In its second major model release this week, Meta is going back to its open source AI roots.

On Thursday, the tech giant unveiled Muse Spark Open, which it's calling "the most capable US open-weight model to date," and fulfilling its promise it made in April to release the weights of Muse Spark. It represents the first meaningful contribution to the US open source market since the company's Llama models.

Notably, Meta said that this is the first model of its size class capable of running on two Apple M5 Ultra Mac Studios with the latest MLX framework. The model was specifically optimized to run on Mac Studio with M5 Ultra, with Meta CEO Mark Zuckerberg specifically calling out the work of Apple CEO John Ternus in a tweet announcing the models.

"We believe the future is for everyone, and that includes the freedom to run advanced models wherever, including at home," Meta said in the announcement.

The model is natively multimodal, clocking in at 816 billion total parameters with 42 billion active parameters and a one million-token context window. Meta said it was built to handle coding and agentic work, as well as long-horizon tasks, complex software engineering and multi-step reasoning.

• In benchmarks for model context protocol, academic use, deep reasoning and hallucination, the model beats out competitors from Thinking Machines, Nvidia's Nemotron, Mistral, and Chinese labs like Moonshot, Z.ai and Alibaba.
• However, Moonshot's controversial Kimi K3 still has it beat in the Terminal-Bench 2.1 and DeepSWE 1.1 benchmarks for coding and software development.

Muse Spark Open is available today through several API platforms, including Baseten, Fireworks AI, Groq, Modal, Nebius, OpenRouter and Together AI, and model weights can be downloaded from Amazon, Google Cloud, Hugging Face and Microsoft.

But Meta isn't the only company seeking to claim its piece of the US open source ecosystem. Also on Thursday, Nvidia unveiled Nemotron 3.5 Super VL, an open, multimodal model built specifically for complex, agentic enterprise workflows. The model features 120-billion parameters with 12 billion active, which balances "model capacity with inference efficiency," Nvidia said in its announcement.

The model is customizable for domain specific data, tools and policies, and performs well across coding, web research, document intelligence, cybersecurity and video understanding tasks, the company said. The model weights are available today, as well as the open datasets on Hugging Face.

Our Deeper View

In the US, the open source AI ecosystem is still finding its feet. But increasingly, enterprises and organizations are turning to open source models as a cost effective alternative to the expensive proprietary APIs that companies like OpenAI and Anthropic are pushing. Though Nvidia and Meta likely have different motivations for wanting to feed the open source ecosystem, with Nvidia's strategy largely driven by designing its hardware, the result is still more options for enterprises who want to save money without wanting to take on the security risks that Chinese open source models may present.