models/Alibaba Qwen/Qwen3-Embedding-0.6b

Alibaba Qwen

Qwen3-Embedding-0.6b

embedding

The Qwen3 Embedding model series is the latest proprietary model of the Qwen family, specifically designed for text embedding and ranking tasks.

MODALITIES

embedding

INPUT

$0.012 /1M

OUTPUT

$0 /1M

RELEASED

2025-06-18

Qwen3-Embedding-0.6b (qwen/qwen3-embedding-0.6b) is a embedding model from Alibaba Qwen, released 2025-06-18. Context window: — tokens; max output —. Pricing via AIgateway: input $0.012/M tokens, output $0/M tokens. Call it via https://api.aigateway.sh/v1/embeddings — set model="qwen/qwen3-embedding-0.6b". Best for: RAG, Semantic search, Recommendation.

model · qwen/qwen3-embedding-0.6bfamily · Qwen

Use this model

model: qwen/qwen3-embedding-0.6b

curl https://api.aigateway.sh/v1/embeddings \
  -H "Authorization: Bearer $AIGATEWAY_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"qwen/qwen3-embedding-0.6b","input":"the quick brown fox"}'

curl https://api.aigateway.sh/v1/embeddings \ -H "Authorization: Bearer $AIGATEWAY_API_KEY" \ -H "Content-Type: application/json" \ -d '{"model":"qwen/qwen3-embedding-0.6b","input":"hello"}'

Capabilities

Strengths

Semantic similarity
Vector search

Use cases

RAGSemantic searchRecommendation

Pricing

Input$0.012 / 1M tokens

Output$0 / 1M tokens

You pay pass-through · 5% applied at credit top-up, not per-call.

See API example →Compare API reference See usage ranking →

Collections

More embedding models →More from Alibaba Qwen →Frontier models →Free-tier models →

API schema

Call Qwen3-Embedding-0.6b from any OpenAI SDK

POST https://api.aigateway.sh/v1/embeddings·Content-Type: application/json·Auth: Bearer sk-aig-...

Request body

json

{
  "model": "qwen/qwen3-embedding-0.6b",
  "input": "Text to embed, or an array of strings for batch."
}

Response

json

{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "index": 0,
      "embedding": [0.0123, -0.0456, 0.0789, /* ... */]
    }
  ],
  "model": "qwen/qwen3-embedding-0.6b",
  "usage": { "prompt_tokens": 5, "total_tokens": 5 }
}

Quickstart

from openai import OpenAI
client = OpenAI(base_url="https://api.aigateway.sh/v1", api_key="sk-aig-...")

r = client.embeddings.create(model="qwen/qwen3-embedding-0.6b", input="hello world")
print(r.data[0].embedding[:5])

Errors

401authentication_errorInvalid or missing API key

402insufficient_creditsWallet empty (PAYG only)

404not_foundUnknown model or endpoint

429rate_limit_errorOver per-minute limit — see Retry-After header

500server_errorUpstream provider failed (retryable)

503service_unavailableUpstream saturated (retryable)

Full docs →API reference →OpenAPI spec →llms.txt →

Frequently asked questions

What is Qwen3-Embedding-0.6b?

The Qwen3 Embedding model series is the latest proprietary model of the Qwen family, specifically designed for text embedding and ranking tasks. It is a embedding model from Alibaba Qwen, accessible via AIgateway's OpenAI-compatible API at slug qwen/qwen3-embedding-0.6b.

How much does Qwen3-Embedding-0.6b cost via AIgateway?

Input costs $0.012 per 1M tokens; output costs $0.000 per 1M tokens. Pass-through plus a 5% platform fee applied at top-up, not per call.

How do I call Qwen3-Embedding-0.6b from my code?

Point the OpenAI SDK at https://api.aigateway.sh/v1 with your AIgateway key and set model to "qwen/qwen3-embedding-0.6b". The request and response shapes match OpenAI exactly.

Does Qwen3-Embedding-0.6b support streaming, tool calling, vision, and JSON mode?

Streaming — no. Tool calling — no. Vision — no. JSON mode — no. Prompt caching — no.

What are the best use cases for Qwen3-Embedding-0.6b?

RAG, Semantic search, Recommendation. Key strengths: Semantic similarity; Vector search.

Can I bring my own Alibaba Qwen API key (BYOK)?

Yes. Attach a Alibaba Qwen key in your AIgateway dashboard and this model flips to pass-through — you pay Alibaba Qwen directly and AIgateway waives the 5% platform fee on those calls.