LongCat-Flash-Chat is released: the 560B parameter large model opens a new era of AI inference with 100 TPS
The LongCat-Flash-Chat launched by the Meituan team has 560B total parameters and dynamic activation of 18.6B-31.3B as the core highlights, combined with 20T training data and 100+ token/s inference speed, and has achieved leading results in TerminalBench and τ²-Bench. It is not only a performance breakthrough for large models, but also provides new options for AI tools, automated agents, and intelligent workflows.
1. Core Highlights
1. 560B parameters + dynamic activation architecture
LongCat-Flash-Chat adopts Mixture-of-Experts (expert hybrid architecture), although the total parameters are as high as 560B, but the actual inference only activates about 27B parameters, which not only ensures intelligent performance, but also controls computing costs.
2. High-speed inference: The 100+ token/s
artificial intelligence model achieves inference performance of 100 tokens per second, meeting the low latency requirements of large-scale applications, and is suitable for agent tasks, terminal tool calls, and real-time interaction scenarios.
(1) Performance evaluation: TerminalBench vs. τ²-Bench
The model scored 39.5 on TerminalBench and 67.7 on τ²-Bench, demonstrating its strong processing capabilities for tool usage and complex tasks, proving its AI tool attributes.
2. Value to AI Toolstation
1. Intelligent Agent Implementation
AIToolstation can be combined with ChatGPT to generate task plans, Claude to verify security logic, and then LongCat-Flash-Chat to execute complex commands to achieve an automated process from prompt to execution.
2. Balance between cost and performance
Dynamic activation reduces redundant calculations, allowing AI to improve inference efficiency while maintaining the intelligence of large models. This means that enterprises can achieve higher throughput with the same computing power.
(1) Implementation plan suggestions
:a. Use SGLang or vLLM as the inference engine
b. ChatGPT to generate prompts and dialogue templates
c. Claude conducts security compliance checks
d. LongCat is responsible for efficient execution and task scheduling
3. Application
Scenario 1: Terminal Operation and Automated O&M
AI tools can quickly handle command-line tasks, script execution, and log analysis, improving DevOps and R&D efficiency.
2. Data processing and multitasking interaction
Combined with Claude and ChatGPT, LongCat can play a role in scenarios such as data scraping, knowledge organization, and batch summary generation, promoting the construction of automated workflows.
4. Limitations and future trends
1. Engineering and hardware threshold
Although dynamic activation reduces the demand for video memory, multi-machine communication and distributed inference still require high engineering experience and are not suitable for lightweight environments.
2. Future direction
The large model will continue to strengthen Agent and execution capabilities, ChatGPT and Claude are in planning and security control, and LongCat is executing at high speed, and the three work together to form a complete link of intelligence and automation.
5. References
LongCat-Flash-Chat model card
https://huggingface.co/meituan-longcat/LongCat-Flash-Chat
LongCat Official site: https://longcat.ai
LongCat-Flash Technical Report: https://arxiv.org/abs/2509.01322
Frequently Asked Questions (Q&A).
Q: What are the advantages of LongCat-Flash-Chat over traditional large models?
A: Using a dynamic activation mechanism, the inference only requires about 27B of computation, which not only has the knowledge reserve of the 560B model, but also maintains high speed and low latency.
Q: How do I integrate LongCat-Flash-Chat with AI Toolstation?
A: Inference services can be deployed using SGLang or vLLM, and ChatGPT generates prompts upstream, Claude reviews security policies, and finally hands them over to LongCat for execution.
Q: What does the TerminalBench vs. τ²-Bench score say?
A: The two are closer to the real scene, and the high score indicates that the model performs well in tool calling, terminal operation and complex task execution, and is suitable for intelligent agent applications.
Q: Is it possible to completely replace ChatGPT or Claude?
A: LongCat is more suitable for execution and reasoning acceleration, while ChatGPT and Claude are stronger than planning and reviewing.
