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Training and deployment pricing

This topic describes the billing rules and pricing for model training and model deployment on Alibaba Cloud Model Studio.

Training billing

Text generation models – Qwen

For the training workflow, see Fine-tune Qwen. After training completes, deploy the new model before evaluating or calling it.
MethodBilled by training tokens
FormulaModel training fee = (Total tokens in training data + Total tokens in mixed training data) × Number of epochs × Training unit price (Minimum billing unit: 1 token)
View the estimated training fee at the bottom of the model training console, and click Computing Details to view the total number of training tokens, number of epochs, and training unit price.

Unit price for training

The following table lists the unit prices for training pre-trained models. The unit price for training a custom model matches that of the corresponding pre-trained model.
  • Qwen
  • Qwen-VL

Model service

Model code

Price

Qwen3.8-27B

qwen3.8-27b

¥0.05/1K tokens

Qwen3.7-Plus-2026-05-26

qwen3.7-plus-2026-05-26

¥0.35/1K tokens

Qwen3.6-Flash-2026-04-16

qwen3.6-flash-2026-04-16

¥0.05/1K tokens

Qwen3.6-Plus-2026-04-02

qwen3.6-plus-2026-04-02

¥0.3/1K tokens

Qwen3.5-27B

qwen3.5-27b

¥0.05/1K tokens

Qwen3.5-9B

qwen3.5-9b

¥0.02/1K tokens

Qwen3.5-4B

qwen3.5-4b

¥0.015/1K tokens

Qwen3.5-Flash-2026-02-23

qwen3.5-flash-2026-02-23

¥0.05/1K tokens

Qwen3.5-Plus-2026-02-15

qwen3.5-plus-2026-02-15

¥0.3/1K tokens

Qwen3-32B

qwen3-32b

¥0.04/1K tokens

Qwen3-30B-A3B-Instruct-2507

qwen3-30b-a3b-instruct-2507

¥0.03/1K tokens

Qwen3-14B

qwen3-14b

¥0.03/1K tokens

Qwen3-8B

qwen3-8b

¥0.006/1K tokens

Qwen3-4B-Instruct-2507

qwen3-4b-instruct-2507

¥0.006/1K tokens

Qwen3-1.7B

qwen3-1.7b

¥0.0045/1K tokens

Qwen3-0.6B

qwen3-0.6b

¥0.003/1K tokens

Qwen2.5-72B-Instruct

qwen2.5-72b-instruct

¥0.15/1K tokens

Qwen2.5-32B-Instruct

qwen2.5-32b-instruct

¥0.03/1K tokens

Qwen2.5-14B-Instruct

qwen2.5-14b-instruct

¥0.03/1K tokens

Qwen2.5-7B-Instruct

qwen2.5-7b-instruct

¥0.006/1K tokens

Qwen-Plus-Character-2025-11-06

qwen-plus-character-2025-11-06

¥0.15/1K tokens

Image generation models – Wan

For the training workflow, see Fine-tune image generation models. After training completes, deploy the new model before calling it.

Method

Billed by training tokens

Formula

Model training fee = Total training tokens × Training unit price (Billing unit: per 1,000 tokens)

Formula for total training tokens

Training Tokens Total ≈ max_steps × LstepWhere:
  • max_steps: A hyperparameter specified during training, representing the maximum number of training steps (configured when creating a fine-tuning job).
  • Lstep: The token consumption per step. The formula is: Lstep = ∑i∈batch Litem(i) ≤ Lmax Lstep is approximately equal to Lmax. Lmax is determined by the max_token_length and generation_type, as shown below:

generation_type

max_token_length

Lmax

t2i (text-to-image)

1k

12,800

2k

23,220

i2i (image-to-image)

1k

23,220

2k

32,000

The above formula provides an approximation. Actual billing is based on the usage field returned by the system.

Model

Code

Training price (per 1K tokens)

Wan image generation

wan2.7-image-pro

$0.015

Wan image generation

wan2.7-image

$0.015

Suppose you fine-tune the wan2.7-image-pro model for t2i. The parameters are: max_steps = 200, max_token_length = "1k", and the training price is $0.015 per 1,000 tokens:
  • From the table: Lmax = 12,800 (generation_type=t2i, max_token_length=1k), Lstep ≈ Lmax = 12,800
  • Total training tokens ≈ 200 × 12800 = 2560000 = 2560 thousand tokens
  • Model training fee ≈ 2560× 0.015 = $38.4

Image generation models – Qwen

For the training workflow, see Fine-tune image generation models. After training completes, deploy the new model before calling it.

Method

Billed by training tokens

Formula

Model training fee = Total training tokens × Training unit price (Billing unit: per 1,000 tokens)

Formula for total training tokens

Total training tokens = n_epochs × N × (max_pixels / Compression ratio) × GPU coefficientWhere:
  • n_epochs: A hyperparameter specified during training, representing the number of training epochs (configured when creating a fine-tuning job).
  • N: The total number of images in the training set.
  • max_pixels: A hyperparameter specified during training, representing the maximum number of pixels per image (configured when creating a fine-tuning job). Valid values are 1k (1024×1024) and 2k (2048×2048).
  • Compression ratio: The fixed VAE compression ratio, which is 1024 (32×32).
  • GPU coefficient: A resource scheduling coefficient that is dynamically adjusted based on job scheduling.

Model

Code

Training price (per 1K tokens)

Qwen image generation

qwen-image-2.0

$0.003

Qwen image generation

qwen-image-2.0-pro

$0.003

Suppose you fine-tune the qwen-image-2.0 model with a training set of 1 image. The parameters are: n_epochs = 1000, max_pixels = "1k", the training price is $0.003 per 1,000 tokens, and the GPU coefficient is 8:
  • Tokens per image = max_pixels / Compression ratio = 1024×1024 / 1024 = 1024
  • Total training tokens = 1000 × 1 × 1024 × 8 = 8192000 = 8192 thousand tokens
  • Model training fee = 8192 × 0.003 = $24.576
The following table estimates token consumption and fees per training image, assuming a GPU coefficient of 8. For a training set of N images, multiply the values by N.

max_pixels

n_epochs

Estimated tokens

Estimated fee (USD)

1k

800

6,553,600

19.66

1k

1,000

8,192,000

24.58

1k

2,000

16,384,000

49.15

2k

800

26,214,400

78.64

2k

1,000

32,768,000

98.30

2k

2,000

65,536,000

196.61

  • The GPU coefficient is dynamically adjusted based on job scheduling. The value 8 in the example and the table above is used only to illustrate the calculation. For actual token consumption, see the usage field returned by the Query a fine-tuning job operation. Your bill is the final authority on fees.
  • batch_size does not affect billing or the total training tokens.

Video generation models – Wan

For the training workflow, see Fine-tuning video generation models. After training completes, deploy the new model before calling it.

Method

Billed by training tokens

Formula

Model training fee = Total training tokens × Training unit price (Billing unit: per 1,000 tokens)

Formula for total training tokens

Training Tokens Total = (∑i=1N billing duration of videoi) × (max_pixels / 1024) × n_epochsWhere:
  • N: Total number of videos in the training set.
  • max_pixels: A hyperparameter specified during training, representing the maximum number of pixels for a video (configured when creating a fine-tuning job).
  • n_epochs: A hyperparameter specified during training, representing the number of loops (configured when creating a fine-tuning job).
    • The conversion between n_epochs and steps is: steps = n_epochs × ⌈dataset_size / batch_size⌉, i.e., n_epochs = steps / ⌈dataset_size / batch_size⌉.
    • When the dataset contains only 1 sample and batch_size = 1, n_epochs = steps. We recommend a total of at least 800 steps.
  • Billing duration calculation rule for a single video: First, round the original video duration (in seconds) to the nearest integer, then determine the final value based on model limits.
    • wan2.7 model: Billing duration=min(10, rounded duration), meaning a single video is billed for a maximum of 10 seconds.
    • wan2.6 model: Billing duration=min(10, rounded duration), meaning a single video is billed for a maximum of 10 seconds.
    • wan2.5 model: Billing duration=min(10, rounded duration), meaning a single video is billed for a maximum of 10 seconds.
    • wan2.2 model: Billing duration=min(5, rounded duration), meaning a single video is billed for a maximum of 5 seconds.

Model

Code

Training price (per 1K tokens)

Image-to-video (first frame)

wan2.7-i2v

$0.3

wan2.6-i2v

$0.08

wan2.5-i2v-preview

$0.05

wan2.2-i2v-flash

$0.03

Image-to-video (first and last frames)

wan2.7-i2v

$0.3

wan2.2-kf2v-flash

$0.03

Billing examples

  1. wan2.7-i2v cost estimation (single data)
Assume a training set contains 1 video with a duration of 10 seconds. With batch_size = 1 (recommended), n_epochs = steps / ⌈1(dataset_size) / 1(batch_size)⌉ = steps.Training unit price = $0.3/thousand tokens. Taking max_pixels = 36864 and n_epochs = 800 as an example:
  • Total training tokens = 10 × (36864 / 1024) × 800 = 288,000 = 288 thousand tokens
  • Model training fee = 288 × $0.3 = $86.4

max_pixels

Common steps

n_epochs

Estimated tokens

Estimated cost (USD)

36864

800

800

288,000

$86.4

1,000

1,000

360,000

$108

2,000

2,000

720,000

$216

65536

800

800

512,000

$153.6

1,000

1,000

640,000

$192

2,000

2,000

1,280,000

$384

102400

800

800

800,000

$240

1,000

1,000

1,000,000

$300

2,000

2,000

2,000,000

$600

  1. wan2.7-i2v cost estimation (multiple data)
Assume a training set contains 2 videos with durations of 3.4 seconds and 11.5 seconds. Parameters: max_pixels = 36864, n_epochs = 800. Training unit price = $0.3/thousand tokens:
  • Duration calculation:
    • Video 1: 3.4 seconds is rounded to 3. Billable duration = min(10, 3) = 3.
    • Video 2: 11.5 seconds is rounded to 11. Billable duration = min(10, 11) = 10.
    • Total billable duration = 3 + 10 = 13 seconds.
  • Total training tokens = 13 × (36864/1024) × 800 = 374,400 = 374.4 thousand tokens.
  • Model training fee = 374.4 × 0.3 = $112.32.
  1. wan2.5-i2v-preview cost estimation (multiple data)
Suppose you fine-tune the wan2.5 model. The training set contains two videos: 3.4 seconds and 11.5 seconds. The parameters are max_pixels = 36864 and n_epochs = 400. The unit training price is $0.05 per 1,000 tokens.
  • Duration calculation:
    • Video 1: 3.4 seconds is rounded to 3. Billable duration: min(10, 3) = 3 seconds.
    • Video 2: 11.5 seconds is rounded to 11. Billable duration: min(10, 11) = 10 seconds.
    • Total billable duration: 3 + 10 = 13 seconds.
  • Total training tokens = 13 × (36864 / 1024) × 400 = 187,200 = 187.2 thousand tokens.
  • Model training fee = 187.2 × 0.05 = $9.36.

Deployment billing

Text generation models: Qwen

  • Usage duration billing (Provisioned Throughput)
  • Usage duration billing (Dedicated Throughput)
  • By model Token usage
  • Billing by usage duration (model unit)
Cost = Usage duration × (Input TPM unit price × Input TPM + Output TPM unit price × Output TPM)Postpaid is calculated by hour: the usage duration unit is hours, and the unit price takes the "1 hour continuous" column in the table below; prepaid is calculated by day: the usage duration unit is days, and the unit price takes the "1 day continuous" column in the table below.
  • Prepaid orders take effect immediately after payment, valid for N days until 23:59 on day N. If ordered after 22:00, the expiration date is automatically extended by 1 day.
  • After a prepaid order expires, the service will be stopped with a 2-hour delay, and resources will be retained for 14 hours after stopping before being released.
  • Prepaid orders support early termination; the used portion is settled at a 1.2x coefficient for refund. For details, see Refund rules for configuration downgrades.
  • For post-paid billing, if your account is in arrears, the deployed resources will be retained and billed for 24 hours, during which the service can still be used normally. After 24 hours, the system stops billing, the model deployment enters an arrears state, and the underlying resources will be deleted, but the model deployment task will be retained. After you pay off the arrears, the system will reallocate resources and resume usage (fees will continue to accrue after resumption). If you do not want to continue incurring charges, you can delete the model deployment task; once deleted successfully, billing will stop.
When the model input exceeds the maximum input Token, the relevant call will automatically switch to the pay-as-you-go mode of the current model; when the purchased TPM is exceeded, it is handled according to the overflow policy selected at creation ("Auto-overflow" switches to pay-as-you-go, "PTU capacity only" returns 429). At this time, inference performance may degrade and will be governed by the public traffic of the current snapshot model in the workspace, and the fee is charged according to the model invocation (pay-as-you-go) standard.
  • At this time (only under the "Auto-overflow" policy), the call API response Header will include: x-dashscope-ptu-overflow:true.
  • For TPM statistics, go to: Model Monitoring.
For specific fee-reduction and refund rules for scale-in (downgrade) scenarios, please refer to: Refund rules for configuration downgrades.
PTU deployment supports stepped capacity coefficients and cache discounts for long inputs. For details, see Provisioned Throughput long input and cache.
  • North China 2 (Beijing)
  • Singapore
  • Qwen
  • DeepSeek
  • Qwen-VL
  • GLM

Model name

Model code

Max input tokens

Postpaid input

Per 10K TPM/hour

Postpaid output

Per 1K TPM/hour

Prepaid input

Per 10K TPM/day

Prepaid output

Per 1K TPM/day

Qwen3.8-Max

qwen3.8-max

1M

$3.96

$1.188

$47.53

$14.258

Qwen3.7-Flash-2026-07-15

qwen3.7-flash-2026-07-15

128K

$0.066

$0.026

$0.792

$0.317

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

$0.066

$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: $0.07

Thinking: $0.28

$3.36

Non-thinking: $0.84

Thinking: $3.36

Image generation models – Wan

Deployment is free. Invocations are billed at the standard rate of the fine-tuned base model. For the training workflow, see Fine-tune image generation models.

Model ID

LoRA Deployment & Invocation Price

wan2.7-image-pro

$0.075/image

wan2.7-image

$0.03/image

Image generation models – Qwen

Deployment is free. Invocations are billed at the standard rate of the fine-tuned base model. For the training and deployment workflow, see Fine-tune image generation models.

Model ID

LoRA Deployment & Invocation Price

qwen-image-2.0

$0.035/image

qwen-image-2.0-pro

$0.075/image

Video generation models – Wan

Fine-tuned Wan video generation models:
  • Model deployment: Free
  • Model invocation: Billed at the standard rate of the fine-tuned base model. For details, see model pricing

FAQ

Q: When does billing for model deployment start?

A: Billing starts when the model status changes to Running. No charges apply during Deploying, Overdue Payment, or Deployment Failed.

Q: Am I charged if I cancel a training job?

A: Yes. If you cancel training manually, you are charged for all tokens processed before cancellation. Training jobs interrupted by system errors or other non-user causes are not charged.

Q: How do I view invocation statistics for a deployed model?

A: Visit the Model Monitoring (Singapore), Model Monitoring (Virginia), or Model Monitoring (Beijing) page.
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