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Embedding 3

zhipuzhipu/embedding-3

Zhipu AI Embedding 3 β€” latest text embedding model. Default 2048-dim, supports custom dimensions (256, 512, 1024, 2048).

Embedding-3 β€” Official Zhipu AI Documentation β†—Zhipu AI Open Platform (bigmodel.cn) β†—

Zhipu AI Embedding-3 is the third-generation text embedding model from Beijing-based Zhipu AI (ζ™Ίθ°± AI), the research lab behind the GLM series of language models. Released as part of the bigmodel.cn open platform, Embedding-3 is a significant step forward from the fixed-dimension Embedding-2, introducing adjustable output dimensions (256 / 512 / 1024 / 2048) and a 3,072-token per-input limit on TheRouter (Zhipu's overview also mentions 8K). The model is designed for Chinese-English bilingual semantic understanding, with particular strength in Chinese-dominant retrieval corpora.

For TheRouter operators, Embedding-3 is the recommended embedding model for Chinese-first RAG, search, and recommendation pipelines. TheRouter's listed input price is $0.081 per million tokens as of 2026-11-04; check the pricing table for the current rate. Zhipu's direct price is separate. Its matryoshka-style dimension selection allows operators to trade off storage cost and vector search latency β€” for example, using 256-dim vectors for large-scale approximate search and 2048-dim for high-fidelity retrieval. The model is accessible through the OpenAI-compatible /v1/embeddings endpoint at api.therouter.ai using the zhipu/embedding-3 model identifier.

Best for
  • β€’ Chinese-English bilingual RAG pipelines: semantic search over mixed-language knowledge bases where Chinese documents dominate
  • β€’ High-volume Chinese-language corpora: compare the current input-token rate and retrieval quality against other available embedding models
  • β€’ Dimension-flexible deployments: select dimensions (256–2048) to balance vector index size and retrieval quality; test changes on your corpus
  • β€’ Recommendation systems and content deduplication over large Chinese-language text corpora (news, e-commerce, user reviews)
Reach for something else if
  • β€’ English-language retrieval: evaluate candidate models on your own corpus; OpenAI text-embedding-3-large is not currently listed on TheRouter
  • β€’ Multimodal (image + text) embedding tasks: Cohere Embed V4 supports interleaved image/text inputs; Embedding-3 is text-only
  • β€’ Cross-lingual semantic tasks beyond Chinese and English: models with broader multilingual MTEB coverage (e.g., Cohere Embed V4, multilingual-e5-large) are more reliable
Context Length
3K
Max Output
--
Input Priceper 1M tokens
$0.081/ 1M tokens

Modalities

text→embedding

Pricing Breakdown

TypeRate
Input$0.081 / 1M tokens

Supported Parameters

inputdimensionsencoding_formatuser

Specifications

Context window3,072 tokens per input on TheRouter (Zhipu overview: 8K)docs.bigmodel.cn β†—verified
Output dimensions256, 512, 1024, or 2048 (default: 2048)docs.bigmodel.cn β†—verified
Batch size limitUp to 64 strings per requestdocs.bigmodel.cn β†—verified
List price (Zhipu direct)Β₯0.5 per million tokensdocs.bigmodel.cn β†—verified
Matryoshka dimension reductionSupported β€” select 256/512/1024/2048 via the dimensions parameterdocs.bigmodel.cn β†—verified
ModalityText β†’ embedding (text-only input; no image or audio support)docs.bigmodel.cn β†—verified
Generation (predecessor)Zhipu Embedding-2 (1024-dim fixed, 512-token context)docs.bigmodel.cn β†—verified

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/embeddings   -H "Content-Type: application/json"   -H "Authorization: Bearer $THE_ROUTER_API_KEY"   -d '{"model":"zhipu/embedding-3","input":["A sample document"]}'

API guide

Embeddings (OpenAI-compatible)

Access Embedding-3 through the standard OpenAI /v1/embeddings endpoint at api.therouter.ai. Pass the dimensions parameter (256, 512, 1024, or 2048) to select your output size. Batch up to 64 strings per request for maximum throughput.

cURL
curl https://api.therouter.ai/v1/embeddings \
  -H "Authorization: Bearer $THEROUTER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "zhipu/embedding-3",
    "input": ["ζ™Ίθ°± AI ζ˜―δΈ­ε›½ι’†ε…ˆηš„ AI η ”η©ΆζœΊζž„", "Zhipu AI is a leading Chinese AI research lab"],
    "dimensions": 1024
  }'

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Similar models

Cross-provider sibling models
openai/text-embedding-3-small

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openai/text-embedding-3-large

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cohere/embed-v4
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Frequently asked

Which dimension should I use?

2048 (default) delivers the highest semantic fidelity and is recommended for precision-critical retrieval such as legal or medical knowledge bases. 1024 is a practical balance for most production RAG stacks. 512 suits large-scale approximate nearest-neighbor (ANN) search where index storage cost matters. 256 is suitable for real-time similarity scoring where latency is the primary constraint. All four dimensions share the same token cost per request.

Can I use Embedding-3 with LangChain, LlamaIndex, or Spring AI?

Yes. Because TheRouter exposes Embedding-3 through the standard OpenAI embeddings API at api.therouter.ai, any framework that supports the OpenAI embeddings client works out of the box. For LangChain and LlamaIndex, set the base URL to https://api.therouter.ai/v1 and the model to zhipu/embedding-3. For Spring AI, set spring.ai.openai.embedding.options.model=zhipu/embedding-3 and spring.ai.openai.embedding.options.dimensions=1024 (or your preferred size).

How does Embedding-3 compare to OpenAI text-embedding-3-small?

As of 2026-11-04, text-embedding-3-small is not in the current TheRouter catalog. Embedding-3 is listed at $0.081 per million input tokens and 3,072 tokens per input; consult the live catalog for current rates and available alternatives. Compare retrieval quality on your own dataset.

Fact ledger β€” every claim on this page traces here
sourceURLretrieved
Context windowdocs.bigmodel.cn β†—2026-06-09verified
Output dimensionsdocs.bigmodel.cn β†—2026-06-09verified
Batch size limitdocs.bigmodel.cn β†—2026-06-09verified
List price (Zhipu direct)docs.bigmodel.cn β†—2026-06-09verified
Matryoshka dimension reductiondocs.bigmodel.cn β†—2026-06-09verified
Modalitydocs.bigmodel.cn β†—2026-06-09verified
Generation (predecessor)docs.bigmodel.cn β†—2026-06-09verified
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