The News
In late April 2026, multiple developers published comparative tests of Chinese AI models on the same coding tasks via X/Twitter. Models included GLM-5.1 (Zhipu), Kimi K2.6 (Moonshot AI), DeepSeek V4 Pro (DeepSeek), and Qwen 3.6 Max Preview (Alibaba Tongyi Qianwen).
This is not an official benchmark score, but a real-world development scenario comparison, making the results more valuable for practical model selection.
Testing Methodology
Multiple developers used similar testing approaches:
- The same coding prompt (usually a medium-complexity full-stack project)
- No additional prompt engineering
- Evaluation dimensions included: code structure, reasoning process, final usability
Model-by-Model Results
GLM-5.1: Developer-Grade Code Structure
GLM-5.1 demonstrated the most human developer-like code organization across multiple tests:
- Clear file structure and modular division
- Standardized function naming and commenting style
- Comprehensive error handling logic
From a tester’s words: “GLM wrote the most senior developer-style code structure.”
In the coding tier ranking, GLM-5.1 sits at the same level as Kimi K2.6 (entry tier).
Kimi K2.6: Explains Decisions Like a Teacher
Kimi K2.6’s unique advantage lies in decision explanation transparency:
- Every step comes with clear reasoning
- Suitable for development scenarios requiring understanding of code logic
- Agent swarm capabilities give it an extra edge in complex projects
“Kimi explains every decision like a teacher.”
K2.6’s agent swarm and long-horizon coding capabilities are also a bonus—it doesn’t just write code, it can plan and execute multi-step tasks.
DeepSeek V4 Pro: Reasoning Engine-Level Thinking
DeepSeek’s performance can be summarized as structured reasoning:
- Analysis first, then coding—step-by-step reasoning process
- 1M token context window suitable for ultra-long code files
- Reliable in precise tasks like invoice data verification (did not fabricate data)
“DeepSeek thinks step-by-step like a reasoning engine.”
DeepSeek V4 Pro ranked slightly below GLM-5.1 and Kimi K2.6 in multiple comparisons, but the gap is minimal.
Qwen 3.6: Most Efficient Code Output
Qwen 3.6 Max Preview is characterized by output efficiency and code cleanliness:
- Generated code structure is clear with minimal redundancy
- Fastest output speed in some tests
- Higher code maintainability
“Qwen produced the cleanest code structure I’ve tested.”
In this comparison, Qwen 3.6 was classified as “below entry tier,” but this categorization is more about the specific test prompt’s bias than an absolute capability gap.
Tier Summary
Based on cross-verification from multiple developers:
| Tier | Models |
|---|---|
| Entry Tier | GLM-5.1 ≈ Kimi K2.6 > DeepSeek V4 Pro |
| Near Entry | Qwen 3.6 Max Preview > MiniMax M2.7 |
Note: This ranking is based on subjective evaluation from specific test tasks and does not represent absolute ordering across all scenarios.
Selection Advice
- Need standardized code structure: Choose GLM-5.1
- Need to understand decision logic: Choose Kimi K2.6
- Need ultra-long context: Choose DeepSeek V4 Pro
- Need efficient output: Choose Qwen 3.6
- Agent swarm scenarios: Kimi K2.6 has a clear advantage
An Interesting Detail
In the invoice data verification test, MiniMax M2.7 and MiMo-V2.5-Pro exhibited data fabrication issues, while DeepSeek V4 Flash, GPT-5.5, and GLM-5.1 all completed the task. This reminds us: in scenarios requiring precision, model selection matters more than price.
Pricing Reference
For long-term use, Ollama Cloud’s Coding Plan Max ($80/month) can support 800 million tokens per month for heavy agent usage. In comparison, official API pay-per-use may be higher in heavy usage scenarios.
Chinese AI models in the coding domain are rapidly closing the gap with international models. For most daily development tasks, these models can already provide trustworthy assistance.