Official model documentation
Fields: model identity
Gemini Embedding 2 is Google's first multimodal embedding model. We currently support mapping text and images into a unified vector space for semantic search and retrieval-augmented generation (RAG). It supports…
- Context
- 8K
- Max output
- Unknown
- Input
- $0.20
- Output
- $0.00
Recommended use cases
Strengths in this dataset
- 8,192-token context window
- text, image, file, audio, video input
- 5 supported API parameters listed
Limits and caveats
- Provider behavior and pricing can change; verify the linked sources before production use.
Capability
Model-native facts
Model- Reasoning
- Unknown
- Open weights
- Unknown
Provider endpoint facts
Provider endpoint- Tool calling
- Not supported
- Structured output
- Supported
- Streaming
- Unknown
- Prompt cache
- Unknown
- Batch
- Unknown
- Fine-tuning
- Unknown
Gemini Embedding 2 Provider pricing
Provider endpoint: google/gemini-embedding-2
- Input
- $0.20 per 1M tokens
- Output
- $0.00 per 1M tokens
- Cached input
- Unknown per 1M tokens
- Image output
- Unknown per 1M tokens
Sources and verification
OpenRouter Models API
Fields: identity, description, modalities, context window, maximum output, pricing, supported parameters
Frequently asked questions
Answers are generated from the same sourced model and provider facts shown above.
Gemini Embedding 2 is Google's first multimodal embedding model. We currently support mapping text and images into a unified vector space for semantic search and retrieval-augmented generation (RAG). It supports…
Model specifications and prices may vary by provider and change over time. AIToolly displays sources and verification dates so users can confirm critical details before production use.