BGE M3

Embeddings

Multi-Functionality, Multi-Linguality, and Multi-Granularity embeddings model.

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 BGE M3 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 bge-m3. Credentials come from your portal, with £15 of free credit on signup.

curl https://api.node.uk/api/v1/models/bge-m3/v1/embeddings \
  -H "Authorization: Bearer $NODE_GATEWAY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"model": "bge-m3", "input": "The text to embed"}'

from openai import OpenAI

client = OpenAI(
    base_url="https://api.node.uk/api/v1/models/bge-m3/v1",
    api_key=NODE_GATEWAY_TOKEN,
)

vectors = client.embeddings.create(
    model="bge-m3",
    input=["The text to embed"],
)

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