Embedding & reranking models

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Model selection

Choose a model based on your input data type and use case.

• Text or code — Use text-embedding-v4. Best general-purpose text embedding model, supporting task instructions (instruct), sparse vectors, and other advanced features.
• Multimodal content — Use tongyi-embedding-vision-plus for cross-modal retrieval (text-to-image, image-to-image, text-to-video). For cost-sensitive scenarios, use tongyi-embedding-vision-flash.
• Large-scale data — Use qwen3.7-text-embedding or text-embedding-v4 with Batch API to significantly reduce costs.

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How many dimensions?

Large-scale search where storage matters → 256 or 512. General use → 1024 (default, good balance). Maximum accuracy on benchmarks → 1536 or 2048.

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Text-only data?

Use text-embedding-v4 instead — faster, cheaper, more dimension options. Multimodal embedding is for cross-modal retrieval (text-to-image, text-to-video).

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Accuracy vs speed

Best accuracy → tongyi-embedding-vision-plus (dimensions up to 1152). Budget or latency-sensitive → tongyi-embedding-vision-flash (up to 768).

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Reranking

Improve RAG precision → add qwen3-rerank after your embedding search. Re-scores top-N results with cross-attention for better ranking quality. Limits: 500 documents per request, 4,000 tokens per item, 30,000 tokens per request.

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Migrate from closed-source models

Replacing OpenAI, Cohere, or Voyage embeddings? Use these QwenCloud equivalents.

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All models

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Learn more