One API key for Chinese AI models. Route to Qwen, Deepseek

China LLM Gateway - Unified interface for Chinese LLMs including Qwen, DeepSeek, GLM, Baichuan. OpenAI compatible, one API Key for all models.

Security Scan

The skill's requests, examples, and included Python client all align with a unified Chinese-LLM gateway that uses a single AISA_API_KEY and the api.aisa.one endpoint — nothing requests unrelated credentials or system access.

Purpose & Capability

Name/description claim a unified gateway for Chinese models and the package requires only curl, python3, and AISA_API_KEY. The included client talks to https://api.aisa.one and exposes model selection, streaming, and comparison features that match the stated purpose.

Instruction Scope

SKILL.md provides examples and an OpenAI-compatible API endpoint (api.aisa.one). Instructions and examples only reference the AISA_API_KEY and network calls to the gateway; there are no instructions to read unrelated files, system secrets, or send data to third-party endpoints.

Install Mechanism

No install spec — instruction-only plus an included Python script. No downloads from untrusted URLs or archive extraction. Uses system Python and curl (reasonable for this CLI client).

Credentials

Only a single env var (AISA_API_KEY) is required and it is the primary credential used by the client. No unrelated tokens, keys, or config paths are requested.

Persistence & Privilege

always is false and the skill does not request permanent system or cross-skill changes. disable-model-invocation is default (agent may call it autonomously) which is normal; this is not itself a red flag.

Assessment

This skill appears coherent: it uses one API key (AISA_API_KEY) and calls https://api.aisa.one as documented. Before installing, confirm you trust the AIsa provider and the api.aisa.one domain, do not reuse high-privilege or cloud provider keys, and rotate the key if needed. If you plan to run the included Python script, review the full script locally and run it in a constrained environment (sandbox/container) if you have concerns. Be aware that all prompts and any sensitive data you send will go to the AIsa service and may incur usage costs; avoid sending secrets in prompts.

OpenClaw CN-LLM 🐉

China LLM Unified Gateway. Powered by AIsa.

One API Key to access all Chinese LLMs. OpenAI compatible interface.

Qwen, DeepSeek, GLM, Baichuan, Moonshot, and more - unified API access.

🔥 What You Can Do

Intelligent Chat

"Use Qwen to answer Chinese questions, use DeepSeek for coding"

Deep Reasoning

"Use DeepSeek-R1 for complex reasoning tasks"

Code Generation

"Use DeepSeek-Coder to generate Python code with explanations"

Long Text Processing

"Use Qwen-Long for ultra-long document summarization"

Model Comparison

"Compare response quality between Qwen-Max and DeepSeek-V3"

Supported Models

Qwen (Alibaba)

Model	Input Price	Output Price	Features
qwen3-max	$1.37/M	$5.48/M	Most powerful general model
qwen3-max-2026-01-23	$1.37/M	$5.48/M	Latest version
qwen3-coder-plus	$2.86/M	$28.60/M	Enhanced code generation
qwen3-coder-flash	$0.72/M	$3.60/M	Fast code generation
qwen3-coder-480b-a35b-instruct	$2.15/M	$8.60/M	480B large model
qwen3-vl-plus	$0.43/M	$4.30/M	Vision-language model
qwen3-vl-flash	$0.86/M	$0.86/M	Fast vision model
qwen3-omni-flash	$4.00/M	$16.00/M	Multimodal model
qwen-vl-max	$0.23/M	$0.57/M	Vision-language
qwen-plus-2025-12-01	$1.26/M	$12.60/M	Plus version
qwen-mt-flash	$0.168/M	$0.514/M	Fast machine translation
qwen-mt-lite	$0.13/M	$0.39/M	Lite machine translation

DeepSeek

Model	Input Price	Output Price	Features
deepseek-r1	$2.00/M	$8.00/M	Reasoning model, supports Tools
deepseek-v3	$1.00/M	$4.00/M	General chat, 671B parameters
deepseek-v3-0324	$1.20/M	$4.80/M	V3 stable version
deepseek-v3.1	$4.00/M	$12.00/M	Latest Terminus version

Note: Prices are in M (million tokens). Model availability may change, see marketplace.aisa.one/pricing for the latest list.

Quick Start

export AISA_API_KEY="your-key"

API Endpoints

OpenAI Compatible Interface

POST https://api.aisa.one/v1/chat/completions

Qwen Example

curl -X POST "https://api.aisa.one/v1/chat/completions" \
  -H "Authorization: Bearer $AISA_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen3-max",
    "messages": [
      {"role": "system", "content": "You are a professional Chinese assistant."},
      {"role": "user", "content": "Please explain what a large language model is?"}
    ],
    "temperature": 0.7,
    "max_tokens": 1000
  }'

DeepSeek Example

# DeepSeek-V3 general chat (671B parameters)
curl -X POST "https://api.aisa.one/v1/chat/completions" \
  -H "Authorization: Bearer $AISA_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "deepseek-v3",
    "messages": [{"role": "user", "content": "Write a quicksort algorithm in Python"}],
    "temperature": 0.3
  }'

# DeepSeek-R1 deep reasoning (supports Tools)
curl -X POST "https://api.aisa.one/v1/chat/completions" \
  -H "Authorization: Bearer $AISA_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "deepseek-r1",
    "messages": [{"role": "user", "content": "A farmer needs to cross a river with a wolf, a sheep, and a cabbage. The boat can only carry the farmer and one item at a time. If the farmer is not present, the wolf will eat the sheep, and the sheep will eat the cabbage. How can the farmer safely cross?"}]
  }'

# DeepSeek-V3.1 Terminus latest version
curl -X POST "https://api.aisa.one/v1/chat/completions" \
  -H "Authorization: Bearer $AISA_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "deepseek-v3.1",
    "messages": [{"role": "user", "content": "Implement an LRU cache with get and put operations"}]
  }'

Qwen3 Code Generation Example

curl -X POST "https://api.aisa.one/v1/chat/completions" \
  -H "Authorization: Bearer $AISA_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen3-coder-plus",
    "messages": [{"role": "user", "content": "Implement a thread-safe Map in Go"}]
  }'

Parameter Reference

Parameter	Type	Required	Description
model	string	Yes	Model identifier
messages	array	Yes	Message list
temperature	number	No	Randomness (0-2, default 1)
max_tokens	integer	No	Maximum tokens to generate
stream	boolean	No	Stream output (default false)
top_p	number	No	Nucleus sampling parameter (0-1)

Response Format

{
  "id": "chatcmpl-xxx",
  "object": "chat.completion",
  "created": 1234567890,
  "model": "qwen-max",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "A large language model (LLM) is a deep learning-based..."
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 30,
    "completion_tokens": 150,
    "total_tokens": 180,
    "cost": 0.001
  }
}

Streaming Output

curl -X POST "https://api.aisa.one/v1/chat/completions" \
  -H "Authorization: Bearer $AISA_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen-plus",
    "messages": [{"role": "user", "content": "Tell a Chinese folk story"}],
    "stream": true
  }'

Returns Server-Sent Events (SSE) format:

data: {"id":"chatcmpl-xxx","choices":[{"delta":{"content":"Once"}}]}
data: {"id":"chatcmpl-xxx","choices":[{"delta":{"content":" upon"}}]}
...
data: [DONE]

Python Client

CLI Usage

# Qwen chat
python3 {baseDir}/scripts/cn_llm_client.py chat --model qwen3-max --message "Hello, please introduce yourself"

# Qwen3 code generation
python3 {baseDir}/scripts/cn_llm_client.py chat --model qwen3-coder-plus --message "Write a binary search algorithm"

# DeepSeek-R1 reasoning
python3 {baseDir}/scripts/cn_llm_client.py chat --model deepseek-r1 --message "Which is larger, 9.9 or 9.11? Please reason in detail"

# DeepSeek-V3 chat
python3 {baseDir}/scripts/cn_llm_client.py chat --model deepseek-v3 --message "Tell a story" --stream

# With system prompt
python3 {baseDir}/scripts/cn_llm_client.py chat --model qwen3-max --system "You are a classical poetry expert" --message "Write a poem about plum blossoms"

# Model comparison
python3 {baseDir}/scripts/cn_llm_client.py compare --models "qwen3-max,deepseek-v3" --message "What is quantum computing?"

# List supported models
python3 {baseDir}/scripts/cn_llm_client.py models

Python SDK Usage

from cn_llm_client import CNLLMClient

client = CNLLMClient()  # Uses AISA_API_KEY environment variable

# Qwen chat
response = client.chat(
    model="qwen3-max",
    messages=[{"role": "user", "content": "Hello!"}]
)
print(response["choices"][0]["message"]["content"])

# Qwen3 code generation
response = client.chat(
    model="qwen3-coder-plus",
    messages=[
        {"role": "system", "content": "You are a professional programmer."},
        {"role": "user", "content": "Implement a singleton pattern in Python"}
    ],
    temperature=0.3
)

# Streaming output
for chunk in client.chat_stream(
    model="deepseek-v3",
    messages=[{"role": "user", "content": "Tell a story about an idiom"}]
):
    print(chunk, end="", flush=True)

# Model comparison
results = client.compare_models(
    models=["qwen3-max", "deepseek-v3", "deepseek-r1"],
    message="Explain what machine learning is"
)
for model, result in results.items():
    print(f"{model}: {result['response'][:100]}...")

Use Cases

1. Chinese Content Generation

# Copywriting
response = client.chat(
    model="qwen3-max",
    messages=[
        {"role": "system", "content": "You are a professional copywriter."},
        {"role": "user", "content": "Write a product introduction for a smart watch"}
    ]
)

2. Code Development

# Code generation and explanation
response = client.chat(
    model="qwen3-coder-plus",
    messages=[{"role": "user", "content": "Implement a thread-safe Map in Go"}]
)

3. Complex Reasoning

# Mathematical reasoning
response = client.chat(
    model="deepseek-r1",
    messages=[{"role": "user", "content": "Prove: For any positive integer n, n³-n is divisible by 6"}]
)

4. Visual Understanding

# Image understanding
response = client.chat(
    model="qwen3-vl-plus",
    messages=[
        {"role": "user", "content": [
            {"type": "text", "text": "Describe the content of this image"},
            {"type": "image_url", "image_url": {"url": "https://example.com/image.jpg"}}
        ]}
    ]
)

5. Model Routing Strategy

MODEL_MAP = {
    "chat": "qwen3-max",           # General chat
    "code": "qwen3-coder-plus",    # Code generation
    "reasoning": "deepseek-r1",    # Complex reasoning
    "vision": "qwen3-vl-plus",     # Visual understanding
    "fast": "qwen3-coder-flash",   # Fast response
    "translate": "qwen-mt-flash"   # Machine translation
}

def route_by_task(task_type: str, message: str) -> str:
    model = MODEL_MAP.get(task_type, "qwen3-max")
    return client.chat(model=model, messages=[{"role": "user", "content": message}])

Error Handling

Errors return JSON with error field:

{
  "error": {
    "code": "model_not_found",
    "message": "Model 'xxx' is not available"
  }
}

Common error codes:

• 401 - Invalid or missing API Key
• 402 - Insufficient balance
• 404 - Model not found
• 429 - Rate limit exceeded
• 500 - Server error

Pricing

Model	Input ($/M)	Output ($/M)
qwen3-max	$1.37	$5.48
qwen3-coder-plus	$2.86	$28.60
qwen3-coder-flash	$0.72	$3.60
qwen3-vl-plus	$0.43	$4.30
deepseek-v3	$1.00	$4.00
deepseek-r1	$2.00	$8.00
deepseek-v3.1	$4.00	$12.00

Price unit: $ per Million tokens. Each response includes usage.cost and usage.credits_remaining.

Get Started

1. Register at aisa.one
2. Get API Key
3. Top up (pay-as-you-go)
4. Set environment variable: export AISA_API_KEY="your-key"

Full API Reference

See API Reference for complete endpoint documentation.