With new open models, Meta pitches another reboot of

What happened

Meta has announced its intention to focus on open-weight large language models. 2, its more powerful model, in the next few weeks. Alongside these announcements, Meta CEO Mark Zuckerberg published a more than 6,000-word essay outlining the company’s philosophy about AI systems and governance moving forward.

It follows several statements by other Big Tech and AI company leaders debating the merits of open-weight models, proprietary labs, and the practice of distillation—something that some Chinese labs have reportedly done to build models that compete with the latest efforts from Anthropic and others.

” On the topic of distillation, Meta and Zuckerberg wrote: However, the majority of the essay focused on the notion of decentralized AI systems that are distributed widely. ” “People’s diverse values represent different tradeoffs they would make on important issues. There is no technological solution that can align with everyone’s opposing interests and values at once,” the essay says.

” Instead, Meta argues here that models should be personalized to the needs and values of individuals or groups of individuals. ” Meta has been lagging behind other big tech companies and major frontier labs for foundation models. Its models haven’t seen the kind of adoption that those developed by OpenAI or Anthropic have.

Why it matters

The essay aims to differentiate Meta from companies like OpenAI and Anthropic, which develop proprietary models and which have lobbied the US government for help competing against large-scale distillation—which involves using an existing model to train a new one—or open-weight models by Chinese labs. Muse Glimmer is a 30 billion parameter model with a 128,000-token context window by default.

It is distilled from Muse Spark, the larger and more capable model that Meta launched earlier this year. Glimmer is meant to run on users’ local machines, rather than via a cloud service or an API. 0 license.

Muse Spark was introduced in April as a closed, proprietary, frontier-class model—Meta’s first major model release after a significant shake-up of the company’s AI teams last year, and a departure from its focus on models that are, by some definition, open. 1 in July, it introduced its first paid service—again, a departure from its previous strategy.

2 was released on August 5 and was accompanied by Muse Code, a terminal coding agent. Developers have generally found that Muse Code doesn’t quite match the frontier models from Anthropic or OpenAI in capability, but it competes well on cost—meaning it has similar positioning to many open-weight models from Chinese labs.

As a smaller model designed to run on consumer GPUs, Muse Glimmer won’t compete on that level at all—but it reflects a growing movement to bring some inference to local devices to reduce reliance and spending on the models produced by the big labs like Anthropic and OpenAI.

Alongside the model releases, Mark Zuckerberg is credited as the author of a lengthy open letter that details Meta’s corporate strategy with AI, and a broader argument for how AI should be developed, distributed, and regulated.

What to watch

OpenAI and Anthropic have aggressively targeted enterprise customers, releasing powerful models and harnesses for knowledge work tasks like software development, and they have made significant inroads and generated substantial revenue from this strategy. Meta has not seen the same level of success.

Meta also saw a total overhaul of its AI division last year, when former Meta AI chief scientist Yann LeCun was replaced by former Scale AI CEO Alexandr Wang. The reset led to a change in focus.