Meta Platforms on Thursday released developer access to its Muse Spark AI alongside an upgraded version, marking a strategic shift as the tech giant begins charging for a model it previously offered free — a move that pits it directly against Anthropic and OpenAI while raising questions about the company's commitment to open-source AI development.
The social media giant touted Muse Spark 1.1 as its most capable model for real-world coding and agentic tasks, part of what it calls delivering "personal superintelligence." Meta said the upgraded model can write and debug code, use software and external tools, understand text, images and video, and carry out complex multi-step tasks with less human intervention. In April, Meta debuted Muse Spark, the first text and reasoning AI model from the superintelligence team it assembled last year to close the gap with rivals in the heated competition for AI supremacy.
From Free Preview to Paid Access
Developers in the United States can now access Muse Spark in public preview on Meta Model API, letting them test prompts, compare outputs and prototype integrations. The API is a key element for AI systems, acting like a digital bridge for developers that allows them to use the model's capabilities in their own software systems. Meta was testing the Application Programming Interface with partners in a private preview during its launch.
Those who sign up for the API receive $20 in free credits to test the model before switching to pay-as-you-go pricing. The access is priced at $1.25 per million input tokens and $4.25 per million output tokens, above OpenAI's entry-level GPT-5 mini and Anthropic's low-cost Claude Haiku 4.5, but below Anthropic's higher-end Claude Sonnet 4.6 model. Meta CEO Mark Zuckerberg said in a post on X, "Our focus is on delivering strong agentic and multimodal models at very low cost."
The new model is now available in Thinking mode in the Meta AI app and on the website. It's also expected to replace existing Llama models powering chatbots on WhatsApp, Instagram, Facebook and Meta's collection of smart glasses. The release follows a company announcement on Tuesday expanding generative AI tools across its apps by rolling out Muse Image, its first image-generation model from Meta Superintelligence Labs.
The Infrastructure Race and Its Costs
Separately, an internal memo reviewed by Reuters said Meta Platforms plans to start manufacturing an artificial intelligence chip from September as part of its plan to boost overall computing power to 14 gigawatts next year. The tech firm's data center chip, code-named "Iris," is part of a four-generation project for Meta Training and Inference Accelerators that it will design in-house. The plan is to use custom-built silicon to improve the AI that powers its Facebook and Instagram social media platforms.
Testing the chip took only six weeks and found no major issues, the memo showed. Meta tailored the chip for its own needs and is working with Broadcom to help design it and Taiwan Semiconductor Manufacturing Co to manufacture it. The approach is likely to help the firm lower its massive computing costs and gain more independence from chip suppliers such as Nvidia and Advanced Micro Devices.
The chip is meant to augment the large quantities of graphics processing units used for AI applications that Meta purchases from Nvidia and AMD. However, adopting the latest GPUs at a firm as large as Meta "has been a heavy lift, and it has cost us time," the memo showed. Meta unveiled Iris under its technical name in March along with three other AI processors. It plans to launch a chip about every six months through 2027, whereas typically firms release AI chips at intervals of a year or more.
Staggering Energy and Financial Investment
This year, Meta plans to deploy seven gigawatts of computing infrastructure. To reach that total, Meta added 1 gigawatt in the first half of the year and forecasts adding another 5.5 gigawatts by the end of the year, the memo said. One gigawatt of energy is enough to power about 800,000 homes. The company plans to double capacity again next year to reach a total of 14 gigawatts in 2027.
Meta expects to spend as much as $145 billion on AI infrastructure this year, a significant portion of Big Tech's more than $700 billion projected outlay on the technology. To expand computing infrastructure, Meta has secured long-term, multi-year supply agreements with Samsung Electronics for memory chips, Sandisk for flash storage and Sumitomo Electric for fiber-optic equipment. Sandisk declined to comment. Samsung Electronics and Sumitomo Electric did not respond to requests for comment.
Components such as memory and AI chips have experienced a surge in demand as tech companies race to expand data centers to keep pace with AI's thirst for computing power. Memory and other chip prices have risen rapidly and substantially enough that "chipflation" has become a macroeconomic concern, Morgan Stanley analysts said.
Why This Matters:
Meta's pivot from free, open-source AI models to paid developer access marks a significant shift in strategy that reflects the crushing financial pressure of the AI arms race. The company's plan to spend up to $145 billion on infrastructure this year — enough to fund multiple European Green Deal initiatives — raises urgent questions about the sustainability and societal benefit of this investment. The energy demand alone is staggering: Meta's planned 14 gigawatts by 2027 could power more than 11 million homes, at a time when Europe is struggling to transition away from fossil fuels and manage energy costs for ordinary households. The rise of "chipflation" threatens to ripple through the broader economy, potentially driving up costs for consumer electronics and digital services that millions depend on daily. As Big Tech consolidates control over AI infrastructure through vertical integration — designing chips, building data centers, and charging for model access — the risk grows that this transformative technology will be shaped primarily by corporate profit motives rather than democratic deliberation or public interest. European policymakers must ensure that AI regulation includes not just algorithmic accountability but also scrutiny of the environmental and economic footprint of this infrastructure race.