LongCat Flash: Complete Guide

The LongCat Flash Chat model has recently been released by a Chinese company, surprising the AI community with its innovative approach to computational efficiency.

What is LongCat Flash?

LongCat Flash Chat is a Mixture of Experts (MOE) model with a total of 560 billion parameters. It introduces a unique mechanism called zero computation experts, which dynamically allocates computation power to important tokens based on their significance.

Instead of activating all parameters at once, the model activates 18.6 to 31.3 billion parameters depending on contextual requirements. On average, 27 billion parameters are activated per token, making the model highly efficient while still delivering strong performance. LongCat Flash Model

This strategy is referred to as efficient computation utilization and is one of the core innovations of LongCat Flash.

Why It Stands Out?

When we look at the current world of large language models, 27 billion active parameters might not seem very large. However, the real breakthrough here is how efficiently this activation happens. This allows the model to perform complex tasks without the need to fully activate its entire 560-billion parameter base.

Another point that makes LongCat Flash unique is its training speed:

The team trained this massive model on 20 trillion tokens in just 30 days.
This is incredibly fast compared to other companies like OpenAI, which take months to pre-train base models.

The Company Behind It: Muan

The model is developed by a company called Muan. Interestingly, Muan is not a typical tech giant like Microsoft, Google, or Baidu.

Muan operates in diverse areas, including delivery services and grocery technology.
The name "Muan" roughly translates to Beautiful Group Reviews.
This makes the release even more surprising, similar to a company like Uber suddenly releasing a model that competes with top AI companies.

Despite not being a traditional foundation model company, Muan has delivered a powerful open-source AI tool with an MIT license, allowing anyone to use and build on top of it.

Key Features of LongCat Flash

Here are the most important features that make LongCat Flash stand out:

1. Mixture of Experts (MOE) Design

Only 27 billion parameters are activated on average for each token.
Balances computational efficiency and performance.
Reduces the overall workload while maintaining accuracy.

2. Zero Computation Experts

Dynamically assigns computational resources to more significant tokens.
Prioritizes critical tasks, avoiding unnecessary parameter activation.

3. Fast Training

Completed pre-training on 20 trillion tokens in 30 days.
Faster iteration cycles for future model improvements.

4. Large Context Length

Supports a 128,000-token (128K) context window.
Ideal for handling long conversations and documents.

5. Agentic and Coding Focus

Optimized for agent tasks and coding-related work.
During mid-training, reasoning and coding capabilities are enhanced.

6. Multi-Stage Training Pipeline

Pre-training → Mid-training → Post-training.
Mid-training phase: Improves reasoning and coding while preparing for agentic tasks.
Uses specialized controllers to create complex tasks that require iterative reasoning and interaction.

7. Open Source MIT License

Fully open-sourced with an MIT license.
Encourages the community to experiment, improve, and deploy freely.

Table Overview of Specifications

Feature	Details
Total Parameters	560 Billion
Active Parameters per Token	27 Billion (average)
Parameter Range per Token	18.6 – 31.3 Billion
Context Window Length	128,000 tokens (128K)
Training Data Size	20 Trillion Tokens
Training Duration	30 Days
License	MIT License
Best Use Cases	Agentic tasks, Coding, Reasoning
Company	Muan

Understanding Scaling Laws

Scaling laws play a major role in the development of LongCat Flash. A scaling law explains how different factors like:

Data volume
Compute resources
Model size

…affect the performance of a model.

By strategically increasing these elements, the model's performance can improve. LongCat Flash was designed with scaling laws in mind, allowing it to grow in size and capability while staying efficient.

Performance Benchmarks

LongCat Flash excels in certain benchmarks, particularly in coding and agentic tasks.

Benchmark	LongCat Flash Score	Competitor Model
Terminal Bench	39.5%	DeepSeek V3.1 – lower, Claude 4 Sonnet – 40.7%
SWEBench Verified	60.4%	Lower than Kim K2
Agentic Tasks	Top performer among Gemini 2.5 Flash, Claude 4 Sonnet, GPT-4.1, Kimik K2, Quen 3e, DeepSeek V3.1

Key Observations

Strong performance in Terminal Bench, close to Claude 4 Sonnet.
Slightly lower SWEBench Verified score compared to expectations.
Outperforms all competitors in agentic task benchmarks like PI2 Bench and VA Bench.

How to Use LongCat Flash

If you want to try out LongCat Flash yourself, here’s how you can do it step-by-step:

Step 1: Access the Chat Interface

Visit the official website: longcat.hat.
You can directly chat with the model online.

Step 2: Explore via VChat

The model is also integrated with VChat, a Chinese platform.
Sign up and start chatting with LongCat Flash.

Step 3: Test Use Cases

Here are some ideas to test the model’s capabilities:

Coding Tasks: Ask it to debug or write scripts.
Agentic Tasks: Test reasoning workflows that require step-by-step problem-solving.
Long Contexts: Provide long documents or conversations to see how it handles 128K tokens.

Step 4: Build with the MIT License

Download the open-source model.
Integrate it into your projects for free.
Modify and fine-tune as needed.

Training Process Explained

LongCat Flash uses a multi-stage training process:

1. Pre-Training Phase

The model collects knowledge by being trained on 20 trillion tokens.
This is the base training stage.

2. Mid-Training Phase

Focuses on reasoning and coding skills.
Extends the context window to 128K tokens.
Builds a framework that defines task difficulty using:
- Information processing
- Toolset complexity
- User interaction

3. Post-Training Phase

Aligns the model’s outputs with user expectations.
Prepares it for deployment in real-world applications.

Practical Applications

LongCat Flash is designed to excel in several specific areas:

Coding Assistance: Debugging, code generation, and terminal operations.
Agentic Systems: Building intelligent agents that can solve complex problems step-by-step.
Extended Conversations: Handling long-form dialogues with a 128K context window.
Research & Development: Since it's open source, developers can study and experiment with its structure.

FAQs

1. What makes LongCat Flash unique compared to other models?

Its efficient computation strategy activates only 27 billion parameters per token while maintaining high performance.

2. Who created LongCat Flash?

It was developed by Muan, a company primarily known for services like delivery and grocery tech.

3. Can I use LongCat Flash for free?

Yes, it has an MIT open-source license, so you can download, modify, and deploy it freely.

4. How long does it take to train this model?

The pre-training process was completed in just 30 days, which is incredibly fast for a model of this size.

5. What tasks is the model best suited for?

It is ideal for agentic tasks, reasoning, and coding-related work.

Final Thoughts

LongCat Flash is an impressive release from a relatively unknown company. With its Mixture of Experts design, efficient computation strategy, and fast training capabilities, it offers a unique approach to large language models.

The fact that Muan, a company not traditionally known for foundational AI models, managed to develop and open-source this tool makes it even more surprising. It shows how quickly the AI landscape is evolving and how new players can make significant contributions.

If you're interested in exploring this model, head to longcat.hat and experience its capabilities firsthand. This release represents a major step forward for open-source AI and could inspire a wave of innovation across the field.

LongCat Flash: 560B Parameter Open MoE Model