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

# BGE-M3

> 1024-dimension embeddings across 100+ languages, built for retrieval that has to work in more than English. The 8K window takes a whole page without chunking. Reach for it when the corpus is multilingual and the per-million rate matters.

## Overview

1024-dimension embeddings across 100+ languages, built for retrieval that has to work in more than English. The 8K window takes a whole page without chunking. Reach for it when the corpus is multilingual and the per-million rate matters.

## Specs

| Field | Value |
| - | - |
| Model ID | `bge-m3` |
| Best for | Multilingual RAG, cross-language search |
| Context window | 8,192 |
| Input modalities | Text |
| Output modalities | Text |
| Speed | Fast |
| Cost band | Cheap |
| Release stage | Stable |

## Pricing

| | Per 1M tokens |
| - | :-: |
| Input | \$0.0135 |
| Output | \$0.00 |

## Example

```python theme={null}
from openai import OpenAI

client = OpenAI(base_url="https://kymaapi.com/v1", api_key="kyma-...")

response = client.embeddings.create(
    model="bge-m3",
    input=["first document", "second document"],
)
```

## Agent query example

Ask the API which models fit, instead of hardcoding an id:

```bash theme={null}
curl "https://kymaapi.com/v1/models?recommended_for=search-documents&quality_tier=strong"
```


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