Model Marketplace

Tongyi Multimodal Embedding

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Overview

Embedding-Vision is a vision-centric multimodal embedding model powered by an LLM, featuring outstanding domain-specific performance and high cost-effectiveness in various domains (e.g., e-commerce, photo galleries, security, autonomous driving). With support for text, image, and video, it is applicable to downstream retrieval tasks, including text-to-image, image-to-image, text-to-video and video-to-video.

Input

TextImageVideo

Output

Features

Prefix Completion

Enable Partial Mode when calling the Qwen API to make the model continue strictly from your provided prefix text.View docs

Function Calling

Use function calling to connect large language models with external tools and systems.View docs

Cache

Context Cache stores shared prefixes for long-context requests to reduce repeated computation, improve latency, and lower cost.View docs

Structured Outputs

Structured Outputs help ensure the model returns a JSON string in the expected format.View docs

Batches

Asynchronously process requests in batches to reduce costs.View docs

Web Search

Enable web search so the model can answer with real-time retrieved data.View docs

Fine-tuning

Train models on sample data to better adapt them to specific tasks.View docs

Pricing

  • Image Input
    $0.09Per 1M tokens
  • Text Input
    $0.09Per 1M tokens

Rate Limits

  • TPMTokens Per Minute
    200K
  • RPMRequests Per Minute
    600

API Reference

Call API
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import dashscope

text = "Multimodal embedding model sample"
input = [{'text': text}]
resp = dashscope.MultiModalEmbedding.call(
    model="tongyi-embedding-vision-plus",
    input=input
)

print(resp)