Alibaba (Qwen) / text-embedding-v3

Text Embedding V3

Text Embedding V3 turns text into embeddings for semantic search, RAG retrieval, clustering, and similarity matching.

Knowledge search

Context

tokens

Input

$0.084

/ 1M tokens

Output

$0.120

/ 1M tokens

Quality

#57

Elo 1197

Overview

What this model is good for

Text Embedding V3 turns text into embeddings for semantic search, RAG retrieval, clustering, and similarity matching.

Model capabilities

Streaming

Supports streamed responses for chat, assistants, and interfaces that need visible incremental output.

Model maker

Model maker and access

This section shows the model maker, SeaLink model ID, protocol, and pricing information.

Alibaba (Qwen)

Called through SeaLink's unified account, balance, and OpenAI-compatible API.

Protocol

Embeddings API

Base URL: https://test.sealink.io/v1

Pricing

Pricing and cost sense

Prices come from the current model catalog. Simple estimates help users judge whether the model fits production volume.

Input$0.084/ 1M tokens

Output$0.120/ 1M tokens

Light production run

100K input + 25K output

$0.011

Higher-volume run

1M input + 250K output

$0.114

Actual billing follows usage logs and billing records; caching and media pricing depend on the endpoint.

API

Copy into your code

The model page should make it clear which model to copy, which base URL to use, and where to get an API Key.

model

text-embedding-v3

base_url

https://test.sealink.io/v1

Auth

Bearer $SEALINK_API_KEY

cURL

curl https://test.sealink.io/v1/embeddings \
  -H "Authorization: Bearer $SEALINK_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "text-embedding-v3",
    "input": "SeaLink gives you one API for leading AI models."
  }'

Python (OpenAI SDK)

from openai import OpenAI
client = OpenAI(base_url="https://test.sealink.io/v1", api_key="<your-sealink-key>")

resp = client.embeddings.create(
    model="text-embedding-v3",
    input="SeaLink gives you one API for leading AI models.",
)
print(resp.data[0].embedding[:5])

Node.js (OpenAI SDK)

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://test.sealink.io/v1",
  apiKey: process.env.SEALINK_API_KEY,
});

const resp = await client.embeddings.create({
  model: "text-embedding-v3",
  input: "SeaLink gives you one API for leading AI models.",
});
console.log(resp.data[0].embedding.slice(0, 5));