Meta's Avocado Model Tests Whether Speed Beats Perfection

Meta has an internal memo circulating that says its new AI model, codenamed Avocado, is performing better than leading open-source base models. The catch is that Avocado hasn't been through post-training yet—the polishing phase that typically makes AI models useful to actual humans.

This matters because of what happened with Llama 4.

According to The Information, which obtained the memo dated January 20th, Avocado represents Meta's most capable pre-trained base model to date. Product manager Megan Fu wrote that the model was "competitive with leading post-trained models in knowledge, visual perception, and multilingual performance." That's the equivalent of saying your rough draft reads like someone else's final edit.

In AI development, pre-training is when a model learns patterns from vast datasets—books, code, images, the accumulated digital exhaust of human knowledge. Post-training refines that raw capability using human feedback, making the model safer and more reliable. Most AI products only become genuinely useful after that second phase. If Avocado is already competitive without it, Meta's foundational work is stronger than expected.

The company also claims efficiency gains that border on the implausible: 10 times better compute efficiency on text tasks compared to its previous model, Maverick, and 100 times better than Behemoth, a version of Llama 4 that Meta delayed indefinitely last year. Those numbers deserve skepticism, but they're at least directionally interesting. Efficiency matters in AI because these models consume staggering amounts of energy and money to train.

The Llama 4 Problem

Here's what makes this announcement more complicated than Meta would prefer: Llama 4 was a disaster.

The model launched with benchmark results that looked suspiciously good. Then it became clear why—Meta had manipulated the testing conditions to inflate performance. Yann LeCun, Meta's chief AI scientist, essentially admitted this publicly. Engineers started removing their names from the Llama 4 paper. People resigned. Mark Zuckerberg disbanded the entire generative AI organization and created a new lab called Meta Super Intelligence Labs.

That's not a routine organizational shuffle. That's a public execution followed by a rebuild from scorched earth.

The episode raised obvious questions about Meta's judgment and integrity in AI development. When a company gets caught fudging benchmarks, it loses the benefit of the doubt on future claims. So when Meta now says Avocado is performing well—even in an internal memo that wasn't meant for public consumption—there's a credibility gap to navigate.

At the World Economic Forum in Davos last month, Meta's CTO Andrew Bosworth spoke carefully about the company's AI progress, describing models as "very good" without elaboration. Zuckerberg himself was quoted saying: "I expect our first models will be good, but more importantly, they will show the rapid trajectory that we're on."

Notice what he's emphasizing: trajectory, not dominance. That's smart messaging from someone who learned an expensive lesson about overpromising.

The Speed Versus Quality Question

The video analysis I watched made an interesting argument about xAI, Elon Musk's AI company, proving that velocity matters more than perfection in this market. Grok went from version 1 in late 2023 to version 4.1 in November 2025, climbing from rank 33 to number one on AI leaderboards. The company kept shipping, kept iterating, kept learning publicly.

Meta, by contrast, has been largely absent. Llama 3 came out. Llama 4 bombed. And since then—silence, at least publicly.

The argument goes that Meta has the resources to recover: $70 billion in AI infrastructure spending, massive compute capacity, proprietary data from billions of social media users, and a completely rebuilt AI team featuring former executives from Scale AI and GitHub plus one of ChatGPT's co-creators.

What they apparently lacked was velocity. If the xAI comparison holds, Meta's path forward isn't about dropping one perfect model—it's about establishing a credible release cadence that demonstrates momentum.

That's harder than it sounds when your last major release was a public embarrassment.

What Remains Unknown

There's speculation that Avocado might not be open-source, which would represent a significant strategic shift. Meta built its AI credibility partly by making Llama models freely available, enabling researchers and developers to build on its work. Going closed-source would protect Meta's intellectual property from competitors like DeepSick, which has been accused of building on open-source models without equivalent contribution back.

But it would also mean competing directly with OpenAI, Anthropic, and Google in the closed-model game—a different kind of fight requiring different capabilities.

The other unknown is whether Avocado's pre-training performance will survive contact with real-world use cases. Internal benchmarks have a way of being overly optimistic. The model that looks brilliant in controlled testing sometimes falls apart when millions of users start doing unpredictable things with it.

Meta also faces an awareness problem. The video notes that most people use ChatGPT and maybe 10-15% even know Gemini or Claude exist. Meta's AI models, for all their technical capability, have yet to penetrate public consciousness the way OpenAI has. WhatsApp integration helps, but it's not the same as being the default answer when someone says "ask AI."

The Deeper Pattern

I've covered enough product launches to recognize the pattern here. A company suffers a credibility-damaging failure. Leadership makes dramatic changes—new teams, new processes, new messaging. Then they face the difficult task of proving those changes were real, not performative.

Meta has the resources to succeed. The question is whether they've genuinely fixed what broke with Llama 4, or whether they've just created new organizational structures that will produce different flavors of the same problems.

The leaked memo suggests they're being more careful about internal claims, which is progress. Bosworth's cautious comments at Davos suggest they've learned something about managing expectations. But we won't know if that learning goes deeper than messaging until Avocado ships and people outside Meta's walls can actually evaluate it.

The AI development cycle has compressed to the point where companies are releasing major updates every few months. Meta can recover from Llama 4, but only if Avocado—or whatever they end up calling it—delivers on these internal claims and they maintain velocity afterward. One successful model won't rebuild credibility. A pattern of successful models might.

Bob Reynolds is Senior Technology Correspondent for Buzzrag