text-embedding-3-large

Model Overview

text-embedding-3-large is our most capable embedding model for both english and non-english tasks. Embeddings are a numerical representation of text that can be used to measure the relatedness between two pieces of text. Embeddings are useful for search, clustering, recommendations, anomaly detection, and classification tasks.

Key Features

  • High performance (3/4 dots rating)
  • Slow speed (2/5 lightning bolts rating)
  • Most capable embedding model
  • Text input and output support
  • Batch API and Embeddings endpoint support

Technical Specifications

  • Pricing: $0.13 per 1M tokens
  • Supports: Input: text only; Output: text only
  • Features: Batch API support, Embeddings endpoint support

Snapshots

  • text-embedding-3-large

Positioning and Use Cases

text-embedding-3-large is our most capable embedding model for both english and non-english tasks. Embeddings are useful for search, clustering, recommendations, anomaly detection, and classification tasks.

Rate Limits

  • Free tier: 100 RPM, 2,000 RPD, 40,000 TPM
  • Tier 1: 3,000 RPM, 1,000,000 TPM, 3,000,000 batch queue limit
  • Tier 2: 5,000 RPM, 1,000,000 TPM, 20,000,000 batch queue limit
  • Tier 3: 5,000 RPM, 5,000,000 TPM, 100,000,000 batch queue limit
  • Tier 4: 10,000 RPM, 5,000,000 TPM, 500,000,000 batch queue limit
  • Tier 5: 10,000 RPM, 10,000,000 TPM, 4,000,000,000 batch queue limit

Documentation

Official Documentation

OpenAI

Pioneer in AI, globally renowned for GPT series models

text-embedding-3-large

Parameters Unknow

text-embedding-3-large Most capable embedding model

Official: $0.13 Our Price: $0.10.4 Save 20%

Frequently Asked Questions

What is the uptime guarantee?
We guarantee 99.9% uptime with our enterprise-grade infrastructure and redundant systems.
How is pricing calculated?
Pricing is based on the number of tokens processed. Both input and output tokens are counted in the final cost.
What is the difference between GPT-4 and GPT-4 Turbo?
GPT-4 Turbo is the latest version with improved performance, longer context window, and more recent knowledge cutoff date.