AI at Meta

Together with the AI community, we are pushing the boundaries of what’s possible through open science to create a more connected world.

[AI at Meta @AIatMeta]

Introducing Muse Spark, the first in the Muse family of models developed by Meta Superintelligence Labs.

Muse Spark is a natively multimodal reasoning model with support for tool-use, visual chain of thought, and multi-agent orchestration.

Muse Spark is available today at https://t.co/wHkMPH82ZH and the Meta AI app. We’re also making it available in private preview via API to select partners, and we hope to open-source future versions of the model.

Learn more: https://t.co/PloE9q5x96

[AI at Meta @AIatMeta]

Big congrats to our SAM 3D team for receiving a Best Paper Honorable Mention at #CVPR26! This prestigious recognition underscores their incredible work pushing the boundaries of computer vision.

Read the paper here: https://t.co/jr59xlzwlX

[AI at Meta @AIatMeta]

@xiaolonw @Meta Welcome to the team! 🚀

[AI at Meta @AIatMeta]

Today we’re announcing an agreement with Amazon Web Services to bring tens of millions of AWS Graviton cores to our compute portfolio.

This partnership marks an expansion of our diversified AI infrastructure and will help scale systems behind Meta AI and agentic experiences that serve billions of people.

Learn more: https://t.co/cmeATOB7Jc

[Summer Yue @summeryue0]

🚀 Muse Spark Safety & Preparedness Report for Meta AI is out.

We start with our pre-deployment assessment under Meta's Advanced AI Scaling Framework, covering chemical and biological, cybersecurity, and loss of control risks. Our assessment flagged potentially elevated chem/bio risk, so we implemented safeguards and validated mitigations before deployment - bringing residual risk to within acceptable levels.

Beyond the Framework, we also share findings and early explorations of model behavior (honesty, intent understanding, etc.), jailbreak robustness, eval awareness, and more.

We're sharing this report to give a closer look at how we evaluate advanced AI safety. Always more work to do, and we welcome feedback from the community.

https://t.co/azpKHwu7x9

[AI at Meta @AIatMeta]

@MartinJosifoski Great to see this research out in the world, great work @MartinJosifoski and team!

[AI at Meta @AIatMeta]

@JeanRemiKing We're proud to partner on this initiative and we can't wait to see how the research community responds. Congrats!

[AI at Meta @AIatMeta]

🔜

[Alexandr Wang @alexandr_wang]

the muse spark API will be coming soon!

we have been thrilled with the amount of excitement amongst developers who want to try muse spark inside their agentic harnesses

stay tuned!

[AI at Meta @AIatMeta]

Try Muse Spark today via the Meta AI app or https://t.co/SSo3nt4aQ4.

[AI at Meta @AIatMeta]

Check out some cool ways the community has been putting Muse Spark to work (and play) 🧵👇

1/ https://t.co/SAaOe6sO7O

[Pietro Schirano @skirano]

Ok this is actually pretty impressive and I truly didn't see any model doing this before or being able to do it to this extent.

When I asked Muse Spark from Meta to convert this image into code, it cut out the assets from the screens so it could use them correctly!

[AI at Meta @AIatMeta]

5/ https://t.co/mtEUo3Appi

[Nainish Rai @Nain1sh]

That clip is the part most people will underestimate.

Image-to-code was already impressive

What Meta’s Muse Spark seems to be doing is one level higher:

it’s not just recreating pixels, it’s inferring product logic.

I gave it a calendar screenshot and I am blown

[AI at Meta @AIatMeta]

@meNishantK @MetaNewsroom @WhatsApp @instagram @facebook @messenger One step at a time, excited about the future! 🚀

[Artificial Analysis @ArtificialAnlys]

Meta is back! Muse Spark scores 52 on the Artificial Analysis Intelligence Index, behind only Gemini 3.1 Pro, GPT-5.4, and Claude Opus 4.6. Muse Spark is the first new release since Llama 4 in April 2025 and also Meta's first release that is not open weights

Muse Spark is a new model from @Meta evaluated on Artificial Analysis. We were given early access by Meta to independently benchmark the model. It is the first frontier-class model from Meta since Llama 4 Maverick was released in April 2025, and notably the first @AIatMeta model that is not being released as open weights. The release follows Meta's reorganization of its AI efforts under Meta Superintelligence Labs, and signals that Meta is re-entering the frontier race after roughly a year of relative quiet.

For context, Llama 4 Maverick and Scout scored 18 and 13 respectively on the Artificial Analysis Intelligence Index as non-reasoning models at the time of their release, while Muse Spark scores 52. Muse Spark essentially closes the gap between to the frontier in a single release.

The model is not open source and is not yet accessible via an API but Meta has shared they expect this to come soon. Meta is also integrating Muse Spark into their first party products including their Meta AI chat product, Facebook, Instagram and Threads.

Key takeaways from our benchmarks:
➤ Muse Spark scores 52 on the Artificial Analysis Intelligence Index, placing it within the top 5 models we have benchmarked. It sits ahead of Claude Sonnet 4.6, GLM-5.1, MiniMax-M2.7, Grok 4.20 and behind Gemini 3.1 Pro Preview, GPT-5.4 and Claude Opus 4.6

➤ Muse Spark is notably token efficient for its intelligence level. It used 58M output tokens to run the Intelligence Index, comparable to Gemini 3.1 Pro Preview (57M) and notably lower than Claude Opus 4.6 (Adaptive Reasoning, max effort, 157M), GPT-5.4 (xhigh, 120M) and GLM-5 (110M)

➤ Muse Spark is the second-most capable vision model we have benchmarked. It scores 80.5% on MMMU-Pro, behind only Gemini 3.1 Pro Preview (82.4%)

➤ Muse Spark performs strongly on reasoning and instruction-following evaluations. It scores 39.9% on HLE, trailing only Gemini 3.1 Pro Preview (44.7%) and GPT-5.4 (xhigh, 41.6%). The model also achieved 5th highest in CritPT with a score of 11%, an eval that is focused on difficult physics research questions. This is substantially above above Gemini 3 Flash (9%) and Claude 4.6 Sonnet (3%)

➤ Agentic performance does not stand out. On GDPval-AA, our evalaution focused on real world work tasks, Muse Spark scores 1427, behind both Claude Sonnet 4.6 at 1648 and GPT-5.4 at 1676, but ahead of Gemini 3.1 Pro Preview at 1320. On On TerminalBench Hard, Muse Spark trails Claude Sonnet 4.6, GPT-5.4, and Gemini 3.1 Pro. Muse Spark joins others in achieving a high τ²-Bench Telecom score of 92%

Key model details:
➤ Modalities: Multimodal including text and vision input, text output
➤ License: Proprietary, Meta's first frontier model not released as open weights
➤ Availability: No public API at the time of publishing. Meta expects to provide API access soon. Meta has started integration into their first party AI offering Meta AI and inside Facebook, Instagram, and Threads

[AI at Meta @AIatMeta]

With Muse Spark, we are on a predictable and efficient scaling trajectory. We look forward to sharing increasingly capable models on the path to personal superintelligence soon.

[AI at Meta @AIatMeta]

To build personal superintelligence, our model’s capabilities should scale predictably and efficiently. Below, we share how we study and track Muse Spark’s scaling properties along three axes: pretraining, reinforcement learning, and test-time reasoning. 🧵👇

Let’s start with pretraining. Over the last 9 months, we rebuilt our pretraining stack with improvements to model architecture, optimization, and data curation, enabling us to increase the capability we can extract from every unit of compute. To rigorously evaluate our new recipe, we fit a scaling law to a series of small models and compare the training FLOPs required to hit a specific level of performance.

The results: we can reach the same capabilities with over an order of magnitude less compute than our previous model, Llama 4 Maverick, making Muse Spark significantly more efficient than the leading base models available for comparison.

[AI at Meta @AIatMeta]

To spend more test-time reasoning without drastically increasing latency, we can scale the number of parallel agents that collaborate to solve hard problems. While standard test-time scaling has a single agent think for longer, scaling Muse Spark with multi-agent thinking enables superior performance with comparable latency.