They chose to compare against Open AI’s mid tier model Terra instead of Sol and still lost some benchmark against it.
They left Opus in and got beat in all but one benchmark.
Nothing wrong with trying to improve, but why the marketing games?
Instead of trying to say in the post you’re “closer” to frontier, first set a clear goal to beat the Chinese labs on price or performance and demonstrate it convincingly.
Then when your ready, come back and talk frontier without playing hide the model.
jjice 38 seconds ago [-]
While I won't take their limited benchmarks with much salt, if it actually is this close to opus, but at a third the cost, that's pretty solid. Now, Terra is pretty damn affordable too and you're right that it's suspicious that they don't put Sol in there at all.
krm01 15 minutes ago [-]
We can throw benchmarks in the bin by now. Each one I've seen is heavily biased and skewed. It holds very little reliable data points (unfortunately)
bradfa 53 minutes ago [-]
If you got the $20 in free credits from Meta for signing up when muse-spark-1.1 was release, please note that there's now small print stating "While using free credits your content may be used for product improvement" which was not present at muse-spark-1.1 launch when the credits were given out.
If you don't mind Meta retaining your data, the "Contributor" pricing is deepseek-v4-flash-level of low, roughly 1/10th normal muse-spark API pricing currently. Attractive if you're OK with them retaining and using your data.
giancarlostoro 24 minutes ago [-]
The API costs for the version of their model that feeds things back to meta is also drastically lower.
tristanj 36 minutes ago [-]
Meta is offering a 10x discount on input ($0.10 vs. $1.25/Mtok) and 20x discount on output ($0.20 vs. $4.25/Mtok) if you opt in to let them train on your data.
This makes it a very interesting alternative to Deepseek for personal work where I don't care about the training - judging by the AA benchmarks it seems like overall cost per task is similar to the new Deepseek Flash but with better benchmarks (and inbuilt vision capabilities).
GodelNumbering 25 minutes ago [-]
I think that's a fair offering tbh
deno 18 minutes ago [-]
I think it's limited to US or at least EU is excluded.
mchusma 1 hours ago [-]
This is a nice release and a solid improvement over Spark 1.1. It compares favorably with Grok 4.5. Not SOTA, but solid releases. I think they need to really get this more competitive with Deepseek V4 Flash / Luna pricing to move the needle.
handzhiev 58 minutes ago [-]
If you are happy to share data for training, the contributor mode offers amazing price $0.10 / $0.20
wxw 53 minutes ago [-]
Last I heard, everyone at Meta was using Claude Code.
Any insiders know how Muse Code is doing internally?
GodelNumbering 37 minutes ago [-]
If there were, do you believe it would be in their interest to answer this publicly?
georgemcbay 25 minutes ago [-]
> > Any insiders know how Muse Code is doing internally?
> If there were, do you believe it would be in their interest to answer this publicly?
If it were being adopted like gangbusters in their organization, sure!
So... the fact that nobody is volunteering the information is probably a valid signal of how things are actually going...
youre-wrong3 30 minutes ago [-]
[dead]
Laurel1234 15 minutes ago [-]
The only company less trustworthy than OpenAI and Anthropic is meta.
Very interesting they have a way cheaper "contributor" version "used to improve our products", how much of that is price discrimination vs the data being that valuable?
Roughly DeepSeek V4 Flash pricing, though you can get V4 from providers that don't train on your data
ipsum2 2 hours ago [-]
I wonder why they didn't compare with GPT-5.6-sol, only Terra?
wmf 2 hours ago [-]
Clearly they're positioning it as a mid model.
minimaxir 1 hours ago [-]
Which is in itself a bit weird as mid models nowadays are a golden mean fallacy. Terra is much less popular than both Luna (cost-sensitive) and Sol (performance-sensitive).
Claude Sonnet is a weird exception to the mid models because Anthropic doesn't do much with Haiku and Opus is too big.
redox99 45 minutes ago [-]
But why include Opus then?
woadwarrior01 1 hours ago [-]
Haven't you seen the kernel optimization case study at the bottom of the page? They compare against GPT-5.6 Sol and their model is worse.
logicchains 1 hours ago [-]
Presumably because it's worse than Sol, same reason they compared it to Opus 5 not Fable.
Handy-Man 1 hours ago [-]
Their bigger model is not ready - watermelon code name was still being prepared for release as of a month ago
arjie 37 minutes ago [-]
Somewhat surprised that Meta with all their resources couldn’t make a model that matches Composer on any frontier. All the Sparks are dominated by some other model everywhere along the frontier. Nothing fancy here since Llama defined the open model.
The use traces must be crucial to functionality which is why they’re keeping prices so low.
wmf 20 minutes ago [-]
They rebooted less than one year ago so this is decent progress. Obviously users don't care about progress though.
arjie 27 seconds ago [-]
Yeah, progress is useful as an internal metric, but I'm going to measure against the present frontier unfortunately. Eager to see what they come up with in the future.
Interesting that they have separate API pricing for "we can train on your data" (whereas iirc most of the big players either make that distinction only between subscriptions and API usage, or train on everything). Wonder how it compares to Deepseek V4 Flash given that they're similar on pricing and data policy.
IceWreck 49 minutes ago [-]
Ive been poking with the muse code binary - seems to be written in rust, looks similar to codex but either its a very hard fork (i also see dissimilar things like config format is different, no acp, etc) or is just heavily inspired by it (more likely).
sarjann 37 minutes ago [-]
I do think some of features in their harness seem interesting (workers in separate worktrees at once), recovery from crashes seem interesting.
liviux 1 hours ago [-]
Does this muse code have any muse spark 1.2 usage included? Can't understand from the docs.
Cappybara12 51 minutes ago [-]
Is this becoming a race where we have a usual flow of a company ..
AI models,
Coding agents,
image generation tools,
and more AI models ?
minimaxir 1 hours ago [-]
Muse Spark 1.1 was released July 16th, less than a month ago. A new version release this soon (particularly after Kimi K3's release drastically overshadowed it) is a bit sus and it appears that Meta is trying a first launch do-over.
gaogao 40 minutes ago [-]
Frequent minor version bumps are pretty common these days. Opus 4.7 -> 4.8 was 42 days.
minimaxir 39 minutes ago [-]
Which was in itself a do-over because Opus 4.7 received a lot of bad press on suspicion of being a regression from 4.6.
king_crimson 54 minutes ago [-]
Why does every AI lab feel the need to build their own coding agent…? Don’t we have more than enough already?
43 minutes ago [-]
Bolwin 1 hours ago [-]
> Muse Spark 1.2 is available today in Muse Code and in Meta Model API with expanded global access
Wasn't the previous one us only? This is probably the biggest part of the post
Anyone know if muse code is open source?
AtlanticThird 42 minutes ago [-]
I wish they would add a ZDR endpoint on OpenRouter
kcb 1 hours ago [-]
Open the weights.
fcoury 1 hours ago [-]
Interesting, it seems like their muse code is built upon Codex CLI?
paulkrush 2 hours ago [-]
Muse Spark 1.2 is a coding-focused update to Muse Spark 1.1, with improvements in code generation, complex debugging, codebase understanding, and end-to-end developer workflows. In Muse Spark 1.2, we significantly scaled up training compute on coding tasks while expanding training environment diversity. The model also maintains its strength in other key areas like general agents.
vcryan 23 minutes ago [-]
It seems like one day, Google or Meta might produce a coding model worth discussing. That day is not today.
qphe95 1 hours ago [-]
Theres no actual evidence they didn't just distill Kimi K3
Jabrov 52 minutes ago [-]
Is there any actual evidence that they did?
toephu2 1 hours ago [-]
At this point, it doesn't matter who is distilling from who.
esafak 1 hours ago [-]
If anyone from Meta is reading, please can you publish the cost and latency for each of your benchmarks, like OpenAI does? Show us how the reasoning effort level affects them in 2D charts. This needs to become standard practice.
giancarlostoro 1 hours ago [-]
Will someone at Meta for the love of God make it so none of this stuff goes through Facebook.com? You want customers but most corporate firewalls block social media. Also, a lot of devs do not want their work stuff tied up to their facebook account. For the love of all things show the IG / FB logins as optional and do email as primary.
I am not a fan of Meta but I do cheer for any competitors against OpenAI and Anthropic, the duopoly is getting tiresome.
greyb 40 minutes ago [-]
I honestly think they're kinda banking on piggybacking off of Facebook account integrity systems to avoid the problems that other LLM providers are facing in trying to prevent mass free trial signups for token relays and so forth.
It's not a good system obviously. Google did this as well for Gemini-CLI, but forced it to be linked to personal Google accounts (which caused a great deal of onboarding friction).
hahahaa 40 minutes ago [-]
China says hi.
aanet 59 minutes ago [-]
+10000 to that
Readerium 49 minutes ago [-]
Lol worse than DeepSeek
rvz 1 hours ago [-]
First of all, you have login to use it. Why?
After everything that you have seen with Meta, would you really trust them with a coding agent? You don't even know if your prompts are being analyzed by them on the side or if your code base is being uploaded to them. This goes for the rest of them that have closed harnesses and closed models gated by a login.
Think twice before falling for this announcement and ask yourself what they are not telling you.
Rendered at 21:10:09 GMT+0000 (Coordinated Universal Time) with Vercel.
They left Opus in and got beat in all but one benchmark.
Nothing wrong with trying to improve, but why the marketing games?
Instead of trying to say in the post you’re “closer” to frontier, first set a clear goal to beat the Chinese labs on price or performance and demonstrate it convincingly.
Then when your ready, come back and talk frontier without playing hide the model.
If you don't mind Meta retaining your data, the "Contributor" pricing is deepseek-v4-flash-level of low, roughly 1/10th normal muse-spark API pricing currently. Attractive if you're OK with them retaining and using your data.
https://developer.meta.com/ai/models/muse-spark/
Any insiders know how Muse Code is doing internally?
> If there were, do you believe it would be in their interest to answer this publicly?
If it were being adopted like gangbusters in their organization, sure!
So... the fact that nobody is volunteering the information is probably a valid signal of how things are actually going...
Very interesting they have a way cheaper "contributor" version "used to improve our products", how much of that is price discrimination vs the data being that valuable?
Roughly DeepSeek V4 Flash pricing, though you can get V4 from providers that don't train on your data
Claude Sonnet is a weird exception to the mid models because Anthropic doesn't do much with Haiku and Opus is too big.
The use traces must be crucial to functionality which is why they’re keeping prices so low.
Interesting that they have separate API pricing for "we can train on your data" (whereas iirc most of the big players either make that distinction only between subscriptions and API usage, or train on everything). Wonder how it compares to Deepseek V4 Flash given that they're similar on pricing and data policy.
Wasn't the previous one us only? This is probably the biggest part of the post
Anyone know if muse code is open source?
I am not a fan of Meta but I do cheer for any competitors against OpenAI and Anthropic, the duopoly is getting tiresome.
It's not a good system obviously. Google did this as well for Gemini-CLI, but forced it to be linked to personal Google accounts (which caused a great deal of onboarding friction).
After everything that you have seen with Meta, would you really trust them with a coding agent? You don't even know if your prompts are being analyzed by them on the side or if your code base is being uploaded to them. This goes for the rest of them that have closed harnesses and closed models gated by a login.
Think twice before falling for this announcement and ask yourself what they are not telling you.