Meta Developed a Secret Model Called "Avocado" — What It Says About the Company's Strategy

In the race for AI leadership, the same names keep showing up in headlines: OpenAI, Anthropic, Google, Microsoft, NVIDIA, Amazon. But behind every publicly announced model sit dozens of unfinished projects, internal prototypes and experiments that never reach a wide audience. One of them turned out to be "Avocado," an undocumented internal Meta model whose story offers a glimpse into the real kitchen of AI development and gives surprisingly much to think about.

Meta's Public Strategy and LLaMA

Although in public perception Meta trails the race leaders, OpenAI and Google, the company has consistently built a strong AI position through its family of open LLaMA models. Since the first release in 2023, it has progressed to LLaMA 3 and later versions, positioning itself as an alternative to the closed GPT and Claude ecosystems. The strategy works: developers worldwide use LLaMA as the basis for their own products, while Meta strengthens its reputation as a player willing to share technology with the community. The public side of this story looks convincing and consistent.

The Hidden "Avocado" Project

Behind it, however, lies another, far less visible reality. "Avocado" is a model that became known through internal sources. The project was developed in parallel with the main LLaMA line, but for some reason was never brought to a public release. Perhaps it failed to clear internal quality thresholds. Perhaps it did not fit strategic priorities or lagged competitors on key metrics. The exact details are unknown, but the very existence of the project raises a natural question: by what criteria do large tech companies decide what to release to market and what to leave in the lab?

By What Criteria Do They Decide What to Release

Technical performance is only one factor. The competitive landscape plays no smaller role: if a model does not outperform what is already available on the market, the value of a release evaporates, especially for a company that positions itself as a leader in open AI. Safety questions are just as critical: even a minor weakness in a model's behavior can cause reputational damage out of proportion to the benefit of publishing. Finally, there is operational readiness: every public release requires full support infrastructure, including documentation, an API, monitoring systems and a response team. "Avocado" apparently failed one or more of these filters.

Why Unfinished Projects Are Valuable

But that does not make the project a failure. Real progress in AI is not a linear process of shipping one model after another. Behind every successful LLM stand several "avocados," prototypes that helped engineering teams find the limits of the possible, refine architectural decisions and learn what actually works in practice. Unfinished projects hold a significant share of real knowledge: they form the foundation on which the next generation of models is built. The race of AI agents, which tech giants are running today, has made internal work an especially sensitive subject. Any leak about an unfinished project immediately lands in the spotlight of media, analysts and competitors.

Practical Takeaway

The "Avocado" story is neither a scandal nor a sensation. It is a rare window into the reality of AI development, where release decisions weigh dozens of parameters, not just lines in benchmark tables. For product teams and startups watching this race, there is a practical takeaway: not every working development has to become a public product. Knowing when to stop a project is as strategically important a skill as knowing how to launch one. Companies that win consistently in AI know how to build new models, but also how to make considered decisions about which of them deserve to go public.

*Meta is designated an extremist organization and banned in Russia.

In brief:

• Meta had an undocumented model, "Avocado," developed in parallel with LLaMA but never released publicly.
• The release decision depends on technical performance, the competitive landscape, safety and operational readiness.
• Practical takeaway: knowing when to stop a project is as important a skill as knowing how to launch one.