GoogleFastHigh

Gemini Embedding 2

Google's second-generation Gemini embedding model and the successor to gemini-embedding-001. Generates vector representations for semantic search, clustering, classification, and RAG at $0.20 per million text input tokens.

372/0credits

input / output · per 1M tokens

Successor to gemini-embedding-001 (retiring 2028-05-14)

Matryoshka Representation Learning (MRL) for flexible dimensionality

Optimized for semantic search, clustering, classification, and RAG

Text input billed at $0.20 per 1M tokens; no output-token charge

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Test this model instantly in the Console Playground — no code required

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Model Specifications

Context Window

tokens

Max Output

tokens

Training Cutoff

2025

Compatible SDK

OpenAI, Google AI

Capabilities

Vision

Function Calling

Streaming

JSON Mode

System Prompt

Token Pricing (per 1M tokens)

Token Type	Credits	USD Equivalent
Input Tokens	372	$0.25
Output Tokens	0	$0.00

* 1,500 credits ≈ $1 (actual charges may vary based on usage)

Quick Start

curl -X POST "https://api.core.today/llm/gemini/v1beta/openai/embeddings" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer cdt_your_api_key" \
  -d '{
  "model": "gemini-embedding-2",
  "input": "What is the meaning of life?"
}'

Parameters

Parameter	Type	Required	Default	Description
`input`	string \| array	Yes	-	Text or array of texts to embed.
`model`	string	Yes	gemini-embedding-2	Model identifier.
`dimensions`	integer	No	3072	Output embedding dimensionality (MRL). Smaller values reduce storage and search cost; L2-normalize truncated vectors before cosine similarity.

Examples

Text Embedding

Generate embeddings for semantic search

curl -X POST "https://api.core.today/llm/gemini/v1beta/openai/embeddings" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer cdt_your_api_key" \
  -d '{
  "model": "gemini-embedding-2",
  "input": "What is the meaning of life?"
}'

Tips & Best Practices

1Text input is billed at $0.20 per 1M tokens (371.6 credits/M); audio input is billed at $6.50 per 1M tokens. The OpenAI-compatible embeddings endpoint takes text input

2Vectors are not compatible with gemini-embedding-001 - re-embed your whole corpus when you switch

3Use a smaller `dimensions` value to cut vector storage and search cost

4Batch multiple texts in a single request for efficiency