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Multimodal embedding API
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Multimodal embedding models convert text, images, and videos into embeddings in a shared semantic space to enable cross-modal retrieval, content classification, and similarity search.
Core capabilities
Cross-modal retrieval: Perform semantic searches across different content types, such as text-to-image, image-to-video, or image-to-image.
Semantic similarity: Measure the semantic similarity between different content types in a unified embedding space.
Content classification and clustering: Group, label, and cluster content based on semantic embeddings.
Key feature: Embeddings for all modalities (text, images, and video) share the same semantic space, enabling direct cross-modal matching and comparison using methods such as cosine similarity. See text and multimodal embedding for details on model selection and usage.
Important
This model service is only available in the China (Beijing) region. To call the service, use an API key from this region.
Embedding types
The multimodal embedding model supports two methods for generating embeddings:
Multimodal independent embedding: Generates a separate embedding for each input, such as text, an image, a video, or multiple images, within the
contents. For example, an input of one text string and one image returns two independent embeddings. This is ideal for comparing individual items, such as in image-to-image or text-to-image searches.Multimodal fused embedding: Fuses all inputs in contents into a single embedding to achieve a unified cross-modal semantic representation. This is suitable for scenarios that require a holistic understanding of multimodal content, such as fusing a product image and its description text into a unified representation for retrieval. For
qwen3-vl-embedding, you enable fusion by settingenable_fusion=true. The fused embedding supports the following combinations:Text and image fusion
Text and video fusion
Fusing multiple images with text (by passing multiple
imageentries)Fusion of images, video, and text
qwen2.5-vl-embeddingsupports only fused embeddings, not independent embeddings.tongyi-embedding-vision-plusandtongyi-embedding-vision-flashsupport only independent embeddings.
For model introductions, selection guidance, and usage instructions, see Text and multimodal embedding.
Model overview
Singapore
China (Beijing)
| Model | Embedding dimensions | Text length limit | Image size limit | Video size limit | Price (per 1M input tokens) | Free quota (Note) |
| tongyi-embedding-vision-plus | 1152 | 1,024 tokens | Up to 3 MB per image. Supports up to 8 images. | Up to 10 MB per video file | Image/Video: $0.09 Text: $0.09 | 1 million tokens Valid for 90 days after activating Model Studio |
| tongyi-embedding-vision-flash | 768 | Image/Video: $0.03 Text: $0.09 |
| Model | Embedding dimensions | Text length limit | Image size limit | Video size limit | Price (per 1M input tokens) |
| qwen3-vl-embedding | 2560 (default), 2048, 1536, 1024, 768, 512, 256 | 32,000 tokens | Up to 5 images, up to 5 MB per image | Up to 50 MB per video file | Image/Video: $0.258 Text: $0.1 |
| multimodal-embedding-v1 | 1024 | 512 tokens | Up to 8 images, 3 MB each | Up to 10 MB per video file | Free trial |
Input formats and usage limits
| Fused multimodal models | ||||
| Model | Text | Image | Video | Request limit |
| qwen3-vl-embedding | Supports 33 major languages, including Chinese, English, Japanese, Korean, French, and German. | JPEG, PNG, WEBP, BMP, TIFF, ICO, DIB, ICNS, SGI (URL or Base64 supported) | MP4, AVI, MOV (URL only) | Up to 20 content elements per request, with a maximum of 5 images and 1 video. |
| Independent multimodal models | ||||
| Model | Text | Image | Video | Request limit |
| tongyi-embedding-vision-plus | Chinese and English | JPG, PNG, BMP (URL or Base64 supported) | MP4, MPEG, MOV, MPG, WEBM, AVI, FLV, MKV (URL only) | No limit on the number of content elements. The total number of input tokens must not exceed the batch processing token limit. |
| tongyi-embedding-vision-flash | ||||
| multimodal-embedding-v1 | JPG, PNG, BMP (URL or Base64 supported) | Up to 20 content elements per request, with a maximum of 20 text segments, 1 image, and 1 video. |
All models accept text, image, and video inputs, individually or in combination.
tongyi-embedding-vision-plus,tongyi-embedding-vision-flashmodels also supportmulti_imagesfor image sequences.
Model capabilities
| Model | Default dimension | Vector type | Supported inputs | Description |
| qwen3-vl-embedding | 2560 | Independent / Fusion | text, image, video, multiple images | Fusion mode, enabled with the enable_fusion parameter, combines multimodal inputs into a single vector. |
| tongyi-embedding-vision-plus | 1152 | Independent only | text, image, video, multi_images | Supports multi_images sequences (up to 8 images). |
| tongyi-embedding-vision-flash | 768 | |||
| multimodal-embedding-v1 | 1024 | text, image, video | The vector dimension is fixed at 1,024 and cannot be configured. |
Prerequisites
Obtain an API key and export the API key as an environment variable. If you use an SDK to make calls, install the DashScope SDK.
HTTP call
POST https://dashscope.aliyuncs.com/api/v1/services/embeddings/multimodal-embedding/multimodal-embedding
| ### Request | Multimodal independent embedding Multimodal fused embedding > The following example uses the tongyi-embedding-vision-plus model to generate an independent embedding for each input. You can replace the model name with another supported model. The multi_images type is supported only by tongyi-embedding-vision-plus and tongyi-embedding-vision-flash. The qwen3-vl-embedding model also supports a fused embedding mode, which you can enable by setting enable_fusion=true. For details, see the "Multimodal fused embedding" tab. curl curl --silent --location --request POST 'https://dashscope.aliyuncs.com/api/v1/services/embeddings/multimodal-embedding/multimodal-embedding' \ --header "Authorization: Bearer $DASHSCOPE_API_KEY" \ --header 'Content-Type: application/json' \ --data '{ "model": "tongyi-embedding-vision-plus", "input": { "contents": [ {"text": "Multimodal embedding model"}, {"image": "https://img.alicdn.com/imgextra/i3/O1CN01rdstgY1uiZWt8gqSL_!!6000000006071-0-tps-1970-356.jpg"}, {"video": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20250107/lbcemt/new+video.mp4"}, {"multi_images": [ "https://img.alicdn.com/imgextra/i2/O1CN019eO00F1HDdlU4Syj5_!!6000000000724-2-tps-2476-1158.png", "https://img.alicdn.com/imgextra/i2/O1CN01dSYhpw1nSoamp31CD_!!6000000005089-2-tps-1765-1639.png" ] } ] } }' > The qwen3-vl-embedding model supports fused embedding generation. Set enable_fusion=true to combine all inputs into a single embedding. This supports various combinations, such as text and image, text and video, multiple images and text, or a mix of image, video, and text. The following example shows a fusion of multiple images, a video, and text. curl curl --location 'https://dashscope.aliyuncs.com/api/v1/services/embeddings/multimodal-embedding/multimodal-embedding' \ --header "Authorization: Bearer $DASHSCOPE_API_KEY" \ --header 'Content-Type: application/json' \ --data '{ "model": "qwen3-vl-embedding", "input": { "contents": [ {"text": "Product description text"}, {"image": "https://dashscope.oss-cn-beijing.aliyuncs.com/images/256_1.png"}, {"image": "https://img.alicdn.com/imgextra/i3/O1CN01rdstgY1uiZWt8gqSL_!!6000000006071-0-tps-1970-356.jpg"}, {"video": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20250107/lbcemt/new+video.mp4"} ] }, "parameters": { "enable_fusion": true } }' |
| #### Request headers | |
Content-Typestring (Required) The content type of the request. Must be application/json. | |
Authorizationstring (Required) Authenticates the request with a Model Studio API key. Example: Bearer sk-xxxx. | |
| #### Request body | |
modelstring (required) The model name. Select a model from the Model overview. | |
inputobject (required) The input content. Properties contentsarray (required) The content items to process. Each item is a dictionary or string that specifies the content type and value in the format {"modality_type": "input_string_or_image/video_url"}. The supported modality types are text, image, video, and multi_images. > The qwen3-vl-embedding model supports both fused and independent embedding generation. To generate a fused embedding, add the boolean field enable_fusion and set it to true. The qwen2.5-vl-embedding model supports only fused embeddings. - Text: The key is text, and the value is a string. You can also pass the string directly without a dictionary. - Image: Use the image key. The value can be a public URL or a Base64-encoded Data URI. The Base64 format is data:image/{format};base64,{data}, where {format} is the image format, such as jpeg or png, and {data} is the Base64-encoded string. - Multiple images: This type is supported only by the tongyi-embedding-vision-plus, tongyi-embedding-vision-flash models. The key is multi_images, and the value is a list of images. Each item in the list is an image that must follow the format described above. - Video: The key is video. The value must be a publicly accessible URL. parametersobject (optional) Embedding processing parameters. For HTTP calls, you must wrap these parameters in the parameters object. For SDK calls, you can use these parameters directly. Properties output_typestring (optional) The format for the output embedding representation. Currently, only dense is supported. dimensioninteger (optional) The output embedding dimension. Supported values vary by model: - qwen3-vl-embedding: Supports 2560, 2048, 1536, 1024, 768, 512, and 256. The default is 2560. - tongyi-embedding-vision-plus: Does not support this parameter. Returns a fixed 1152-dimension embedding. - tongyi-embedding-vision-flash: Does not support this parameter. Returns a fixed 768-dimension embedding. - multimodal-embedding-v1: Does not support this parameter. Returns a fixed 1024-dimension embedding. fpsfloat (optional) The video frame sampling rate. A smaller value extracts fewer frames. The valid range is [0, 1], and the default is 1.0. instructstring (optional) A custom task description to help the model understand the query's intent. English instructions are recommended and can improve performance by 1% to 5%. enable_fusionbool (optional) Specifies whether to generate a fused embedding. This parameter is supported only by the qwen3-vl-embedding model. When set to true, all multimodal content in the contents array is fused into a single embedding. The default value is false, which generates an independent embedding for each modality. Fused embeddings support combinations such as text and image, text and video, multiple images and text (by passing multiple image items), and a mix of image, video, and text. This is suitable for retrieval scenarios that require a comprehensive understanding of multimodal content. |
| ### Response | Successful response Error response json { "output": { "embeddings": [ { "index": 0, "embedding": [ -0.026611328125, -0.016571044921875, -0.02227783203125, ... ], "type": "text" }, { "index": 1, "embedding": [ 0.051544189453125, 0.007717132568359375, 0.026611328125, ... ], "type": "image" }, { "index": 2, "embedding": [ -0.0217437744140625, -0.016448974609375, 0.040679931640625, ... ], "type": "video" } ] }, "usage": { "input_tokens": 10, "input_tokens_details": { "image_tokens": 896, "text_tokens": 7 }, "output_tokens": 3, "total_tokens": 906 }, "request_id": "1fff9502-a6c5-9472-9ee1-73930fdd04c5" } Note The usage field varies by model. See the following descriptions: - tongyi-embedding-vision-* series models: Return input_tokens (sum of text and image tokens), input_tokens_details (including image_tokens and text_tokens), output_tokens, and total_tokens. The response example above is for this type of model. - qwen3-vl-embedding: Returns only input_tokens (text tokens only, including system template tokens), image_tokens, and total_tokens (= input_tokens + image_tokens). Does not return input_tokens_details or output_tokens. Example: json { "usage": { "input_tokens": 43, "image_tokens": 1247, "total_tokens": 1290 } } Note - qwen2.5-vl-embedding: Returns only input_tokens and image_tokens. Does not return total_tokens, input_tokens_details, or output_tokens. - multimodal-embedding-v1: Returns input_tokens, image_tokens, image_count, and duration. Does not return total_tokens, input_tokens_details, or output_tokens. json { "code":"InvalidApiKey", "message":"Invalid API-key provided.", "request_id":"fb53c4ec-1c12-4fc4-a580-cdb7c3261fc1" } |
outputobject Task output. Properties embeddingsarray A list of the resulting embeddings, where each object corresponds to an input element. Properties indexint The index of the result in the input list. embeddingarray The dimension of the generated array of embeddings depends on the model and the dimension parameter. typestring The input type for this result. text, image, video, and multi_images correspond to text, image, video, and multi-image inputs, respectively. Special types include: fusion is the type returned by the qwen3-vl-embedding model in fused embedding mode; vl is the type returned by the qwen3-vl-embedding model in independent embedding mode. | |
request_idstring Unique request identifier for tracing and troubleshooting. | |
codestring Error code. Returned only for failed requests. See Error codes. | |
messagestring Detailed error message. Returned only for failed requests. See Error codes. | |
usageobject Statistics about token usage. Properties input_tokensint The number of tokens in the input content for the current request. For the qwen3-vl-embedding and qwen2.5-vl-embedding models, this value includes only text tokens (including system template tokens) and does not include image or video tokens. For the tongyi-embedding-vision-* series models, this value includes the total number of text, image, and video tokens. input_tokens_detailsobject A detailed breakdown of input tokens. This field is returned only by the tongyi-embedding-vision-* series models. It is not returned by the qwen3-vl-embedding, qwen2.5-vl-embedding, or multimodal-embedding-v1 models. Properties image_tokensint The number of tokens for the input images or videos. text_tokensint The number of tokens for the input text. output_tokensint The number of tokens in the output for the current request. This field is returned only by the tongyi-embedding-vision-* series models. total_tokensint The total number of input and output tokens. This field is returned by the qwen3-vl-embedding and tongyi-embedding-vision-* models, but not by the qwen2.5-vl-embedding or multimodal-embedding-v1 models. For the qwen3-vl-embedding model, total_tokens = input_tokens + image_tokens. image_tokensint The number of tokens for the input images or videos in the current request. The system samples frames from input videos, with the maximum number of frames controlled by the system configuration, and then calculates the tokens based on the processed result. This field is returned as a top-level field only by the qwen3-vl-embedding, qwen2.5-vl-embedding, and multimodal-embedding-v1 models. For the tongyi-embedding-vision-* series models, the image token count is included in input_tokens_details.image_tokens. image_countint The number of images in the input for the current request. This field is returned only by the multimodal-embedding-v1 model. durationint The duration of the input video in seconds. This field is returned only by the multimodal-embedding-v1 model. |
SDK usage
The SDK's
inputparameter maps toinput.contentsin the HTTP request body, but their structures are different.
Code examples
Image embedding
Video embedding
Text embedding
Fused embedding
Multi-image fused embedding
2026-03-06 snapshot version
Image URL
Local image
python
import dashscope
import json
from http import HTTPStatus
# Replace with your image URL.
image = "https://dashscope.oss-cn-beijing.aliyuncs.com/images/256_1.png"
input = [{'image': image}]
# Call the model API.
resp = dashscope.MultiModalEmbedding.call(
model="tongyi-embedding-vision-plus",
input=input
)
if resp.status_code == HTTPStatus.OK:
result = {
"status_code": resp.status_code,
"request_id": getattr(resp, "request_id", ""),
"code": getattr(resp, "code", ""),
"message": getattr(resp, "message", ""),
"output": resp.output,
"usage": resp.usage
}
print(json.dumps(result, ensure_ascii=False, indent=4))To generate an embedding from a local image, convert the image to a Base64 string:
python
import dashscope
import base64
import json
from http import HTTPStatus
# Read the image and convert it to Base64. Replace xxx.png with your image file.
image_path = "xxx.png"
with open(image_path, "rb") as image_file:
# Read the file and convert it to Base64.
base64_image = base64.b64encode(image_file.read()).decode('utf-8')
# Set the image format.
image_format = "png" # Change this to your image's format (e.g., jpg, bmp).
image_data = f"data:image/{image_format};base64,{base64_image}"
# Input data
input = [{'image': image_data}]
# Call the model API.
resp = dashscope.MultiModalEmbedding.call(
model="tongyi-embedding-vision-plus",
input=input
)
if resp.status_code == HTTPStatus.OK:
result = {
"status_code": resp.status_code,
"request_id": getattr(resp, "request_id", ""),
"code": getattr(resp, "code", ""),
"message": getattr(resp, "message", ""),
"output": resp.output,
"usage": resp.usage
}
print(json.dumps(result, ensure_ascii=False, indent=4))Currently, the model only supports video input via URL. Local video files are not supported.
python
import dashscope
import json
from http import HTTPStatus
# Replace with your video URL.
video = "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20250107/lbcemt/new+video.mp4"
input = [{'video': video}]
# Call the model API.
resp = dashscope.MultiModalEmbedding.call(
model="tongyi-embedding-vision-plus",
input=input
)
if resp.status_code == HTTPStatus.OK:
result = {
"status_code": resp.status_code,
"request_id": getattr(resp, "request_id", ""),
"code": getattr(resp, "code", ""),
"message": getattr(resp, "message", ""),
"output": resp.output,
"usage": resp.usage
}
print(json.dumps(result, ensure_ascii=False, indent=4))python
import dashscope
import json
from http import HTTPStatus
text = "General multimodal representation model example"
input = [{'text': text}]
# Call the model API.
resp = dashscope.MultiModalEmbedding.call(
model="tongyi-embedding-vision-plus",
input=input
)
if resp.status_code == HTTPStatus.OK:
result = {
"status_code": resp.status_code,
"request_id": getattr(resp, "request_id", ""),
"code": getattr(resp, "code", ""),
"message": getattr(resp, "message", ""),
"output": resp.output,
"usage": resp.usage
}
print(json.dumps(result, ensure_ascii=False, indent=4))python
import dashscope
import json
import os
from http import HTTPStatus
# Fuses text, image, and video into a single fused embedding.
# Ideal for use cases like cross-modal retrieval and image search.
text = "This is a test text for generating a multimodal fused embedding."
image = "https://dashscope.oss-cn-beijing.aliyuncs.com/images/256_1.png"
video = "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20250107/lbcemt/new+video.mp4"
# Input includes text, image, and video. Set enable_fusion=True to generate a fused embedding.
input_data = [\
{"text": text},\
{"image": image},\
{"video": video}\
]
resp = dashscope.MultiModalEmbedding.call(
# If the environment variable is not set, provide your Model Studio API key, e.g., api_key="sk-xxx".
api_key=os.getenv("DASHSCOPE_API_KEY"),
model="qwen3-vl-embedding",
input=input_data,
enable_fusion=True,
# Optional: Specify the embedding dimension. Valid values: 2560, 2048, 1536, 1024, 768, 512, and 256. Default: 2560.
# parameters={"dimension": 1024}
)
print(json.dumps(resp, ensure_ascii=False, indent=4))Use qwen3-vl-embedding to fuse multiple images and text into a single embedding. To fuse multiple images, pass multiple image items. This is ideal for semantic retrieval using multi-angle product images and a text description.
python
import dashscope
import json
import os
from http import HTTPStatus
# Fuses multiple product images and a description into a single embedding.
# Ideal for comprehensive semantic retrieval using multi-angle product images and a text description.
text = "White sports shoes, lightweight and breathable, suitable for running and daily wear."
image1 = "https://dashscope.oss-cn-beijing.aliyuncs.com/images/256_1.png"
image2 = "https://img.alicdn.com/imgextra/i3/O1CN01rdstgY1uiZWt8gqSL_!!6000000006071-0-tps-1970-356.jpg"
# Pass multiple image items and set enable_fusion=True to fuse all inputs into a single embedding.
input_data = [\
{"text": text},\
{"image": image1},\
{"image": image2}\
]
resp = dashscope.MultiModalEmbedding.call(
# If the environment variable is not set, provide your Model Studio API key, e.g., api_key="sk-xxx".
api_key=os.getenv("DASHSCOPE_API_KEY"),
model="qwen3-vl-embedding",
input=input_data,
enable_fusion=True
)
print(json.dumps(resp, ensure_ascii=False, indent=4))This example shows how to use the
tongyi-embedding-vision-plus-2026-03-06model and itsres_level(resolution) andmax_video_frames(video frames) parameters. Built on the Qwen3 foundation, this model supports 30+ languages and generates both independent and fused embeddings.
python
import dashscope
import json
import os
from http import HTTPStatus
# Demonstrates using the res_level (resolution) and max_video_frames (video frames) parameters.
image = "https://dashscope.oss-cn-beijing.aliyuncs.com/images/256_1.png"
video = "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20250107/lbcemt/new+video.mp4"
text = "This is a visual multimodal representation model."
input_data = [\
{"text": text},\
{"image": image},\
{"video": video}\
]
resp = dashscope.MultiModalEmbedding.call(
api_key=os.getenv("DASHSCOPE_API_KEY"),
model="tongyi-embedding-vision-plus-2026-03-06",
input=input_data,
dimension=1152, # Valid values: 1152, 1024, 512, 256, 128, 64
res_level=1, # Resolution level: 0, 1, 2, or 3. The default is 1.
max_video_frames=64 # Maximum number of sampled video frames. Default: 8. Maximum: 64.
)
if resp.status_code == HTTPStatus.OK:
result = {
"status_code": resp.status_code,
"request_id": getattr(resp, "request_id", ""),
"output": resp.output,
"usage": resp.usage
}
print(json.dumps(result, ensure_ascii=False, indent=4))To generate a fused embedding with the 2026-03-06 version, place text, image, and video in the same content object. The model fuses all inputs into a single embedding with the type fused.
python
import dashscope
import json
import os
from http import HTTPStatus
# To create a fused embedding, place text and image in the same content object.
# The model fuses all inputs into a single embedding of type `fused`.
text = "White sports shoes, lightweight and breathable, suitable for running and daily wear."
image = "https://dashscope.oss-cn-beijing.aliyuncs.com/images/256_1.png"
input_data = [\
{"text": text, "image": image}\
]
resp = dashscope.MultiModalEmbedding.call(
api_key=os.getenv("DASHSCOPE_API_KEY"),
model="tongyi-embedding-vision-plus-2026-03-06",
input=input_data,
dimension=1152
)
if resp.status_code == HTTPStatus.OK:
result = {
"status_code": resp.status_code,
"request_id": getattr(resp, "request_id", ""),
"output": resp.output,
"usage": resp.usage
}
print(json.dumps(result, ensure_ascii=False, indent=4))Output example
json
{
"status_code": 200,
"request_id": "40532987-ba72-42aa-a178-bb58b52fb7f3",
"code": "",
"message": "",
"output": {
"embeddings": [\
{\
"index": 0,\
"embedding": [\
-0.009490966796875,\
-0.024871826171875,\
-0.031280517578125,\
...\
],\
"type": "text"\
}\
]
},
"usage": {
"input_tokens": 10,
"input_tokens_details": {
"image_tokens": 0,
"text_tokens": 10
},
"output_tokens": 1,
"total_tokens": 11
}
}Error codes
If the model call fails and returns an error message, see Error codes for resolution.
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Core capabilities
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