Meta Delays Launch of Llama 4 “Behemoth” to Refine AI Performance, Will Ship Smaller Models First

Menlo Park, CA — Meta has announced a strategic delay in the release of its most ambitious artificial intelligence model to date, “Behemoth,” a next-generation large language model under the Llama 4 family. Originally slated for debut at Meta’s annual developer conference this past April, Behemoth’s rollout has been postponed after internal evaluations showed it underperformed on critical AI benchmarks.

This decision underscores Meta’s growing commitment to quality over speed in the highly competitive generative AI landscape.

“We’re committed to building AI models that meet the highest standards of performance and safety,” a Meta spokesperson said. “Behemoth simply wasn’t there yet.”

Why the Delay?

According to sources familiar with the project, Behemoth failed to meet internal targets on key measures of reasoning, factual accuracy, and reliability—particularly in high-stakes use cases such as enterprise productivity and code generation. Meta’s research teams reportedly found that while the model showed promise in raw scale, it struggled with hallucination rates, contextual drift, and response latency under stress testing.

Rather than push forward with a model that could fail to meet market expectations, Meta opted to pause and optimizebefore going public—an increasingly common move as model complexity skyrockets.

Smaller, Smarter: “Scout” and “Maverick” Still On Track

While Behemoth remains in the lab, Meta confirmed it is moving ahead with the release of two smaller Llama 4 variants—Scout and Maverick—which are scheduled to ship later this year. These models are designed to offer leaner, more efficient performance for a wide range of use cases, from mobile applications to lightweight enterprise deployments.

Despite their smaller size, Meta says both models will feature notable advancements over the Llama 3 generation, including:

• Improved instruction-following and multi-turn dialogue

• Reduced hallucination and better factual grounding

• Faster inference speeds and energy-efficient deployment profiles

Meta appears to be positioning Scout and Maverick as nimble, dependable workhorses for developers and businesses, while Behemoth is being retooled for a more powerful, possibly multi-modal future.

What This Means for Meta’s AI Roadmap

The decision to delay Behemoth may slow Meta’s sprint to dominate the frontier model space—territory currently contested by OpenAI, Google DeepMind, Anthropic, and xAI. However, it also suggests a disciplined and mature shiftin Meta’s AI strategy: prioritizing reliability, safety, and real-world utility over raw scale and launch theatrics.

With Llama 3 already serving as the backbone of Meta’s open-source AI initiative, the success of Llama 4’s Scout and Maverick variants will be a litmus test for the company’s next phase of AI innovation.

TL;DR:

• Meta has delayed the release of “Behemoth,” its largest Llama 4 model, due to performance issues uncovered in internal testing.

• The company will instead focus on launching two smaller, optimized Llama 4 variants—Scout and Maverick—later this year.

• Meta says the delay reflects a deliberate quality-first approach to AI development, as it continues to refine its models for performance, safety, and real-world applicability.