Embeddings · Qwen (Alibaba Cloud)
Run Qwen3 Embedding 8B in the EU through an OpenAI-compatible embeddings API
Qwen3 Embedding 8B turns text into vectors for semantic search, retrieval-augmented generation, clustering and classification. It covers more than 100 languages, including programming languages, and reads up to 32K tokens per input. On inference.eu you call it through the OpenAI embeddings format, with your documents processed in EU data centres and never used for training.
- 018B
- 02EU data centres
- 03Apache 2.0
What Qwen3 Embedding 8B is
Qwen3 Embedding 8B is the largest model in Alibaba's Qwen3 Embedding series, released in June 2025 under the Apache 2.0 licence. It is built on the Qwen3 foundation model, has 8 billion parameters and 36 layers, and produces embeddings of up to 4096 dimensions.
It accepts inputs of up to 32K tokens, so long documents can be embedded without heavy chunking. It supports more than 100 natural and programming languages, which makes it useful for multilingual and cross-lingual retrieval as well as code search. The same series includes reranking models for a second, more precise ranking step.
What it does well, and where it falls short
The model's strengths are retrieval quality across many languages, long inputs and flexibility. It supports Matryoshka representation learning, so you can request shorter vectors, from 32 up to 4096 dimensions, and trade a little accuracy for much smaller indexes. It is also instruction-aware: a short task description on the query side typically improves results by 1 to 5 percent, according to Qwen.
The trade-off is size. At 8 billion parameters it is far heavier than encoders such as bge-m3 or multilingual-e5-large-instruct, so embedding very large corpora takes more compute. If your documents are short and your index is huge, a smaller model may be the practical choice.
Calling Qwen3 Embedding 8B through the API
inference.eu uses the OpenAI request format, so any OpenAI SDK, LangChain, LlamaIndex or vector database integration that calls /v1/embeddings works unchanged. Set the base URL to the inference.eu API, use your inference.eu key and set the model to qwen3-embedding-8b.
Pass a string or a list of strings as input. Use the dimensions parameter to request shorter vectors that fit your index. For queries, prefix the text with a one-line task, for example Instruct: Given a web search query, retrieve relevant passages that answer the query followed by Query: and the question. Documents need no instruction. Qwen recommends writing instructions in English, even for other languages.
Data residency for Qwen3 Embedding 8B
Embedding a knowledge base means sending the whole corpus to the model: contracts, tickets, wikis and customer records. On inference.eu, Qwen3 Embedding 8B runs on infrastructure in the European Union, so this text is processed in EU data centres, is not sent to Alibaba in China and is never used to train any model.
Every customer receives a data processing agreement under Article 28 of the GDPR and our subprocessor list, which helps you document the RAG pipeline in your records of processing. Billing is in euros. The Apache 2.0 licence allows commercial use without user limits.
import os
from openai import OpenAI
client = OpenAI(base_url="https://api.inference.eu/v1", api_key=os.environ["INFERENCE_EU_API_KEY"])
result = client.embeddings.create(
model="qwen3-embedding-8b",
input=["Where is my data processed?"],
)
print(len(result.data[0].embedding))What teams use it for
- 1
Multilingual enterprise search
Index documents in German, French, English and other languages and find them with a query in any of them.
- 2
Retrieval for RAG assistants
Retrieve the right passages from long policies, manuals or contracts before a chat model writes the answer.
- 3
Code search
Find functions and snippets across repositories from a plain-language description of what the code does.
- 4
Clustering and classification
Group support tickets or feedback by topic and route them using vector similarity.
Qwen3 Embedding 8B: common questions
The questions buyers, developers and legal teams ask us most. Something missing? Ask us directly.
Related
- BGE-M3Run bge-m3 on inference.eu: a compact multilingual embedding model for search and RAG, 100+ languages and 8192-token inputs, processed in the EU.Embeddings
- multilingual-e5-large-instructRun multilingual-e5-large-instruct on inference.eu: compact embeddings in 100 languages for search, RAG and classification, processed in the EU.Embeddings
- BGE Reranker v2 M3Run bge-reranker-v2-m3 on inference.eu: a light multilingual reranker that sorts search and RAG results by relevance, processed in the EU.Reranking
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