Model card

BGE-M3

lx1-bge-m3EmbeddingsLive in the catalog

A widely-used open embedding that handles dense, sparse, and multi-vector retrieval in one model, across 100+ languages and inputs up to 8K tokens. A dependable, well-understood default for multilingual RAG.

Max input8K tokens

Input · per Mtok$0.02

Dimensions1,024

Served byLayer X1 engine

Where it earns its keep[01/03]

01Dense, sparse, and multi-vector in one model
02100+ languages
038K input for longer passages

Capabilities

EmbeddingsYes

Turns text into a fixed-length vector for semantic search, RAG, clustering, and dedupe. Call it on /v1/embeddings with a string or array of inputs — no chat, tools, or reasoning.

Behind the endpoint[02/03]

One endpoint. Served by our engine.

BGE-M3 is served through the Layer X1 engine — zero-downtime serving is the design target, not a status-page apology. You request it by name; everything else is our problem.

Call it by name

curl https://api.layerx1.in/v1/embeddings \
  -H "authorization: Bearer lx1_your_key" \
  -H "content-type: application/json" \
  -d '{
    "model": "lx1-bge-m3",
    "input": "text to embed"
  }'

OpenAI-style clients work too — POST the same model name to /v1/embeddings with a Bearer key. See the docs for both dialects.

Your agent doesn't change.
Everything underneath does.

$export ANTHROPIC_BASE_URL=https://api.layerx1.in

Get a key Read the docs

Start free · no card · Starter from $5/mo

One endpoint. Served by our engine.

Your agent doesn't change.Everything underneath does.

Your agent doesn't change.
Everything underneath does.