Google: EmbeddingGemma 300M

One API key. Live prices. Ready-to-run code.

EmbeddingGemma 300M is Google DeepMind's small multilingual embedding model for search, classification and clustering.

Served on OneInfer by DeepInfra. Use it with the same API key and pay-as-you-go wallet as every other model in the catalog.

ProviderDeepInfragoogle/embeddinggemma-300m

Call EmbeddingGemma 300M in 4 steps

Step 1 of 4

Create a key in the Console. The same key works for every model on OneInfer.

OverviewProvidersPricingBenchmarksAPI and quickstartReviews
Input tokens
$0.0022 / 1M tokens
Context
2K
Input
text
Output
embeddings
Tool calling
Not listed

About this model

EmbeddingGemma 300M is Google DeepMind's small multilingual embedding model for search, classification and clustering. It makes 768-dimension vectors, or shorter ones on request (dimensions), from inputs of up to 2048 tokens.

Input modalities

  • text

    Accepted as model input

Providers

Available routing options for this model through OneInfer.

  • DeepInfra
    Available
    Input tokens
    $0.0022 / 1M tokens
    Routing
    OneInfer optimized

Pricing

Current OneInfer pricing for this model.

UsagePrice
Input tokens$0.0022 / 1M tokens

Performance and benchmarks

Independent evaluations and live cluster operational metrics

We haven’t measured this model yet.

API guide

API docs

  1. Generate your access token

    Use your API key to generate the JWT access token required by the Models API.

  2. Add the JWT token

    Copy the generated JWT token and replace JWT_TOKEN in the example below.

  3. Check your credits

    If your balance is too low, before calling the model.

  4. Run the API example

    Choose your preferred language, copy the example, and send your first model request.

    curl -X POST https://api.oneinfer.ai/v1/ula/embeddings \
  -H "Authorization: Bearer YOUR_JWT_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
  "model": "google/embeddinggemma-300m",
  "input": [
    "OneInfer routes each request to a provider.",
    "Embeddings turn text into vectors."
  ]
}'

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