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Meta has once again delayed the release of its flagship large language model, Behemoth, which was initially scheduled for April, then pushed to June, and now postponed until at least the fall. This decision comes amid internal doubts about whether the enhancements in Behemoth are substantial enough to warrant its launch as a new large language model for broad adoption. Developers reportedly struggled to deliver significant improvements over previous iterations, raising concerns about the platform´s readiness and value in the rapidly evolving Artificial Intelligence landscape.
The delay reflects a pattern observed across the Artificial Intelligence industry, where developing new, more powerful language models is proving increasingly difficult. In April, Meta had described Llama 4 Behemoth as its most advanced and intelligent model to date, intended to set the standard for future development. However, instead of Behemoth, Meta recently released two other models, Llama 4 Scout and Llama 4 Maverick, which the company claims perform comparably to Google Gemini 3 and OpenAI´s GPT-4o. This pivot underscores the challenge of achieving transformative breakthroughs as model sophistication increases.
Meta´s situation mirrors trends at other leading Artificial Intelligence companies. OpenAI, for example, has also delayed the release of its anticipated GPT-5 model, opting instead to introduce smaller, incremental updates like o3 and 04-mini. According to CEO Sam Altman, the postponement is an opportunity to significantly improve GPT-5 before launch. Underlying these industry-wide stalls are two primary issues: a shortage of new high-quality training data, and the escalating financial costs associated with developing and training increasingly large models. As a result, organizations across the sector are reassessing their approaches to releasing next-generation Artificial Intelligence, with Meta´s Behemoth delay serving as the latest high-profile example of these mounting challenges.
