Embeddings
The Qwen3 Embedding model series is the latest proprietary model of the Qwen family, specifically designed for text embedding and ranking tasks.
What it's best for
Best for search, retrieval-augmented generation (RAG), clustering and deduplication. Embed your documents once, store the vectors, then retrieve the most relevant chunks at question time and hand them to a chat model for the answer. Priced on input tokens only.
Pricing
Prices updated daily — last generated 2026-07-08.
Billing is metered per request in GBP on the same monthly invoice as your apps — no subscription, no minimum. We list every model once, at the cheapest route we can serve it on; if we have to fail over to a more expensive route, we absorb the difference and your price does not change.
Where your data is processed
Requests to Qwen3 Embedding 0.6B are routed to a vetted partner provider and processed on that provider's infrastructure — they leave our infrastructure, and the UK-residency guarantee that applies to our UK-hosted models does not apply here. We hold the provider credentials server-side and route on cost and availability. If you need prompts that never leave our own infrastructure, use one of our UK-hosted models.
Call it in two minutes
The gateway is OpenAI-compatible: point your SDK or HTTP client at
api.node.uk and use the model id qwen3-embedding-0.6b.
Credentials come from your portal, with £15
of free credit on signup.
curl https://api.node.uk/api/v1/models/qwen3-embedding-0.6b/v1/embeddings \
-H "Authorization: Bearer $NODE_GATEWAY_TOKEN" \
-H "Content-Type: application/json" \
-d '{"model": "qwen3-embedding-0.6b", "input": "The text to embed"}'from openai import OpenAI
client = OpenAI(
base_url="https://api.node.uk/api/v1/models/qwen3-embedding-0.6b/v1",
api_key=NODE_GATEWAY_TOKEN,
)
vectors = client.embeddings.create(
model="qwen3-embedding-0.6b",
input=["The text to embed"],
)Related models
Browse the full model catalogue, read which model for which task, or see how the AI Gateway works.