Best Chinese LLMs for Programming in 2026: Complete Guide (DeepSeek, Qwen, GLM Compared)

Mar 3, 2026

I’ve been paying OpenAI way too much for GPT-4. $2,000+ a month for coding assistance. That’s insane. So I decided to test five Chinese LLMs to see if they could actually replace GPT-4 for real programming work.

The results surprised me. Not just that some of them were good—but that they handle 80-90% of coding tasks at a fraction of the cost.

Here’s what I found.

The Test Setup

I tested these models on actual work projects, not toy examples:

Python data science codebases
JavaScript Node.js applications
React frontend projects
Legacy code debugging
Feature generation from scratch
Large-scale refactoring

I was looking for something specific: which model handles what kind of task best. Not “which one got the highest benchmark score,” but “which one helps me ship code faster.”

The Surprising Results

GLM-4.6:        Best context understanding
DeepSeek-V3.2:  Fastest + cheapest
Qwen3-Max:      Best instruction following
Qwen3-Coder:    Best open-weight model
Yi-Lightning:   Avoid for coding

GLM-4.6 was way better at understanding project context than I expected. DeepSeek-V3.2-Exp is stupid fast and cheap, though sometimes it overcomplicates simple things. Qwen3-Max crushed it for following exact instructions—no surprise additions, no creative interpretations.

Yi-Lightning honestly felt like the weakest performer.

GLM-4.6: The Context King

I had a bug in a React app where authentication worked but the dashboard showed “Access Denied.” This bug spanned three files: auth-context.tsx, dashboard.tsx, and api-client.ts.

I pasted all three files into GLM-4.6 and asked what was wrong.

It immediately identified the issue: the auth context was storing a JWT token, but the dashboard component was checking a boolean flag instead of verifying the token. The fix required changes in two files, and GLM-4.6 explained how the authentication flow actually worked across the entire application.

What impressed me wasn’t just that it found the bug—it understood how the pieces fit together.

GLM-4.6 has a 200K context window with 30% better token efficiency than previous versions. This means it can actually understand large codebases, not just the snippet you pasted.

When I used it for a complex refactoring of a TypeScript project with 50+ files, it correctly identified all the dependencies and suggested changes that didn’t break anything.

Why this matters: for complex refactoring, legacy code debugging, or architectural decisions, you need a model that understands context. GLM-4.6 delivers.

The tradeoff: it’s mid-tier pricing. Not the cheapest option, and slower than DeepSeek for simple tasks. But for complex work, it’s worth it.

DeepSeek-V3.2-Exp: Speed and Value

I needed a quick prototype of a to-do app. Nothing fancy—just add/remove tasks, mark as complete, local storage persistence.

I pasted the requirements into DeepSeek-V3.2-Exp.

Three seconds later, I had a working React component with Tailwind styling. It ran on the first try.

The speed was impressive, but what really got my attention was the cost: $0.28 per million input tokens, $0.42 per million output tokens. And 90% discount on cached content.

I did the math: for a heavy user doing 500 coding tasks daily, DeepSeek costs $150-300/month. GPT-4 would be $2,250-4,500/month. That’s 87-93% savings.

DeepSeek also offers 5 million free tokens for new users, and the API is OpenAI-compatible. You literally just change the base URL and swap the model name—no code changes.

The catch: sometimes it overcomplicates simple solutions. I asked it to write a basic Python script to parse a JSON file, and it included error handling for edge cases I didn’t need and added type hints I didn’t ask for.

But for rapid prototyping and high-volume coding workflows, the speed and cost savings outweigh the occasional over-engineering.

Qwen3-Max: The Instruction Perfectionist

Here’s where things got interesting.

I needed an API endpoint with exact specifications: POST /api/users with email, password, and name. Email must be valid regex format. Password must be 12+ characters with uppercase, lowercase, number, and special character. Return 201 with specific fields on success, return 400 with specific error messages for each validation failure.

I emphasized: “Do NOT deviate from this specification.”

Qwen3-Max implemented exactly what I asked for. No extra fields. No additional validation. No “helpful” error handling I didn’t request. Just the exact specification.

When I tried the same prompt with DeepSeek, it added user registration email confirmation logic. When I tried it with GLM-4.6, it added rate limiting middleware.

Both of those additions might be good ideas, but they weren’t what I asked for.

Qwen3-Max can handle up to 252K context window—the largest among the models I tested. It’s excellent for test-driven development where you need the model to implement tests and code that exactly match your requirements.

The downside: pricing varies significantly by configuration, and it can be overly literal. If you forget to mention something in your specification, it won’t add it.

For strict requirements, API development with precise specs, or test-driven development, Qwen3-Max is the tool.

Qwen3-Coder-480B: The Open-Weight Leader

This one’s for the self-hosters.

Qwen3-Coder-480B is available via Nebius for easy deployment, and it’s still the best open-weight coding model I’ve found.

If you’re running a large organization with strict data requirements, or you want to fine-tune a model for your specific codebase, or you just want full control over your AI infrastructure, this is the way to go.

The tradeoff: you need infrastructure and technical expertise. There’s no free lunch here.

But if you’re doing 500+ coding tasks daily, the math works in your favor. The API costs drop to zero—you’re just paying for infrastructure. One-time setup, ongoing savings.

Which Model Should You Use?

It depends on what you’re doing. Here’s how I decide:

Need to churn through 20 small coding tasks today?
→ DeepSeek-V3.2-Exp (fast + cheap)

Debugging a bug that spans multiple files?
→ GLM-4.6 (context understanding)

Implementing an API with strict specifications?
→ Qwen3-Max (exact instruction following)

Need to refactor a large codebase?
→ GLM-4.6 (architectural understanding)

Building a quick prototype?
→ DeepSeek-V3.2-Exp (speed)

Writing tests with exact requirements?
→ Qwen3-Max (precision)

Self-hosting for data security?
→ Qwen3-Coder-480B (control)

For individual developers, I’d recommend: start with DeepSeek-V3.2-Exp for daily coding, use GLM-4.6 for complex refactoring tasks. Budget $10-50/month covers most needs.

For small teams (3-10 developers): standardize on DeepSeek-V3.2-Exp for 80% of tasks, use GLM-4.6 for architectural decisions, consider Qwen3-Max for strict API development. Budget $50-200/month.

For large organizations: self-host Qwen3-Coder-480B for data security, use GLM-4.6 for complex enterprise applications, keep DeepSeek for rapid prototyping.

Cost-Benefit Analysis

Here’s what your monthly costs look like based on usage volume:

Light User (20-50 tasks/day):
  DeepSeek:    $5-15
  GLM-4.6:     $15-30
  Qwen3-Max:   $10-25

Medium User (100-200 tasks/day):
  DeepSeek:    $25-60
  GLM-4.6:     $75-120
  Qwen3-Max:   $50-100

Heavy User (500+ tasks/day):
  DeepSeek:    $150-300
  GLM-4.6:     $450-750
  Qwen3-Max:   $300-600
  GPT-4:       $2,250-4,500

For heavy users, the savings are massive: $1,950-4,200 monthly (87-93% reduction) compared to GPT-4.

Migration Strategy

Don’t switch everything at once. Here’s how I migrated:

Week 1: Test DeepSeek-V3.2-Exp on 20% of tasks. Monitor quality. Adjust prompts if needed.

Week 2: Add GLM-4.6 for complex tasks. Compare quality to previous solutions.

Week 3: Evaluate Qwen3-Max for strict requirements. Track where it shines.

Week 4: Decide on final model mix based on usage patterns.

By week 4, I was using DeepSeek for 70% of tasks, GLM-4.6 for 25%, and Qwen3-Max for 5%. Quality was comparable to GPT-4, and costs were down 90%.

Additional Models Worth Mentioning

The Reddit discussion also highlighted a few other options:

MiniMax-M2: “it’s quite good” for coding, worth considering if pricing works for you
Kimi K2.5: Mentioned as worth testing, though I haven’t personally evaluated it
Xiaomi Mimo: Referenced in discussion but details were sparse

These aren’t in my top tier, but they’re worth exploring if the pricing or availability works better for your region.

What About Yi-Lightning?

Avoid it for serious programming work.

It was consistently the weakest performer in my testing. Every task I gave it produced inferior results compared to the other models.

There are better alternatives at similar price points. Don’t waste your time.

The Bottom Line

Chinese LLMs have matured into serious programming tools. You don’t need GPT-4 anymore for most coding tasks.

Default choice: DeepSeek-V3.2-Exp for speed and cost
Complex work: GLM-4.6 for context understanding
Strict specs: Qwen3-Max for precision
Self-hosting: Qwen3-Coder-480B for control

The key insight: don’t stick with just one model. Use the right tool for each job. You’ll save thousands monthly without sacrificing quality.

My workflow now: DeepSeek for the bulk of daily coding, GLM-4.6 when I hit a complex refactor, Qwen3-Max when specs need to be exact.

Quality hasn’t dropped. Costs have plummeted. Productivity is up.

That’s the combination I was looking for.

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!