- Text Embeddings
bge-base-en-v1.5 is a base-sized English text embedding model from BAAI’s BGE (BAAI General Embedding) series, optimized for semantic similarity and retrieval. It generates 768-dimensional embeddings for…
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Gemini Embedding 001
Gemini Embedding 001 is Google’s production-grade text embedding model that provides high-quality, multilingual vector representations for retrieval, classification, and other language tasks.
Providers Comparison
Speed
Output tokens per second · Higher is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
End-to-End Response Time
Seconds · Lower is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
Price
USD per 1M tokens (blended) · Lower is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
About the model
What is Gemini Embedding 001?
Gemini Embedding 001 is Google’s generally available text embedding model built on the Gemini family and optimized for strong performance on benchmarks like MTEB Multilingual. It is mainly used to convert text into dense vector embeddings for applications such as semantic search, retrieval-augmented generation, and recommendation systems. It is also widely applied to tasks like text classification, clustering, and similarity measurement across more than 100 languages. It follows earlier Google embedding offerings (such as text-embedding and multilingual-embedding models) and is part of the broader Gemini model family developed by Google DeepMind.
Model capabilities
5 Core Capabilities
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Text Embedding
Generates dense vector representations of text inputs for tasks like semantic similarity, retrieval, recommendations, and clustering.
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Semantic Search
Enables semantic search over documents by embedding queries and passages into a shared vector space for relevance scoring.
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Multilingual Embeddings
Produces embeddings for multiple languages, allowing cross-lingual similarity search and analysis across diverse multilingual text data.
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Document Encoding
Encodes sentences, paragraphs, or full documents into fixed-length vectors useful for downstream ML models and analytics pipelines.
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Content Recommendation
Supports building recommendation systems by embedding items and user signals, enabling similarity-based content and product suggestions.
Use cases
6 Most Valuable Use Cases
- Semantic Text Search
- Document Similarity Matching
- Customer Intent Tagging
- Legal Case Retrieval
- Recommendation Ranking
- Multilingual Text Clustering
Transparent pricing
Cost Comparison
LLM API offers the lowest-cost, lowest-latency embeddings versus Gemini and other major providers.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | 80ms | 120k tps | 99.99% | $0.05 | $0.00 | 200K tokens |
| Global | ~180ms | ~40k tps | 99.9% | ~$0.10 | $0.00 | ~32K tokens | |
| OpenAI | Global | ~160ms | ~60k tps | 99.9% | ~$0.10 | $0.00 | ~200K tokens |
| Azure | US East | ~190ms | ~50k tps | 99.9% | ~$0.11 | $0.00 | ~32K tokens |
Performance benchmarks
Technical Specifications
| Metric | Gemini Embedding 001 | text-embedding-3-large (OpenAI) | text-embedding-004 (Google) |
|---|---|---|---|
| Dimensions | 768 | 3072 | 768 |
| Max Input Tokens | ~8K | 8K | ~8K |
| Price per 1M Tokens | ~$0.10 | $0.13 | ~$0.10 |
| Throughput | ~800 tps | ~1,000 tps | ~800 tps |
| Avg Latency | ~120ms | ~100ms | ~110ms |
| Uptime | 99.9% | 99.9% | 99.9% |
30-day usage via LLM API
- 5.8B
- Embedding tokens processed (30 days)
- 42M
- Embedding API requests (30 days)
- 260K
- Developers using this model (30 days)
- 99.98%
- Avg API uptime (30 days)
Architecture & Integration
Why Build on LLM.API?
One unified API. Every major model. Built-in reliability, cost control, and observability.
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Unified AI Routing
Dynamically route each request to the best model across providers based on latency, quality, and cost—without changing your app code or client integration.
One endpoint, any model -
Cost-Aware Controls
Enforce per-model and per-project cost policies with smart price-based routing and guardrails, so you never blow your AI budget in production again.
Optimize spend by default -
Automatic Fallbacks
Recover instantly from model or provider failures with configurable failover chains, ensuring your critical AI flows keep working even when upstreams break.
Resilient by design -
Deep Observability
Get full visibility into prompts, latencies, errors, and costs across all models and vendors with structured logs, traces, and dashboards out of the box.
See every token -
Task-Oriented Abstractions
Call high-level tasks—chat, generation, extraction, tools—through a stable schema, while LLM.API handles provider quirks, parameters, and prompt shaping underneath.
Code to tasks, not models -
High-Throughput Batch
Run massive prompt batches through multiple providers with automatic chunking, retries, and aggregation, so you can backfill, evaluate, and retrain at scale.
Ship bulk workloads fast
Decision guide
When to Use — When NOT to Use
Use it if...
- You need general-purpose text embeddings for semantic search across diverse domains and topics.
- You need multilingual text embeddings that work reasonably well across many major languages.
- You need embeddings integrated tightly with other Google Cloud or Gemini services and tooling.
- Your use case involves building recommendation or retrieval systems over short to medium texts.
- Your use case involves clustering or deduplicating large volumes of user-generated text content.
- You need a managed, scalable embedding API without operating your own vectorization infrastructure.
- Your use case involves hybrid search, combining Gemini Embedding 001 with keyword or metadata filters.
Avoid if...
- You need embeddings for non-text modalities like images, audio, video, or structured tables.
- Your workload requires open-source, self-hostable embeddings without dependence on a cloud provider.
- You need extremely long-context embeddings for very large documents exceeding typical limits.
- Your workload requires strict on-prem or air-gapped deployment with no external API calls.
- You need domain-specific embeddings already fine-tuned for specialized scientific or medical corpora.
- Your workload requires deterministic, version-pinned embedding behavior over many years for compliance.
- You need ultra-low-latency on-device embedding generation without any network round-trips.
FAQ
Frequently Asked Questions
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What is Gemini Embedding 001?
Gemini Embedding 001 is a Google model that converts text into vector embeddings for semantic search, retrieval, clustering, and recommendation tasks.
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What is Gemini Embedding 001 best suited for?
It is best for semantic search, document retrieval, deduplication, topic clustering, recommendation systems, and building retrieval-augmented generation (RAG) pipelines.
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How do I access Gemini Embedding 001 through LLM.API?
Call the LLM.API embeddings endpoint with the provider set to Google and the model name set to "Gemini Embedding 001".
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What input modalities does Gemini Embedding 001 support via LLM.API?
Via LLM.API it is typically used as a text-embedding model, accepting plain text strings and returning dense numeric vector representations.
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What is the context window for Gemini Embedding 001 inputs?
Gemini Embedding 001 supports relatively long text inputs, but you should verify exact maximum token limits in the current LLM.API documentation.
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How fast is Gemini Embedding 001 when called through LLM.API?
Latency is generally low and suitable for real-time search, but depends on request size, concurrency, and your proximity to LLM.API servers.
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How is Gemini Embedding 001 priced on LLM.API?
Pricing is typically per input token or character processed, so check the LLM.API pricing page for the latest Gemini Embedding 001 rates.
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How does Gemini Embedding 001 compare to other embedding models on LLM.API?
It generally offers strong semantic quality and compatibility with Google’s ecosystem, while some alternatives may prioritize lower cost or domain specialization.
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Does Gemini Embedding 001 support multilingual embeddings?
Gemini Embedding 001 supports multiple languages, but coverage and quality vary by language, so test your target languages for accuracy.
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What are the main limitations of Gemini Embedding 001?
Limitations include potential loss of fine-grained information, sensitivity to domain shifts, input size caps, and no direct generative or reasoning capabilities.
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Can I use Gemini Embedding 001 for RAG with other LLMs on LLM.API?
Yes, you can embed documents with Gemini Embedding 001, store vectors, then use them to retrieve context for any compatible generative model.
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How do I control embedding dimensionality with Gemini Embedding 001 on LLM.API?
Embedding dimensionality is fixed by the model configuration, so you cannot change it at request time through LLM.API.
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