Google DeepMind (Gemini) / gemini-embedding-001
Gemini Embedding 001 stands out for Speed- and cost-oriented model tier and 2K-token context window. It is best suited to semantic search and RAG and similarity matching.
Context
2K
token
Input
$0.135
/ 1M tokens
Output
$0
/ 1M tokens
Quality
—
No score
Overview
Gemini Embedding 001 stands out for Speed- and cost-oriented model tier and 2K-token context window. It is best suited to semantic search and RAG and similarity matching.
Model maker
Google DeepMind (Gemini)
Called through SeaLink's unified account, balance, and OpenAI-compatible API.
Protocol
Embeddings API
Base URL: https://api.sealink.io/v1
Pricing
The final charge follows generated usage and the applicable selector, such as resolution, duration, input type, or provider unit.
API
model
gemini-embedding-001base_url
https://api.sealink.io/v1Auth
Bearer $SEALINK_API_KEYcurl https://api.sealink.io/v1/embeddings \-H "Authorization: Bearer $SEALINK_API_KEY" \-H "Content-Type: application/json" \-d '{"model": "gemini-embedding-001","input": "SeaLink gives you one API for leading AI models."}'
from openai import OpenAIclient = OpenAI(base_url="https://api.sealink.io/v1", api_key="<your-sealink-key>")resp = client.embeddings.create(model="gemini-embedding-001",input="SeaLink gives you one API for leading AI models.",)print(resp.data[0].embedding[:5])
import OpenAI from "openai";const client = new OpenAI({baseURL: "https://api.sealink.io/v1",apiKey: process.env.SEALINK_API_KEY,});const resp = await client.embeddings.create({model: "gemini-embedding-001",input: "SeaLink gives you one API for leading AI models.",});console.log(resp.data[0].embedding.slice(0, 5));