Qwen3 Embedding 0.6B

Overview

1024-dimension embeddings from the smallest of the Qwen3 retrieval line, at the same rate as its larger siblings. The 32K window is the reason to reach for it: long documents embed whole while the vector stays small enough to index cheaply.

Specs

Field	Value
Model ID	`qwen3-embedding-0.6b`
Best for	High-volume indexing, long documents on a budget
Context window	32,768
Input modalities	Text
Output modalities	Text
Speed	Fast
Cost band	Cheap
Release stage	Stable

Pricing

	Per 1M tokens
Input	$0.0135
Output	$0.00

Example

from openai import OpenAI

client = OpenAI(base_url="https://kymaapi.com/v1", api_key="kyma-...")

response = client.embeddings.create(
    model="qwen3-embedding-0.6b",
    input=["first document", "second document"],
)

Agent query example

Ask the API which models fit, instead of hardcoding an id:

curl "https://kymaapi.com/v1/models?recommended_for=search-documents&quality_tier=strong"

​Overview

​Specs

​Pricing

​Example

​Agent query example

Overview

Specs

Pricing

Example

Agent query example