Key Takeaways:
Meituan's decision to open-source its trillion-parameter LongCat-2.0 under an MIT license gives small merchants AI tools built on proprietary delivery-network data that general-purpose models cannot replicate.
Meituan's open-source release of LongCat-2.0, a trillion-parameter large language model under the permissive MIT license, arms small merchants with AI tools built on proprietary delivery-network data that general-purpose models cannot match.
Citi maintained its Buy rating on Meituan with a 113 Hong Kong dollar target price, saying the open-source strategy will strengthen the company's local services leadership. "This strategic move will further consolidate its leading position in the local life services market," the bank said in a July 13 report.
LongCat-2.0's agentic architecture is designed for real-world task execution, drawing on Meituan's accumulated dispatch algorithm experience and data from millions of daily delivery orders. The model's differentiation lies in its access to proprietary offline transaction and operations data — a dataset no general-purpose LLM provider can replicate. By contrast, Tencent's Hy3, with 295 billion total parameters and 21 billion activated, targets broader coding and reasoning benchmarks under an Apache 2.0 license, while MiniMax is preparing a 2.7 trillion-parameter M3 Pro model expected as early as the third quarter.
For Meituan, the open-source release serves dual purposes: attracting external developers to build on its platform while driving internal R&D efficiency. Citi sees this as a moat-builder that deepens relationships with both merchants and consumers. The bank's 113 HKD target implies roughly 20 percent upside from current levels.
Why Proprietary Data Matters More Than Model Size
The AI industry's focus on raw parameter counts and benchmark scores masks a more important distinction: access to unique, high-frequency transaction data. Meituan processes tens of millions of daily delivery orders, generating a real-time dataset of consumer behavior, merchant inventory, and logistics optimization that no general-purpose model can access. This data advantage is reinforced by the company's years of dispatch algorithm refinement — a domain-specific expertise that general LLMs lack.
The open-source release under MIT license removes adoption barriers for small and medium enterprises, allowing them to integrate Meituan's AI capabilities without licensing costs. This mirrors a broader industry trend: Meta released Muse Spark 1.1 with a 1 million-token context window and agentic capabilities, while SpaceXAI launched Grok 4.5 trained with Cursor on tens of thousands of Nvidia GB300 GPUs for coding and agentic tasks. OpenAI's GPT-5.6 family, including its flagship Sol model, targets general-purpose reasoning.
The Investment Case
Citi's Buy rating reflects confidence that Meituan's data moat will prove durable even as the AI model field becomes more crowded. The bank's analysis suggests that Meituan's AI-driven marketing and business insights for SME merchants create switching costs that general-purpose AI providers cannot easily overcome. This is particularly relevant as US enterprise surveys show only 18 percent average AI adoption, suggesting the market for practical, domain-specific AI applications remains underpenetrated.
Meituan's approach contrasts with the broader industry's cost pressures. Chamath Palihapitiya recently noted that his company's AI inference token costs are doubling every 45 days with only about 5 percent productivity improvement, highlighting the economic challenges facing general-purpose model providers. Meituan's proprietary data advantage may insulate it from these dynamics by enabling more targeted, higher-value AI applications.
This article is for informational purposes only and does not constitute investment advice.
