Model Filter

Model Type

Features

Context Windown

Maxmium Output

Provider

Recommend

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.

whisper-1

Whisper is a general-purpose speech recognition model, trained on a large dataset of diverse audio. You can also use it as a multitask model to perform multilingual speech recognition as well as speech translation and language identification.

gpt-4o-2024-05-13

GPT-4o (“o” for “omni”) is our versatile, high-intelligence flagship model. It accepts both text and image inputs, and produces text outputs (including Structured Outputs). It is the best model for most tasks, and is our most capable model outside of our o-series models.

bge-reranker-v2-m3

Lightweight reranker model, possesses strong multilingual capabilities, easy to deploy, with fast inference.

bge-m3

BGE-M3 is a versatile multilingual embedding model supporting dense, sparse, and multi-vector retrieval across 100+ languages and handling inputs from sentences to long documents (8,192 tokens).

gpt-3.5-turbo

GPT-3.5 Turbo models can understand and generate natural language or code and have been optimized for chat using the Chat Completions API but work well for non-chat tasks as well. As of July 2024, use gpt-4o-mini in place of GPT-3.5 Turbo, as it is cheaper, more capable, multimodal, and just as fast. GPT-3.5 Turbo is still available for use in the API.

gpt-3.5-turbo-0125

GPT-3.5 Turbo models can understand and generate natural language or code and have been optimized for chat using the Chat Completions API but work well for non-chat tasks as well. As of July 2024, use gpt-4o-mini in place of GPT-3.5 Turbo, as it is cheaper, more capable, multimodal, and just as fast. GPT-3.5 Turbo is still available for use in the API.

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.