As a long-time Claude Code user, I’m always searching for cost-effective AI programming assistants. From DeepSeek and GLM-4.5 to Kimi-k2, I’ve tested multiple models. Recently, Meituan’s open source LongCat-Flash-Chat caught my attention, and after several days of intensive testing, I must say: this is one of my most satisfying experiences to date.
LongCat-Flash-Chat: Meituan’s Technical Showcase
LongCat-Flash-Chat is Meituan’s open source large language model, built on the company’s extensive data and technical expertise accumulated in the local services domain. This model not only demonstrates Meituan’s capabilities in the AI field but, more importantly, provides developers with a genuinely practical free AI tool.
Unlike some commercial models, LongCat-Flash-Chat is completely open source and free to use, reflecting Meituan’s support for the open source community and commitment to technological innovation.
Integration Process: Super Simple with Multi-Model Launcher
Integrating LongCat-Flash-Chat with Claude Code is incredibly simple, especially if you’re already using my multi-model launcher from my previous article. The setup takes just seconds:
Super Simple Configuration
If you have my multi-model launcher set up, simply add your LongCat API key:
export LONGCAT_API_KEY=your_longcat_api_key_here
Then use the model with:
claude --model LongCat-Flash-Chat
For those who prefer the traditional approach, you can also configure it, following the official - documentation. The LongCat API platform is fully compatible with the Anthropic Claude API format, making integration seamless.
Three Surprises: Why LongCat-Flash-Chat Impressed Me
1. Generous Token Allocation: From 100K to 5 Million/Day
The most surprising aspect of LongCat-Flash-Chat is its token allocation policy. By default, new users receive 100,000 free tokens per day, which is already quite generous for daily development. But what truly amazed me was: after submitting an application, the quota can be expanded to 5 million tokens per day!
To put this number in perspective, 5 million tokens is equivalent to:
- Reading approximately 2,000 pages of technical documentation
- Analyzing dozens of medium-sized codebases
- Engaging in all-day programming conversations without quota concerns
Most importantly, this is completely free - there are currently no paid options available. In today’s landscape where AI services typically charge fees, this generous quota policy is a developer’s dream come true.
2. Impressive Response Speed
LongCat-Flash-Chat’s response speed is among the fastest of all the models I’ve tested. Whether it’s code generation, question answering, or file analysis, responses are almost instantaneous.
In practical usage, I observed:
- Code Generation: Complex function implementations typically complete within 2-3 seconds
- File Analysis: Medium-sized project analysis is about 30% faster than DeepSeek
- Continuous Conversation: Almost no latency, maintaining a smooth programming experience
This speed advantage is particularly noticeable during extended programming sessions, significantly improving development efficiency.
3. Excellent Coding Capabilities
In terms of code quality, LongCat-Flash-Chat is on par with DeepSeek. After multiple rounds of testing, I found:
- Code Accuracy: Generated code has clear logic and low error rates
- Architecture Understanding: Capable of understanding complex project structures and providing reasonable refactoring suggestions
- Best Practices: Follows industry standards with consistent coding style
- Problem Solving: Provides effective solutions for specific bugs
In most programming tasks, LongCat-Flash-Chat performs comparably to DeepSeek, sometimes even excelling in specific domains.
One Minor Issue to Note: Tool Usage Habits
While the overall experience is very satisfying, I noticed that LongCat-Flash-Chat has a small habit that requires some adaptation:
The model sometimes outputs code directly instead of using tools to modify files.
This manifests as:
- When I request file modifications, the model may display the complete modified code
- Rather than directly using Edit tools like other models
- I need to explicitly instruct it to “please use the Edit tool to modify the file” for it to perform file operations
This habit requires some initial adaptation, but once instructions are clear, the model correctly uses tools. I believe this may be related to the model’s training data or default behavior settings.
Additional Issues Encountered
During my continued testing, I encountered two additional issues that are worth mentioning:
1. Token Length Limitations
I occasionally encountered errors related to token length exceeding 8192 tokens. According to the official documentation, this can be resolved by setting:
export CLAUDE_CODE_MAX_OUTPUT_TOKENS="6000"
This environment variable limits the maximum output tokens, preventing the model from generating responses that exceed the platform’s token limits.
2. Rate Limit Issues (429 Errors)
During peak usage hours, particularly in the evenings, I experienced rate limit errors (HTTP 429). The official documentation suggests setting:
export CLAUDE_CODE_DISABLE_NONESSENTIAL_TRAFFIC=1
This setting reduces non-essential background traffic that Claude Code might generate. However, I found that this doesn’t completely solve the rate limit issues during high-traffic periods. The service appears to become essentially unusable during peak hours, which is something to be aware of if you plan to use LongCat-Flash-Chat during these times.
I’ll continue monitoring this issue to see if it’s a temporary problem or a persistent limitation of the free service.
Comparison with Other Models
For a more objective evaluation of LongCat-Flash-Chat, I compared it with my commonly used models:
| Feature | LongCat-Flash-Chat | DeepSeek | GLM-4.5 | Kimi-k2 |
|---|---|---|---|---|
| Free Quota | 5M tokens/day | Limited free | Subscription | 3 RPM limit |
| Response Speed | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐ |
| Code Quality | ⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐ |
| Tool Usage | ⭐⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐ |
| Cost Efficiency | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐ | ⭐⭐ |
From this comparison, LongCat-Flash-Chat has clear advantages in free quota and response speed, with excellent code quality, though tool usage habits require some adaptation.
Recommended Usage Scenarios
Based on my experience, I recommend using LongCat-Flash-Chat in the following scenarios:
Best Suited For:
- Large-scale Code Analysis: Leverage the 5M token quota for deep project analysis
- Rapid Prototyping: Use its fast response to quickly validate ideas
- Learning New Technologies: Unlimited conversation quota is ideal for deep learning
- Cost-Sensitive Projects: Completely free, perfect for budget-conscious individual developers
Scenarios Requiring Adaptation:
- Complex File Operations: Need clear instructions to ensure proper tool usage
- Precise Code Modifications: May require multiple confirmations of modification content
- Peak Hour Usage: May experience rate limits during high-traffic periods
Tips for Applying for 5M Token Quota
If you also want to obtain the 5M token/day quota, here are some application tips:
- Detail Your Use Case: Explain your specific development needs in the application
- Show Technical Background: Briefly introduce your tech stack and project experience
- Express Long-term Usage Intent: Indicate your plan for long-term use and potential feedback contribution
- Maintain Professionalism: Formal application tone increases approval chances
My application was approved within tens of minutes, with a very smooth process.
Conclusion: A Free AI Programming Assistant Not to Be Missed
After thorough testing and usage, LongCat-Flash-Chat has left a deep impression on me. Meituan’s open source project not only demonstrates technical capability but, more importantly, provides developers with a genuinely practical, free, and efficient AI programming tool.