curl -X POST 'https://api.openai.com/v1/embeddings' \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -H 'Content-Type: application/json' \
  -d '{
  "input": "The quick brown fox jumped over the lazy dog",
  "model": "text-embedding-3-small"
}'

# POST /embeddings (OpenAI API)
Base URL: https://api.openai.com/v1
Auth: Authorization; OpenAI API key; required.
Request formats: application/json.
Last verified: 2026-10-20

> Create embeddings

## Critical Gotchas
* The Responses API is the primary interface for new text and tool workflows; Chat Completions remains a separate compatibility API.
* Audio transcription uploads use multipart form data rather than a JSON request body.

## cURL
curl -X POST 'https://api.openai.com/v1/embeddings' \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -H 'Content-Type: application/json' \
  -d '{
  "input": "The quick brown fox jumped over the lazy dog",
  "model": "text-embedding-3-small"
}'

## Parameters
| Name | In | Type | Required | Description |
| :--- | :--- | :--- | :--- | :--- |
| input | body | string | Yes | Input text to embed, encoded as a string or array of tokens. To embed multipl... |
| model | body | string | Yes | ID of the model to use. You can use the [List models](https://developers.open... |
| encoding_format | body | string | No | The format to return the embeddings in. Can be either `float` or [`base64`](h... |
| dimensions | body | integer | No | The number of dimensions the resulting output embeddings should have. Only su... |
| user | body | string | No | A unique identifier representing your end-user, which can help OpenAI to moni... |

## Response example
{"data":[{}],"model":"string"}

---
Need an API key? Get one at https://platform.openai.com/api-keys

POST

POST /embeddings — OpenAI API

OpenAI API key (required); https://api.openai.com/v1

OpenAI API

Create embeddings

Base URL: https://api.openai.com/v1
Auth: Authorization: Bearer <OPENAI_API_KEY>
Last verified: 2026-10-20 · upstream hash matched

Actions Try in browser→

Agents: curl -H "Accept: text/markdown" this URL
→ 316 tokens · Vary: Accept

Critical gotchas

The Responses API is the primary interface for new text and tool workflows; Chat Completions remains a separate compatibility API.

Audio transcription uploads use multipart form data rather than a JSON request body.

cURL

curl -X POST 'https://api.openai.com/v1/embeddings' \  -H "Authorization: Bearer $OPENAI_API_KEY" \  -H 'Content-Type: application/json' \  -d '{  "input": "The quick brown fox jumped over the lazy dog",  "model": "text-embedding-3-small"}'

Parameters

Name	In	Type	Required	Description
input	body	string	Yes	Input text to embed, encoded as a string or array of tokens. To embed multiple inputs in a single request, pass an array of strings or array of token arrays. The input must not exceed the max input tokens for the model (8192 tokens for all embedding models), cannot be an empty string, and any array must be 2048 dimensions or less. [Example Python code](https://cookbook.openai.com/examples/how_to_count_tokens_with_tiktoken) for counting tokens. In addition to the per-input token limit, all embedding models enforce a maximum of 300,000 tokens summed across all inputs in a single request.
model	body	string	Yes	ID of the model to use. You can use the [List models](https://developers.openai.com/api/reference/resources/models/methods/list) API to see all of your available models, or see our [Model overview](https://developers.openai.com/api/docs/models) for descriptions of them.
encoding_format	body	string	No	The format to return the embeddings in. Can be either `float` or [`base64`](https://pypi.org/project/pybase64/).
dimensions	body	integer	No	The number of dimensions the resulting output embeddings should have. Only supported in `text-embedding-3` and later models.
user	body	string	No	A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse. [Learn more](https://developers.openai.com/api/docs/guides/safety-best-practices#implement-safety-identifiers).

Response 200 OK

{
  "data": [
    {
      "index": 0,
      "embedding": [
        0
      ],
      "object": "embedding"
    }
  ],
  "model": "string",
  "object": "list",
  "usage": {
    "prompt_tokens": 0,
    "total_tokens": 0
  }
}

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