> ## AI Agent Context
> Get the complete documentation index at: https://aistudio.google.com/llms.txt
> Use this file to discover all available pages.
> For the full documentation in a single file, fetch: https://aistudio.google.com/llms-full.txt

# Gemini Embedding model

A specialized engine for high-dimensional vector representation, providing
efficient numerical mapping of text and images. The Gemini Embedding model is
best for semantic search, document retrieval, and recommendation systems that
require fast, scalable similarity calculations across large datasets.

## Documentation  {% id="documentation" %}

Visit the [Embeddings](/docs/embeddings) page for full coverage
of features and capabilities.

## gemini-embedding-001  {% id="gemini-embedding-001" %}

{% table variant="model" %}
  * Property
  * Description

  ---

  *     {% section class="model-property" %}
  {% icon name="id_card" /%}Model code

    {% /section %}

  * **Gemini API**
    `gemini-embedding-001`

  ---

  *     {% section class="model-property" %}
  {% icon name="save" /%}Supported data types

    {% /section %}

  *     **Input** Text

    **Output** Text embeddings


  ---

  *     {% section class="model-property" %}
  {% icon name="token_auto" /%}Token limits{% sup %}[[*]](/docs/tokens){% /sup %}

    {% /section %}

  *     **Input token limit** 2,048

    **Output dimension size** Flexible, supports: 128 - 3072, Recommended: 768, 1536, 3072


  ---

  *     {% section class="model-property" %}
  {% icon name="123" /%}Versions

    {% /section %}

  *     Read the [model version patterns](/docs/models#model-versions) for more details.

    *   Stable: `gemini-embedding-001`


  ---

  *     {% section class="model-property" %}
  {% icon name="calendar_month" /%}Latest update

    {% /section %}

  * June 2025

  ---

  *     {% section class="model-property" %}
  {% icon name="paid" /%}Pricing

    {% /section %}

  * [Pricing](/docs/pricing#gemini-embedding)
{% /table %}