Embedding Modelqwen3-embedding-8b

Qwen3 Embedding 8B

Dense Vector

Dense semantic vector embedding model with high retrieval accuracy for enterprise RAG and semantic search.

AvailableEncoder TransformerProprietary

Params

8B Dense

Context

32K

Max Output

4096 dim

API Surface/v1/embeddings

Capabilities & Performance

SOTA MTEB retrieval performance
Ultra-low cost at $0.057/M tokens down to $0.043/M wholesale

Transparent Pricing

Lane	Public Rate	Cached
Realtime API	$0.057 / $0.000	N/A
Batch Queue	Batch rates available on rollout	N/A

Prices per 1M tokens. Cached prompt rate applies on prefix hits.

100% OpenAI-Compatible Code

Drop-in API Examples

Get API Key

Python (OpenAI SDK)

from openai import OpenAI

client = OpenAI(
    base_url="https://api.batchin.tech/v1",
    api_key="BATCHIN_API_KEY"
)

resp = client.chat.completions.create(
    model="qwen3-embedding-8b",
    messages=[{"role": "user", "content": "Benchmark system architecture performance and cost profile."}]
)

print(resp.choices[0].message.content)

JavaScript / Node.js

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://api.batchin.tech/v1",
  apiKey: process.env.BATCHIN_API_KEY,
});

const resp = await client.chat.completions.create({
  model: "qwen3-embedding-8b",
  messages: [{ role: "user", content: "Benchmark system architecture performance and cost profile." }],
});

console.log(resp.choices[0]?.message?.content);

cURL / Shell

curl https://api.batchin.tech/v1/chat/completions \
  -H "Authorization: Bearer $BATCHIN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen3-embedding-8b",
    "messages": [{"role":"user","content":"Benchmark system architecture performance and cost profile."}]
  }'

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