GoogleFastHigh

Gemini Embedding 001

Google's text embedding model for generating vector representations. Optimized for semantic search, clustering, and similarity tasks.

0.1 credits

per 1K tokens

High-quality text embeddings

2,048 token max input per request

Output dimensions: 128–3,072 (default 3,072; recommended 768 / 1,536 / 3,072)

Matryoshka Representation Learning (MRL) for flexible dimensionality

8 task types: SEMANTIC_SIMILARITY, CLASSIFICATION, CLUSTERING, RETRIEVAL_DOCUMENT, RETRIEVAL_QUERY, CODE_RETRIEVAL_QUERY, QUESTION_ANSWERING, FACT_VERIFICATION

Optimized for semantic search, clustering, classification, and RAG

Run it right now

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

Use with AI Assistant

Copy usage instructions for Claude, ChatGPT, or other AI

llms.txt

Model Specifications

Context Window

tokens

Max Output

tokens

Training Cutoff

August 2024

Compatible SDK

Google AI

Capabilities

Vision

Function Calling

Streaming

JSON Mode

System Prompt

Token Pricing (per 1M tokens)

Token Type	Credits	USD Equivalent
Input Tokens	300	$0.30
Output Tokens	0	$0.00

* 1 credit ≈ $0.001 (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-001",
  "input": "What is the meaning of life?"
}'

Parameters

Parameter	Type	Required	Default	Description
`input`	string \| array	Yes	-	Text or array of texts to embed. Max 2,048 input tokens per item.
`model`	string	Yes	gemini-embedding-001	Model identifier.
`dimensions`	integer	No	3072	Output embedding dimensionality. Range: 128–3,072. Recommended: 768, 1,536, 3,072. Manual L2 normalization required for non-3,072 dimensions.
`task_type`	string	No	-	Optimize embedding for a downstream task. One of: SEMANTIC_SIMILARITY, CLASSIFICATION, CLUSTERING, RETRIEVAL_DOCUMENT, RETRIEVAL_QUERY, CODE_RETRIEVAL_QUERY, QUESTION_ANSWERING, FACT_VERIFICATION. SEMANTIC_SIMILARITYCLASSIFICATIONCLUSTERINGRETRIEVAL_DOCUMENTRETRIEVAL_QUERYCODE_RETRIEVAL_QUERYQUESTION_ANSWERINGFACT_VERIFICATION

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-001",
  "input": "What is the meaning of life?"
}'