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qwen3-vl-embedding API pricing & access

qwen3-vl-embedding API pricing: $0.2572 per 1M tokens. Alibaba Qwen3-VL multimodal embedding model.

qwen3-vl-embedding API access

Alibaba Qwen3-VL multimodal embedding model.

Compare supported API endpoints and capabilities, then access qwen3-vl-embedding through the unified API gateway.

Category
vector
Supported APIs
OpenAI Chat Completions API
API endpoints
POST /v1/chat/completions

qwen3-vl-embedding API pricing

Base input price
$0.2572 per 1M tokens
Base output price
$0.2572 per 1M tokens

Base prices are shown in USD before group-specific adjustments. Open the live pricing page for current access-group prices.

About qwen3-vl-embedding

Qwen3 VL Embedding generates retrieval vectors for text, image and video content. Official documentation distinguishes fused and independent vectors; evaluate text-to-image search and mixed-media retrieval.

Use fixed cases for the tasks described above. Integration follows the listed endpoint, selected route parameters and current group pricing; official capabilities do not establish acceptance for every channel. Compare qwen3-vl-rerank.

Official reference 1, checked 2026-10-14.

Use cases and prompting

  1. Validate supported media and vector output through the route’s multimodal embedding contract.
  2. Choose fused or independent vectors and keep model, dimensions and preprocessing fixed per index.
  3. Test known pairs for text-to-image and mixed retrieval; validate video sampling and limits separately.
  4. Rebuild indexes on upgrades and record retrieval quality, media errors and usage before reranking.

API documentation.

How to call qwen3-vl-embedding

qwen3-vl-embedding is served through tbuy's unified gateway. Existing OpenAI or Anthropic SDK code keeps working: point base_url at https://modelsok.com and use your tbuy API key.

curl (OpenAI-compatible)

curl https://modelsok.com/v1/chat/completions \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model": "qwen3-vl-embedding", "messages": [{"role": "user", "content": "Hello"}]}'

Python (openai SDK)

from openai import OpenAI

client = OpenAI(base_url="https://modelsok.com/v1", api_key="YOUR_API_KEY")
response = client.chat.completions.create(
    model="qwen3-vl-embedding",
    messages=[{"role": "user", "content": "Hello"}],
)
print(response.choices[0].message.content)

Frequently asked questions about qwen3-vl-embedding

What is qwen3-vl-embedding?

Alibaba Qwen3-VL multimodal embedding model.

How do I call qwen3-vl-embedding?

Create an API key with access to qwen3-vl-embedding, then use the exact model ID and a supported endpoint from the API access section. Request fields depend on the selected endpoint.

How is qwen3-vl-embedding priced?

Pricing depends on the selected provider group and the model billing unit. Refer to the current input, output, request, or media prices in the pricing section.

How should I evaluate qwen3-vl-embedding for my project?

Start with the use cases and prompting guidance on this page, then evaluate the model with representative inputs from your project.

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