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01

Providers + pricing

CapabilitiesProvider details
● Your own key · billed by your provider · AnyRouter fee $0
OVHcloud
ovhcloud-byok
$0.01list$0list
Unavailable
● Free pool · donated keys · not available for this model — donate a key
02

Try it

POST /api/v1/embeddings
import OpenAI from "openai" const client = new OpenAI({  apiKey: process.env.ANYROUTER_API_KEY,  baseURL: "https://anyrouter.dev/api/v1",}) const resp = await client.embeddings.create({  model: "baai/bge-multilingual-gemma2",  input: "The quick brown fox jumps over the lazy dog",})console.log(resp.data[0].embedding.slice(0, 8))
Set ANYROUTER_API_KEY · edits on the demo update this code
Live demo
Cosine similarity
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03

Uptime + latency

—recent checks · all routes

No health checks recorded for this model yet.

24h
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Uptime & Health
No uptime data yet

These providers haven't been health-probed for this model yet. The router still routes around upstreams that fail live requests — uptime fills in once probe history accrues.

BGE Multilingual Gemma 2

  • OVHcloud
Embedtext → [0.12, -0.4, …]3584dimensions
Context
8K
Input
$0.01
Output
$0
TTFT
—
Uptime
—
Routes
1
04

Your access

Credits
Your own key · AnyRouter fee $0

Run BGE Multilingual Gemma 2 on your own key — your requests are billed by the provider. Pool callers pay AnyRouter credits.

No BYOK keys configured for this model yet.

Share a key with the pool to earn credits for every request it serves, covering your plan cost.

Free poolnot in pool yetDonate key →
Create API key for this model
Embedding vectors
Vector dimensions3,584
Max input8,192 tokens
Price$0.01 / 1M tokens
Request parameters
inputmodelencoding_format
ArchitectureTransformer
Categoryembedding
ReleasedJul 25, 2024
Modalities
→
Capabilities
Embeddings are fixed-length vectors — compare them with cosine similarity for semantic search, RAG retrieval, clustering, and deduplication. Embed queries and documents with the same model, or the distances are meaningless.
05

About

BAAI's bge-multilingual-gemma2 is a compact multilingual text embedding model built on Gemma 2, trained across 100+ languages with an 8,192-token input window. It returns a 3,584-dimension dense vector.

Released 2024-07-25 · params: input · model · encoding_format

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BGE Multilingual Gemma 2
OVHcloud upstream share card
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