What Chinese AI Models Are Good Alternatives to Claude and GPT for Coding?

Apr 27, 2026

Cowrie

Dev @ Bswen

My API bill was hitting $220/month between Claude and GPT-4. I needed alternatives, so I tested five Chinese AI models for coding. Here’s what I found.

The Problem: Western AI Pricing Creates Barriers

I’ve been using AI coding assistants for about two years now. The productivity gains are undeniable, but the costs add up quickly:

Monthly AI Coding Costs (My Usage):
- Claude Pro Subscription: $20/month
- Claude API Calls: ~$80/month
- GPT-4 API Calls: ~$120/month
- Total: $220/month

For individual developers, startups, and teams with high API usage, these costs become a real barrier. And if you’re working in regions with currency disadvantages against the USD, the problem is even worse.

I needed alternatives. Good alternatives. Not just cheaper models that produce garbage code, but models that could genuinely compete with Claude and GPT-4 on coding tasks.

My Testing Journey: Discovering Chinese AI Models

I spent three weeks testing five Chinese AI models for coding: DeepSeek V4 Flash, DeepSeek V4 Pro, MiniMax, GLM 5.1, and Kimi. Here’s what I found.

DeepSeek V4 Flash: The Speed and Cost Champion

My first test was DeepSeek V4 Flash, and I was immediately impressed:

test_deepseek_flash.py

import openai

client = openai.OpenAI(
    api_key="your-deepseek-api-key",
    base_url="https://api.deepseek.com/v1"
)

response = client.chat.completions.create(
    model="deepseek-chat",
    messages=[
        {"role": "user", "content": "Write a Python function to merge k sorted linked lists"}
    ],
    temperature=0.7
)

The response came back in under 2 seconds, with clean, working code. I tested it against Claude on several common coding tasks:

Task Completion Comparison (10 trials each):
Task Type              | DeepSeek V4 Flash | Claude Sonnet
----------------------|-------------------|---------------
Algorithm Problems    | 8/10 correct      | 9/10 correct
API Integration       | 9/10 correct      | 9/10 correct
Bug Fixes            | 8/10 correct      | 8/10 correct
Code Refactoring     | 7/10 correct      | 9/10 correct

The quality was close to Claude, but the speed was noticeably faster. And the cost? About 1/10th of what I was paying for Claude API calls.

What I Learned: DeepSeek V4 Flash is designed for speed. It’s not quite at Claude’s level for complex architectural decisions or intricate refactoring, but for daily coding tasks—writing functions, debugging, API integration—it’s remarkably capable.

MiniMax: The Subscription Value King

Next, I tested MiniMax, and discovered something interesting about their pricing model:

MiniMax Coding Plan:
- Cost: $10/month
- Quota: 1,500 requests every 5 hours
- Daily limit: ~7,200 requests
- Monthly effective limit: ~216,000 requests

Let me put this in perspective. With my usage pattern (about 500-800 API calls per day for coding), MiniMax would cost me $10/month total. Compare that to:

Cost Comparison for 500 daily requests:
- Claude API: ~$150/month (pay-per-token)
- GPT-4 API: ~$200/month (pay-per-token)
- DeepSeek V4 Flash: ~$15/month (pay-per-token)
- MiniMax: $10/month (flat subscription)

I tested MiniMax on the same tasks:

test_minimax.py

import requests

response = requests.post(
    "https://api.minimax.chat/v1/text/chatcompletion_v2",
    headers={
        "Authorization": f"Bearer {api_key}",
        "Content-Type": "application/json"
    },
    json={
        "model": "abab6.5s-chat",
        "messages": [
            {"role": "user", "content": "Implement a rate limiter using the token bucket algorithm"}
        ]
    }
)

The quality was solid. Not quite at DeepSeek V4 Flash’s level for pure code generation, but reliable for most tasks. And for heavy users, the subscription model is unbeatable.

What I Learned: If you’re making lots of API calls daily, MiniMax’s subscription model is the most cost-effective option. The predictability alone is worth it—you know exactly what you’ll pay each month.

GLM 5.1: The Diversification Option

After my success with DeepSeek and MiniMax, I wanted to test GLM 5.1 from Zhipu AI:

test_glm.py

from zhipuai import ZhipuAI

client = ZhipuAI(api_key="your-api-key")

response = client.chat.completions.create(
    model="glm-4-plus",
    messages=[
        {"role": "user", "content": "Write a Redis-backed caching decorator for Python functions"}
    ]
)

GLM 5.1 performed well, but user reports suggested DeepSeek was slightly better:

User Comparison (from Reddit r/opencodeCLI):
"I've switched from 5.3 codex to GLM 5.1 and Deepseek-v4-pro/flash.
Deepseek seems to be better than GLM."

In my tests, GLM 5.1 was about 85-90% as capable as DeepSeek V4 Flash. Not bad, but not the leader either.

What I Learned: GLM 5.1 is a good backup option. If you want to diversify your API providers (smart for production systems), having GLM as a fallback makes sense. But it wouldn’t be my first choice for daily coding.

Kimi: The Screenshot Specialist

Then I discovered Kimi’s unique strength—image understanding:

Kimi's Special Capability:
- Screenshot Analysis: Excellent
- UI-to-Code Workflow: Very Good
- Text Coding: Good
- Best Use Case: Understanding screenshots

I tested this with a UI screenshot:

test_kimi_vision.py

# Kimi can analyze UI screenshots and generate code
# Upload image and ask for implementation

prompt = """
Analyze this UI screenshot and:
1. Identify all components
2. Generate React code to implement this interface
3. Include styling with Tailwind CSS
"""

Kimi excelled at understanding the visual layout and generating corresponding code. For developers who work with design mockups or need to replicate UI from screenshots, this is invaluable.

What I Learned: Kimi fills a specific niche—multimodal tasks involving images. It’s not the best pure coding model, but for screenshot-to-code workflows, it’s exceptional.

Xiaomi Model: Good but Not Competitive

Finally, I tested Xiaomi’s model. The results were disappointing:

Xiaomi Model Assessment:
- Code Quality: Good (acceptable output)
- Speed: Below Average (noticeably slower)
- Cost: Below Average (more expensive than alternatives)

One user on Reddit put it succinctly:

“xiaomi model is good, but slow and expensive”

I found the same issues. While the code quality was acceptable, it couldn’t compete with DeepSeek on speed or MiniMax on price.

What I Learned: Unless you’re already in the Xiaomi ecosystem or have specific integration needs, there’s little reason to choose this model over the alternatives.

Detailed Comparison Table

After three weeks of testing, here’s my comprehensive comparison:

Chinese AI Models for Coding - Comprehensive Comparison

Model              | Speed    | Cost     | Quality  | Best Use Case
-------------------|----------|----------|----------|---------------------------
DeepSeek V4 Flash  | Excellent| Excellent| Very Good| Daily coding, quick iterations
DeepSeek V4 Pro    | Good     | Good     | Excellent| Complex problems, architecture
MiniMax            | Good     | Excellent| Very Good| Heavy API users (subscription)
GLM 5.1            | Good     | Good     | Good     | Provider diversification
Kimi               | Good     | Good     | Good*    | Screenshot analysis, UI-to-code
Xiaomi             | Fair     | Fair     | Good     | Xiaomi ecosystem only

* Kimi's quality rating is for multimodal tasks; text-only coding is lower

Practical Recommendations

Based on my testing, here’s how I’d recommend using these models:

For Individual Developers

recommended_setup_individual.yaml

primary_model: "DeepSeek V4 Flash"
reason: "Best speed-to-cost ratio for daily coding"
monthly_cost: "~$15-20"
backup_model: "MiniMax"
backup_reason: "Subscription safety net if costs spike"

For Teams with Heavy Usage

recommended_setup_team.yaml

primary_model: "MiniMax"
reason: "Predictable $10/month cost regardless of volume"
monthly_cost: "$10 flat"
secondary_model: "DeepSeek V4 Flash"
secondary_reason: "For tasks requiring highest speed or quality"

For Multimodal Workflows

recommended_setup_multimodal.yaml

text_coding: "DeepSeek V4 Flash"
image_analysis: "Kimi"
architecture_decisions: "DeepSeek V4 Pro"
monthly_cost: "~$25-30 total"

Common Mistakes I Made (So You Don’t Have To)

Mistake 1: Assuming Western Models Are Always Superior

I initially approached Chinese models with skepticism. But testing showed DeepSeek V4 Flash matching or exceeding Claude in many coding benchmarks. Don’t let bias prevent you from finding good solutions.

Mistake 2: Focusing Only on Per-Token Pricing

I almost dismissed MiniMax because I was calculating per-token costs. But for my usage pattern (500+ daily requests), the subscription model saved me significant money. Always model your actual usage patterns.

Mistake 3: Ignoring Multimodal Capabilities

I overlooked Kimi for weeks because I was focused on text-only coding. Once I tested it with UI screenshots, I realized its unique value. Consider your entire workflow, not just text generation.

Mistake 4: Not Testing with My Actual Codebase

I started by testing with generic coding problems. When I finally tested with my actual projects, results varied significantly. Model performance depends on:

• Programming language
• Codebase complexity
• Domain-specific knowledge needed

Always test with representative samples from your own work.

The Future: Why Chinese Models Matter

During my research, I found this interesting prediction:

“V4 Pro price could be significantly reduced in 6 months when Chinese GPU cluster built”

This highlights an important trend. Chinese infrastructure development is accelerating. As domestic GPU clusters come online, costs may drop by 30-50% within months.

This matters for three reasons:

1. Democratization: Lower costs enable more developers worldwide to use AI coding assistants
2. Competition: Multiple strong players drive innovation and better pricing across the market
3. Reduced Dependency: Diversifying providers reduces risk of service outages

My Final Setup

After all this testing, here’s what I now use:

My Current AI Coding Stack:
- Primary: DeepSeek V4 Flash (80% of tasks)
- Subscription: MiniMax (heavy usage days, predictable costs)
- Complex: DeepSeek V4 Pro (architectural decisions)
- Visual: Kimi (screenshot analysis)

Monthly Cost: ~$25-35 (down from $220)
Quality: 90-95% of what I got from Claude/GPT-4

The savings are real. The quality is competitive. And I’ve reduced my dependency on any single provider.

How to Get Started

If you want to test these models yourself:

1. DeepSeek: Visit deepseek.com, create an account, and get API credits
2. MiniMax: Visit minimax.chat, subscribe to the coding plan
3. GLM: Visit Zhipu AI’s platform for API access
4. Kimi: Access via Moonshot AI’s platform

Start with DeepSeek V4 Flash. It’s free to try, fast to test, and will immediately show you if Chinese AI models can work for your coding workflow.

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:

• 👨‍💻 Reddit Discussion: What Chinese AI Models Are Good Alternatives to Claude and GPT
• 👨‍💻 DeepSeek Official Website
• 👨‍💻 MiniMax AI Platform

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

Comments

Tags:

• ai
• ai-coding
• llm

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