EmbeddingGemma 300M
EmbeddingGemma 300M is a 300-million-parameter text embedding model from Google with a 2048-token context window.
Google
DeepInfraCompact text embedding model from Google
EmbeddingGemma 300M is a text embedding model developed by Google and released in September 2025. It belongs to the Gemma model family and is designed to convert text into dense vector representations, which can then be used for tasks such as semantic search, retrieval-augmented generation, clustering, and classification. With 300 million parameters, it sits on the smaller end of the embedding model spectrum, making it suitable for use cases where computational efficiency matters.
The model accepts text input and produces fixed-dimensional embeddings within a 2048-token context window. It is served through DeepInfra and is accessible without requiring users to manage their own infrastructure. EmbeddingGemma 300M is well suited for developers building search pipelines, recommendation systems, or any application that requires comparing or retrieving text based on semantic similarity.
What EmbeddingGemma 300M supports
Text Embedding
Converts input text into dense vector representations for downstream tasks. Supports sequences up to 2048 tokens per input.
Semantic Search
Enables similarity-based retrieval by encoding queries and documents into a shared vector space. Useful for building search and ranking pipelines.
Document Retrieval
Supports retrieval-augmented generation (RAG) workflows by producing embeddings that can be indexed and queried at scale.
Text Clustering
Groups semantically related texts by comparing their vector representations. Applicable to topic modeling and content organization tasks.
Classification Support
Provides embeddings that can serve as input features for downstream classification models. Works with standard machine learning frameworks.
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Get Started FreeCommon questions about EmbeddingGemma 300M
What is the context window for EmbeddingGemma 300M?
EmbeddingGemma 300M supports a context window of 2048 tokens per input.
Who developed EmbeddingGemma 300M?
EmbeddingGemma 300M was developed by Google and is part of the Gemma model family, released in September 2025.
What type of model is EmbeddingGemma 300M?
It is a text embedding model, meaning it converts text into dense vector representations rather than generating text responses.
What can I use EmbeddingGemma 300M for?
Common use cases include semantic search, retrieval-augmented generation (RAG), text clustering, and classification tasks that require comparing or retrieving text by meaning.
Is pricing information available for EmbeddingGemma 300M?
Pricing details are not currently listed in the model metadata. You can check the DeepInfra model page for the latest pricing information.
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