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GET
/
models
/
{model_name}
PINECONE_API_KEY="YOUR_API_KEY"

curl "https://api.pinecone.io/models/llama-text-embed-v2" \
    -H "Api-Key: $PINECONE_API_KEY" \
    -H "X-Pinecone-Api-Version: 2025-10"

{
  "model": "llama-text-embed-v2",
  "short_description": "A high performance dense embedding model optimized for multilingual and cross-lingual text question-answering retrieval with support for long documents (up to 2048 tokens) and dynamic embedding size (Matryoshka Embeddings).",
  "type": "embed",
  "vector_type": "dense",
  "default_dimension": 1024,
  "modality": "text",
  "max_sequence_length": 2048,
  "max_batch_size": 96,
  "provider_name": "NVIDIA",
  "supported_metrics": [
    "Cosine",
    "DotProduct"
  ],
  "supported_dimensions": [
    384,
    512,
    768,
    1024,
    2048
  ],
  "supported_parameters": [
    {
      "parameter": "input_type",
      "required": true,
      "type": "one_of",
      "value_type": "string",
      "allowed_values": [
        "query",
        "passage"
      ]
    },
    {
      "parameter": "truncate",
      "required": false,
      "default": "END",
      "type": "one_of",
      "value_type": "string",
      "allowed_values": [
        "END",
        "NONE",
        "START"
      ]
    },
    {
      "parameter": "dimension",
      "required": false,
      "default": 1024,
      "type": "one_of",
      "value_type": "integer",
      "allowed_values": [
        384,
        512,
        768,
        1024,
        2048
      ]
    }
  ]
}

PINECONE_API_KEY="YOUR_API_KEY"

curl "https://api.pinecone.io/models/llama-text-embed-v2" \
    -H "Api-Key: $PINECONE_API_KEY" \
    -H "X-Pinecone-Api-Version: 2025-10"

{
  "model": "llama-text-embed-v2",
  "short_description": "A high performance dense embedding model optimized for multilingual and cross-lingual text question-answering retrieval with support for long documents (up to 2048 tokens) and dynamic embedding size (Matryoshka Embeddings).",
  "type": "embed",
  "vector_type": "dense",
  "default_dimension": 1024,
  "modality": "text",
  "max_sequence_length": 2048,
  "max_batch_size": 96,
  "provider_name": "NVIDIA",
  "supported_metrics": [
    "Cosine",
    "DotProduct"
  ],
  "supported_dimensions": [
    384,
    512,
    768,
    1024,
    2048
  ],
  "supported_parameters": [
    {
      "parameter": "input_type",
      "required": true,
      "type": "one_of",
      "value_type": "string",
      "allowed_values": [
        "query",
        "passage"
      ]
    },
    {
      "parameter": "truncate",
      "required": false,
      "default": "END",
      "type": "one_of",
      "value_type": "string",
      "allowed_values": [
        "END",
        "NONE",
        "START"
      ]
    },
    {
      "parameter": "dimension",
      "required": false,
      "default": 1024,
      "type": "one_of",
      "value_type": "integer",
      "allowed_values": [
        384,
        512,
        768,
        1024,
        2048
      ]
    }
  ]
}

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:2025-10
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.

​
vector_type
string

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

​
default_dimension
integer

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

The maximum tokens per sequence supported by the model.

Required range: x >= 1
Example:

512

​
max_batch_size
integer

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[]

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

​
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.

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