Qwen3 Embedding 8B API.
Find meaning, not just keywords.

Qwen3 Embedding 8B turns text into vectors for semantic search and retrieval. Use it to find relevant passages before a separate language model writes an answer.

€5 in free credits for every new customer.

Try Qwen3 Embedding 8B

From €0.00774 per million input tokens. View the pricing details.

Embeddings & semantic search
with Qwen3 Embedding 8B.

Practical starting points for Qwen3 Embedding 8B. Choose one task, define the expected output and measure the result on your own examples.

Build semantic search

Embed document chunks and queries with the same configuration, then compare them in your vector index.

Supply context for RAG

Retrieve useful passages for a user’s question and send those passages to a chat model. Embeddings retrieve; they do not generate the answer.

Group related content

Explore document clustering or duplicate detection using vector similarity. Evaluate thresholds on your own collection.

Explore the embeddings & semantic search workflow ↗

Qwen3 Embedding 8B pricing

Qwen3 Embedding 8B
API pricing.

These are catalogue “From” rates in EUR. Dynamic pricing can change the applicable price. Confirm your model, request route and active billing unit before estimating a workload.

See Qwen3 Embedding 8B on the rate card ↗

Input / 1M tokens: From€0.00774

A concrete example
100,000 input tokens = €0.000774 at the displayed starting rates.

Illustration only; excludes retries, other models and paid tools. Catalogue updated 2026-10-22.

Check current rates ↗

API integration

How to use
the Qwen3 Embedding 8B API.

Use the Blade model ID qwen3-embedding-8b and keep your API key in your backend environment. Verify account access and supported parameters before connecting production traffic.

Create a Blade API key in the console.
Run pip install openai and set BLADE_API_KEY.
Check the model list below, then use the console’s confirmed inference example for this model.

Read the API reference ↗

python

import os
from openai import OpenAI

client = OpenAI(
    base_url="https://api.models.blade.sh/v1",
    api_key=os.environ["BLADE_API_KEY"],
    timeout=120.0,
)

model_id = "qwen3-embedding-8b"
models = client.models.list()
match = next(
    (item for item in models.data
     if item.id == model_id),
    None,
)
if match is None:
    raise RuntimeError("Model not listed for this account")
print(match.model_dump_json(indent=2))

Install openai and set BLADE_API_KEY. This checks the account’s model list; it does not run inference. Copy the confirmed model-specific inference request from the console before integrating.

Qwen3 Embedding 8B API FAQ.

What can I use Qwen3 Embedding 8B for?

Qwen3 Embedding 8B turns text into vectors for semantic search and retrieval. Use it to find relevant passages before a separate language model writes an answer. Its Blade model ID is qwen3-embedding-8b.

How much does Qwen3 Embedding 8B cost on Blade?

Input / 1M tokens: from €0.00774. These are dynamic catalogue starting rates as of 2026-10-22, not fixed prices for every request. Check the console for current rates.

What should I confirm before integrating?

Blade lists a 7k context limit, which differs from the upstream model’s maximum. The current public API reference does not document an embeddings route; confirm request format, dimensions and normalization in the console.

Model reference: Publisher model reference ↗. Hosted configuration and pricing: Blade catalogue, 2026-10-22.

Choose by task, input type and billing unit. Browse all models ↗

Qwen3 Embedding 8B API.Find meaning, not just keywords.

Embeddings & semantic searchwith Qwen3 Embedding 8B.