Describe a model

curl --request GET \
  --url https://api.pinecone.io/models/{model_name} \
  --header 'Api-Key: <api-key>' \
  --header 'X-Pinecone-Api-Version: <x-pinecone-api-version>'

import requests

url = "https://api.pinecone.io/models/{model_name}"

headers = {
    "X-Pinecone-Api-Version": "<x-pinecone-api-version>",
    "Api-Key": "<api-key>"
}

response = requests.get(url, headers=headers)

print(response.text)

const options = {
  method: 'GET',
  headers: {'X-Pinecone-Api-Version': '<x-pinecone-api-version>', 'Api-Key': '<api-key>'}
};

fetch('https://api.pinecone.io/models/{model_name}', options)
  .then(res => res.json())
  .then(res => console.log(res))
  .catch(err => console.error(err));

<?php

$curl = curl_init();

curl_setopt_array($curl, [
  CURLOPT_URL => "https://api.pinecone.io/models/{model_name}",
  CURLOPT_RETURNTRANSFER => true,
  CURLOPT_ENCODING => "",
  CURLOPT_MAXREDIRS => 10,
  CURLOPT_TIMEOUT => 30,
  CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
  CURLOPT_CUSTOMREQUEST => "GET",
  CURLOPT_HTTPHEADER => [
    "Api-Key: <api-key>",
    "X-Pinecone-Api-Version: <x-pinecone-api-version>"
  ],
]);

$response = curl_exec($curl);
$err = curl_error($curl);

curl_close($curl);

if ($err) {
  echo "cURL Error #:" . $err;
} else {
  echo $response;
}

package main

import (
	"fmt"
	"net/http"
	"io"
)

func main() {

	url := "https://api.pinecone.io/models/{model_name}"

	req, _ := http.NewRequest("GET", url, nil)

	req.Header.Add("X-Pinecone-Api-Version", "<x-pinecone-api-version>")
	req.Header.Add("Api-Key", "<api-key>")

	res, _ := http.DefaultClient.Do(req)

	defer res.Body.Close()
	body, _ := io.ReadAll(res.Body)

	fmt.Println(string(body))

}

HttpResponse<String> response = Unirest.get("https://api.pinecone.io/models/{model_name}")
  .header("X-Pinecone-Api-Version", "<x-pinecone-api-version>")
  .header("Api-Key", "<api-key>")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://api.pinecone.io/models/{model_name}")

http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true

request = Net::HTTP::Get.new(url)
request["X-Pinecone-Api-Version"] = '<x-pinecone-api-version>'
request["Api-Key"] = '<api-key>'

response = http.request(request)
puts response.read_body

{
  "default_dimension": 256,
  "max_batch_size": 96,
  "max_sequence_length": 512,
  "modality": "text",
  "model": "example-embedding-model",
  "provider_name": "Embedding Model Provider",
  "short_description": "An example embedding model.",
  "supported_dimensions": [
    256,
    512
  ],
  "supported_metrics": [
    "cosine",
    "euclidean"
  ],
  "supported_parameters": [
    {
      "allowed_values": [
        "value1",
        "value2"
      ],
      "parameter": "example_required_param",
      "required": true,
      "type": "one_of",
      "value_type": "string"
    },
    {
      "allowed_values": [
        "value1",
        "value2"
      ],
      "default": "value1",
      "parameter": "example_param_with_default",
      "required": false,
      "type": "one_of",
      "value_type": "string"
    },
    {
      "default": 5,
      "max": 10,
      "min": 0,
      "parameter": "example_numeric_range",
      "required": false,
      "type": "numeric_range",
      "value_type": "integer"
    }
  ],
  "type": "embed",
  "vector_type": "dense"
}

GET

models

{model_name}

Describe a model

curl --request GET \
  --url https://api.pinecone.io/models/{model_name} \
  --header 'Api-Key: <api-key>' \
  --header 'X-Pinecone-Api-Version: <x-pinecone-api-version>'

import requests

url = "https://api.pinecone.io/models/{model_name}"

headers = {
    "X-Pinecone-Api-Version": "<x-pinecone-api-version>",
    "Api-Key": "<api-key>"
}

response = requests.get(url, headers=headers)

print(response.text)

const options = {
  method: 'GET',
  headers: {'X-Pinecone-Api-Version': '<x-pinecone-api-version>', 'Api-Key': '<api-key>'}
};

fetch('https://api.pinecone.io/models/{model_name}', options)
  .then(res => res.json())
  .then(res => console.log(res))
  .catch(err => console.error(err));

<?php

$curl = curl_init();

curl_setopt_array($curl, [
  CURLOPT_URL => "https://api.pinecone.io/models/{model_name}",
  CURLOPT_RETURNTRANSFER => true,
  CURLOPT_ENCODING => "",
  CURLOPT_MAXREDIRS => 10,
  CURLOPT_TIMEOUT => 30,
  CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
  CURLOPT_CUSTOMREQUEST => "GET",
  CURLOPT_HTTPHEADER => [
    "Api-Key: <api-key>",
    "X-Pinecone-Api-Version: <x-pinecone-api-version>"
  ],
]);

$response = curl_exec($curl);
$err = curl_error($curl);

curl_close($curl);

if ($err) {
  echo "cURL Error #:" . $err;
} else {
  echo $response;
}

package main

import (
	"fmt"
	"net/http"
	"io"
)

func main() {

	url := "https://api.pinecone.io/models/{model_name}"

	req, _ := http.NewRequest("GET", url, nil)

	req.Header.Add("X-Pinecone-Api-Version", "<x-pinecone-api-version>")
	req.Header.Add("Api-Key", "<api-key>")

	res, _ := http.DefaultClient.Do(req)

	defer res.Body.Close()
	body, _ := io.ReadAll(res.Body)

	fmt.Println(string(body))

}

HttpResponse<String> response = Unirest.get("https://api.pinecone.io/models/{model_name}")
  .header("X-Pinecone-Api-Version", "<x-pinecone-api-version>")
  .header("Api-Key", "<api-key>")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://api.pinecone.io/models/{model_name}")

http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true

request = Net::HTTP::Get.new(url)
request["X-Pinecone-Api-Version"] = '<x-pinecone-api-version>'
request["Api-Key"] = '<api-key>'

response = http.request(request)
puts response.read_body

{
  "default_dimension": 256,
  "max_batch_size": 96,
  "max_sequence_length": 512,
  "modality": "text",
  "model": "example-embedding-model",
  "provider_name": "Embedding Model Provider",
  "short_description": "An example embedding model.",
  "supported_dimensions": [
    256,
    512
  ],
  "supported_metrics": [
    "cosine",
    "euclidean"
  ],
  "supported_parameters": [
    {
      "allowed_values": [
        "value1",
        "value2"
      ],
      "parameter": "example_required_param",
      "required": true,
      "type": "one_of",
      "value_type": "string"
    },
    {
      "allowed_values": [
        "value1",
        "value2"
      ],
      "default": "value1",
      "parameter": "example_param_with_default",
      "required": false,
      "type": "one_of",
      "value_type": "string"
    },
    {
      "default": 5,
      "max": 10,
      "min": 0,
      "parameter": "example_numeric_range",
      "required": false,
      "type": "numeric_range",
      "value_type": "integer"
    }
  ],
  "type": "embed",
  "vector_type": "dense"
}

Authorizations

Api-Key

string

header

required

An API Key is required to call Pinecone APIs. Get yours from the console.

Headers

X-Pinecone-Api-Version

string

default:2026-07

required

Required date-based version header

Path Parameters

model_name

string

required

The name of the model to look up.

Response

The model details.

Represents the model configuration including model type, supported parameters, and other model details.

model

string

required

The name of the model.

Example:

"multilingual-e5-large"

short_description

string

required

A summary of the model.

Example:

"multilingual-e5-large"

type

string

required

The type of model (e.g. 'embed' or 'rerank').

Example:

"embed"

supported_parameters

object[]

required

List of parameters supported by the model.

Show child attributes

vector_type

string

Whether the embedding model produces 'dense' or 'sparse' embeddings.

default_dimension

integer<int32>

The default embedding model dimension (applies to dense embedding models only).

Required range: 1 <= x <= 20000

Example:

1024

modality

string

The modality of the model (e.g. 'text').

Example:

"text"

max_sequence_length

integer<int32>

The maximum tokens per sequence supported by the model.

Required range: x >= 1

Example:

512

max_batch_size

integer<int32>

The maximum batch size (number of sequences) supported by the model.

Required range: x >= 1

Example:

96

provider_name

string

The name of the provider of the model.

Example:

"NVIDIA"

supported_dimensions

integer<int32>[]

The list of supported dimensions for the model (applies to dense embedding models only).

Required range: 1 <= x <= 20000

supported_metrics

string[]

The distance metrics supported by the model for similarity search.

A distance metric that the embedding model supports for similarity searches. Possible values: cosine, euclidean, or dotproduct.

APIs

Database

Inference

Admin

SDKs

CLI

Tools

Describe a model

Authorizations

Headers

Path Parameters

Response