Alibaba Cloud Model Studio:Model deployment

Time-based billing (Provisioned Throughput)

Cost = Usage Duration × (Input TPM Unit Price × Input TPM + Output TPM Unit Price × Output TPM)

For the pay-as-you-go method, usage is billed hourly, and the unit price is based on the hourly rates in the table below. For the subscription method, usage is billed daily, and the unit price is based on the daily rates in the table below.

• Subscription orders take effect immediately after payment. An N-day subscription is valid until 23:59 on the Nth day. If an order is placed after 22:00, the expiration date is automatically extended by one day.

• After a subscription order expires, the service is stopped after a 2-hour grace period. After the service is stopped, the resources are retained for 14 hours and then released.

• Subscription orders cannot be terminated early.

• For the pay-as-you-go method, if your account has an overdue payment, the deployed resources are retained and continue to be billed for 24 hours, during which the service remains available. After 24 hours, the system stops billing, and the model deployment enters an overdue state. The underlying resources are deleted, but the model deployment task is retained. After you pay the overdue amount, the system reallocates resources, restores the service, and resumes billing. To stop incurring charges, you must delete the model deployment task. Billing stops after the task is successfully deleted.

If the model input exceeds the maximum input tokens or the purchased TPM, the call automatically switches to the pay-as-you-go mode for the current model. In this case, inference performance may decrease and will be subject to the public traffic control of the current snapshot model in the workspace. Costs are charged based on the model invocation (pay-as-you-go) standard.

• In this case, the API call returns a header that contains x-dashscope-ptu-overflow:true.

• To view TPM statistics, go to Model Monitoring.

For the specific refund rules for scale-in scenarios (downgrades), see Refund rules for downgrades.

Time-based billing (Model Unit)

Cost = Usage Duration (hours) × Number of Model Units × Model Unit Price

For the pay-as-you-go method, the "Model Unit Price" is the "Hourly Price" from the table below. For the monthly subscription method, the formula is: Number of Months × Number of Model Units × Monthly Price.

• For subscriptions, if you unsubscribe within the first month, the daily unit price (≈ monthly unit price / 30) is charged at 1.2 times the standard rate. Usage for less than a day is billed as a full day.

By model token usage

Cost = Number of Input Tokens × Input Unit Price + Number of Output Tokens × Output Unit Price (Minimum billing unit: 1 token)

• Billing by model token usage is only supported after you have completed Supervised Fine-Tuning (SFT) for the following foundation models and you have obtained a custom model.

DashScope

import os
import dashscope

messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Who are you?"},
]
dashscope.base_http_api_url = 'https://dashscope-intl.aliyuncs.com/api/v1'
response = dashscope.Generation.call(
    # If the environment variable is not configured, replace the next line with: api_key="sk-xxx",
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    model="qwen3-max-xxx-xxx",  # Replace with the code generated after the model is successfully deployed
    messages=messages,
    result_format="message",
    enable_thinking=False,
)
print(response)

OpenAI compatible interface

import os
from openai import OpenAI


client = OpenAI(
    # If the environment variable is not configured, replace the next line with: api_key="sk-xxx",
    api_key=os.getenv('DASHSCOPE_API_KEY'),
    base_url="https://dashscope-intl.aliyuncs.com/compatible-mode/v1",
)

completion = client.chat.completions.create(
    model="qwen3-max-xxx-xxx",  # Replace with the code generated after the model is successfully deployed
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Who are you?"},
    ],
    extra_body={"enable_thinking": False},
)
print(completion)