> ## Documentation Index
> Fetch the complete documentation index at: https://www.meilisearch.com/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Binary quantization

> Compress embedding vectors to reduce storage and improve indexing speed while using larger, more capable models.

Binary quantization compresses embedding vectors by representing each dimension with a single bit instead of a full floating-point number. This dramatically reduces storage requirements and speeds up vector operations, making it practical to use larger, higher-quality embedding models that produce more dimensions.

## Why use binary quantization

Larger embedding models (1536+ dimensions) generally produce better semantic search results because they capture more nuance in the meaning of text. However, storing and comparing high-dimensional vectors is expensive in terms of disk space, memory, and CPU time.

Binary quantization solves this trade-off:

| Without BQ                            | With BQ                                  |
| ------------------------------------- | ---------------------------------------- |
| Each dimension stored as 32-bit float | Each dimension stored as 1 bit           |
| 1536-dim vector = 6 KB                | 1536-dim vector = 192 bytes              |
| Slower indexing at high dimensions    | Significantly faster indexing            |
| Full precision similarity             | Approximate similarity (still effective) |

The key insight is that **a large model with binary quantization often outperforms a small model without it**. For example, OpenAI's `text-embedding-3-large` (3072 dimensions) with binary quantization typically produces better search results than `text-embedding-3-small` (1536 dimensions) at full precision, while using less storage.

## When to use it

Binary quantization is most effective when:

* Your dataset contains **more than 1M documents** with embeddings
* You use a model with **1400+ dimensions** (the more dimensions, the better BQ works, because there is more information to preserve even after quantization)
* You want to **reduce disk usage** and **speed up indexing** without switching to a smaller model
* Storage or memory is a constraint in your deployment

Binary quantization is less effective with low-dimensional models (under 512 dimensions), where the information loss from quantization has a more noticeable impact on search quality.

## Enable binary quantization

Set `binaryQuantized` to `true` in your embedder configuration:

<CodeGroup>
  ```bash theme={null}
  curl \
    -X PATCH 'MEILISEARCH_URL/indexes/products/settings/embedders' \
    -H 'Content-Type: application/json' \
    -H 'Authorization: Bearer MEILISEARCH_KEY' \
    --data-binary '{
      "default": {
        "binaryQuantized": true
      }
    }'
  ```
</CodeGroup>

This works with any embedder source ([OpenAI](/capabilities/hybrid_search/how_to/configure_openai_embedder), [Cohere](/capabilities/hybrid_search/how_to/configure_cohere_embedder), [HuggingFace](/capabilities/hybrid_search/how_to/configure_huggingface_embedder), [REST](/capabilities/hybrid_search/how_to/configure_rest_embedder), or user-provided).

### Example: OpenAI with a large model

Use OpenAI's largest embedding model with binary quantization for the best balance of quality and efficiency:

<CodeGroup>
  ```bash theme={null}
  curl \
    -X PATCH 'MEILISEARCH_URL/indexes/products/settings/embedders' \
    -H 'Content-Type: application/json' \
    -H 'Authorization: Bearer MEILISEARCH_KEY' \
    --data-binary '{
      "default": {
        "source": "openAi",
        "apiKey": "OPEN_AI_API_KEY",
        "model": "text-embedding-3-large",
        "binaryQuantized": true
      }
    }'
  ```
</CodeGroup>

<Warning>
  **Activating binary quantization is irreversible.** Once enabled, Meilisearch converts all vectors and discards the original full-precision data. The only way to recover the original vectors is to re-index all documents in a new embedder without binary quantization.
</Warning>

## Impact on search quality

Binary quantization reduces the precision of vector similarity calculations. In practice, the impact on search quality depends on the model and dataset:

* **High-dimensional models (1500+ dims)**: minimal quality loss, often imperceptible
* **Medium-dimensional models (512-1500 dims)**: slight quality reduction, acceptable for most use cases
* **Low-dimensional models (under 512 dims)**: noticeable quality reduction, not recommended

The [ranking pipeline](/capabilities/full_text_search/advanced/ranking_pipeline) mitigates this further in [hybrid search](/capabilities/hybrid_search/overview) mode, where keyword matching compensates for any precision loss in the vector component.

## Recommended models with binary quantization

| Provider    | Model                     | Dimensions | Good with BQ?   |
| ----------- | ------------------------- | ---------- | --------------- |
| OpenAI      | `text-embedding-3-large`  | 3072       | Excellent       |
| OpenAI      | `text-embedding-3-small`  | 1536       | Good            |
| Cohere      | `embed-english-v3.0`      | 1024       | Good            |
| Cohere      | `embed-multilingual-v3.0` | 1024       | Good            |
| HuggingFace | `BAAI/bge-large-en-v1.5`  | 1024       | Good            |
| HuggingFace | `BAAI/bge-small-en-v1.5`  | 384        | Not recommended |

## Next steps

<CardGroup cols={2}>
  <Card title="Choose an embedder" href="/capabilities/hybrid_search/how_to/choose_an_embedder">
    Compare embedding providers for your use case
  </Card>

  <Card title="Custom hybrid ranking" href="/capabilities/hybrid_search/advanced/custom_hybrid_ranking">
    Tune the balance between keyword and vector search
  </Card>

  <Card title="Composite embedders" href="/capabilities/hybrid_search/advanced/composite_embedders">
    Use different models for indexing and search
  </Card>

  <Card title="Performance tuning" href="/capabilities/full_text_search/advanced/performance_tuning">
    Optimize overall search performance
  </Card>
</CardGroup>