Track Hyper | Meituan Open Sources LongCat-Flash: Where Is the Strategy Heading?

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Author: Zhou Yuan / Wallstreetcn

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

The model adopts the industry-popular MoE (Mixture-of-Experts) architecture, with a total parameter count as high as 560 billion (560B), but only 18.6 to 31.3 billion parameters are activated per inference, averaging about 27 billion, with an average activation rate of only 4.8%.

Despite such a low activation rate, according to Meituan officials, "the model shows obvious advantages in several agent-related tests, and inference speed can exceed 100 tokens/s."

Currently, the model's code and weights are all open source, and it uses the MIT License (MIT: one of the world's most popular and permissive open-source software licenses).

Besides the technological significance, this move mainly reflects Meituan's deeper considerations regarding its AI strategy.

From Parameter Stacking to Engineering Balance

In today's competition around large models, simple parameter scale is no longer a novel topic.

The industry has been through the "whose model is bigger" stage; now, the key is to find a balance between computing constraints and deployment efficiency.

Meituan's LongCat-Flash adopts the MoE route, activating experts on demand on top of a huge total parameter base.

The result: the model retains massive potential representational capacity, but the actual inference overhead is controlled at the level of common medium-to-large models.

In practical application, engineering details are critical.

Traditional MoE models are prone to routing instability and high communication costs; Meituan introduced "zero-computation experts" in the routing mechanism, allowing some tokens to quickly skip computation and thus ensure overall efficiency; at the same time, overlapping computation and communication with ScMoE alleviates bottlenecks in multi-node deployment.

These modifications are not flashy, but tackle the true pain points of MoE implementation: how to ensure models can run fast and stably reproduce under real hardware and scheduling conditions.

Different from some recent large models emphasizing chain-of-thought reasoning or long-chain logic, Meituan officially defines LongCat-Flash as a "non-thinking foundation model."

This positioning implies Meituan's new understanding of application scenarios.

Meituan does not attempt to prove the model's multi-step reasoning capabilities in academic tests, instead focusing on agent tasks: tool usage, workflow orchestration, environment interaction, and multi-turn information processing—actual application layers.

This approach highly aligns with Meituan's business logic.

Meituan's local life services form a complex system, involving merchant information, delivery times, geographic location, inventory status, and payment rules.

A user's request usually goes through coordination and decision-making across multiple subsystems.

If the model can be called and interacted with as a tool at each step, AI can transform from a mere conversational assistant to a true process engine.

Thus, compared with showcasing a model's "thinking depth", Meituan values stable execution more, which is clearly more valuable for the business.

In Meituan's official description, LongCat-Flash's inference speed exceeds 100 tokens/s, which is highlighted as a "significant advantage".

For industry insiders, speed is never an isolated metric, but a key variable that directly maps to deployment costs and user experience.

MoE architectures naturally challenge throughput: unstable expert routing can cause large differences in request latency, and multi-GPU communication might hamper overall efficiency.

Meituan can still claim high throughput under huge total parameters because of routing and communication optimization. More importantly, the model supports mainstream inference frameworks, including SGLang and vLLM.

This means enterprise users do not need to overhaul their deployment stacks to reproduce the test results fairly directly.

From a commercial perspective, enterprises actually care more about per-token cost and stability under large-scale concurrency.

A model may perform excellently in a single-machine environment, but if latency fluctuates under real traffic, or batch requests show higher error rates, it is difficult to become a true productivity tool.

Meituan's choice is to solve scalability and throughput at the architectural level, then open up the deployment framework for developers to evaluate cost curves themselves.

This "give a runnable baseline, then let the market verify" approach is likely more meaningful for practical applications than empty performance comparisons.

The Implicit Signal of Open Source and Licensing

Unlike many domestic vendors who only open some weights or impose "non-commercial" restrictions, Meituan has taken a more thorough open-source strategy: releasing both weights and code, with MIT licensing.

This choice has significant implications in both legal and ecosystem dimensions.

Legally, the MIT license is the least restrictive, allowing free modification, distribution, and commercial use, with virtually no additional obstacles for enterprise applications; for companies wanting to integrate the model into their products, this is a very friendly signal.

From the ecosystem perspective, MIT licensing means Meituan is willing to make the model a public asset, enabling more developers to build on, redevelop and experiment. This can accelerate model iteration and give Meituan a louder voice in fierce open-source competition.

On a practical level, Meituan chose to release on both GitHub and Hugging Face, which are the mainstream channels for developer communities and model distribution respectively, ensuring quick reach and use.

So, behind the open-source move is actually Meituan's campaign for developer ecosystem: whoever can attract more early developers to experiment with their model is more likely to form application chains and tool ecosystems later on.

In the public model card, Meituan shows LongCat-Flash's test results on several benchmarks: outstanding performance on agent-centric benchmarks like TerminalBench, τ²-Bench, AceBench, and VitaBench, while scores on general QA, mathematics, and code are basically on par with other leading large models.

This indicates that LongCat-Flash is not aiming to completely surpass the current mainstream models, but rather chooses a differentiated competition path: its strength lies in multi-tool collaboration, environment interaction, and workflow orchestration, aligning closely with Meituan’s emphasized application scenarios.

If a developer wants to build a QA assistant, it may not be superior to other open-source models; but for building agents involving multi-tool calls, information integration, and workflow execution, LongCat-Flash hits the market demand precisely.

For Meituan, open source is not just for external display, but is also combined with internal business practice.

Meituan’s local life scenarios are a natural testbed for agents: delivery workflow, merchant info, real-time inventory, and user interaction form a complex ecosystem.

If the model can steadily handle tool invocation and workflow orchestration in this ecosystem, Meituan's operational efficiency, user experience, and overall platform competitiveness will all be enhanced.

This is why Meituan is not focused on solving complex logic reasoning, but on reliably invoking tools to complete tasks.

Meituan wants a model that can stably perform millions of tool calls and reduce system error rates; obviously, Meituan believes this is more valuable in reality than a model that just leads academic tests by a few points.

The open-source of LongCat-Flash is not just an internal affair for Meituan.

In terms of industry value, Meituan provides a directly usable, high-performance MoE model. Especially as agent applications become a focus of the industry, this open-source base emphasizing tool invocation and workflow orchestration can accelerate application exploration.

This spillover may appear in two ways: on one hand, small and medium teams can quickly validate their agent products without building a base model from scratch; on the other, more industry scenarios (such as logistics scheduling, customer service, knowledge management) may also use the model for experimentation.

These scenarios may not be identical to Meituan's local life, but share the complexity and tool-dependency in workflow.

Through the MIT open-source license, Meituan is providing these scenarios with a low-threshold infrastructure.

For developers, LongCat-Flash provides an open model trained and optimized for agent scenarios, that can be directly applied to task flows requiring tool collaboration; for enterprise users, the real test is embedding the model into their existing systems and managing the resulting compliance, monitoring, and cost issues.

In this process, the most important thing is not the model's raw accuracy, but its stability and controllability in workflows: whether it can degrade gracefully on call failure, adapt quickly to external environment changes, and maintain performance under high concurrency.

Only after solving these problems, can Meituan's open-source model truly become part of a business system instead of just a technology showcase.

Given how much Meituan values real-world application, it's clear that open-sourcing LongCat-Flash is not just technical showmanship, but is a clear strategic statement: Meituan chooses a path distinct from emphasizing "thinking" abilities, focusing on agent capabilities in tool invocation and workflow execution, and solving MoE landing difficulties through engineering optimization.

With the MIT license's complete openness, this serves not just Meituan's own business but the entire industry ecosystem.

In the future, the true value of LongCat-Flash will not lie in its parameter scale, but in whether it can stably operate in complex business processes, driving agent applications from experimentation into large-scale deployment.

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