# Gemini Embedding 001

Gemini Embedding 001 is a unified text embedding model that delivers strong multilingual retrieval and classification benchmark results while offering flexible output dimensions through Matryoshka Representation Learning (MRL), designed for production semantic search, Retrieval-Augmented Generation (RAG), and cross-lingual retrieval pipelines.

- **Model ID:** `google/gemini-embedding-001`
- **Type:** embedding
- **Providers:** google, vertex
- **Context window:** Not applicable
- **Maximum output tokens:** 0
- **Pricing:** $0.15/1M input tokens
- **Canonical page:** https://vercel.com/ai-gateway/models/gemini-embedding-001

## Supported parameters

Detailed capability metadata has not been reported for this model.

## Example

```ts
import { embed } from 'ai';

const result = await embed({
  model: 'google/gemini-embedding-001',
  value: 'Sunny day at the beach',
})
```

## About

Gemini Embedding 001 reached general availability on May 20, 2025 after ranking highly on the Massive Text Embedding Benchmark (MTEB) Multilingual leaderboard since its experimental launch. Gemini Embedding 001 shows stronger retrieval and classification results than Google's earlier text embedding models on MTEB Multilingual across tasks spanning domains including science, legal, finance, and coding.

The model supports over 100 languages and accepts up to 2,048 input tokens per request. Its MTEB Multilingual leaderboard position reflects evaluations across diverse languages and task types, meaning the benchmark advantage spans cross-lingual retrieval and multilingual classification, not just English-language retrieval.

A key architectural feature is Matryoshka Representation Learning (MRL), which lets you reduce output vector dimensions from the default 3,072 to smaller sizes. Google recommends 3,072, 1,536, or 768 dimensions for the highest quality. This flexibility helps balance retrieval accuracy against vector database storage costs. A corpus embedded at 768 dimensions uses significantly less storage than the same corpus at 3,072, with a measurable but bounded quality tradeoff.

The model uses the `embed_content` endpoint and costs $0.15 per million input tokens. A free tier is available for experimentation, with higher rate limits in the paid tier.

## What to consider

When planning vector storage, decide on your target output dimension (3072, 1536, or 768) before ingesting embeddings, as changing dimensions later requires re-embedding your full corpus.

## When to use

### Best For

- **Multilingual semantic search**, building retrieval systems that must return results across 100+ languages from a single embedding space, without maintaining separate per-language indexes
- **Retrieval-Augmented Generation (RAG) pipelines**, embedding document corpora for dense retrieval that feeds into a generative model, where high-quality passage retrieval directly determines answer accuracy
- **Cross-domain classification**, strong performance across science, legal, finance, and coding domains, making it suitable for classification tasks that span multiple subject areas
- **Storage-cost-optimized vector databases**, using MRL to reduce dimensions to 768 or 1,536 for large corpora where full-precision embeddings would be cost-prohibitive, with a controlled quality trade-off

### Consider Alternatives When

- Your application requires embedding images, video, audio, or documents in addition to text (Gemini Embedding 2 provides natively multimodal embeddings in a single shared space)
- Your input documents exceed 2,048 tokens and you cannot chunk them effectively, as requests above the limit will require truncation or segmentation
- You need real-time streaming or generative output rather than vector representations

## Best for

- **Multilingual semantic search:** building retrieval systems that must return results across 100+ languages from a single embedding space, without maintaining separate per-language indexes
- **Retrieval-augmented generation (RAG) pipelines:** embedding document corpora for dense retrieval that feeds into a generative model, where high-quality passage retrieval directly determines answer accuracy
- **Cross-domain classification:** strong performance across science, legal, finance, and coding domains, making it suitable for classification tasks that span multiple subject areas
- **Storage-cost-optimized vector databases:** using MRL to reduce dimensions to 768 or 1,536 for large corpora where full-precision embeddings would be cost-prohibitive, with a controlled quality trade-off

## Consider alternatives

- **Multimodal embedding needs:** Your application requires embedding images, video, audio, or documents in addition to text (Gemini Embedding 2 provides natively multimodal embeddings in a single shared space)
- **Inputs exceed 2,048 tokens:** Your documents cannot be chunked effectively and requests above the limit will require truncation or segmentation
- **Streaming or generative output:** You need real-time streaming or generated text rather than vector representations

## Frequently asked questions

### How does gemini-embedding-001 rank on MTEB Multilingual?

It ranks highly on the MTEB Multilingual leaderboard, a position it has maintained since its experimental launch.

### What is Matryoshka Representation Learning and how does it affect output dimensions?

MRL is a training technique that nests information across dimension scales, allowing the model to produce embeddings that remain meaningful when truncated to smaller sizes. Google recommends 3,072, 1,536, or 768 dimensions; the default is 3,072 for highest quality.

### How many languages does gemini-embedding-001 support?

The model supports over 100 languages, consistent with its strong multilingual benchmark results.

### What is the maximum input token length per request?

A maximum of 2,048 input tokens per embedding request.

### What is the pricing?

This page lists the current rates. Multiple providers can serve Gemini Embedding 001, so AI Gateway surfaces live pricing rather than a single fixed figure.

### How is gemini-embedding-001 priced on AI Gateway?

This page lists the current rates. Multiple providers can serve Gemini Embedding 001, so AI Gateway surfaces live pricing rather than a single fixed figure.

## Links

- [Model page](https://vercel.com/ai-gateway/models/gemini-embedding-001)
- [AI Gateway documentation](https://vercel.com/docs/ai-gateway)
- [Provider model documentation](https://ai.google.dev/gemini-api/docs/embeddings)
- [Provider pricing](https://cloud.google.com/vertex-ai/generative-ai/pricing)