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01

Providers + pricing

CapabilitiesProvider details
● Credits · billed by AnyRouter
deepinfra
deepinfra
$0.01
$0
● Your own key · billed by your provider · AnyRouter fee $0
DeepInfra
deepinfra-byok
$0
$0
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-0.6b",  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
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 0.6BDisabled since Aug 19, 2026

The platform Workers AI host no longer serves this catalog id (model_unavailable). Remaining routes require a user-owned key, so the model is unlisted rather than advertised as a live hosted embedding model.

  • deepinfra
  • DeepInfra
Embedtext → [0.12, -0.4, …]1024dimensions
Context
33K
Input
$0.01
Output
$0
TTFT
—
Uptime
—
Routes
2
04

Your access

Credits
Your own key · AnyRouter fee $0

Run Qwen3 Embedding 0.6B 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 dimensions1,024
Max input32,768 tokens
Price$0.01 / 1M tokens
Request parameters
inputmodeldimensionsencoding_format
ArchitectureTransformer
Categoryembedding
ReleasedApr 23, 2026
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 model for text embedding and ranking tasks.

Released 2026-04-23 · params: input · model · dimensions · encoding_format

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Qwen3 Embedding 0.6B
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