On September 1st, Meituan officially released and open-sourced its self-developed large language model LongCat-Flash-Chat. This marks the first time Meituan has made a complete large language model product available to the industry and developers.

The model employs the industry-popular MoE (Mixture-of-Experts) architecture, with a total parameter scale of 560 billion (560B). However, only 18.6-31.3 billion parameters are activated during each inference, averaging approximately 27 billion parameters with an average activation rate of just 4.8%. Despite such a low activation rate, according to Meituan, "the model demonstrates significant advantages in multiple agent-related tests while achieving inference speeds exceeding 100 tokens/s."

Currently, both the model code and weights are fully open-sourced under the MIT License, one of the world's most popular and permissive open-source software licenses. This move reflects not only technical significance but also Meituan's deeper strategic considerations in artificial intelligence.

**From Parameter Stacking to Engineering Balance**

In today's large language model competition, sheer parameter scale is no longer a novelty. The industry has moved beyond the "whose model is bigger" phase, with the focus now on finding balance between computational constraints and deployment efficiency.

Meituan's LongCat-Flash chose the MoE route, achieving on-demand activation through expert routing while maintaining an extremely large total parameter count. The result is a model that retains massive potential representational capability while controlling actual inference costs to levels comparable to conventional medium-to-large models.

Engineering details are crucial in practical applications. Traditional MoE models often face issues with unstable routing and high communication costs. Meituan addressed these by introducing "zero-computation experts" in the routing mechanism, allowing some tokens to quickly bypass computation to ensure overall efficiency. Simultaneously, the ScMoE approach increases overlap between computation and communication, alleviating bottlenecks in multi-node deployments.

These modifications may not be flashy, but they address the real pain points of MoE deployment: ensuring models run both fast and stable under real hardware and scheduling conditions.

Unlike recent large language models that emphasize chain-of-thought reasoning and long-chain logic, LongCat-Flash is officially defined by Meituan as a "non-thinking foundation model." This positioning reflects Meituan's reconceptualization of application scenarios.

Rather than proving the model's multi-step reasoning capabilities in academic tests, Meituan focuses on agent tasks: tool calling, task orchestration, environment interaction, and multi-turn information processing for practical applications. This orientation aligns perfectly with Meituan's business logic.

Meituan's local lifestyle services constitute a complex system involving merchant information, delivery timelines, geographical locations, inventory status, and payment rules. A single user request often requires coordination and decision-making across multiple subsystems. If the model can complete calls and interactions as a tool in each segment, it can transform AI from a simple conversational assistant into a genuine process engine.

Therefore, compared to showcasing the model's "thinking depth," Meituan prioritizes the model's stable execution capability, which clearly offers greater business value.

In Meituan's official description, LongCat-Flash achieves inference speeds exceeding 100 tokens/s, an indicator emphasized as a "significant advantage." For industry professionals, speed is never an isolated metric but a key variable directly mapping to deployment costs and user experience.

The MoE architecture inherently challenges throughput: unstable expert routing can cause significant differences in processing times for different requests, while multi-card communication may drag down overall efficiency. Meituan's ability to claim high throughput despite high total parameter scale relies on optimizations in routing and communication.

More importantly, this model can adapt to mainstream inference frameworks, including SGLang and vLLM. This means enterprise users can relatively directly reproduce test results without major deployment stack modifications.

From a business perspective, enterprises are more concerned with per-token costs and stability under large-scale concurrency. A model may perform brilliantly in single-machine environments, but if it shows unstable latency under real traffic or significantly increased error rates with batch requests, it cannot truly become a productivity tool.

Meituan's approach is to first solve scalability and throughput issues at the architectural level, then let developers evaluate cost curves through open deployment frameworks. This "provide a workable baseline first, then let the market validate" approach may have more practical significance in real applications than hollow performance comparisons.

**Hidden Implications of Open Source and Licensing**

Unlike many domestic companies that only release partial weights or attach "non-commercial use restrictions," Meituan has adopted a more thorough open-source strategy: simultaneous release of weights and code under the MIT license. This choice has significant implications in both legal and ecosystem dimensions.

From a legal perspective, the MIT license has minimal restrictions, allowing free modification, distribution, and commercial use with virtually no additional barriers for enterprise applications. This is undoubtedly a friendly signal for companies hoping to integrate the model into their products.

From an ecosystem standpoint, the MIT license means Meituan is willing to treat the model as a public asset, enabling more developers to conduct secondary development and experimentation. This can not only accelerate model iteration but also help Meituan make a bigger impact in fierce open-source competition.

In practical terms, Meituan chose to release simultaneously on GitHub and Hugging Face, platforms representing developer communities and mainstream model distribution channels respectively, ensuring rapid access and adoption.

Behind this open-source move is actually Meituan launching a battle for developer ecosystem dominance: whoever can attract more developers to experiment with their model in the early stages is more likely to form application chains and tool ecosystems subsequently.

In the public model card, Meituan showcased LongCat-Flash's test results across multiple benchmark dimensions: outstanding performance in agent-focused evaluations like TerminalBench, τ²-Bench, AceBench, and VitaBench, while maintaining levels comparable to first-tier large language models in common dimensions like general Q&A, mathematics, and coding.

This indicates LongCat-Flash is not designed to comprehensively surpass existing mainstream models but chooses a differentiated competitive path: the model's strength lies in multi-tool collaboration, environment interaction, and process orchestration, highly consistent with Meituan's emphasized application scenarios.

If developers aim to build a Q&A assistant, it may not outperform other open-source models; but for building agents involving multi-tool calling, information integration, and chain execution, LongCat-Flash's positioning precisely hits market demand.

For Meituan, open sourcing is not merely an external showcase but a result of integration with internal business practices. Meituan's local lifestyle scenarios naturally serve as the ideal testing ground for agents: delivery chains, merchant information, real-time inventory, and user interactions form a complex ecosystem. If the model can stably undertake tool calling and process orchestration roles in this ecosystem, Meituan's operational efficiency, user experience, and overall platform competitiveness will improve.

This explains why Meituan doesn't focus on solving more complex logical reasoning problems but concentrates on more robust tool calling for task completion. Meituan wants a model that can stably complete millions of tool calls and reduce system error rates; clearly, Meituan believes this has more practical value than a model leading by a few percentage points in academic tests.

**Industry Value and Future Outlook**

LongCat-Flash's open sourcing extends beyond Meituan's internal affairs. For the entire industry, Meituan provides a directly usable high-performance MoE model. Particularly as agent applications gradually become an industry focus, an open-source foundation emphasizing tool calling and process orchestration capabilities can accelerate application exploration industry-wide.

This spillover effect may manifest in two ways: first, small and medium teams can quickly validate their agent products based on the model without building underlying models from scratch; second, more industry scenarios (such as logistics scheduling, customer service systems, knowledge management) may also experiment using this model. While these scenarios may not completely align with Meituan's local lifestyle services, they share similarities in process complexity and tool dependency.

Through MIT open-source licensing, Meituan essentially provides low-barrier infrastructure for these scenarios. For developers, LongCat-Flash's value lies in providing an open model trained and optimized for agent dimensions, directly applicable to task chains requiring tool collaboration. For enterprise users, the real test is how to embed the model into existing systems while handling resulting compliance, monitoring, and cost issues.

In this process, the most noteworthy aspect is not the model's accuracy but its stability and controllability in processes: whether it can promptly downgrade when calls fail, quickly adapt when external environments change, and maintain consistent performance under high concurrency. Only by solving these issues can Meituan's open-source model truly become part of business systems rather than merely a technical demonstration.

Given Meituan's emphasis on the model's practical value, open-sourcing LongCat-Flash is clearly not mere technical showmanship but a clear strategic statement: Meituan has chosen a path different from emphasizing "thinking," focusing on agent capabilities for tool calling and process execution while solving MoE deployment challenges through engineering optimization.

The MIT license's thorough open-source nature means Meituan's choice serves not only its internal business but also opens to the entire industry ecosystem. In the future, LongCat-Flash's true value will not lie in parameter scale but in whether it can operate stably in complex business chains, driving agent applications from experimentation to large-scale deployment.