Terra Cuts Costs, Boosts Performance... OpenAI Speeds Up Agent Ecosystem Expansion
Computer-task specialist "Terra"
90% cheaper than Astra
OpenAI is accelerating the expansion of its ecosystem by diversifying the roles of AI agents and the cost of model tokens. Its strategy is to broaden the range of options—from agents that handle tasks continuously to features that support decision-making and cost-effective models specialized in computer control—in an effort to reduce the burden on businesses.
On October 27, OpenAI unveiled a new AI model, GPT-6.1 Terra. The model is designed to improve its ability to perform computer-based tasks such as drafting proposals, interpreting technical drawings and editing videos, with a focus on handling repetitive office work quickly and affordably. OpenAI said Terra scored 15 percentage points higher than its predecessor, GPT-5.6 Terra, on Agent Last Exam, a benchmark that evaluates agents' ability to perform tasks.
The cost of tokens has been cut in half compared with previous models. Its standard API pricing is 50% lower than that of the previous model, GPT-5.6 Terra, and the next-tier model, GPT-6.1 Sol, and 10 times lower than that of GPT-6 Astra. Terra costs $1 per 1 million input tokens and $5 per 1 million output tokens.
OpenAI is building an ecosystem in which users can choose and combine AI models according to their needs—for example, using high-performance models for complex tasks and relatively inexpensive models for repetitive ones.
Dots, unveiled on September 29, uses its own cloud computers to handle multiple steps of tasks continuously on users' behalf. According to OpenAI, Dots is rapidly gaining users in South Korea, with the country ranking third worldwide in weekly active users as of October.
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OpenAI is also enhancing features that help agents make decisions by providing outcomes rather than generating text. On October 22, the company officially launched Decisions API, powered by GPT-6 Luna. It helps AI agents make straightforward decisions, such as routing customer inquiries to the appropriate department or prioritizing tasks.
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