# EmbeddingGemma 300M

> 768-dimension embeddings from a 300M-parameter model. Built for on-device and high-volume indexing — the cheapest way to embed a large corpus, at a fraction of the usual per-million rate.

Human version: https://kymaapi.com/models/embeddinggemma-300m
Live JSON: `GET https://kymaapi.com/v1/models` (no auth required)

## Facts

- **Model ID**: `embeddinggemma-300m` — pass this as `model` in the request body
- **Creator**: Google
- **Released**: 2025-09-04
- **Context window**: 2K tokens
- **Price**: $0.0027 in / $0.00 out per 1M, cached input 10%
- **Capabilities**: tools, caching

## Call it

```bash
curl https://kymaapi.com/v1/chat/completions \
  -H "Authorization: Bearer $KYMA_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model": "embeddinggemma-300m", "messages": [{"role": "user", "content": "Hello"}]}'
```

## Positioning

EmbeddingGemma 300M is a lightweight, 768-dimension text embedding model optimized for high-volume indexing and budget-constrained retrieval. Reach for it when you need to process large text corporas quickly without paying premium embedding rates.

## About

Google’s EmbeddingGemma 300M generates 768-dimension text embeddings from a compact 300M-parameter architecture. It is designed specifically for bulk corpus indexing, semantic deduplication, and retrieval-augmented generation pipelines where cost efficiency and throughput matter more than maximum representational depth.

On Kyma, the model runs through an OpenAI-compatible endpoint with automatic request failover and exact cost reporting in the usage object. It supports prompt caching, which bills repeated prefixes at 10% of the standard input rate, and ships with a $0.50 signup credit to test throughput.

The model accepts text input up to a 2048-token context window and outputs fixed-length vectors. It does not support text generation, reasoning, vision, or structured outputs, and it is strictly an embedding-only endpoint.

## Use cases

- **Bulk Document Indexing** — Index large text corporas efficiently while keeping per-token embedding costs minimal.
- **Budget RAG Retrieval** — Power semantic search in retrieval pipelines where high throughput outweighs the need for larger vector dimensions.
- **Semantic Text Deduplication** — Identify and remove duplicate or near-duplicate entries across large datasets using fast vector similarity.
- **High-Volume Data Processing** — Process massive text batches quickly with a lightweight model optimized for speed and low latency.

## Not ideal for

Do not use this model for documents exceeding 2048 tokens, tasks requiring generative text or reasoning, or applications that demand high-dimensional embeddings for fine-grained semantic discrimination.

## Pick something else when

- You need higher-dimensional embeddings for complex semantic tasks → `qwen3-embedding-8b`

## See also

- All models: https://kymaapi.com/models.md
- Pricing: https://kymaapi.com/pricing.md
- Other models by Google: https://kymaapi.com/models?q=Google