Gemini Embedding 001 is Google’s text embedding model for mapping language into vectors. It supports semantic retrieval, similarity, clustering, and classification workflows.
Key strengths
- Text semantic vectors
- Retrieval-oriented output
- Similarity comparison
- Google Gemini API integration
Use cases
- Semantic search
- RAG retrieval
- Text clustering
- Document classification
Google's google/gemini-embedding-001 is a dense vector embedding model. It maps text into a semantic vector space optimized for retrieval, clustering, classification, recommendation, and RAG retrieval pipelines.
Compatible with the OpenAI `/embeddings` endpoint, returning numerical representations that measure semantic similarity between pieces of text. Well-suited for high-throughput indexing of large corpora at low cost.
google/gemini-embedding-001 is fully OpenAI-compatible — drop in your existing OpenAI Python or Node SDK and switch `baseURL` to `https://api.tokenlx.ai`. TokenLX transparently routes your requests to the optimal provider endpoint while preserving streaming, function-calling, and structured-output semantics.
Performance
Compare different providers across TokenLX · All locations.
Effective Pricing
Actual cost per million tokens across providers over the past 7 days.
Recent activity
Total usage per day on TokenLX (last 30 days).
Sample code & API
TokenLX normalizes requests and responses across providers. Use any OpenAI SDK or our native SDK.
import requests
headers = {
"Authorization": "Bearer sk-aihub-...",
"Content-Type": "application/json",
}
# Text embeddings
response_1 = requests.post(
"https://api.tokenlx.ai/v1/embeddings",
headers=headers,
json={"model": "gemini-embedding-001", "input": ["AIHub makes model routing simple."], "dimension": "1024", "encoding_format": "float"},
)
print(response_1.json())Replace sk-aihubrouter-… with your key from the dashboard.