How to Use Chinese AI Models (MiniMax, Kimi, GLM) for Coding Assistance

Apr 4, 2026

Western AI coding assistants like Claude and GPT-4 are expensive. Teams burn through monthly limits quickly, and the subscription costs add up. I needed an alternative that wouldn’t break the budget but could still handle real coding work.

I discovered that Chinese AI models accessible through OpenCode offer a practical solution. Models like MiniMax M2.5, MiniMax M2.7, Kimi 2.5 Mini, and GLM-5 can handle coding tasks effectively when you match the right model to the right task phase.

The Cost Problem

Premium AI assistants charge premium prices. Claude Pro costs $20/month, and teams hit rate limits fast. For budget-constrained teams building internal tools and SaaS products, these costs become unsustainable.

The alternative? Chinese AI models through OpenCode cost around $10/month per developer. That’s roughly half the price while maintaining usable quality for most coding tasks.

Model Selection: What Works

Not all Chinese models are created equal. Through testing, I found that different models excel at different phases of the development workflow:

GLM-5 shines at planning and reasoning tasks. Use it for:

Designing API structures
Planning component architecture
Analyzing problem requirements
Writing design documents

MiniMax M2.7 handles implementation well. Use it for:

Generating code from design specs
Implementing functions
Writing unit tests
Debugging and fixing issues

Kimi 2.5 Mini works as a lightweight option for simpler tasks where you don’t need the full power of the larger models.

The Two-Phase Workflow

The key insight: don’t use the same model for everything. Split your workflow into planning and implementation phases.

# Planning Phase
Model: GLM-5
Purpose: Architecture, Design, Reasoning
Tasks:
  - Design API structure
  - Plan component architecture
  - Analyze problem requirements
  - Write design documents

# Implementation Phase
Model: MiniMax M2.7
Purpose: Coding, Execution
Tasks:
  - Generate code from design
  - Implement functions
  - Write unit tests
  - Debug and fix issues

This workflow diagram shows the approach:

┌─────────────────┐         ┌──────────────────┐
│  Planning Phase │         │ Implementation    │
│                 │   ───>  │ Phase            │
│  Model: GLM-5   │         │ Model: MiniMax   │
│  Architecture   │         │ M2.7             │
│  Design         │         │ Code Generation  │
│  Reasoning      │         │ Debugging        │
└─────────────────┘         └──────────────────┘

Real-World Performance

My team builds internal tools and SEO-focused SaaS products. The work is “fairly complex” as I describe it—not hardcore big-tech engineering, but substantial enough that we need capable AI assistance.

The results surprised me. When I plan properly and prompt clearly, these models handle tasks I didn’t expect them to manage well. The Chinese models (Kimi 2.5 Mini, MiniMax M2.5, MiniMax M2.7, GLM-5) are solid performers.

One redditor in r/opencodeCLI summed it up: “If you plan and prompt properly, they can handle a lot more than you’d expect.”

Prompting Matters More

Chinese models need clearer instructions than Claude or GPT-4. They don’t infer as much from context. This means:

Be explicit about what you want
Provide more context upfront
Break complex tasks into smaller steps
Use structured prompts with clear sections

The extra effort in prompting pays off in cost savings. At 10-20x cheaper than Western alternatives, the trade-off works for many use cases.

Common Mistakes to Avoid

I made these mistakes when starting out:

Using one model for everything: This wastes capability and increases costs. GLM-5 costs less than MiniMax M2.7 for planning tasks, but MiniMax M2.7 generates code faster. Match model to task.

Poor prompting: Chinese models need more guidance. Vague prompts that work with Claude fail here. Take time to write clear, structured instructions.

Assuming they can’t code: This misconception comes from outdated information. The current generation of Chinese models handles real coding work competently.

Setting Up in OpenCode

OpenCode provides a unified interface to access these models. Here’s what you need:

Install OpenCode CLI
Configure your API keys for MiniMax, Kimi, or GLM
Set up model aliases in your config
Create workflow scripts that route tasks to the right model

The setup process takes about 30 minutes. Once configured, you can switch between models seamlessly.

When to Stick with Premium Models

Chinese models aren’t perfect for everything. I still use Claude or GPT-4 for:

Complex architectural decisions requiring nuanced reasoning
Tasks with ambiguous requirements
Code reviews where I need the highest quality analysis
Situations where I can’t afford to iterate on prompts

For routine coding, implementation, and well-defined tasks, Chinese models handle the job at a fraction of the cost.

Cost Comparison

Here’s a rough breakdown of monthly costs per developer:

Western Premium Models:
- Claude Pro: $20/month
- GPT-4 Plus: $20/month
- Team plans: $25-30/month per user

Chinese Models via OpenCode:
- MiniMax M2.7: ~$5-10/month (usage-based)
- GLM-5: ~$3-8/month (usage-based)
- Combined workflow: ~$10/month typical

The savings multiply across teams. For a 10-person team, that’s $100-200/month versus $200-300/month for premium alternatives.

Practical Example

Last week, I needed to build a content management system for a client’s blog. Here’s how I split the work:

Planning with GLM-5:

Designed the database schema
Planned the API endpoints
Structured the component hierarchy
Wrote the technical spec

Implementation with MiniMax M2.7:

Generated the Express.js routes
Created React components
Wrote database queries
Built the admin interface

The entire project took three days. Total AI cost: under $5. With premium models, the same work would have cost $15-20 in API calls alone.

Getting Started

If you want to try Chinese AI models for coding:

Start with OpenCode as your interface
Set up GLM-5 for planning tasks
Add MiniMax M2.7 for implementation
Create clear prompt templates for common tasks
Track your costs to see the savings

The learning curve takes about a week. After that, the workflow becomes natural.

Final Thoughts

Chinese AI models offer a viable alternative for budget-conscious developers. They’re not perfect replacements for Claude or GPT-4 in every scenario, but they handle most coding tasks competently when you match the right model to the right phase.

The key is workflow design: plan with GLM-5, implement with MiniMax M2.7, and invest time in writing clear prompts. For teams building internal tools, SaaS products, or SEO-focused applications, the cost savings make this approach worth exploring.

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!

In this post, I shared my experience using Chinese AI models for coding assistance. I covered the two-phase workflow with GLM-5 and MiniMax M2.7, real-world performance insights, and practical setup guidance. The cost savings are significant—roughly half of premium alternatives—while maintaining usable quality for most coding tasks.