Meta unveils its most powerful AI model, nearing top competitors
Meta unveils its most powerful AI model, nearing top competitors
摘要: TECHNOLOGY
Meta unveils its most powerful AI model, nearing top competitors
Muse Spark 1.3 marks Meta's most aggressive push yet into closed, propri
正文
TECHNOLOGY
Meta unveils its most powerful AI model, nearing top competitors
Muse Spark 1.3 marks Meta's most aggressive push yet into closed, proprietary AI as the company races to close the gap with OpenAI and Anthropic.
Editorial Team
Sep. 2, 2026
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Photo: Tima Miroshnichenko / Pexels
Meta spent years being the company that gave away its AI models for free. That era is quietly ending.
On September 2, 2026, Meta released Muse Spark 1.3, its most capable AI model to date, and opened API access to developers the following day on a paid basis. The move is a meaningful signal: the company best known for making its Llama models freely available is now building a proprietary frontier product it intends to monetize directly.
Alexandr Wang, Meta’s chief AI officer, said the model’s capabilities are increasingly comparable to leading offerings from OpenAI and Anthropic.
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From Llama to Muse: a quiet strategic pivot
The Muse family represents a genuine philosophical shift for Meta’s AI division. The company formalized the transition in April 2026 with the first Muse Spark release, then shipped version 1.1 in July and version 1.2 on August 5, before arriving at 1.3 less than a month later.
The Llama series, which made Meta a favorite among researchers and developers who wanted to run powerful models locally without licensing fees, is no longer the primary vehicle for the company’s frontier ambitions. Muse Spark is closed, API-gated, and paid, which puts it structurally in the same lane as GPT-4o and Claude.
Meta did not abandon openness entirely. Muse Glimmer, a distilled variant released in August 2026, is designed to run on consumer-grade GPUs and remains open-weight.
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Meta’s capital expenditure guidance for 2026 projects spending between $130 billion and $145 billion on infrastructure this year, directed at data centers, custom silicon, and compute capacity needed to train an