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OpenAI launches GPT-6.1 Terra for lower-cost computer use

OpenAI has launched GPT-6.1 Terra in ChatGPT Work, Codex and its API, positioning the model for visual reasoning and computer-use tasks at lower token prices than GPT-6.1 Sol. The availability is real, while performance and safety comparisons remain largely company-reported.

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OpenAI API model page for GPT-6.1 Terra
GPT-6.1 Terra model specifications · Credit: OpenAI View source

OpenAI launched GPT-6.1 Terra on October 26, making the new model available in ChatGPT Work, Codex and the OpenAI API. The company positions Terra as a faster, more economical model for visual reasoning, computer use, coding and routine knowledge work rather than as a replacement for its highest-capability systems.

A computer-use model aimed at high-volume work

The release focuses on tasks that move from understanding a screen or document to taking an action: reading invoices, filling forms, interpreting technical drawings and editing media. OpenAI says Terra improves substantially over GPT-5.6 Terra on its Agents’ Last Exam, MMMU-Pro, OSWorld and Terminal-Bench evaluations, while acknowledging that GPT-6.1 Sol and GPT-6 Astra remain stronger choices for the most complex coding tasks.

OpenAI lists standard API prices of $1 per million input tokens, $0.05 per million cached input tokens and $5 per million output tokens. Those prices are half the standard token rates OpenAI cites for GPT-5.6 Terra and GPT-6.1 Sol, turning cost—not just peak benchmark performance—into the core product claim.

Availability is confirmed; comparative performance needs independent testing

Terra is available to Plus, Pro, Enterprise and Edu users in ChatGPT Work and Codex and through the API under the identifier gpt-6.1-terra. OpenAI says the same plan allowance covers more Terra tasks than higher-cost models, though practical usage depends on task length, reasoning effort and product limits.

The company says Terra meets the Critical threshold for cybersecurity under its Preparedness Framework and retains safeguards from GPT-6 Astra. Those safety and benchmark results are company evaluations; independent testing will be needed to establish how reliably the model performs across third-party desktop environments and adversarial workloads.

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