KymaKyma
Google

Google

EmbeddingGemma 300M

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.

Modalities

Text → Text

Input

$0.0027 /1M

Output

$0.00 /1M

Cached input

$0.00027 /1M90% off

Context

2K

Speed

fast

Performance

Live production data from real requests on Kyma — not synthetic benchmarks.

Rank

#52

of 74 active models

Tokens served

1.1K

all-time

Total requests3
Platform share0.0%

Pricing

Pay per token. Cached input is billed at 10% of the input rate.

$0.0027 /1M input$0.00 /1M output
Hobby10 req/day · 2K in / 500 out
~$0.00/mo
Production1,000 req/day · 2K in / 500 out
~$0.16/mo
Scale20,000 req/day · 2K in / 500 out
~$3.24/mo
+ Estimate your workload
1,000
2,000
500
30%

Estimated monthly cost

$0.1183

$0.0039 / day on EmbeddingGemma 300M

Same workload on:

Gemini 3.5 Flash Lite$74.92+63256%
Gemini 3.6 Flash$273+231064%

Estimates use list pricing with cached input billed at the 90%-discount rate. Actual bills depend on real token counts — every response includes its exact cost.

When to use EmbeddingGemma 300M

Updated 2026-07-31

Where this model earns its cost — and where it doesn't.

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.

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.

How it compares

Against the peers people actually weigh it against.

SpecEmbeddingGemma 300MGemini 3.5 Flash LiteGemini 3.6 Flash
Input /1M$0.0027$0.405$2.025
Output /1M$0.00$3.375$10.125
Context2K1M1M
ToolsYesYesYes
ReasoningNoNoYes
Speedfastfastfast

Quick start

Up and running in under two minutes.

  1. 1

    Create an API key

    Sign up and grab a key from the dashboard — $0.50 free credit, no card required.

    Get API key →
  2. 2

    Make your first request

    Drop in your key and send a chat completion — fully OpenAI-compatible.

    curl https://kymaapi.com/v1/chat/completions \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "embeddinggemma-300m",
    "messages": [
      {"role": "user", "content": "Explain prompt caching in one paragraph."}
    ]
  }'
  3. 3

    Stream responses

    Add "stream": true to receive tokens as they arrive.

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

FAQ

Common questions about this model.

What is the context window of EmbeddingGemma 300M?

EmbeddingGemma 300M has a 2K-token context window — roughly 3 pages of text in a single request.

How much does the EmbeddingGemma 300M API cost?

$0.0027 per 1M input tokens and $0.00 per 1M output tokens, with cached input at $0.00027/1M — a 90% discount on repeated prompt prefixes. No subscription; you pay only for what you use.

Does EmbeddingGemma 300M support function calling?

Yes — EmbeddingGemma 300M supports tool/function calling and structured outputs (JSON mode), so it works with agent frameworks out of the box.

How do I use EmbeddingGemma 300M?

Kyma is OpenAI-compatible: point your SDK's base URL at https://kymaapi.com/v1, use your Kyma API key, and set the model to embeddinggemma-300m. Signing up is free and includes $0.50 of credit — no card required.

What is the maximum input length for this model?

The context window is capped at 2048 tokens per request. Longer inputs must be chunked before embedding.

Does Kyma support prompt caching for this endpoint?

Yes. Repeated prompt prefixes are billed at 10% of the standard input rate, reducing costs for batch processing with shared headers or instructions.

Can I use this model to generate text or answer questions?

No. This is an embedding-only model that outputs fixed-length vectors. It does not support text generation, reasoning, or vision tasks.

Start with $0.50 free credit — no card required.Create account →

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