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Meta's next AI model, 'Avocado,' signals shift to closed development

Joanna Gao, Taipei; Elaine Chen, DIGITIMES Asia 0

Credit: AFP

Meta is reportedly developing a new AI model, code-named "Avocado," slated for release in the spring of 2026.

Unlike its popular Llama series, which embraced an open-source approach, Avocado is expected to adopt a closed-source model. During its training, the project reportedly drew on multiple models developed by competitors, including Alibaba's Qwen, Google's Gemma, and OpenAI's proprietary systems.

Bloomberg reported that while it remains unclear which specific Qwen model was used, the news has already drawn attention in China, where Qwen had long sought to catch up to Llama. The situation now appears reversed, with Qwen serving as a reference point for Llama's successor.

According to the South China Morning Post, historically, Chinese companies have leveraged Llama to build their own AI models, sometimes sparking controversy. For instance, in 2023-2024, AI startup 01.AI, founded by Kai-Fu Lee, faced criticism for not fully disclosing the use of Llama in its training process.

Interest in Chinese open-source models surged in early 2025 with the breakout success of DeepSeek. Both DeepSeek and Qwen have become leading examples of China's rapid AI innovation, accelerating model development and adoption. In November, Alibaba's Qwen3-based app, Qwen Chat, entered public testing and surpassed 10 million downloads within a week, outpacing the initial launches of ChatGPT and DeepSeek. Alibaba aims to position Qianwen as "the gateway to AI-powered daily life," signaling a shift from its traditional enterprise focus toward consumer applications.

While Chinese developers continue to embrace open-source models, Meta's Avocado project marks a clear pivot toward closed-source development. CEO Mark Zuckerberg has invested heavily in building Meta's AI engineering teams and is personally monitoring the project's progress, underscoring the company's high stakes in the next generation of AI.

Article edited by Jack Wu