when i try to explain SF to my non-tech friends, i usually end up using the term "money bukkake" at some point in the night
this is what we in the business call framemogging
Deutsche Bank: Muse remains significantly ahead ChatGPT in terms of downloads and DAUs through 36 days since launch.
$META
muse wonder full on webdev arena
not bad, but clearly lots of room for us to improve!
Exciting news: Muse Wonder Full (Max) by @AIatMeta has just debuted in the Code Arena: WebDev at #6 overall with 1726 pts, putting Meta in the top 3 labs!
This is +75 pts and 11 places above Muse Wonder (Max) at #17 (1651 pts), and +67 pts and 8 places above Muse Spark 1.3 (Max) at #14 (1659 pts).
Muse Wonder Full (#6) also landed +16 pts above Claude Sonnet 5.5 (#17), and +48 pts above Gemini 4 Argon (#10). With this score, Muse took the #3 lab spot (previously held by Google).
Across categories, Muse Wonder Full (Max) landed:
- #4 in Data & Analytics and in Consumer Product
- #5 in Brand & Marketing
- #6 in Content Creation Tools
- #7 in Gaming and in Simulations
- #8 in Reference-Based Design
Congrats to the @AIatMeta team and @alexandr_wang on this release!
let me tell you… not many were saying this last summer
oh how much can change in a year… let’s do one more 😎
if i was to work for any lab it would be meta.
as much as i love claude models, how autonomous grokbot is and the reliability of codex, its clear the world is heading towards agents working in the background as we get to spend more time doing what we love.
community building and entertainment will be huge (it already is). creator economy is about to roadster flip over everything. meta distribution via instagram and partnering up with apple means they’re going to own a major % of the consumer end.
mark zuckerberg recently said something in the recent cheeky pint podcast that really stuck with me. "the highest use of super intelligence is invention and creation, not automation". the more i use agents the more im realising this myself. more time to connect with loved ones, more time to work on passion projects and more time to solve harder problems. no one cares how much faster you’re flying through excel spreadsheets. or how much money you’re making. its really time to have fun. you need to dream bigger.
thats all i’ve ever wanted to do and if progress in si stops here i would still be fully content with life. and the crazy thing is i haven’t even tried muse yet (im from uk/eu and they’re setting us back 100 years right now🥲). but i love how meta trained the model to work for personal use case in mind and this aligns with my philosophy: “more human, by design”. i see where this all heading so much clearer now.
personal super intelligence. i dont even like saying its bigger than the internet anymore because thats obvious. truly once in a civilisation moment like alexandr said.
muse wonder full on VulcanBench
pretty competitive overall with Astra, Opus 5.5, and Sol 6.1, but definitely slower which is something we’re working on ⚡️
Okay my Muse Wonder Full benchmark is now complete with @VulcanBench - busy weekend so might not get to put all the data and model cards together until next week.
But I wanted to share a quick peek at the combined score and how it compares to other models.
The TL;DR is, with Muse Wonder Full, Medium effort seems to be the sweet spot, but, you need to take tokens-per-task and cost-per-task into account when deciding what model to use.
In the two tables below, first is combined accuracy score, second is time per task.
Right now Muse Wonder Full is quite a bit slower than Opus 5.5, much slower than GPT-6.1 Sol, and way slower than GPT-6 Astra.
Overall, based on what I'm seeing, I would use Opus 5.5 or GPT-6.1 Sol over Muse Full at this point. You'll get the same or better accuracy faster and cheaper.
More to come in model cards and deeper analysis on the site, but it's the weekend and I'm hanging with family more than doing benchmarks so might be a day or two.
You should do the same! 🖖
after an exciting week of launches (muse wonder [full] & muse spark open), i want to write a note of appreciation to the amazing research teams at meta superintelligence labs on reaching such a special milestone ♥️
we started MSL and TBD a little more than a year ago and set a goal of producing a frontier model within a year or so. given where llama4 landed, that was an incredibly audacious goal, and we knew it wouldn't be easy. many people told us it would be impossible. we knew it was unlikely, but still worth doing. a remarkable group of researchers and engineers joined the effort from inside and outside meta. every single person came in, picked up a shovel, and got to work with earnestness and urgency. nobody asked whose job it was.
we assembled a team of the most brilliant people i have ever had the opportunity to work with (and remember, i was an HRT intern 🙃). i am surrounded by ambitious, visionary, technically excellent, hard-working, pragmatic, thoughtful scientists, engineers, and product folks—people who will chase a weird loss spike past midnight just because they need to know why. these models are the product of their passion, hard work, and brilliance.
somewhat paradoxically, when we started TBD, we set a principle of no deadlines. we knew we had an astronomical amount of work ahead of us—across pretraining, post-training, RL, multimodal, infrastructure, safety, data, AND frontier research. there was no use setting arbitrary timelines—we just had to get to work, be thoughtful, and build the foundations of a frontier research effort in record time. and run after run, this team did exactly that.
our researchers are magicians. i can only describe building these models with this team as a wondrous journey of scientific discovery. i am constantly amazed by what we can accomplish with scaling. i will remember this past year for the rest of my life—the late nights, the broken runs, the evals that came back better than any of us dared to hope. watching this team create wonder after wonder has been a true and deep gift.
more than just the models we released this week, we have built a machine that builds new models. it often sounds like a cop-out to say labs can just turn the crank to keep scaling, but now i have seen it first-hand. sure, it's a lot of work and a careful process, but we have built a scalable system to keep advancing at the frontier, and i am really proud of how fast this team keeps making these models better.
lastly, i get to work with researchers who care deeply about how their work benefits humanity. it's easy to forget when everything is changing so fast, but the whole damn point is to make people's lives better. time and time again, i have seen our researchers, engineers, and product folks build our technology with care so it can truly be good for the world—whether that's building the best models for muse, thoughtfully open-sourcing strong models, or making sure the alignment and security of our systems are rock solid. this is a team of people who never forget there's a real person on the other side of every prompt.
thank you to everyone at MSL for being part of this crazy journey over the past year. i'm so grateful for every one of you, and our work is not done!