AIsa CN-LLM Route

View on GitHub -> Chinese LLM routing through AIsa. Send Chinese-language tasks to Qwen, DeepSeek, GLM, Baichuan, and related model families.

Install

First, install the AIsa CLI if you have not already:

npm install -g @aisa-one/cli

Then install the skill:

aisa skills install cn-llm

What can agents do with it?

Chinese prompts

Route Chinese-language tasks to strong Chinese LLMs.

Model selection

Choose between Qwen, DeepSeek, GLM, Baichuan, and related models.

Bilingual workflows

Switch between Chinese and global model families.

Agent defaults

Give agents practical routing heuristics.

🔥 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 console.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 scripts/cn_llm_client.py chat --model qwen3-max --message "Hello, please introduce yourself"

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

# DeepSeek-R1 reasoning
python3 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 scripts/cn_llm_client.py chat --model deepseek-v3 --message "Tell a story" --stream

# With system prompt
python3 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 scripts/cn_llm_client.py compare --models "qwen3-max,deepseek-v3" --message "What is quantum computing?"

# List supported models
python3 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

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

Full API Reference

See API Reference for complete endpoint documentation.

Get started

Sign up at aisa.one (new accounts start with $2 free credit).
Generate an API key from the console.

Set your key and install the skill:

export AISA_API_KEY="your-key"
npm install -g @aisa-one/cli
aisa skills install cn-llm

Start a new agent session so the runtime loads the updated skill instructions.

Chinese LLMs

Overview of Chinese model families in AIsa.

AIsa LLM Router

General-purpose model routing across providers.

Model Catalog

Browse supported model IDs.

​Install

​What can agents do with it?

Chinese prompts

Model selection

Bilingual workflows

Agent defaults

​🔥 What You Can Do

​Intelligent Chat

​Deep Reasoning

​Code Generation

​Long Text Processing

​Model Comparison

​Supported Models

​Qwen (Alibaba)

​DeepSeek

​Quick Start

​API Endpoints

​OpenAI Compatible Interface

​Qwen Example

​DeepSeek Example

​Qwen3 Code Generation Example

​Parameter Reference

​Response Format

​Streaming Output

​Python Client

​CLI Usage

​Python SDK Usage

​Use Cases

​1. Chinese Content Generation

​2. Code Development

​3. Complex Reasoning

​4. Visual Understanding

​5. Model Routing Strategy

​Error Handling

​Pricing

​Get Started

​Full API Reference

​Get started

​Related