Models
zen3-embedding
High-quality text embedding model with 3072 dimensions and 8K context.
zen3-embedding
Text Embeddings
High-quality text embeddings for search, clustering, and retrieval. 3072-dimensional dense vectors optimized for semantic similarity.
Specifications
| Property | Value |
|---|---|
| Model ID | zen3-embedding |
| Dimensions | 3,072 |
| Context Window | 8K tokens |
| Tier | pro max |
| Input Price | $0.39 / 1M tokens |
| Output Price | $0.39 / 1M tokens |
Capabilities
- Semantic search by meaning, not keywords
- RAG pipeline retrieval
- Document clustering and deduplication
- Classification feature generation
- 3072-dimensional dense vectors
API Usage
curl https://api.hanzo.ai/v1/embeddings \
-H "Authorization: Bearer $HANZO_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "zen3-embedding",
"input": "Zen LM is a family of frontier AI models"
}'from hanzoai import Hanzo
client = Hanzo(api_key="hk-your-api-key")
response = client.embeddings.create(
model="zen3-embedding",
input=["Hello world", "Zen LM models"],
)
for embedding in response.data:
print(f"Vector dim: {len(embedding.embedding)}")Response Format
{
"object": "list",
"data": [
{
"object": "embedding",
"index": 0,
"embedding": [0.0023, -0.0091, 0.0152, "..."]
}
],
"model": "zen3-embedding",
"usage": {
"prompt_tokens": 8,
"total_tokens": 8
}
}See Also
- Embeddings API -- Endpoint documentation
- zen3-nano -- Lightweight text model
- Pricing -- Full pricing table