Embedding / MODEL API
qwen3-embedding-0.6b
Input $0.12 / 1M tokens
OVERVIEW
Model overview
Qwen3-Embedding-0.6B has the following features: Model Type: Text Embedding Supported Languages: 100+ Languages Number of Parameters: 0.6B Context Length: 32k Embedding Dimension: Up to 1024, supports user-defined output dimensions ranging from 32 to 1024
Specifications
- Category
- Embedding
- Input modalities
- text
- Output modalities
- text
MODEL PRICING
Model API pricing
Embedding
USD per 1M tokens| Model | Input | Output |
|---|---|---|
| qwen3-embedding-0.6b | $0.12 | — |
Public platform rates refresh every five minutes. A dash means a rate is not provided; it does not mean free. Check the console for final charges.
BUILT FOR YOUR STACK
API quickstart
Expand connection setup and run the preparation commands first. Then save and run the Python or cURL example.
Connection setup & preparationActual API address, model ID, and environment variables
- API address
https://api.ecohash.com/v1- API model ID
qwen3-embedding-0.6b
Replace YOUR_API_KEY with the full inference API key issued in the console. Run setup in macOS/Linux or a compatible shell. JSON cURL examples use the Python standard library to build the request body.
python -m pip install openai
export GIGACOMMA_API_BASE_URL='https://api.ecohash.com/v1'
export GIGACOMMA_MODEL_ID='qwen3-embedding-0.6b'
export GIGACOMMA_API_KEY="YOUR_API_KEY"GIGACOMMA_API_KEY is the environment variable name. Its value is an inference API key from the console, not an OpenAI API key. Get an API key
import osfrom openai import OpenAIMODEL_ID = os.environ["GIGACOMMA_MODEL_ID"]client = OpenAI( base_url=os.environ["GIGACOMMA_API_BASE_URL"], api_key=os.environ["GIGACOMMA_API_KEY"],)response = client.embeddings.create( model=MODEL_ID, input="Semantic search over your documents",)print(response.data[0].embedding)