cn-llm

name: cn-llm

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

homepage: https://openclaw.ai

metadata: {"openclaw":{"emoji":"🐉","requires":{"bins":["curl","python3"],"env":["AISA_API_KEY"]},"primaryEnv":"AISA_API_KEY"}}

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.