Name: Text Embedding 3 Large API
Brand: OpenAI

Overview

text-embedding-3-large Description

Basic Information

Model Name: text-embedding-3-large
Developer/Creator: OpenAI
Release Date: January 25th, 2024
Version: text-embedding-3-large
Model Type: Text Embedding

Overview:text-embedding-3-large is a next-generation embedding model that offers superior performance and flexibility. It converts text into high-dimensional numerical representations that are highly effective for various machine learning tasks.
Key Features:
- Top Performance: The highest performing embedding model with significant improvements over predecessors.
- Flexible Embedding Size: Supports dimensions from 256 up to 3072, allowing for trade-offs between performance and resource usage.
- Native Support for Shortening Embeddings: Developers can shorten embeddings without significant loss in conceptual representation.
Intended Use:
- High-Performance Search: Optimal for applications requiring precise and fast search results.
- Advanced Clustering: Suitable for sophisticated data analysis and clustering tasks.
- Enhanced Recommendations: Provides accurate recommendations by understanding text similarities.
- Robust Anomaly Detection: Efficiently identifies outliers in large datasets.
- Detailed Diversity Measurement: Analyzes the diversity of large text corpora.
- Accurate Classification: Highly effective in classifying complex text data.
Language Support:Offers improved support for multiple languages, making it suitable for global applications.

Technical Details

Architecture:Advanced transformer-based architecture designed for high-dimensional embeddings and superior performance.
Training Data:Trained on an extensive and diverse dataset to capture a wide array of linguistic nuances.
Data Source and Size:Includes billions of text entries, ensuring a comprehensive understanding of language.
Diversity and Bias:Ensures high diversity in training data to mitigate biases and enhance reliability.

Performance Metrics

Comparison to Other Models:
- MIRACL Score: Increased from 31.4% (ada-002) to 54.9%.
- MTEB Score: Improved from 61.0% (ada-002) to 64.6%.
Accuracy:Delivers top-tier accuracy across multiple benchmarks.
Speed:Optimized for faster processing times despite larger dimensionality.
Robustness:Maintains high performance across a variety of input types and contexts.

Uptime and performance

Uptime (24h): 67.0%
Uptime (7d): 95.0%
Availability (24h): 99.0%
Error rate (24h): 1.0%
End-to-end latency (24h median): 356ms

Measured on the AI/ML API gateway across live traffic. Figures are medians unless stated. Last updated 2026-10-12.

Use cases

High‑accuracy semantic search

Build enterprise-grade search engines: queries map to documents or products using rich embeddings for high relevance across languages.

Multilingual recommendation systems

Use embeddings to cluster and match content/users in diverse languages — ideal for global apps (e‑commerce, media, etc.).

Advanced RAG pipelines

Combine embeddings with vector databases for retrieval-augmented agents that need deep semantic understanding.

Content classification & clustering

Structure large multilingual datasets through vector similarity and clustering for tagging, summarization, and moderation.

Anomaly & outlier detection

Detect unusual or off-topic content in data streams by measuring embedding distances.

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How to use Text Embedding 3 Large API

Install any OpenAI-compatible SDK, point it at api.aimlapi.com/v1, and set the model to openai/text-embedding-3-large.

import requests

r = requests.post(
    "https://api.aimlapi.com/v1/embeddings",
    headers={"Authorization": "Bearer " + AIMLAPI_KEY},
    json={
      "model": "openai/text-embedding-3-large",
      "input": "The quick brown fox"
    },
)
print(r.json())

const r = await fetch("https://api.aimlapi.com/v1/embeddings", {
  method: "POST",
  headers: {
    Authorization: `Bearer ${process.env.AIMLAPI_KEY}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    "model": "openai/text-embedding-3-large",
    "input": "The quick brown fox"
  }),
});
console.log(await r.json());

curl -X POST https://api.aimlapi.com/v1/embeddings \
  -H "Authorization: Bearer $AIMLAPI_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"openai/text-embedding-3-large","input":"The quick brown fox"}'

OpenAI-compatible — swap the base URL and it works with your existing SDK.