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DeepSeek vs GLM-4.6 vs Qwen: Which Chinese LLM is Best for Coding?

Cowrie
Cowrie
Dev @ Bswen

I spent two weeks testing five Chinese LLMs side-by-side for coding tasks. What I found surprised me: there’s no single “best” model, but the right choice depends entirely on what you’re trying to do.

Here’s the short answer: use GLM-4.6 for complex refactoring, DeepSeek-V3.1 for quick fixes, and Qwen3-Max when you need precise instruction following.

The Setup

I tested DeepSeek (V3.1, V3.2-Exp), GLM-4.6, Qwen3-Max, Qwen3-235B, and Yi-Lightning on real coding tasks: bug fixes, API generation, refactoring, and test writing. No synthetic benchmarks—actual work on actual codebases.

GLM-4.6: The Deep Thinker

“Way better at understanding project context than I expected.”

That was my first reaction. GLM-4.6 doesn’t just generate code—it thinks through problems. When I asked it to refactor an authentication module to follow SOLID principles, it didn’t just spew code. It:

  1. Analyzed the existing architecture
  2. Proposed a step-by-step refactoring plan
  3. Identified dependencies and potential breaking changes
  4. Generated clean, well-structured code with comments explaining WHY it made each decision

This is where GLM-4.6 shines: complex architecture decisions and multi-file refactoring. The 200K token context window means it can actually understand your entire codebase, not just the file you’re currently editing.

On public benchmarks, GLM-4.6 scores 73.8% on SWE-bench and beats DeepSeek-V3.1-Terminus and Claude Sonnet 4. But the real difference isn’t in the scores—it’s in how it handles nuance.

DeepSeek: Speed and Value King

DeepSeek-V3.2-Exp is stupid fast. But there’s a catch: sometimes it overcomplicates simple stuff.

I asked V3.2 to fix a null pointer exception. The fix worked, but it also rewrote three unrelated functions “for better performance.” That’s great when you want it, annoying when you don’t.

DeepSeek-V3.1 is more balanced. It gives you the speed and absurdly cheap pricing (under $1 for most tasks) without the tendency to over-engineer.

DeepSeek-Coder-V2 is particularly strong: it rivals GPT-4-Turbo-0409 in code generation, understanding, debugging, and completion. The Fill-In-the-Middle (FIM) completion is 5.1% better than the previous version.

For quick fixes and simple features, DeepSeek-V3.1 is your best bet. It’s fast, cheap, and gets the job done without forcing you to justify every change.

Qwen3: Precision Over Creativity

“Tell it to do something specific and it does exactly that, no creative liberties.”

That’s Qwen3-Max in a nutshell. When I asked for a REST API with exactly four endpoints (POST /users, GET /users/:id, PUT /users/:id, DELETE /users/:id), Qwen3-Max generated exactly four endpoints. No PATCH, no bulk operations, no “helpful” additions.

GLM-4.6 would have suggested extra endpoints “for completeness.” DeepSeek would have added some reasonable features. Qwen3-Max follows instructions literally.

Qwen3-235B is similar but slightly better at handling ambiguous requirements. If your spec is fuzzy, use 235B instead of Max.

Qwen3-Coder is optimized for terminal-based workflows and CI/CD integration. It excels at security audits and performance analysis—tasks where you want precision, not creativity.

Honestly, Yi-Lightning felt like the weakest of the bunch. It kept giving generic StackOverflow-style answers that weren’t particularly helpful for actual coding tasks. I can’t recommend it for coding compared to the top three.

When to Use What

Model Decision Matrix
┌─────────────────────────────┬───────────────┬─────────────────┐
│ Use Case                    │ Best Choice   │ Alternative     │
├─────────────────────────────┼───────────────┼─────────────────┤
│ Complex architecture        │ GLM-4.6       │ DeepSeek-V3.1   │
│ Multi-file refactoring      │ GLM-4.6       │ DeepSeek-Coder  │
│ Quick bug fixes             │ DeepSeek-V3.1 │ Qwen3-Max       │
│ Test generation             │ Qwen3-Max     │ GLM-4.6         │
│ Security audits             │ Qwen3-Coder   │ GLM-4.6         │
│ Performance analysis        │ Qwen3-Coder   │ DeepSeek        │
│ Exact spec implementation    │ Qwen3-Max     │ DeepSeek-V3.1   │
│ Cost-sensitive projects     │ DeepSeek      │ Qwen3           │
│ Ambiguous requirements      │ Qwen3-235B    │ GLM-4.6         │
│ Long-context refactoring    │ GLM-4.6       │ -               │
└─────────────────────────────┴───────────────┴─────────────────┘

My Final Workflow

After testing, I ended up using all three models strategically:

  1. GLM-4.6 for complex architecture decisions and refactoring—when I need the model to understand the big picture and think through problems

  2. DeepSeek-V3.1 for quick fixes and simple features—when speed and cost matter more than deep analysis

  3. Qwen3-Max when I need something done exactly as specified—no creative interpretation, just precision

The Hybrid Approach

Smart developers don’t pick one model and stick with it. They use the right tool for the job:

  • Start with DeepSeek-V3.1 for most coding tasks (best balance of cost and performance)
  • Switch to GLM-4.6 when working on complex refactoring or system design
  • Use Qwen3-Max when you have strict specifications or need exact instruction following

This hybrid approach gives you the best of each model’s strengths while minimizing their weaknesses. GLM-4.6’s 200K context window comes at a premium price, but you’re only paying for it when you actually need that context. DeepSeek’s speed and cheap pricing become advantages, not compromises, when you use it for appropriate tasks.

Bottom Line

If I had to pick just one: DeepSeek-V3.1. It’s the most balanced for general coding work.

But if you’re serious about productivity, use all three strategically based on the task at hand. The small mental overhead of remembering which model excels at what pays off in faster, higher-quality code.

And skip Yi-Lightning for coding. It’s just not competitive with the top three.

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!

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