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text-embedding-3-small

text-embedding-3-small

View docs

Convert text into 1536-dimensional vectors for semantic search and retrieval.

Pricing: From 4.5003 Credits / 1M tokens(≈ $0.0225)
Output: Embeddings
Added to catalog: Oct 30, 2026

Input

Examples

Vector format

Default: "float"

Texts to embed*

Item 1

Item 2

Item 3

Default: Three semantic-search example texts

Estimate the cost first. Generation starts only after confirmation.

OutputFill example

3 inputs; 1536 dimensions per vector. Cosine similarity: related=0.6519, unrelated=0.1142. First eight coordinates: [-0.0158538818359375,0.0147552490234375,-0.0030040740966796875,-0.0113525390625,0.048828125,-0.020294189453125,-0.0293426513671875,0.03948974609375]

README

Convert text into 1536-dimensional vectors for semantic search and retrieval.

Model capabilities

Input modalities: Text
Output modalities: Not confirmed

Parameters

Fields accepted in the request body for this model.

inputobject

Required

encoding_formatenum

Optional

Values: floatDefault: float

Pricing

Credits / 1M tokens

Standard

Input4.5003≈ $0.0225

Bill actual quantity

Minimum per call: 0 Credits · Rounding is applied once to the total cost

Only listed usage types are supported for automatic billing

Pricing follows the selected specifications

Prices are billed per unit consumed with no monthly minimum. See billing guide for Credits estimates and actual usage charges.

Code

View docs →

POST https://orux.top/api/v1/embeddings

Code examples

Request body

curl 'https://orux.top/api/v1/embeddings' \
  -H 'Authorization: Bearer YOUR_API_KEY' \
  -H 'Content-Type: application/json' \
  -d '{
  "input": [
    "社区咖啡馆提供安静的阅读空间。",
    "想找一家可以看书喝咖啡的店。",
    "明天的航班将在上午起飞。"
  ],
  "encoding_format": "float",
  "model": "text-embedding-3-small"
}'

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Code examples

Response examples

{
  "object": "list",
  "data": [{ "object": "embedding", "index": 0, "embedding": [0.0023, -0.009, "..."] }],
  "model": "text-embedding-3-small",
  "usage": { "prompt_tokens": 8, "total_tokens": 8 }
}

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Frequently asked questions

About text-embedding-3-small pricing, capabilities and API access on Orux AI.

What is text-embedding-3-small?

text-embedding-3-small: Convert text into 1536-dimensional vectors for semantic search and retrieval.

How much does text-embedding-3-small cost on Orux AI?

Input: 4.5003 Credits; output: 0 Credits, per 1M tokens. See the pricing section on this page for text-embedding-3-small pricing and applicable conditions. Charges depend on the selected parameters and recorded usage.

Where can I access the text-embedding-3-small API?

Create an Orux AI API key and send an authenticated POST request to https://orux.top/api/v1/embeddings, using text-embedding-3-small as the model identifier. Try the playground on this page and use the API example for the exact request format.