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Text Embedding 3 Large is OpenAI’s high‑capacity embedding model optimized for semantic search, retrieval, and clustering tasks. It provides high‑quality vector representations of text with strong performance across diverse domains.
Output tokens per second · Higher is better
Seconds · Lower is better
USD per 1M tokens (blended) · Lower is better
About the model
Text Embedding 3 Large is an OpenAI model that converts text into high‑dimensional vector embeddings for downstream machine learning and retrieval applications. It is primarily used for tasks such as semantic search, reranking, and building retrieval‑augmented generation (RAG) systems over large document collections. It is also used for similarity search, clustering, classification features, and other applications that rely on dense vector representations of text. It is part of OpenAI’s text-embedding-3 family, succeeding earlier OpenAI embedding models like the text-embedding-ada family.
Model capabilities
Generates high-quality vector representations of text optimized for semantic tasks like clustering, retrieval, and similarity search.
Enables retrieval of conceptually related documents by comparing embedding vectors instead of relying purely on keyword matching.
Supports grouping related texts by embedding them into a shared vector space for downstream clustering and topic analysis.
Produces embeddings that capture meaning across multiple languages, enabling cross-lingual similarity and retrieval workflows.
Provides embeddings usable as features for training classifiers to categorize documents by topic, intent, or other labels.
Use cases
Transparent pricing
LLM API offers the lowest-cost, highest-capacity embeddings versus comparable Text Embedding 3 Large tiers.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | ~110ms | ~65K tokens/s | 99.99% | ~$0.02 | $0.00 | ~1M tokens |
| OpenAI | Global | ~180ms | ~40K tokens/s | 99.9% | $0.13 | $0.00 | ~8192 tokens |
| Azure OpenAI | US East | ~190ms | ~35K tokens/s | 99.9% | ~$0.14 | $0.00 | ~8192 tokens |
| Anthropic | US West | ~200ms | ~30K tokens/s | 99.9% | ~$0.15 | $0.00 | ~200K tokens |
Performance benchmarks
| Metric | Text Embedding 3 Large (OpenAI) | text-embedding-3-large (OpenAI) | text-embedding-ada-002 (OpenAI) |
|---|---|---|---|
| Dimensions | 3072 | 3072 | 1536 |
| Max Input Tokens | 8192 | 8192 | 8192 |
| Price per 1M Tokens | $0.13 | $0.13 | $0.10 |
| Avg Latency | ~220ms | ~250ms | ~260ms |
| Throughput | ~1,200 tps | ~1,000 tps | ~950 tps |
| Uptime | 99.9% | 99.9% | 99.9% |
30-day usage via LLM API
Architecture & Integration
One unified API. Every major model. Built-in reliability, cost control, and observability.
Define policies once and let LLM.API automatically route each call to the best model across providers, balancing latency, accuracy, and availability with no client changes.
One endpoint, every model.Set hard budgets and price tiers, then let LLM.API choose cheaper models by default and escalate only when needed, cutting spend without touching application code.
Spend less per token.When a provider is slow, degraded, or down, LLM.API retries and fails over to healthy models, preserving SLAs and user experience without manual incident playbooks.
Resilience by default.Get centralized traces, logs, and metrics for every request across all models and providers, making debugging, performance tuning, and cost attribution straightforward.
See every token.Express work as tasks—chat, generation, extraction, tools—and let LLM.API pick the right model and configuration, so you ship features instead of juggling parameters.
Code to tasks, not models.Submit massive job batches through a single endpoint with built-in concurrency control, retries, and progress tracking, maximizing throughput while protecting upstream systems.
Scale jobs, not scripts.Decision guide
FAQ
Text Embedding 3 Large is an OpenAI text-only embedding model optimized for high-quality, dense vector representations of longer texts.
It is best for high-accuracy semantic search, retrieval-augmented generation, clustering, recommendations, and other tasks needing rich semantic text similarity.
Text Embedding 3 Large supports input sequences up to 8,191 tokens in length.
Text Embedding 3 Large supports text input only and outputs numerical embedding vectors.
Pricing is typically per 1,000 input tokens, with LLM.API applying OpenAI’s base rates plus any LLM.API-specific fees or discounts.
Embedding models are generally low-latency, and Text Embedding 3 Large is suitable for real-time or near–real-time semantic search workloads.
Use LLM.API’s embeddings endpoint, specify provider "openai" and model "text-embedding-3-large," and pass your text inputs in the request body.
Text Embedding 3 Large offers higher embedding quality and accuracy, while Text Embedding 3 Small is cheaper and faster but slightly less accurate.
Yes, Text Embedding 3 Large supports multiple languages, making it suitable for cross-lingual semantic search and similarity tasks.
It cannot generate text or process images, may encode training-data biases, and its performance degrades if inputs exceed the token limit.
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