Meituan LongCat-Flash-Chat: A Technical Breakthrough in Efficient Large Language Models

Introduction: Redefining Efficiency in AI Language Models

In the rapidly evolving field of artificial intelligence, where larger models often equate to better performance, a significant challenge has emerged: how to maintain exceptional capabilities while managing overwhelming computational demands. Meituan’s LongCat-Flash-Chat represents a groundbreaking solution to this problem—a sophisticated language model that delivers top-tier performance through innovative engineering rather than simply scaling parameter count.

This 560-billion-parameter model introduces a revolutionary approach to computational allocation, dynamically activating only between 18.6 and 31.3 billion parameters based on contextual needs. This strategic design allows LongCat-Flash-Chat to achieve remarkable efficiency without compromising on the advanced capabilities expected from state-of-the-art language models, particularly in agentic tasks that require complex reasoning and tool interaction.

Architectural Innovation: The Core of LongCat’s Efficiency

Dynamic Computation Mechanism

Traditional large language models typically engage their entire parameter set for every token processed, creating significant computational overhead. LongCat-Flash-Chat challenges this convention with its innovative “zero-computation experts” mechanism within its Mixture-of-Experts (MoE) architecture.

The model intelligently assesses the importance of each token and allocates computational resources accordingly. Rather than applying equal processing power to all elements of text, it identifies which tokens require deeper analysis and which can be handled more efficiently. This approach activates between 18.6 and 31.3 billion parameters from the total 560 billion, maintaining an average of approximately 27 billion activated parameters per token.

This dynamic allocation is managed through an expert bias system adjusted by a PID-controller, ensuring consistent computational load while responding flexibly to varying contextual demands.

Shortcut-Connected MoE Design

As MoE models scale, communication between expert modules often becomes a performance bottleneck. LongCat-Flash-Chat addresses this fundamental challenge through its Shortcut-connected MoE (ScMoE) design, which expands the computation-communication overlap window.

This architectural innovation, combined with customized infrastructure optimizations, enables training at massive scale across tens of thousands of accelerators while supporting high-throughput, low-latency inference. The system achieves impressive performance of over 100 tokens per second (TPS), making it both powerful and practical for real-world applications.

Comprehensive Training and Scaling Framework

Hyperparameter Transfer Strategy

Developing effective scaling strategies remains one of the most significant challenges in large model development. The LongCat team successfully applied a hyperparameter transfer strategy to their massive model, predicting optimal configurations by leveraging results from smaller proxy models with theoretical guarantees.

This approach allowed them to bypass much of the trial-and-error typically associated with large model training, significantly reducing development time and computational costs while ensuring stable performance.

Model Growth Initialization

Rather than relying on conventional initialization methods, the team implemented a model-growth mechanism based on a refined half-scale checkpoint. This innovative approach resulted in improved performance compared to traditional initialization techniques, providing a stronger foundation for the full-scale model.

Multi-Pronged Stability Suite

To ensure training stability—a critical concern with models of this scale—the developers incorporated several advanced techniques:

  • Principled router-gradient balancing to maintain equilibrium across expert modules
  • A hidden z-loss component to suppress massive activations that can destabilize training
  • Fine-tuned optimizer configurations specifically tailored to the model’s architecture

Deterministic Computation for Reliability

Enhancing large-scale cluster training reliability, the team introduced deterministic computation protocols. This guarantees exact reproducibility of experiments and enables detection of Silent Data Corruption (SDC) during the training process. These interventions ensured that LongCat-Flash’s training remained stable throughout, with no irrecoverable loss spikes—a remarkable achievement for a model of this complexity.

Multi-Stage Training Pipeline for Advanced Agentic Capability

Base Model Construction

The development of LongCat-Flash-Chat followed a meticulously designed pipeline that endowed the model with advanced agentic behaviors. Initial efforts focused on constructing a suitable base model for agentic post-training through a two-stage pretraining data fusion strategy specifically designed to concentrate reasoning-intensive domain data.

Mid-Training Enhancements

During the mid-training phase, the team enhanced the model’s reasoning and coding capabilities while extending the context length to 128k tokens to meet agentic post-training requirements. This substantial context window enables the model to maintain coherence over extended interactions, a critical capability for complex agentic tasks.

Multi-Stage Post-Training

Building on this advanced base model, the team implemented a multi-stage post-training process. Recognizing the scarcity of high-quality, high-difficulty training problems for agentic tasks, they designed a novel multi-agent synthesis framework that defines task difficulty across three dimensions:

  1. Information processing complexity
  2. Tool-set complexity
  3. User interaction requirements

This framework uses specialized controllers to generate complex tasks requiring iterative reasoning and environmental interaction, providing the model with the challenging training scenarios needed to develop robust agentic capabilities.

Performance Evaluation: Benchmarking Against Leading Models

LongCat-Flash-Chat has undergone rigorous testing across multiple domains and capabilities. The evaluation results demonstrate its competitive positioning among leading models worldwide.

General Knowledge Capabilities

In broad knowledge assessments, LongCat-Flash-Chat delivers strong performance:

  • MMLU (Massive Multitask Language Understanding): 89.71% accuracy
  • MMLU-Pro: 82.68% accuracy
  • ArenaHard-V2: 86.50% accuracy
  • CEval (Chinese evaluation): 90.44% accuracy
  • CMMLU (Chinese massive multi-task language understanding): 84.34% accuracy

These results place LongCat-Flash-Chat among the top performers in general knowledge tasks, demonstrating its robust understanding across diverse domains.

Instruction Following Capabilities

The model excels at understanding and executing instructions:

  • IFEval (instruction following evaluation): 89.65% accuracy
  • COLLIE: 57.10% accuracy
  • Meeseeks-zh (Chinese instruction following): 43.03% accuracy

These scores indicate strong comprehension of nuanced instructions and the ability to execute them accurately.

Mathematical Reasoning Proficiency

LongCat-Flash-Chat demonstrates advanced mathematical capabilities:

  • MATH500: 96.40% accuracy
  • AIME24 (American Invitational Mathematics Examination): 70.42 average score
  • AIME25: 61.25 average score
  • BeyondAIME: 43.00 average score

These results place the model among the top performers in mathematical reasoning, showcasing its ability to handle complex quantitative problems.

General Reasoning Abilities

The model’s logical reasoning capabilities are equally impressive:

  • GPQA-diamond: 73.23% accuracy
  • DROP (discrete reasoning over paragraphs): 79.06 F1 score
  • ZebraLogic: 89.30% accuracy
  • GraphWalks-128k: 51.05% precision

These scores demonstrate strong performance in tasks requiring complex logical reasoning and information synthesis.

Coding Capabilities

LongCat-Flash-Chat delivers competitive results in programming tasks:

  • LiveCodeBench: 48.02% pass@1
  • Humaneval+: 88.41% pass@1
  • MBPP+ (mostly basic programming problems): 79.63% pass@1
  • SWE-Bench-Verified: 60.40% accuracy
  • TerminalBench: 39.51% accuracy

These results position the model as a capable programming assistant, able to generate and understand code across multiple contexts.

Agentic Tool Use Excellence

Where LongCat-Flash-Chat truly distinguishes itself is in agentic tool use—the ability to interact with external tools and systems:

  • τ²-Bench (telecom): 73.68 average score
  • τ²-Bench (airline): 58.00 average score
  • τ²-Bench (retail): 71.27 average score
  • AceBench: 76.10% accuracy
  • VitaBench: 24.30 average score

These exceptional results, particularly in telecom and retail domains, demonstrate the model’s advanced capabilities in understanding and operating within complex tool ecosystems.

Safety Performance

The model also demonstrates strong safety characteristics:

  • Harmful content avoidance: 83.98% effectiveness
  • Criminal content prevention: 91.24% effectiveness
  • Misinformation identification: 81.72% effectiveness
  • Privacy protection: 93.98% effectiveness

These scores indicate robust safety safeguards across multiple dimensions of potential concern.

Practical Implementation: How to Use LongCat-Flash-Chat

Chat Template Structure

The model uses a structured chat template format detailed in the tokenizer_config.json file. Here are the practical implementation details:

First-Turn Interaction

For initial queries, use the following format:

[Round 0] USER:{your_query} ASSISTANT:

When including a system prompt, use this structure:

SYSTEM:{system_prompt} [Round 0] USER:{your_query} ASSISTANT:

Multi-Turn Conversations

For extended dialogues, construct the prefix by concatenating previous context with the latest query:

SYSTEM:{system_prompt} [Round 0] USER:{query_1} ASSISTANT:{response_1}</longcat_s> [Round 1] USER:{query_2} ASSISTANT:{response_2}</longcat_s> ... [Round N] USER:{latest_query} ASSISTANT:

Here, N represents the current turn index (starting from zero), and </longcat_s> serves as a separator between conversation turns.

Tool Calling Implementation

LongCat-Flash-Chat supports sophisticated tool calling capabilities using this format:

{tool_description}

## Messages

SYSTEM:{system_prompt} [Round 0] USER:{query} ASSISTANT:

The tool_description section should follow this structure:

## Tools

You have access to the following tools:

### Tool namespace: function

#### Tool name: {function_name}

Description: {function_description}

InputSchema:
{json_formatted_parameters}

**Note**: For each function call, return a JSON object with function name and arguments within <longcat_tool_call></longcat_tool_call> XML tags as follows:
<longcat_tool_call>
{"name": "function_name", "arguments": {args_dict}}
</longcat_tool_call>

For multiple simultaneous function calls, each should be wrapped in separate tags:

<longcat_tool_call>
{"name": "first_function", "arguments": {first_args}}
</longcat_tool_call><longcat_tool_call>
{"name": "second_function", "arguments": {second_args}}
</longcat_tool_call>

Deployment Options and Infrastructure

LongCat-Flash-Chat has been adapted for deployment in both SGLang and vLLM environments, providing flexibility for different implementation scenarios. For comprehensive deployment guidance, refer to the detailed Deployment Guide available in the LongCat-Flash-Chat repository.

These implementations support efficient inference while maintaining the model’s advanced capabilities, making it accessible for both research and production applications.

Practical Access: Online Chat Interface

For those interested in experiencing LongCat-Flash-Chat without local deployment, Meituan provides an official chat website where users can interact directly with the model:

Official Chat Website: https://longcat.ai

This interface allows users to test the model’s capabilities across various domains and task types, providing practical insight into its performance and potential applications.

Licensing Information

The model weights for LongCat-Flash-Chat are released under the MIT License, providing considerable freedom for both research and commercial applications. However, this license does not grant rights to use Meituan trademarks or patents.

Contributions to the model repository are similarly licensed under MIT unless otherwise stated. The full license text is available in the LICENSE file.

Responsible Usage Considerations

While LongCat-Flash-Chat demonstrates impressive capabilities across numerous benchmarks, developers should consider several important factors before deployment:

  1. Task-Specific Performance: The model hasn’t been specifically designed or comprehensively evaluated for every possible downstream application. Performance may vary across different use cases.

  2. Language and Domain Variations: Like all large language models, performance may differ across languages and specialized domains. Thorough testing is recommended for specific applications.

  3. High-Risk Scenarios: In sensitive or high-risk applications (healthcare, financial services, etc.), developers should carefully assess accuracy, safety, and fairness before deployment.

  4. Regulatory Compliance: Developers and downstream users are responsible for understanding and complying with all applicable laws and regulations relevant to their use case, including data protection, privacy, and content safety requirements.

  5. Limitation Understanding: Users should maintain awareness of the known limitations of large language models and implement appropriate safeguards and monitoring.

Frequently Asked Questions

What distinguishes LongCat-Flash-Chat from other large language models?

LongCat-Flash-Chat employs a unique Mixture-of-Experts architecture with dynamic computation allocation, activating only 18.6-31.3B of its 560B total parameters based on contextual needs. This approach provides exceptional efficiency while maintaining competitive performance, particularly in agentic tasks and tool use scenarios.

How does the model handle Chinese language tasks?

The model demonstrates strong performance in Chinese language evaluations, achieving 90.44% on CEval and 84.34% on CMMLU—competitive results that indicate robust Chinese language understanding and generation capabilities.

Can I deploy LongCat-Flash-Chat locally?

Yes, the model supports local deployment through adapted implementations for both SGLang and vLLM frameworks. Detailed deployment guidance is available in the project repository.

What types of applications is this model best suited for?

LongCat-Flash-Chat excels in agentic tasks that require complex reasoning, tool interaction, and multi-step problem solving. It’s particularly well-suited for applications in customer service, programming assistance, data analysis, and any scenario requiring interaction with external systems or APIs.

Is fine-tuning supported for specific applications?

While the official documentation doesn’t explicitly detail fine-tuning capabilities, the MIT license typically allows for model adaptation. Users should consult the license terms and monitor repository updates for specific guidance on fine-tuning.

How does the safety performance compare to other models?

LongCat-Flash-Chat demonstrates strong safety performance, with 83.98% effectiveness at avoiding harmful content, 91.24% at preventing criminal content, and 81.72% at identifying misinformation. These scores place it among the safer large language models available.

Conclusion: The Future of Efficient AI

Meituan’s LongCat-Flash-Chat represents a significant advancement in efficient AI architecture, demonstrating that exceptional performance doesn’t necessarily require overwhelming computational resources. Through innovative MoE design, dynamic computation allocation, and sophisticated training methodologies, this model delivers top-tier capabilities while maintaining practical efficiency.

The model’s particular strength in agentic tasks and tool interaction positions it as a valuable resource for developing advanced AI applications that can understand and operate within complex environments. As the field continues to evolve, approaches like those demonstrated in LongCat-Flash-Chat will likely play an increasingly important role in making advanced AI capabilities more accessible and sustainable.

For those interested in the technical details behind these innovations, the comprehensive LongCat-Flash Technical Report provides deeper insight into the architectural choices and training methodologies that make this model unique.

As with any advanced AI system, responsible development and deployment practices remain essential. Users should thoroughly evaluate the model’s performance for their specific use cases and implement appropriate safeguards to ensure ethical and effective application of this technology.