Google Leads With LLMs, Meta and OpenAI Struggle

The early history of large languages models (LLMs) was dominated by OpenAI and, to a lesser extent, Meta. OpenAI’s early GPT models established the frontier of LLM performance, while Meta carved out a healthy niche with open-weight models that delivered strong performance. Open-weight models have publicly accessible code that anyone can use, modify, and deploy freely.

That left some tech giants, including Google, behind the curve. The breakthrough research paper on the transformer architecture that underpins large language models came from Google in 2017, yet the company is often remembered more for its botched launch of Bard in 2023 than for its innovative AI research.

But strong new LLMs from Google, and misfires from Meta and OpenAI, are shifting the vibe.

Llama 4 herd gets off on the wrong hoof

News of Llama 4‘s release unexpectedly came out of Meta on Saturday, 5 April.

If the decision to release a major model on a weekend strikes you as odd, you’re not alone. The timing caught everyone off guard and partially buried the announcement in the following week’s news cycle.

Meta’s new open-weight LLM does have its strengths. Llama 4 is multimodal, which means it can handle images, audio, and other modalities. It comes in three flavors, Llama 4 Behemoth, Maverick, and Scout, which have different sizes and strengths. Llama 4 Scout also boasts a huge context window of up to 10 million tokens. Tokens are the small units of text that LLMs process and gneerate, and the context window is the number of tokens a model can process at once. A larger context window helps the model “remember” and work with larger amounts of text in a single session. Most models have a context window of one million tokens or less.

But reception took a turn for a worse when critics noticed Meta’s sly approach to ranking on LMArena, a site that ranks LLMs based on user votes. The specific Llama 4 model that Meta used for the rankings wasn’t the same model available as part of its general release. In a statement, LMArena said Meta provided “a customized model to optimize for human preference.”

Meta also caught flak for its boast about Llama 4 Scout’s 10-million-token context window. While this figure appears to be technically accurate, a benchmark of long-context performance found that Llama 4 lagged behind competitive models.

Meta also didn’t release a Llama 4 “reasoning” or “thinking” model and held back smaller variants, though Meta says a reasoning model will become available.

“They deviated from the norm of a more systematic release, where they have all their ducks in a row,” says Ben Lorica, founder of the AI consulting company Gradient Flow. “This seems like they wanted to reassure people they have a new model, even if they don’t have all the components, like a reasoning model and smaller versions.”

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