Create deployment - Alibaba Cloud Model Studio - Alibaba Cloud Documentation Center

Create a model deployment task.

Prerequisites

Read Introduction to model deployment and Deploy a model by using the API to understand how model deployment works and the basic workflow on Alibaba Cloud Model Studio.
Configure your API key for Model Studio. For details, see Get an API key.

Model deployment

Endpoint

POST https://dashscope-intl.aliyuncs.com/api/v1/deployments

Request examples

Billing by provisioned throughput units (PTU)

Note

After running the deployment command below, billing starts immediately upon successful deployment—even if you have not yet called the model. Confirm the billing rules before deploying.

The PTU billing mode charges based on the duration of provisioned throughput usage. It suits scenarios requiring stable throughput guarantees, high concurrency, low latency, and predictable traffic. In this mode, throughput/concurrency and generation speed are preset by the platform and cannot be adjusted.

curl "https://dashscope-intl.aliyuncs.com/api/v1/deployments" \
--header "Authorization: Bearer $DASHSCOPE_API_KEY" \
--header 'Content-Type: application/json' \
--data '{
    "name": "my_qwen_flash",
    "model_name": "qwen-flash-2025-07-28",
    "plan": "ptu",
    "ptu_capacity": {
        "input_tpm": 10000,
	"output_tpm": 1000
    }
}'

Billing by model unit usage duration

Note

After running the deployment command below, billing starts immediately upon successful deployment—even if you have not yet called the model. Confirm the billing rules before deploying.
Model unit pay-as-you-go computing resources are allocated on a first-come, first-served basis. If purchase fails, you receive a full refund.

Select the model unit billing method. This mode charges based on model unit usage duration and suits large-scale inference workloads after model fine-tuning. Resources are dedicated, and performance and cost are flexible. Throughput/concurrency and generation speed are customizable.

curl "https://dashscope-intl.aliyuncs.com/api/v1/deployments" \
--header "Authorization: Bearer $DASHSCOPE_API_KEY" \
--header 'Content-Type: application/json' \
--data '{
    "name": "my_qwen_plus",
    "model_name": "qwen-plus-2025-12-01",
    "plan": "mu",
    "deploy_spec": "MU1",
    "enable_thinking": true,
    "capacity": 4,
    "max_context_length": 10000,
    "rpm_limit": 500,
    "tpm_limit": 1000
}'

The model unit deployment mode supports additional settings:

Configuration	Details
Service Name	The custom name of the deployment service.
Select Model	Select the model to deploy, including pre-trained platform models and fine-tuned models.
Model Unit Type	Select the deployment specification. Different specifications correspond to different computing power and performance.
Number of Replicas	Set the initial number of deployment replicas, which affects the service's concurrent processing capability.
Deployment Template	Select a deployment template, such as "Single-Machine Deployment". Different templates correspond to different resource configuration plans. This is only available in the model unit billing mode.
Configure Model Inference Mode	When some models are deployed using the Model Unit method, you can configure the inference mode, maximum context length, and other settings. Instruct - The deployed model performs inference in non-thinking mode. Thinking - The deployed model performs inference in thinking mode.
Maximum Context	This setting is supported for the Model Unit deployment mode of some models. The maximum context length is based on the model type.
Service Throttling	This setting is supported for the Model Unit deployment mode of some models. You can limit the RPM and TPM of model calls.

For details on setting these options via API, see Create a model deployment task using the API.

Billing by token usage

Select the token-based billing method. This mode charges based on token usage and suits cost-sensitive scenarios with low requirements for concurrency and latency. It offers the highest price advantage. Throughput/concurrency and generation speed are preset by the platform and cannot be adjusted.

curl "https://dashscope-intl.aliyuncs.com/api/v1/deployments" \
--header "Authorization: Bearer $DASHSCOPE_API_KEY" \
--header 'Content-Type: application/json' \
--data '{
    "model_name": "qwen3-8b-ft-202511132025-0260",
    "plan": "lora",
    "capacity": 1,
    "name": "qwen3-8b-ft"
}'

The capacity parameter has no effect but must be included. To scale up or down, go to the Model Studio model deployment console and submit a form request.

Request parameters

Parameter

Type

Location

Required

Description

model_name

String

body

Yes

The name of the model to deploy. This corresponds to the model ID in My Models. You can also get this ID from the output of the Create Training Job or Create Import Job operations.

plan

String

body

Yes

The deployment plan. The following billing methods are supported:

Billing method	Plan setting
Billing by model unit	`"plan": "mu"`
Billing by compute unit	`"plan": "cu"`
Provisioned throughput	`"plan": "ptu"`
LoRA shared deployment (billed by token usage)	`"plan": "lora"`

You can quickly find the supported deployment plans for a fine-tuned model in My Models.

Note

Fine-tuned CosyVoice models currently only support "plan": "mu".

name

String

body

Yes

The display name of the model in the console.

capacity

Integer

body

Required only when "plan": "mu" is specified. Specifies the number of resource units for the deployment. The value must be an integer multiple of base_capacity. The constraints vary based on the deploy_spec value. For example, for MU2, the value must be a multiple of 8, while for MU5, it can be 1. Example: "capacity": 1.

Note

CosyVoice models currently provide the following two deployment templates with corresponding capacity constraints:

single-node deployment: capacity must be an integer multiple of 1, such as 1, 2, 3, 4, or 5.
single-node deployment - flagship complex inference edition: capacity must be an integer multiple of 8, such as 8, 16, 24, or 32.

billing_method

String

body

Required only when "plan": "mu" is specified. Currently, only "POST_PAY" (Post-paid) is supported. Example: "billing_method": "POST_PAY".

deploy_spec

String

body

This setting is applicable only when "plan": "mu" is specified.

For details about feature support, see Feature support for model unit deployment.

This parameter is required when "plan": "mu" is specified. Example: "deploy_spec": "MU1".

Note

You can get this value from the template_id field returned by the Get Deployable Model List operation.

enable_thinking

Boolean

body

Supported by some models. You can set this to true or false.

max_context_length

Number

body

Supported by some models. Example: "max_context_length": 131072.

rpm_limit

Number

body

Supported by some models. Specifies the maximum number of requests per minute (RPM).

tpm_limit

Number

body

Supported by some models. Specifies the maximum number of tokens per minute (TPM).

ptu_capacity

Object

body

This setting is applicable only when "plan": "ptu" is specified.

For details about feature support, see Feature Support for PTU Deployment.

If you do not specify this parameter, the system defaults to 10,000 input_tpm and 1,000 output_tpm.

Example: "ptu_capacity": { "input_tpm": 10000, "output_tpm": 1000 }.

ptu_capacity.input_tpm

Number

body

Supported by all models. Specifies the maximum number of input tokens per minute (TPM).

ptu_capacity.output_tpm

Number

body

Supported by all models. Specifies the maximum number of output tokens per minute (TPM).

ptu_capacity.thinking_output_tpm

Number

body

Supported by some models. Specifies the maximum number of provisioned thinking output tokens per minute (TPM).

suffix

String

body

After a model is deployed, a new model name is generated. The suffix parameter specifies the suffix for this new name. It must be globally unique and have a maximum length of 8 characters. You can omit the suffix for the first deployment of a model. If you deploy the same model multiple times, you must specify a unique suffix for each deployment.

See the deployed_model output parameter for more information.

Supported models

View supported features and billing.

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.

Singapore

Qwen

Model name	Model code	Max input tokens	Pay-as-you-go input Per 10k TPM/hour	Pay-as-you-go output Per 1k TPM/hour	Subscription input Per 10k TPM/day	Subscription output Per 1k TPM/day
Qwen3.7-Max-2026-05-20	qwen3.7-max-2026-05-20	256K	$6	$1.8	$72	$21.6
Qwen3.7-Plus-2026-05-26	qwen3.7-plus-2026-05-26	256K	$0.96	$0.384	$11.52	$4.608

Qwen3.6-Plus-2026-04-02	qwen3.6-plus-2026-04-02	128K	$1.2	$0.72	$14.4	$8.64

Qwen3.5-Plus-2026-04-20	qwen3.5-plus-2026-04-20	128K	$0.96	$0.576	$11.52	$6.912

DeepSeek

Model name	Model code	Max input tokens	Pay-as-you-go input Per 10k TPM/hour	Pay-as-you-go output Per 1k TPM/hour	Subscription input Per 10k TPM/day	Subscription output Per 1k TPM/day
DeepSeek-v4-Flash	deepseek-v4-flash	256K	$0.72	$0.144	$8.64	$1.728
DeepSeek-v4-Pro	deepseek-v4-pro	256K	$8.64	$1.728	$103.68	$20.736
DeepSeek-v3.2	deepseek-v3.2	64K	$2.05	$0.616	$24.62	$7.387

Qwen-VL

Model name

Model code

Max input tokens

Pay-as-you-go input

Per 10k TPM/hour

Pay-as-you-go output

Per 1k TPM/hour

Subscription input

Per 10k TPM/day

Subscription output

Per 1k TPM/day

Qwen3-VL-Plus-2025-09-23

qwen3-vl-plus-2025-09-23

128K

$0.48

$0.384

$5.76

$4.608

More models

Model name

Model code

Max input tokens

Pay-as-you-go input

Per 10k TPM/hour

Pay-as-you-go output

Per 1k TPM/hour

Subscription input

Per 10k TPM/day

Subscription output

Per 1k TPM/day

GLM-5.1

glm-5.1

64K

$5.04

$1.584

$64.8

$19.008

China (Beijing)

Qwen

Model name	Model code	Max input tokens	Pay-as-you-go input Per 10k TPM/hour	Pay-as-you-go output Per 1k TPM/hour	Subscription input Per 10k TPM/day	Subscription output Per 1k TPM/day
Qwen3.7-Max-2026-05-20	qwen3.7-max-2026-05-20	256K	$3.96	$1.188	$47.53	$14.258
Qwen3.7-Plus-2026-05-26	qwen3.7-plus-2026-05-26	256K	$0.66	$0.264	$7.92	$3.168

Qwen3.6-Plus-2026-04-02	qwen3.6-plus-2026-04-02	128K	$0.67	$0.397	$7.93	$4.753

Qwen3.5-Plus-2026-04-20	qwen3.5-plus-2026-04-20	128K	$0.26	$0.16	$3.17	$1.9

Qwen3-Max-2025-09-23	qwen3-max-2025-09-23	128K	$1.11	$0.45	$13.32	$5.4

Qwen-Flash-2025-07-28	qwen-flash-2025-07-28	128K	$0.06	$0.06	$0.72	$0.72
Qwen-Plus-2025-12-01	qwen-plus-2025-12-01	128K	$0.28	Non-thinking mode: $0.07 Thinking mode: $0.28	$3.36	Non-thinking mode: $0.84 Thinking mode: $3.36

DeepSeek

Model name	Model code	Max input tokens	Pay-as-you-go input Per 10k TPM/hour	Pay-as-you-go output Per 1k TPM/hour	Subscription input Per 10k TPM/day	Subscription output Per 1k TPM/day
DeepSeek-v4-Flash	deepseek-v4-flash	256K	$0.5	$0.099	$5.94	$1.188
DeepSeek-v4-Pro	deepseek-v4-pro	256K	$5.94	$1.188	$71.3	$14.26
DeepSeek-v3.2	deepseek-v3.2	64K	$1.04	$0.16	$12.48	$1.92
DeepSeek-v3	deepseek-v3	64K	$0.99	$0.396	$11.9	$4.75

Qwen-VL

Model name

Model code

Max input tokens

Pay-as-you-go input

Per 10k TPM/hour

Pay-as-you-go output

Per 1k TPM/hour

Subscription input

Per 10k TPM/day

Subscription output

Per 1k TPM/day

Qwen3-VL-Plus-2025-09-23

qwen3-vl-plus-2025-09-23

128K

$0.35

$4.2

More models

Model name

Model code

Max input tokens

Pay-as-you-go input

Per 10k TPM/hour

Pay-as-you-go output

Per 1k TPM/hour

Subscription input

Per 10k TPM/day

Subscription output

Per 1k TPM/day

GLM-5.1

glm-5.1

64K

$2.97

$1.19

$35.65

$14.26

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.

Note

For the Model Unit pay-as-you-go method, computing power resources are allocated on a first-come, first-served basis. A full refund is issued if the purchase is unsuccessful.

Singapore

Text generation

Model name	Model code	Model unit specification	Hourly price ($) Minimum billing unit: minute	Monthly price ($) Minimum billing unit: day
Qwen3.6-Plus-2026-04-02	qwen3.6-plus-2026-04-02	MU1 x 8	$88	$41,832

Qwen3.5-39B-A17B	qwen3.5-397b-a17b	MU2 x 8	$112	$52,392
Qwen3.5-35B-A3B	qwen3.5-35b-a3b	MU2 x 8	$112	$52,392

Qwen3-32B	qwen3-32b	MU1 x 4	$44	$20,916
Qwen3-32B	qwen3-32b	MU2 x 8	$112	$52,392
Qwen3-14B	qwen3-14b	MU1 x 4	$44	$20,916

GLM-5.1	glm-5.1	MU2 x 8	$112	$52,392

DeepSeek-V4-Flash	deepseek-v4-flash	MU1 x 8	$88	$41,832

Multimodal

Model name

Model code

Model unit specification

Hourly price ($)

Minimum billing unit: minute

Monthly price ($)

Minimum billing unit: day

Qwen3-VL-32B-Instruct

qwen3-vl-32b-instruct

MU2 x 8

$112

$52,392

Qwen3-VL-8B-Instruct

qwen3-vl-8b-instruct

MU1 x 2

$22

$10,458

Model types:

Instruct - The deployed model performs inference in non-thinking mode.

China (Beijing)

Text generation

Qwen

Model name	Model code	Model unit specification	Hourly price ($) Minimum billing unit: minute	Monthly price ($) Minimum billing unit: day
Qwen3.7-Plus-2026-05-26	qwen3.7-plus-2026-05-26	MU3 x 8	$150.72	$72,577.152

Qwen3.6-35B-A3B	qwen3.6-35b-a3b	MU8 x 1	$6.464	$3,080.477
Qwen3.6-35B-A3B	qwen3.6-35b-a3b	MU9 x 1	$7.014	$3,383.024
Qwen3.6-27B	qwen3.6-27b	MU9 x 1	$7.014	$3,383.024
Qwen3.6-Flash-2026-04-16	qwen3.6-flash-2026-04-16	MU1 x 2	$14.852	$7,183.564
Qwen3.6-Plus-2026-04-02	qwen3.6-plus-2026-04-02	MU1 x 8	$59.408	$28,734.256

Qwen3.5-397B-A17B	qwen3.5-397b-a17b	MU2 x 8	$69.312	$33,044.72
		MU3 x 8	$150.72	$72,577.152
		MU6 x 16	$55.008	$26,599.92
Qwen3.5-122B-A10B	qwen3.5-122b-a10b	MU1 x 4	$29.704	$14,367.128
		MU2 x 8	$69.312	$33,044.72
		MU6 x 16	$55.008	$26,599.92
		MU9 x 2	$14.028	$6,766.048
Qwen3.5-35B-A3B	qwen3.5-35b-a3b	MU1 x 2	$14.852	$7,183.564
		MU2 x 8	$69.312	$33,044.72
		MU8 x 1	$6.464	$3,080.477
		MU9 x 1	$7.014	$3,383.024
Qwen3.5-27B	qwen3.5-27b	MU9 x 1	$7.014	$3,383.024
Qwen3.5-9B	qwen3.5-9b	MU8 x 1	$6.464	$3,080.477
Qwen3.5-9B	qwen3.5-9b	MU9 x 1	$7.014	$3,383.024
Qwen3.5-Flash-2026-02-23	qwen3.5-flash-2026-02-23	MU1 x 2	$14.852	$7,183.564
Qwen3.5-Plus-2026-02-15	qwen3.5-plus-2026-02-15	MU1 x 8	$59.408	$28,734.256
Qwen3.5-Plus-2026-02-15	qwen3.5-plus-2026-02-15	MU3 x 8	$150.72	$72,577.152

Qwen3-235B-A22B-Instruct	qwen3-235b-a22b-instruct-2507	MU1 x 4	$29.704	$14,367.128
Qwen3-235B-A22B-Instruct	qwen3-235b-a22b-instruct-2507	MU2 x 8	$69.312	$33,044.72
Qwen3-Next-80B-A3B-Instruct	qwen3-next-80b-a3b-instruct	MU1 x 2	$14.852	$7,183.564
Qwen3-32B	qwen3-32b	MU1 x 4	$29.704	$14,367.128
Qwen3-32B	qwen3-32b	MU6 x 4	$13.752	$6,649.98
Qwen3-30B-A3B	qwen3-30b-a3b	MU9 x 2	$14.028	$6,766.048
Qwen3-30B-A3B-Instruct-2507	qwen3-30b-a3b-instruct-2507	MU1 x 4	$29.704	$14,367.128
Qwen3-30B-A3B-Instruct-2507	qwen3-30b-a3b-instruct-2507	MU2 x 8	$69.312	$33,044.72
Qwen3-8B	qwen3-8b	MU1 x 2	$14.852	$7,183.564
		MU2 x 2	$17.328	$8,261.18
		MU5 x 1	$2.888	$1,394.329
Qwen3-4B	qwen3-4b	MU1 x 2	$14.852	$7,183.564
Qwen3-4B	qwen3-4b	MU5 x 1	$2.888	$1,394.329
Qwen3-1.7B	qwen3-1.7b	MU1 x 2	$14.852	$7,183.564
Qwen3-1.7B	qwen3-1.7b	MU5 x 1	$2.888	$1,394.329
Qwen3-Max-2025-09-23	qwen3-max-2025-09-23	MU2 x 8	$69.312	$33,044.72
Qwen3-Max-2025-09-23	qwen3-max-2025-09-23	MU3 x 8	$150.72	$72,577.152

Qwen2.5-72B	qwen2.5-72b-instruct	MU1 x 4	$29.704	$14,367.128
Qwen2.5-32B	qwen2.5-32b-instruct	MU1 x 4	$29.704	$14,367.128
Qwen2.5-14B	qwen2.5-14b-instruct	MU1 x 2	$14.852	$7,183.564
Qwen2.5-7B	qwen2.5-7b-instruct	MU1 x 2	$14.852	$7,183.564
Qwen2.5-7B	qwen2.5-7b-instruct	MU5 x 1	$2.888	$1,394.329
Qwen2.5-3B-Instruct	qwen2.5-3b-instruct	MU5 x 1	$2.888	$1,394.329

Qwen-Flash-2025-07-28	qwen-flash-2025-07-28	MU1 x 4	$29.704	$14,367.128
Qwen-Plus-2025-07-28	qwen-plus-2025-07-28	MU1 x 4	$29.704	$14,367.128
Qwen-Plus-2025-12-01	qwen-plus-2025-12-01	MU1 x 4	$29.704	$14,367.128

GLM

Model name	Model code	Model unit specification	Hourly price ($) Minimum billing unit: minute	Monthly price ($) Minimum billing unit: day
GLM-5.1	glm-5.1	MU2 x 8	$69.312	$33,044.72
		MU3 x 8	$150.72	$72,577.152
		MU6 x 16	$55.008	$26,599.92
GLM-5	glm-5	MU3 x 8	$150.72	$72,577.152
GLM-4.7	glm-4.7	MU6 x 16	$55.008	$26,599.92

DeepSeek

Model name

Model code

Model unit specification

Hourly price ($)

Minimum billing unit: minute

Monthly price ($)

Minimum billing unit: day

DeepSeek-V4-Flash

deepseek-v4-flash

MU1 x 8

$59.408

$28,734.256

DeepSeek-V3.2

deepseek-v3.2

MU2 x 8

$69.312

$33,044.72

Other models

Model name	Model code	Model unit specification	Hourly price ($) Minimum billing unit: minute	Monthly price ($) Minimum billing unit: day
MiniMax-M2.5	MiniMax-M2.5	MU1 x 8	$59.408	$28,734.256

Kimi-K2.5	kimi-k2.5	MU2 x 8	$69.312	$33,044.72

Multimodal

Qwen-VL

Model name	Model code	Model unit specification	Hourly price ($) Minimum billing unit: minute	Monthly price ($) Minimum billing unit: day
Qwen3-VL-235B-A22B-Instruct	qwen3-vl-235b-a22b-instruct	MU1 x 4	$29.704	$14,367.128
Qwen3-VL-235B-A22B-Thinking	qwen3-vl-235b-a22b-thinking	MU1 x 4	$29.704	$14,367.128
Qwen3-VL-32B-Instruct	qwen3-vl-32b-instruct	MU2 x 8	$69.312	$33,044.72
Qwen3-VL-8B-Instruct	qwen3-vl-8b-instruct	MU1 x 2	$14.852	$7,183.564
Qwen3-VL-Flash-2025-10-15	qwen3-vl-flash-2025-10-15	MU1 x 4	$29.704	$14,367.128
Qwen3-VL-Plus-2025-09-23	qwen3-vl-plus-2025-09-23	MU1 x 4	$29.704	$14,367.128

Qwen-VL-Max-2025-08-13	qwen-vl-max-2025-08-13	MU6 x 4	$13.752	$6,649.98
Qwen-VL-OCR-2025-11-20	qwen-vl-ocr-2025-11-20	MU6 x 4	$13.752	$6,649.98

Qwen Omni

Model name	Model code	Model unit specification	Hourly price ($) Minimum billing unit: minute	Monthly price ($) Minimum billing unit: day
Qwen3.5-Omni-Flash	qwen3.5-omni-flash	MU8 x 1	$6.464	$3,080.477
Qwen3.5-Omni-Flash	qwen3.5-omni-flash	MU9 x 1	$7.014	$3,383.024
Qwen3.5-Omni-Plus	qwen3.5-omni-plus	MU9 x 8	$56.112	$27,064.192

Model types:

Instruct - The deployed model performs inference in non-thinking mode.
Thinking - The deployed model performs inference in thinking mode.

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.

Singapore

Foundation model

Model code

Input

$/1k tokens

Output

$/1k tokens

Qwen3-14B

qwen3-14b

$0.00035

Non-thinking mode: $0.0014

Thinking mode: $0.0042

Response example

The command returns the following:

{
  "request_id": "f2ae64f7-83cc-410c-bc0b-840443f7eb86",
  "output": {
    "deployed_model": "emo-35b3f106-sample01",
    "gmt_create": "2025-06-17T11:00:38.68",
    "gmt_modified": "2025-06-17T11:00:38.68",
    "status": "PENDING",
    "model_name": "emo",
    "base_model": "emo",
    "base_capacity": 1,
    "capacity": 1,
    "ready_capacity": 0,
    "workspace_id": "llm-v71tlv3d***",
    "charge_type": "post_paid",
    "creator": "175805416***",
    "modifier": "175805416***"
  }
}

Response parameters

Parameter	Type	Description
request_id	String	The ID of the request.
output	Object	Details of the deployment task.
deployed_model	String	A unique identifier for the deployed model. This ID is used for API operations, such as querying deployment details, modifying deployment rate limiting, deployment scaling, and deleting deployments, and is also passed as an SDK parameter when you invoke the model.
gmt_create	String	The creation time of the deployment task.
gmt_modified	String	The last modification time of the deployment task.
status	String	The status of the deployment task. PENDING: The task is being created. UPDATING: The task is being updated. RUNNING: The deployment task is running, and the deployed model can process requests. STOPPED: The deployment task is stopped and is not billed. DELETING: The task is being deleted. FAILED: The creation or update of the task failed.
model_name	String	The name of the model used in the deployment task.
base_model	String	The ID of the base model used in the deployment task.
base_capacity	Number	The minimum number of resource units required to run the base model.
capacity	Number	The number of resource units used by the deployment task.
ready_capacity	Number	The number of resource units that are ready to process requests immediately. Resource initialization speed or hardware status can limit this value.
workspace_id	String	The ID of the deployment task's workspace.
charge_type	String	The billing method for the deployment task. `post_paid`: Post-paid.
creator	String	The UID of the user who created the deployment task.
modifier	String	The UID of the user who last modified the deployment task.
plan	String	The billing model for the deployment task. This parameter is not returned for some billing models.
Returned only for Model Unit deployments.
model_unit_spec	String	The model unit specification.
enable_thinking	Boolean	Specifies if Thinking mode is enabled. This feature is only available for certain models.
max_context_length	Number	The maximum context length.
rpm_limit	String	The maximum number of requests per minute (RPM).
tpm_limit	Number	The maximum number of tokens per minute (TPM).
Returned only for provisioned throughput (PTU) deployments.
ptu_capacity	Object	This parameter takes effect only when `"plan": "ptu"` is set. Example: `"ptu_capacity": { "input_tpm": 10000, "output_tpm": 1000 }`.
ptu_capacity.input_tpm	Number	The maximum number of input tokens per minute (TPM) for the deployed model. This feature is supported by all models.
ptu_capacity.output_tpm	Number	The maximum number of output tokens per minute (TPM) for the deployed model. This feature is supported by all models.
ptu_capacity.thinking_output_tpm	Number	The maximum number of thinking output tokens per minute (TPM) for the deployed model. This feature is only available for certain models.

Error response

Response example

{
    "request_id": "ca218d57-b91b-46b2-bd35-c41c6287bcf4",
    "message": "Model: qwen-plus-20230703-cx7f not found!",
    "code": "NotFound"
}

Response parameters

Parameter	Type	Description
request_id	String	The unique ID of the request.
code	String	The error code.
message	String	The error message.

The following errors can occur when a request fails:

Error code	Error message	Reason
NotFound	Model: xxx not found!	You are creating a deployment task with a model that does not exist. You are querying, updating, or deleting a deployment task with a model that does not exist.
Conflict	Deployed model xxx already exists, please specify a suffix.	You are creating a deployment task with a suffix that is already in use.
InvalidParameter	Invalid capacity (xx), capacity must be larger than or equal to 0 and multiples of 1 and less than 1000!	You are creating or updating a deployment task with an invalid number of capacity units.