How Developers in China Can Use Claude and GPT APIs: Complete 2026 Guide

Developers in China usually hit the same three problems when they try to use Claude, GPT, or other overseas AI APIs:

• payment friction, because many official providers do not support Alipay or WeChat Pay
• network instability, because direct access can be inconsistent from some regions
• operating overhead, because managing multiple foreign accounts, keys, and billing dashboards gets messy fast

This guide breaks the problem into three practical paths, from the simplest option to the most flexible.

If you already know you want an OpenAI-compatible migration path, read the 5-minute migration guide next. If you are comparing platforms rather than just trying to unblock access, the pricing comparison and OpenRouter comparison are the two pages worth keeping open in adjacent tabs.

Option 1: Use an AI API aggregator

For most teams, this is the fastest path.

An API aggregator runs the upstream integrations for you. Instead of maintaining separate accounts for OpenAI, Anthropic, and Google, you integrate with one endpoint and one API key.

Why teams choose this route

• RMB payments through Alipay or WeChat Pay
• one API key for 300+ models
• OpenAI-compatible access for faster migration
• fallback capacity when one upstream has issues
• simpler billing and usage tracking

Typical integration flow

1. Create an account and generate an API key
2. Replace base_url and api_key in your existing integration
3. Keep the rest of your OpenAI-compatible code unchanged

from openai import OpenAI

client = OpenAI(
    api_key="sk-lemon-xxx",
    base_url="https://api.lemondata.cc/v1"
)

# Call GPT-4.1
response = client.chat.completions.create(
    model="gpt-4.1",
    messages=[{"role": "user", "content": "Hello"}]
)

# Call Claude Sonnet 4.6 with the same key
response = client.chat.completions.create(
    model="claude-sonnet-4-6",
    messages=[{"role": "user", "content": "Hello"}]
)

If you need Anthropic's native protocol

If your workflow depends on Claude's native features, such as extended thinking or prompt caching, you can still use an Anthropic-native SDK:

from anthropic import Anthropic

client = Anthropic(
    api_key="sk-lemon-xxx",
    base_url="https://api.lemondata.cc"
)

response = client.messages.create(
    model="claude-sonnet-4-6",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Analyze the performance bottlenecks in this code"}]
)

Cost comparison

For a team spending about $50/month on API usage:

Path	Approx. RMB cost	Notes
OpenAI official + Visa	~¥380	includes foreign transaction fees
Anthropic official + Visa	~¥380	similar fee structure
API aggregator + Alipay	~¥365	direct RMB payment

The absolute difference per month may not look dramatic. The operational difference is usually bigger: one account, one billing surface, and one integration point.

What to verify before choosing an aggregator

Do not stop at “it works in curl.” Check the operating details:

• whether model IDs stay close to official names
• whether streaming works through the same endpoint
• whether Claude and Gemini native features are available when you need them
• whether request IDs, rate-limit headers, and billing data are visible enough for debugging
• whether your preferred payment method actually works for recurring top-ups

That checklist matters more than a small headline price difference.

Option 2: Use official provider APIs directly

If you already have an international credit card and stable network access, direct registration is still viable.

OpenAI

1. Visit platform.openai.com
2. Create an account
3. Add a credit card
4. Create an API key

Anthropic

1. Visit console.anthropic.com
2. Create an account
3. Add a credit card
4. Create an API key

Tradeoffs

• network quality may vary by region
• foreign transaction fees add small but persistent overhead
• every provider has separate billing, rate limits, and support workflows
• multi-provider applications often end up with duplicated integration logic

Direct provider access is still a good fit when your team has all three of these:

• stable payment infrastructure for international cards
• a reason to stay close to one vendor's native platform
• internal engineering time to maintain multiple integrations if your stack expands later

If you do not have those three, the “cheaper in theory” route often becomes more expensive in engineering time.

Option 3: Run open-source models locally

If privacy, cost control, or experimentation matter more than access to frontier closed models, local deployment is a strong alternative.

Common model choices

Model	Parameters	Minimum memory	Good for
DeepSeek V3	671B (MoE)	multi-GPU required	strongest open general model
Qwen 2.5 72B	72B	48GB	Chinese-heavy workloads
Llama 3.3 70B	70B	48GB	strong English general tasks
DeepSeek R1 distilled	32B	24GB	reasoning-heavy workloads

Quick start with Ollama

# Install Ollama
curl -fsSL https://ollama.com/install.sh | sh

# Run a model
ollama run qwen2.5:32b

# Use it as an OpenAI-compatible API
curl http://localhost:11434/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model":"qwen2.5:32b","messages":[{"role":"user","content":"Write quicksort in Python"}]}'

Hardware guidance

• Mac Studio class hardware can run large quantized models
• 48GB memory is enough for many 70B-class deployments
• 16GB laptops are usually limited to smaller models

Local deployment is strongest when the problem is privacy, offline work, or deterministic cost control. It is weaker when the requirement is “I need the best frontier coding or reasoning model right now.”

For many teams in China, the practical architecture is hybrid:

• local or regional models for background jobs and privacy-sensitive workloads
• aggregated frontier APIs for coding, reasoning, or premium user-facing paths

That split keeps costs predictable without forcing every use case onto one stack.

Decision Framework

If you need the fastest path to production, start with an aggregator.

If you need strict vendor-native behavior and already have payment + network solved, direct APIs are fine.

If you need privacy and hardware ownership more than frontier capability, local models win.

The mistake is trying to answer this as a pure technical question. For most teams, the deciding variable is operational drag:

• how many keys you need to manage
• how many billing surfaces finance has to reconcile
• how many protocol differences your application code has to absorb
• how often your team has to debug provider-specific behavior

That is why “one endpoint, one key, multiple models” keeps winning in practice.

Tool integrations

Cursor

Settings → Models → OpenAI API Key:

• API Key: sk-lemon-xxx
• Base URL: https://api.lemondata.cc/v1

Continue (VS Code 插件)

{
  "models": [{
    "title": "Claude Sonnet 4.6",
    "provider": "openai",
    "model": "claude-sonnet-4-6",
    "apiBase": "https://api.lemondata.cc/v1",
    "apiKey": "sk-lemon-xxx"
  }]
}

LangChain

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    model="gpt-4.1",
    api_key="sk-lemon-xxx",
    base_url="https://api.lemondata.cc/v1"
)

If your team works in editors first, the Cursor / Cline / Windsurf setup guide is the fastest next step after the base API connection is working.

FAQ

How do teams usually choose between these options?

If you need frontier models and low operational drag, use an aggregator. If you need direct vendor control and already have payment infrastructure, official APIs are fine. If privacy or cost is the top constraint, local models make more sense.

Does an aggregator always add latency?

Not necessarily. For developers in Asia, a regional aggregator can reduce operational friction enough that the overall user experience improves, even if the request path is one hop longer.

Can I still stream responses?

Yes. Standard SSE streaming still works, and native Anthropic protocol support also preserves thinking deltas where the gateway exposes them.

Do model names stay the same?

Usually yes for mainstream models, but do not assume every gateway uses every vendor naming convention verbatim. Test the exact IDs your code will use and keep a small allowlist in application config.

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Create an API key at LemonData, test one OpenAI-compatible call, one Claude-native call if you need it, and then move the rest of your stack only after the smoke tests pass. That keeps the migration boring, which is exactly what you want.