Model Filter

Model Type

Features

Context Windown

Maxmium Output

Provider

Recommend

We have launched the Basic Series economy models, offering higher discounts. Click to view the model comparison >>

1MT: One million tokens. This pricing is based on the conversion rate of ¥2 = $1. If your purchase rate is ¥3.5 = $1, the price should be multiplied by 1.75 accordingly.

text-embedding-3-large

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.

text-embedding-3-small

text-embedding-3-small is our improved, more performant version of our ada embedding model. 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.

gpt-4-all

Using reverse engineering to call the model within the official application and convert it into an API.

gpt-4-gizmo-*

Using reverse engineering to call the model within the official application and convert it into an API.

tts-1

TTS is a model that converts text to natural sounding spoken text. The tts-1 model is optimized for realtime text-to-speech use cases. Use it with the Speech endpoint in the Audio API.

tts-1-hd

TTS is a model that converts text to natural sounding spoken text. The tts-1 model is optimized for realtime text-to-speech use cases. Use it with the Speech endpoint in the Audio API.

dall-e-3

DALL·E is an AI system that creates realistic images and art from a natural language description. DALL·E 3 currently supports the ability, given a prompt, to create a new image with a specific size.

text-embedding-ada-002

text-embedding-ada-002 is our improved, more performant version of our ada embedding model. 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.