Gemini Embedding 001
Google 的向量模型，用于将输入内容转换为数值向量，支持语义检索和相似度计算。
- Starting price
- Input / Output · 1M
- Context
- 2K
- Maximum input window
- Modalities
- →
- Released
- Jul 2025
Pricing by Supplier
Capabilities / Supported modalities
Provider & data privacy
SentencePiece (Gemini)Performance
Benchmarks
Scores on standardized evaluations. Higher percentages are better — and rank percentile shows
Metrics sourced fromArtificial Analysis 2026-09-30·Gemini Embedding 001
This model is not included in the current benchmark snapshot.
Missing models or measurements are not zero scores.
Benchmark charts preserve the source model selection and reasoning settings. Missing models or measurements are not zero scores, and benchmark cost or speed is not this site’s service commitment.
About Gemini Embedding 001
Google 的向量模型，用于将输入内容转换为数值向量，支持语义检索和相似度计算。
Use cases and prompting
Starting points for evaluation; supported inputs and options are listed in API access.
Use cases to explore
- Evaluate semantic search with representative queries and documents.
- Compare retrieval quality on your own knowledge base before connecting a downstream assistant.
Practical tips
Check whether the endpoint returns embeddings or reranks documents. Keep indexing and query preprocessing consistent.
API access
Code samples
/v1beta/models/gemini-embedding-001:generateContent| Parameter | Type | Default / range | Description |
|---|---|---|---|
inputrequired | string | — | Text or array of texts to embed |
dimensions | integer | >= 1 | Truncate embeddings to this many dimensions |
encoding_format | enum | = float | Wire encoding for the embedding vectors |
user | string | — | End-user identifier for abuse monitoring |
Replace <YOUR_API_KEY> with the API key from your token settings.
Authentication
All requests must include Authorization: Bearer <TOKEN> header. Anthropic-formatted endpoints accept the x-api-key header instead.
Generate tokens from the Tokens page; you can scope them to specific models, groups, IPs, and rate-limits.
Supported parameters
| Parameter | Type | Default / range | Description |
|---|---|---|---|
inputrequired | string | — | Text or array of texts to embed |
dimensions | integer | >= 1 | Truncate embeddings to this many dimensions |
encoding_format | enum | = float | Wire encoding for the embedding vectors |
user | string | — | End-user identifier for abuse monitoring |
Rate limits
| Supplier | RPM | TPM | RPD |
|---|---|---|---|
| Google AI Studio | Unlimited | Unlimited | Unlimited |
| Google Vertex | Unlimited | Unlimited | Unlimited |
No restriction
Frequently asked questions about gemini-embedding-001
What is gemini-embedding-001?
Google 的向量模型，用于将输入内容转换为数值向量，支持语义检索和相似度计算。
How do I call gemini-embedding-001?
Create an API key with access to gemini-embedding-001, then use the exact model ID and a supported endpoint from the API access section. Request fields depend on the selected endpoint.
How is gemini-embedding-001 priced?
Pricing depends on the selected provider group and the model billing unit. The current input, output, request, or media prices are shown on this page before sign-up.
What is the context window of gemini-embedding-001?
The model catalog lists a context window of 2048 tokens. Check the selected endpoint for request limits.
How should I evaluate gemini-embedding-001 for my project?
Check whether the endpoint returns embeddings or reranks documents. Keep indexing and query preprocessing consistent.