List Embedding Models

Overview

List all embedding-capable models available through RedPill. This endpoint returns models specifically designed for generating vector embeddings from text, images, or videos.

Embedding models are filtered separately from chat/completion models. Use GET /v1/models for chat models or GET /v1/embeddings/models for embedding models.

List All Embedding Models

curl https://api.redpill.ai/v1/embeddings/models \
  -H "Authorization: Bearer $REDPILL_API_KEY"

{
  "object": "list",
  "data": [
    {
      "id": "openai/text-embedding-3-large",
      "name": "OpenAI: Text Embedding 3 Large",
      "created": 1704067200,
      "input_modalities": ["text", "embeddings"],
      "output_modalities": ["embeddings"],
      "context_length": 8192,
      "max_output_length": 8192,
      "pricing": {
        "prompt": "0.00000013",
        "completion": "0",
        "image": "0",
        "request": "0"
      },
      "supported_sampling_parameters": ["temperature"],
      "supported_features": [],
      "description": "text-embedding-3-large is OpenAI's most capable embedding model for both english and non-english tasks. Embeddings are a numerical representation of text that can be used to measure the relatedness between two pieces of text."
    },
    {
      "id": "openai/text-embedding-3-small",
      "name": "OpenAI: Text Embedding 3 Small",
      "created": 1704067200,
      "input_modalities": ["text", "embeddings"],
      "output_modalities": ["embeddings"],
      "context_length": 8192,
      "max_output_length": 8192,
      "pricing": {
        "prompt": "0.00000002",
        "completion": "0"
      },
      "description": "Improved embedding model with better performance than ada-002"
    },
    {
      "id": "openai/text-embedding-ada-002",
      "name": "OpenAI: Text Embedding Ada 002",
      "created": 1704067200,
      "input_modalities": ["text", "embeddings"],
      "output_modalities": ["embeddings"],
      "context_length": 8192,
      "max_output_length": 8192,
      "pricing": {
        "prompt": "0.0000001",
        "completion": "0"
      },
      "description": "Legacy text embedding model"
    }
  ]
}

Response Fields

object

string

Always "list" for this endpoint

data

array

Array of embedding model objects

Show Model Object Fields

string

Model identifier (e.g., openai/text-embedding-3-large)

name

string

Human-readable model name

created

integer

Unix timestamp of when the model was added

input_modalities

array

Input types the model accepts:

text - Text input
embeddings - Embedding vectors

output_modalities

array

Output types the model produces (always ["embeddings"] for embedding models)

context_length

integer

Maximum input tokens/length

max_output_length

integer

Maximum output dimensions

pricing

object

Cost per unit

Show Pricing Fields

prompt

string

Cost per input token (in USD)

completion

string

Always "0" for embedding models

description

string

Model description and capabilities

Filter by Provider

Get embedding models from a specific provider.

curl https://api.redpill.ai/v1/embeddings/models/openai \
  -H "Authorization: Bearer $REDPILL_API_KEY"

URL Parameters

provider

string

required

Provider name to filter by (e.g., openai, voyage, jina, nomic, cohere)

Available Embedding Providers

Provider	Models	Specialization	Max Dimensions
OpenAI	text-embedding-3-large, 3-small, ada-002	General purpose	3072
Voyage	voyage-3, voyage-3-lite	Semantic search	1024
Jina	jina-embeddings-v3, jina-clip-v2	Multi-modal	1024
Nomic	nomic-embed-text-v1.5, nomic-embed-video	Text & video	768
Cohere	embed-english-v3.0, embed-multilingual	Multilingual	1024
Milvus	milvus-embedding	Vector database	768
Qdrant	qdrant-embedding	Vector search	1536

Create Embeddings

Learn how to generate embeddings →

Model Selection Guide

By Use Case

General Purpose Text Embeddings

Recommended: openai/text-embedding-3-large

Best overall quality
Supports 100+ languages
Customizable dimensions (256-3072)

Budget Option: openai/text-embedding-3-small

5x cheaper
Good performance
Faster generation

Semantic Search & RAG

Recommended: voyage/voyage-3

Optimized for retrieval tasks
Strong semantic understanding
Excellent for document search

Alternative: cohere/embed-english-v3.0

Built-in search/clustering modes
High accuracy for RAG applications

Multi-Modal (Text + Images)

Video Understanding

Recommended: nomic/nomic-embed-video-v1.5

Specialized for video content
Temporal understanding
Scene-level embeddings

Alternative: Extract frames and use jina/jina-clip-v2

Multilingual Support

Recommended: cohere/embed-multilingual-v3.0

100+ languages
Strong cross-lingual performance
No language detection needed

Alternative: openai/text-embedding-3-large

Supports many languages
Consistent quality across languages

Cost-Optimized

Recommended: openai/text-embedding-3-small

$0.02 per 1M tokens
Good quality/price ratio

Ultra-Budget: openai/text-embedding-ada-002

$0.10 per 1M tokens (legacy pricing)
Acceptable for many use cases

Filtering Models

By Capability

import requests

response = requests.get(
    "https://api.redpill.ai/v1/embeddings/models",
    headers={"Authorization": f"Bearer YOUR_API_KEY"}
)

models = response.json()["data"]

# Filter by dimension size
large_models = [m for m in models if m.get("max_output_length", 0) >= 2048]

# Filter by provider
openai_models = [m for m in models if m["id"].startswith("openai/")]

# Filter by pricing
cheap_models = [m for m in models if float(m["pricing"]["prompt"]) < 0.0000001]

# Find models supporting specific input
text_models = [m for m in models if "text" in m["input_modalities"]]

Comparison with Chat Models

Aspect	Embedding Models	Chat Models
Endpoint	`/v1/embeddings/models`	`/v1/models`
Output	Vector representations	Text responses
Use Cases	Search, clustering, similarity	Conversation, generation, reasoning
Pricing	Per input token only	Per input + output token
Dimensions	Fixed (configurable for some)	N/A

Privacy & Security

TEE-Protected Embedding

All embedding generation flows through hardware-protected gateway

Confidential Queries

Your text/images processed in secure enclaves

No Data Retention

Embeddings not stored after generation

No Training Use

Your data never used to train models

Examples

List and Compare Models

import requests

def compare_embedding_models():
    response = requests.get(
        "https://api.redpill.ai/v1/embeddings/models",
        headers={"Authorization": f"Bearer YOUR_API_KEY"}
    )

    models = response.json()["data"]

    print("Model Comparison:\n")
    print(f"{'Model ID':<40} {'Dimensions':<12} {'Cost (per 1M tokens)'}")
    print("-" * 80)

    for model in models:
        model_id = model["id"]
        dimensions = model.get("max_output_length", "N/A")
        cost = float(model["pricing"]["prompt"]) * 1_000_000
        print(f"{model_id:<40} {str(dimensions):<12} ${cost:.4f}")

compare_embedding_models()

Find Cheapest Model

def find_cheapest_model(min_dimensions=768):
    response = requests.get(
        "https://api.redpill.ai/v1/embeddings/models",
        headers={"Authorization": f"Bearer YOUR_API_KEY"}
    )

    models = response.json()["data"]

    # Filter by minimum dimensions
    qualified = [
        m for m in models
        if m.get("max_output_length", 0) >= min_dimensions
    ]

    # Sort by price
    cheapest = min(qualified, key=lambda m: float(m["pricing"]["prompt"]))

    print(f"Cheapest model with {min_dimensions}+ dimensions:")
    print(f"  Model: {cheapest['id']}")
    print(f"  Dimensions: {cheapest['max_output_length']}")
    print(f"  Cost: ${float(cheapest['pricing']['prompt']) * 1_000_000:.4f} per 1M tokens")

    return cheapest["id"]

Error Handling

{
  "error": {
    "message": "Invalid API key",
    "type": "authentication_error",
    "code": 401
  }
}

Next Steps

Generate Embeddings

Create embeddings with these models

Multi-Modal Embeddings

Use image and video embeddings

Supported Models

Browse all available models

Pricing

View embedding costs

Get Started

API Reference

Guides

Integrations

Use Cases

List Embedding Models

Overview

List All Embedding Models

Response Fields

Filter by Provider

URL Parameters

Available Embedding Providers

Create Embeddings

Model Selection Guide

By Use Case

Filtering Models

By Capability

Comparison with Chat Models

Privacy & Security

TEE-Protected Embedding

Confidential Queries

No Data Retention

No Training Use

Examples

List and Compare Models

Find Cheapest Model

Error Handling

Next Steps

Generate Embeddings

Multi-Modal Embeddings

Supported Models

Pricing

Get Started

API Reference

Guides

Integrations

Use Cases

​Overview

​List All Embedding Models

​Response Fields

​Filter by Provider

​URL Parameters

​Available Embedding Providers

Create Embeddings

​Model Selection Guide

​By Use Case

​Filtering Models

​By Capability

​Comparison with Chat Models

​Privacy & Security

TEE-Protected Embedding

Confidential Queries

No Data Retention

No Training Use

​Examples

​List and Compare Models

​Find Cheapest Model

​Error Handling

​Next Steps

Generate Embeddings

Multi-Modal Embeddings

Supported Models

Pricing

Overview

List All Embedding Models

Response Fields

Filter by Provider

URL Parameters

Available Embedding Providers

Model Selection Guide

By Use Case

Filtering Models

By Capability

Comparison with Chat Models

Privacy & Security

Examples

List and Compare Models

Find Cheapest Model

Error Handling

Next Steps