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01

Providers + pricing

CapabilitiesProvider details
● Your own key · billed by your provider · AnyRouter fee $0
OpenRouter
openrouter-byok
$0.02list$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: "qwen/qwen3-embedding-4b",  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
No traffic in the last 24h.
More performance detail
Usage analytics

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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.

Qwen3 Embedding 4B

  • OpenRouter
Embedtext → [0.12, -0.4, …]2560dimensions
Context
33K
Input
$0.02
Output
$0
TTFT
—
Uptime
—
Routes
1
04

Your access

Credits
Your own key · AnyRouter fee $0

Run Qwen3 Embedding 4B 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 dimensions2,560
Max input32,768 tokens
Price$0.02 / 1M tokens
Request parameters
inputmodeldimensionsencoding_format
ArchitectureTransformer
Categoryembedding
ReleasedOct 28, 2025
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

Qwen3 Embedding 4B is the 4B size in the Qwen3 embedding and reranking family, between the 0.6B and 8B cuts. It embeds text over a 32,768-token window at 2,560 dimensions, supports 100+ languages, is instruction-aware for task prefixes, and supports Matryoshka truncation from 32 to 2,560 dimensions. It ranks below the 8B cut on MTEB multilingual but well above the 0.6B cut, at a much lower cost.

Released 2025-10-28 · params: input · model · dimensions · encoding_format

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Qwen3 Embedding 4B
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