Jihui Yang retweeted
Today we (meta) released the most capable US open-weight models. Meta's production inference also runs on SGLang. The best open model on the best open-source inference engine. Try it now 👇
Muse Spark Open is built to plan, act, and use computer across agentic tasks for life and work.
You can now build a powerful personal AI agent with an open stack, and SGLang to make it:
- Ultra-responsive with NVFP4 MegaMoE and sequence parallelism
- Faster for agentic and coding tasks with our DFlash2 draft model
- Customizable for specialized use cases via fine-tuning with Miles
We’re incredibly proud to have made this possible with @AIatMeta, @NVIDIAAI, @AIatAMD, @intel, and @radixark!
Jihui Yang retweeted
Muse Spark Open is built to plan, act, and use computer across agentic tasks for life and work.
You can now build a powerful personal AI agent with an open stack, and SGLang to make it:
- Ultra-responsive with NVFP4 MegaMoE and sequence parallelism
- Faster for agentic and coding tasks with our DFlash2 draft model
- Customizable for specialized use cases via fine-tuning with Miles
We’re incredibly proud to have made this possible with @AIatMeta, @NVIDIAAI, @AIatAMD, @intel, and @radixark!
Today we’re releasing Muse Spark Open, the most capable US open-weight model to date.
Built for coding and agentic work, it excels at long-horizon tasks, complex software engineering, and multi-step reasoning.
Key Model Specs:
→ 816B total / 42B active parameters
→ 1M token context window
→ Natively multimodal across text, image, video, audio
Jihui Yang retweeted
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! 🇺🇲
Jihui Yang retweeted
MUSE WONDER
this is the culmination of over a year of research and infrastructure work in msl
since we originally shipped muse spark in april, we have continued to scale our models across pre-training and RL, now reaching the frontier 🏆
see our model's improvements on the artificial analysis index:
Jihui Yang retweeted
introducing muse wonder
our new frontier model. state-of-the-art at personal agents and professional agents.
our largest and most capable model yet—rivals the big bois
Jihui Yang retweeted
Introducing Muse Wonder, our new family of frontier models with state of the art performance for personal and professional agentic work.
Jihui Yang retweeted
We announced an agreement to acquire a nationwide low-band spectrum license portfolio that will pave the way for @Starlink to become a major mobile carrier in the US.
With this new spectrum and our Gen2 constellation, Starlink Mobile can ensure Americans have access to high-speed mobile broadband no matter where they are → spacex.com/updates#starlink-…
Jihui Yang retweeted
Introducing River Recipes: our open-source initiative for turning promising research into vetted, reproducible training recipes.
Our first recipe tackles text-to-SQL, where our trained open models beat GPT-6 Astra Pro and Claude Opus 5.5 on Arcwise-Plat for less than 1% of the cost.
Read the full breakdown and explore the recipe ↓
river.ai/achieving-frontier-…
Jihui Yang retweeted
you can use a muse gadget to track your wine collection
Muse Gadgets are here. You can have a little Jolly everywhere.
My first one sits by the wine rack. Snap the label, Muse reads the producer and vintage, and the bottle shows up in our personal cellar over Tailscale (Muse has a Tailscale connector support!).
It's a ~$50 sensecap watcher device and took less than an hour to build. You can grab any ESP32 or Raspberry Pi and you can build your own smart home gadget.
Go build one. Happy building!
xitter.cf/natfriedman/status/210…
Jihui Yang retweeted
a week after launch, muse is now the #1 app in the App Store!
it’s been SO exciting to see how much y’all have done with muse. we can’t wait to do more together!
Jihui Yang retweeted
muse for mac is HERE!! your agent can now get stuff done right on your computer — files, messages, calendar, notes, all of it.
you're in control of what it can access, and it always asks before doing anything sensitive.
Add it to the dock - ai.meta.com/muse/download/
Jihui Yang retweeted
@periodiclabs Neon is an early glimpse of the future: physical labs owning their own AI that beats closed models and keeps getting better from their data.
At Periodic, we do hardcore atoms-and-bits work. We build **the full stack of AI for Science** — from physical labs, simulation tools, and lab software (atoms) to training, inference, RL, and sandbox infra (bits) — and we optimize across the full stack to squeeze the juice out of the data and GPUs. I am grateful for the close collaboration from @radixark @sgl_project @nvidia @raydistributed for the infra piece.
Periodic Neon is a 1T-parameter model that we post-trained to analyze experimental results and deployed to our physical labs. This is a step toward putting autonomous discovery into the hands of scientists.
We built high-throughput materials labs in Menlo Park to create a loop between experiments and models. The labs generate fresh data, the models learn from it, and then help us decide what to try next.
Using only 1,300 H200s, plus months of our experimental data, we mid-trained and RL’d an open-source model to surpass GPT-6 Astra on our analysis benchmark. We call it Neon.
This is real footage from our lab. We’re focusing first on hard problems in materials science, including superconductors, magnets, and semiconductor materials.
Read our blog posts below.
Very cool!
We’ve spent the past year building high-throughput labs and infrastructure for large-scale physics experiments in our search for better superconductors and magnets.
We ran our first experiments by hand. Now, experiments run continuously in the labs. We’re building AI to make sense of what they produce and to determine which experiments to run next.
Jihui Yang retweeted
1/ today we're rolling out Muse, our new personal ai assistant. Muse is always-on, wicked fast, can use a browser, connect to your apps, and is designed to be secure.
try it now: muse.ai.
Jihui Yang retweeted
Introducing Muse, the personal agent that understands your goals and works 24/7 to get things done for you.
Jihui Yang retweeted
i really hate to say it, but…
gemini who? 🏎️💨
Meta has released Muse Spark 1.3, their fourth Muse Spark model release in five months. Muse Spark 1.3 (max), which is in limited preview for Meta’s partners, scores 62 on the Artificial Analysis Intelligence Index, behind only Claude Fable 5.1 and Claude Opus 5. The variant available now, Muse Spark 1.3 (xhigh), scores 61 and ties with GPT-5.6 Sol (max) and Grok 4.6 (high). Both variants’ gains come primarily from improvements in agentic work and scientific capabilities
Muse Spark 1.3 (xhigh) enters the Artificial Analysis Intelligence Index at 61, up 4 points from Muse Spark 1.2 (57, August) and 8 points from Muse Spark 1.1 (53, July). It enters tied with GPT-5.6 Sol (max), Grok 4.6 (high), and Claude Opus 5 (high), and behind Claude Fable 5.1 (max, 66), Claude Opus 5 (max, 63), and Claude Fable 5 (max, 62)
Muse Spark 1.3 (max), which is in a limited preview stage, lands at 62. This higher index score is enabled by gains vs. Muse Spark 1.3 (xhigh) in Tau3-Bench Banking (52% vs. 47%) and GDPval-AA v2 (1,754 Elo vs. 1,709). Muse Spark 1.3 (max) is second only to Claude’s Fable and Opus variants in total score
Congratulations to @AIatMeta, @finkd, and @alexandr_wang on the release!
Key Takeaways:
➤ Continued improvement on agentic knowledge work tasks. At the launch of Muse Spark 1.2, we noted its significant gains in agentic knowledge work performance vs. Muse Spark 1.1. The latest iteration continues this trend, with Muse Spark 1.3 (xhigh) demonstrating a notable 12-point gain vs. Muse Spark 1.2 in Tau3-Bench Banking (35% to 47%), a 5-point gain in Terminal-Bench 2.1 (80% to 85%), and a new GDPval-AA v2 Elo of 1709 against its predecessor’s 1615. Muse Spark 1.3 (max) improves further on Tau3-Bench Banking (52%) and GDPval-AA v2 (1,754 Elo). This Tau3-Bench Banking score is #1 among all models. Muse Spark 1.3 (max) achieves these higher agentic work scores by using more turns and total reasoning tokens, reasoning 62% more on GDPval-AA v2 and 28% more on Tau3-Bench Banking compared to Muse Spark 1.3 (xhigh)
➤ The lowest cost per task for any model at 59+ on the Artificial Analysis Intelligence Index. Muse Spark 1.3 (xhigh) costs $0.55 per Intelligence Index task at Meta's unchanged $1.25/$4.25 per 1M token pricing ($0.15 for cached input), with its peers GPT-5.6 Sol (max) and Grok 4.6 (high) costing $0.95 and $0.94 respectively, a 70%+ premium. This places Muse Spark 1.3 (xhigh) on the Pareto frontier for Intelligence vs. Cost per Task. Its cost per task is higher than Muse Spark 1.2 ($0.40 per task), driven by ~57% more input tokens per task on agentic evaluations, with output tokens up only ~8%. Pricing for Muse Spark 1.3 (max) is not yet publicly available
➤ Scientific Reasoning results rose across the board, led by CritPt. CritPt was the standout non-agentic score gain vs. Muse Spark 1.2, with a material +8 points for the xhigh variant (18% to 26%), and GPQA Diamond achieved +4 points (90% to 94%), while Humanity’s Last Exam and SciCode each gained a more modest 2-3 points (45% to 47% and 56% to 59%, respectively). Muse Spark 1.3 (max) achieved roughly similar scores to the xhigh variant, gaining 2 points in Humanity’s Last Exam, tying on GPQA Diamond, and losing a point on CritPt vs. Muse Spark 1.3 (xhigh)
➤ Minor regressions in only two evaluations. Both Muse Spark 1.3 (xhigh) and Muse Spark 1.3 (max) dropped 4 points in AA-LCR (83% to 79%) when compared to Muse Spark 1.2, and AA-Omniscience (Accuracy) fell 3 points for xhigh and 1 point for max. The drops in AA-Omniscience (Accuracy) are due to a higher abstention rate (not answering questions when unsure), which also lowered the hallucination rate for Muse Spark 1.3 (xhigh)
Other model details (xhigh variant):
➤ Context window: 1M tokens, unchanged from Muse Spark 1.2
➤ Pricing: unchanged from Muse Spark 1.2: $1.25/$4.25 per 1M input/output tokens, with cache hits discounted to $0.15 per 1M
➤ Input modalities: text, image, video
➤ Availability: Meta's first-party API and Muse Code
