Since it was making rounds yesterday here is what muse generated for the 3d rubik's cube prompt - https://ashish01.github.io/rubik-muse/by ashish01
- Hold my beer...by Kuyawa
- Just got it working with codex in a container! FYI I think there is a bug most others will run into at the Codex:Muse interface.
It's some kind of parsing or integration error due to what I think is codex not anticipating server-side tool calling and how meta treats those ids... first couple times running codex with muse, it would fail on its first non-web search call.
Got it fixed, not personally sold on the bespoke server-side tool calling and indefinite file storage yet, but also a very cool model that I'm enjoying using so far!
https://github.com/accretional/awesome-muse-spark/blob/main/...
by weitendorf - 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?
- 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.by alansaber
- Gemini 3.5 flash is better than fable at tool calling. Tool calling is probably one of the easier things to do post training for.
- 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.by paytonjjones
- 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.
by xnorswap - Where is the data retention policy information for paid API per-token uses? Every other provider has one and makes it clear how they handle your data. A quick look doesn’t show one for this new offering.by bradfa
- Finally found it: https://dev.meta.ai/legal/
Paid prompts are not used for training. Data retention is not entirely clear, they will keep it if they think you're breaking the rules, but it's not clear how long they keep it if they don't think you're breaking the rules.
by bradfa - This not being available on Openrouter really makes it hard to test. I was going to compare vs Grok 4.5 and GPT-5.6 Luna, but I don't want to deal with signing up for Meta for it unless it checks out. Please Meta make this available.by mchusma
- How is every company able to show itself at the top of every benchmark?by paxys
- They're being called "trust me bro benchmarks" for a reason ( ･ั ﹏ ･ั )by ffsm8
- Anyone deep in the AI realm know which is the gold standard benchmark for coding?by toephu2
- Wait to the exact moment your model is ahead on at least N benchmarks then publish.by logicchains
- At this point comparing to Gemini is a free Bingo space.by Archit3ch
- Not much moat, incremental improvements, cherry picking models to compare.
To be fair, seems more correct to compare against similar strength models if your main edge is pricing.
by adam_arthur - First look what models are worse in a set of self selected benchmarks.
Second, compare to older versions of competitor s models.
Still does not look good? Compare to own previous models.
by morgengold - 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.
by kilroy123 - 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
by Lomlioto - He came to X to post about this instead of his very own meta threads. This just shows how much interested he is to make this thing big, and of course, the cost can stay bearable for us considering all of these cash burn that these companies are doingby Cappybara12
- 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)
by wolttam - While data centers are still using lots of energy created from fossil fuels and many still evaporate water for cooling?
No wonder we still can’t get climate change under control
by croes - 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.
by cpt100 - Meta's local llama models used to be the face of open source AI. The scene has really changed.by alansaber
- Interesting how the prevalent opinion until yesterday seems to have been that OpenAI & Anthropic are irreversibly ahead and now with xAI and Meta at least delivered something that's competitive with useful models and cheap too. Granted, the narrative that the two leading labs are ahead still holds with Fable (and perhaps an upcoming GPT6), but it's not as over as common knowledge by the opinion leaders would have us believe.by Sol-
- meh, models are mostly good enough now. Without a major breakthrough the only thing that matters now is cost.
- People misinterpreted Google being behind as Anthropic and OpenAi being really ahead, when it was really just Google falling behind the same way it did with Tensorflow, Angular and GCP.by logicchains
- Think it’s less a shift in who’s ahead and instead more people acknowledging that making yet another dashboard app worked fine on last years SOTA and doesn’t really need today’s SOTAby Havoc
- > Interesting how the prevalent opinion until yesterday seems to have been that OpenAI & Anthropic are irreversibly ahead
Not the way you're implying?
The GLM 5.2 hype was blowing way before this. Neither xAI nor Meta have really made a difference in a different way - similar results / similar pricing (to GLM 5.2).
by re-thc