Cosmo Du @Answeror

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!

Arena.ai @arena

7 days ago

Muse Wonder (Max) by @AIatMeta has entered Code Arena: WebDev at #15 with 1651 pts and Text Arena at #2 with 1513 pts! In Code Arena: WebDev, Muse Wonder (Max) landed at #15 with 1651 pts. This result is slightly lower thanMuse Spark 1.3 (Max) at #12 with 1659 pts, within 4 pts

keyboard_arrow_left Previous keyboard_arrow_right Next

23 13 301 88K 45

@Muse saved me 50$ on new climbing shoes by finding them on sale on some Italian website, it figured that shipping and US import tax still saves me money, and no other retailer in US would beat the price. I got my shoes delivered in 6 days, I haven't even seen that website or typed any of my details. Just told my Muse what I need, and confirmed purchase once it done full research. I love agents in my personal life. I also keep hearing from friends how happy they are with @Muse. My Metamates who made this possible, you rock!

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!

1.4.8 made Muse Code a lot more token-efficient, so we wanted you to feel it right away. 🎉 Everyone's usage is reset, effective now. Have fun.

Michael Douglas @mjdouglas

6 days ago

Muse Code 1.4.8 is out and is focused on token efficiency. - Auto-review and background agents now use a cheaper model when available, and long auto-reviews stop sooner instead of burning tokens. - We've fixed a bug that routed a small number of users who had enabled

25 12 126 24K 11

1/ muse spark open is the most capable US open-weight model to date. 816B total params, 42B active. 1M-token context. natively multimodal. built for coding and agentic work like long-horizon tasks, hard software engineering, multi-step reasoning. america! 🇺🇲

Muse Spark Open -- weights releasing today -- runs great on new Mac Studios thanks to work with @johnternus and Apple.

Tried out Muse Wonder on a tricky task (figuring out which parts of the OpenHands Agent Canvas are hard for users to use) and it did a great job, impressed! And as someone who's used to gpt models, the conciseness of the verbiage was pretty nice too 😀 Great job by @AIatMeta !

multi turn, long horizon, real world dev, without breaking what already works

Zhiqing Sun @EdwardSun0909

6 days ago

Muse Wonder Full tops VCB 1–100 which measures sustained, multi-turn software development in the real-world setting and mogs on cost!

2 11 75 29K 4

Muse Code (muse-wonder-full-1.0 high) is doing really good. By my vibe testing, It's better than Astra. Not quite as good as Claude Code Fable and 5.5 Opus. But it's much faster than Claude Code. I am doing serious compiler stuff that Codex simply chokes on.

In our health workstream at Meta we did a project where we wanted to see what research papers medical experts could write using a Muse Wonder coding scaffold that could access a database of 5M electronic health records we licensed from a vendor. The goal is that with 5M patients in this database, AI could do research to answer arbitrary questions like “did people who take statin have higher rates of diabetes later in life”? The thing we were betting on here is on what happens when you put human experts, a strong coding model, and access to data all together We worked with a small group of medical experts to help them use this AI tool and the punchline is that they all found that this setup enabled a much faster pace of health research. They generated ten manuscripts in around two weeks, with one researcher (Paddy Ssentongo) generating three. Throughout the process, they collaborated with each other to review manuscripts and write skill files to help our coding scaffold improve at doing research. The medical experts liked how quickly they could test different hypotheses and choose the best ones to continue working on Health research is pretty unique in that it is super important to be able to interpret noisy data and have taste to know which variables are important, which was still a significant value add from human researchers. Given that medicine is one of the oldest fields in human history and it is ultimately about humans, keeping a substantial human component in this type of research is probably important. In the near future I see AI primarily as a tool to facilitate human-led research. With the right scaffold I believe health researchers can be 10x or even 100x more productive using AI tools that have appropriate access to the wealth of data that the world has collected about human health One way to see this work is through the broader lens of AI for science. You can think of science as two parts: (1) collecting raw data about the world, and (2) synthesizing that raw data into understanding. In this project we already had existing data about the world and we aimed to accelerate part (2) using AI. Eventually, I imagine a world where we have such great AI tools that the time to synthesize raw data into understanding is driven towards zero. In that world, science will be substantially faster and most of the work will be focused on collecting the right raw data via physical experiments We did this in a pretty short amount of time (which was the point) and I think the future of scientific research will be more faster and more exciting than ever

AI at Meta @AIatMeta

6 days ago

We’re exploring how AI can accelerate health science research. While AI has made significant breakthroughs in formal sciences like mathematics, health data science depends on imperfect real-world data and consequential choices about cohorts, variables, and causal assumptions.

74 100 717 214K 259

real world work, publicly available

AI at Meta @AIatMeta

6 days ago

We’re exploring how AI can accelerate health science research. While AI has made significant breakthroughs in formal sciences like mathematics, health data science depends on imperfect real-world data and consequential choices about cohorts, variables, and causal assumptions.

74 100 717 214K 259

Muse Code 1.4.8 is out and is focused on token efficiency. - Auto-review and background agents now use a cheaper model when available, and long auto-reviews stop sooner instead of burning tokens. - We've fixed a bug that routed a small number of users who had enabled contributor mode to the full-priced models.

Now I can say it: Had early access to @Meta’s new "Watermelon" for the past few days. Trick questions didn’t fool it, it followed instructions to the word, and as a coding agent it wrote, ran and checked its own code. Creative writing still a bit dry for my taste, but very very very impressive overall. From my initial testings its as good as the benchmarks make it look. Meta really nailed it with their model! P.s.: the benchmarks still surprised me, in a postive way. Didnt see the benchmark evals in advance.

no doubt my eli5 prompts went into training data

Alexandr Wang @alexandr_wang

a week ago

this was a big focus area for muse wonder; our goal was to build a model for everyone!

23 13 350 39K 20

My bet would be Muse is going to win

Deedy @deedydas

a week ago

Meta Muse's retention is out, and it's almost as good as ChatGPT! It's higher than ChatGPT at D7 (>30%) and just lower at D14 (22%). Not quite in the elite D14 tier of WA (67%) YT (59%) Insta (54%) or TikTok/Snap (37%), though. Muse is one of the highest retaining AI products!

205 158 2K 717K 869

trained for real work, tested on real bugs that's muse wonder full

Paweł Huryn @PawelHuryn

7 days ago

So, I tested Muse Wonder Full 1.0 on real work. 2 repos. 105 bugs frontier models missed in Q1 2026. Find and fix what you can. I'm so surprised we need a new thread. The results: GPT-6 Astra (max): 45 Muse Wonder Full 1.0 (max): 44 Fable 5.1 (max): 43 Opus 5.5 (max): 41.7

51 35 426 85K 72

@trevor_strohman @alexandr_wang („• ֊ •„)੭

active focus isn't grinding harder it's staying locked on the goal sometimes the right move is "someone already fixed this, merge theirs"

Theo - t3.gg @theo

a week ago

Asked Opus 5.5, GPT-6.1 Sol, and Muse Wonder Full to fix a bug Opus found the root cause and filed a pr GPT-6.1 Sol asked a question, crashed from the async question tool interrupt, found the bug, fixed it, wrote 300 lines of tests, and explained it in impossible to understand

313 151 8K 917K 1K

@mkumar_ish /feedback please!

Some thoughts on the new Meta release today: - The models from MSL have improved quickly. They’re not just competing for Pareto optimal anymore, but driving for the frontier. - To me, the biggest unlock of these models is their token efficiency. They’re able to see significant performance increases while reasoning less and spending fewer tokens / turns. In Vibe Code Bench ,WF gets the same performance as MS 1.3 in half the number of steps. Smarter models think more efficiently. - Today, Muse Wonder Full is only available in in-app experiences (not via API). It may be that we see an even tighter coupling between models and first party applications. Or that this gating prevents nefarious actors from misusing the most capable intelligence. Not yet clear what the plan is. - MSL is making some serious progress on alignment. We’ll have more to share publicly here soon. Meta hasn’t been as vocal about safety, but to service Muse so widely they have needed to solve some fundamental alignment challenges. - Relatedly, we saw 0 refusals and Wonder did better than Wonder Full on our cyber capabilities benchmarks. This is itself very good! People should not expect their hard economically valuable work to be inhibited by these models. - The models have room for improvement on scientific discovery. They are strong executors, especially in code, but don’t have the intuition for which experiments to try. We’re still running our RSI Index, sharing results soon. If they aren’t able to maintain the frontier, this would be why. - META went up 3% today. I’ve said it before and I’ll say it again. As AI underpins a larger part of our economy, third party benchmarks will be the best value predictor of AI labs. The market for intelligence is rationalizing.