OpenAI Details Which ChatGPT Model to Use for Daily Tasks
Paid ChatGPT users now choose from four distinct generative AI model variants designed to match specific workplace demands, according to details shared by OpenAI executives.
Selecting the Right Model for Daily Operations
Since OpenAI launched ChatGPT in 2022, the platform’s underlying architecture has expanded to include four primary variants for subscribers: GPT-6 Astra, GPT-6.1 Sol, GPT-6.1 Terra, and GPT-6 Luna. Jaeyoung Shin, APAC Delivery Lead at OpenAI, outlined the specific use cases for each model during a workshop held in Jakarta on November 3, 2026, as reported by detikInet.
GPT-6 Astra functions as the primary frontier model within the current lineup. Shin noted that Astra targets high-complexity tasks demanding advanced reasoning capabilities, such as designing campaign strategies and establishing enterprise targets.
“Misalnya, menyusun strategi kampanye dan menetapkan target dengan kualitas tertinggi, kalian dapat mempertimbangkan Astra,” Shin stated during the Jakarta workshop, according to detikInet’s reporting.
The model requires significant token consumption, making it inefficient for routine tasks like email sorting. For basic, repetitive administrative functions such as checking calendars and processing email, Shin recommended GPT-6.1 Luna to secure rapid, low-friction outputs.
OpenAI Recommends Specific Models for Different Corporate Workflows
Technical demands dictate model selection across corporate workflows. OpenAI merekomendasikan model GPT-6.1 Terra bagi yang membutuhkan model untuk pekerjaan kreatif dengan visual atau pembuatan aplikasi. Meanwhile, GPT-6.1 Sol manages complex assignments while optimizing token efficiency against processing speed, bridging the gap before users deploy the resource-heavy Astra.
In addition to model selection, OpenAI incorporates adjustable reasoning levels ranging from low to ultra. This configuration allows operators to scale computational depth based on task complexity, matching simple queries with lower settings while reserving intensive reasoning parameters for strategic analysis.