Leaked internal memos from Meta's Superintelligence Labs have confirmed what many of us suspected: the company's next-generation AI model, codenamed "Avocado," is a significant leap beyond anything in the Llama 4 family. According to a January 20 memo first reported by The Information, Avocado is now "Meta's most capable pre-trained base model to date," achieving 10x compute efficiency gains on text tasks compared to Llama 4 Maverick and 100x efficiency improvements over Behemoth.
For those of us building with large language models in production, these numbers deserve serious attention.
What the Memos Actually Say
The memo, written by Meta Superintelligence Labs product manager Megan Fu, states that Avocado outperforms the best freely available base models in knowledge, visual perception, and multilingual capabilities. What makes this especially noteworthy is that these results come from the pre-trained base model alone, before any post-training refinement such as instruction tuning or RLHF.
The efficiency story is the real headline here. A 10x compute efficiency improvement over Maverick means that organizations could theoretically achieve Llama 4 Maverick-level performance at a fraction of the infrastructure cost. For teams in the Middle East and beyond that are deploying LLMs at scale, this has direct implications for total cost of ownership.
Meta attributes these gains to three factors: improved training data, better technical foundations, and refined training methodologies. While the specifics remain under wraps, this aligns with a broader industry trend where training data quality and architectural innovations are delivering outsized returns compared to simply scaling up compute.
The Open-Source Question
Perhaps the most consequential aspect of the Avocado story is the growing speculation that Meta will move away from the open-source approach that defined the Llama series. Multiple reports indicate that Avocado could be a proprietary, closed model.
This would represent a fundamental shift in Meta's AI strategy. The Llama models became the backbone of countless open-source projects, fine-tuned deployments, and research initiatives around the world. In the UAE and across the Gulf region, many organizations built their Arabic language AI capabilities on top of Llama models precisely because they were open and customizable.
If Meta goes closed with Avocado, the open-source AI ecosystem would lose one of its most important contributors. It would also put Meta in direct competition with OpenAI, Anthropic, and Google rather than serving as the counterweight that kept the open-source path viable.
Inside Meta Superintelligence Labs
Avocado is being developed inside Meta Superintelligence Labs (MSL), a new elite unit that represents a major organizational restructuring of Meta's AI research division. The group is led by Alexandr Wang, the co-founder of Scale AI who was brought in as Meta's chief AI officer.
This restructuring came after a rocky 2025 for Meta's AI efforts. The Llama 4 launch was plagued by delayed releases, questions about benchmark manipulation, and developer disappointment with real-world performance. The internal turbulence ultimately led to the departure of Yann LeCun from his leadership role.
Meta is also developing a second model codenamed "Mango," focused on image and video generation, under the same lab. The company plans to release new models steadily throughout 2026, backed by projected capital expenditures of $115 to $135 billion for the year, a 73% increase over 2025.
What This Means for AI Practitioners
For AI teams evaluating their model strategy, here are the practical takeaways:
- Do not bet everything on one provider. If Meta's Avocado does go closed-source, teams that built exclusively on Llama will need migration plans. Diversifying across model families (Qwen, Mistral, and other open alternatives) is a sensible hedge.
- Efficiency gains matter more than raw capability. A model that delivers comparable performance at 10x lower compute cost changes the economics of deployment entirely. Watch for benchmark comparisons once Avocado enters post-training.
- The open-source vs. proprietary debate is not settled. While Meta may close Avocado, competition from Alibaba's Qwen, DeepSeek, and Mistral continues to push open models forward. The ecosystem is resilient.
- Middle East AI strategy should account for this shift. Organizations in the UAE and Saudi Arabia that relied on Llama for on-premise, sovereign AI deployments should begin evaluating alternatives now rather than waiting for a formal announcement.
Looking Ahead
The Avocado leak signals that the AI industry's center of gravity is shifting again. After a year where open-source models seemed to be closing the gap with proprietary ones, Meta's potential retreat from openness could redraw the competitive landscape.
For the region, this is a reminder that building AI capabilities requires more than adopting the latest model. It requires building the institutional knowledge and infrastructure flexibility to adapt when the landscape changes. The teams that will thrive are those treating model selection as an ongoing strategic decision, not a one-time choice.
I will be watching closely for Avocado's formal release in the first half of 2026. If the efficiency claims hold up in independent benchmarks, this model could redefine what is achievable at reasonable compute budgets. And if Meta does go closed, the ripple effects across the open-source AI community will be felt for years.