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Qwen Text Embedding v3

qwenqwen/text-embedding-v3

Generation-3 multilingual text embedding from Qwen.

text-embedding-v3 is Qwen's generation-3 general-purpose multilingual text embedding model. It produces dense vectors for retrieval, semantic search, clustering, classification, and RAG indexing across Chinese, English, and other major languages.

If you are starting a new project, prefer text-embedding-v4 — v3 is kept here for compatibility with existing indices and pipelines built before the v4 release. TheRouter routes between bailian-cn and bailian-sg per request based on cost.

Multilingual
Trained across Chinese, English, and other major languages with consistent vector geometry.
8K input window
Embed long passages without aggressive chunking — covers most RAG document slices.
OpenAI-compatible
Standard `/v1/embeddings` shape — drop into existing OpenAI embedding pipelines.
Dual-region routing
Selector picks bailian-cn or bailian-sg per request based on cost.
When to use
Existing RAG pipelines and indices already built on Qwen v3 embeddings, or projects that must stay vector-compatible with a prior corpus.
When not to use
New projects — use text-embedding-v4 for higher quality at the same price.
Pricing: $0.12 per MTok of input. TheRouter routes to the cheaper of bailian-cn / bailian-sg per request.
Context Length
8K
Max Output
--
Input Priceper 1M tokens
$0.144/ 1M tokens

Modalities

text→embedding

Pricing Breakdown

TypeRate
Input$0.144 / 1M tokens

Supported Parameters

inputdimensionsencoding_format

API Usage Examples

Use the global api.therouter.ai endpoint shown below for new integrations; the legacy China accelerated endpoint is retired.

cURL
curl https://api.therouter.ai/v1/chat/completions   -H "Content-Type: application/json"   -H "Authorization: Bearer $THE_ROUTER_API_KEY"   -d '{
    "model": "qwen/text-embedding-v3",
    "messages": [
      {"role": "user", "content": "Summarize the key points from this input."}
    ]
  }'
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