PINECONE_API_KEY="YOUR_API_KEY"

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

{
  "models": [
    {
      "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
          ]
        }
      ]
    },
    {
      "model": "multilingual-e5-large",
      "short_description": "A high-performance dense embedding model trained on a mixture of multilingual datasets. It works well on messy data and short queries expected to return medium-length passages of text (1-2 paragraphs)",
      "type": "embed",
      "vector_type": "dense",
      "default_dimension": 1024,
      "modality": "text",
      "max_sequence_length": 507,
      "max_batch_size": 96,
      "provider_name": "Microsoft",
      "supported_metrics": [
        "Cosine",
        "Euclidean"
      ],
      "supported_dimensions": [
        1024
      ],
      "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"
          ]
        }
      ]
    },
    {
      "model": "pinecone-sparse-english-v0",
      "short_description": "A sparse embedding model for converting text to sparse vectors for keyword or hybrid semantic/keyword search. Built on the innovations of the DeepImpact architecture.",
      "type": "embed",
      "vector_type": "sparse",
      "modality": "text",
      "max_sequence_length": 512,
      "max_batch_size": 96,
      "provider_name": "Pinecone",
      "supported_metrics": [
        "DotProduct"
      ],
      "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"
          ]
        },
        {
          "parameter": "return_tokens",
          "required": false,
          "default": false,
          "type": "any",
          "value_type": "boolean"
        }
      ]
    },
    {
      "model": "bge-reranker-v2-m3",
      "short_description": "A high-performance, multilingual reranking model that works well on messy data and short queries expected to return medium-length passages of text (1-2 paragraphs)",
      "type": "rerank",
      "modality": "text",
      "max_sequence_length": 1024,
      "max_batch_size": 100,
      "provider_name": "BAAI",
      "supported_parameters": [
        {
          "parameter": "truncate",
          "required": false,
          "default": "NONE",
          "type": "one_of",
          "value_type": "string",
          "allowed_values": [
            "END",
            "NONE"
          ]
        }
      ]
    },
    {
      "model": "cohere-rerank-3.5",
      "short_description": "Cohere's leading reranking model, balancing performance and latency for a wide range of enterprise search applications.",
      "type": "rerank",
      "modality": "text",
      "max_sequence_length": 40000,
      "max_batch_size": 200,
      "provider_name": "Cohere",
      "supported_parameters": [
        {
          "parameter": "max_chunks_per_doc",
          "required": false,
          "default": 3072,
          "type": "numeric_range",
          "value_type": "integer",
          "min": 1,
          "max": 3072
        }
      ]
    },
    {
      "model": "pinecone-rerank-v0",
      "short_description": "A state of the art reranking model that out-performs competitors on widely accepted benchmarks. It can handle chunks up to 512 tokens (1-2 paragraphs)",
      "type": "rerank",
      "modality": "text",
      "max_sequence_length": 512,
      "max_batch_size": 100,
      "provider_name": "Pinecone",
      "supported_parameters": [
        {
          "parameter": "truncate",
          "required": false,
          "default": "END",
          "type": "one_of",
          "value_type": "string",
          "allowed_values": [
            "END",
            "NONE"
          ]
        }
      ]
    }
  ]
}