The Pulse
OpenAI Releases GPT-6.1 Terra at Half the API Price
OpenAI released GPT-6.1 Terra on October 26, 2026, positioning it as a lower-cost model for visual reasoning and computer use. The model is available in ChatGPT Work and Codex, with API prices of $1 per million input tokens and $5 per milli
AI.info Team · Written with AI · fact-checked ·
OpenAI is pitching GPT-6.1 Terra as a cheaper way to put AI to work across computer screens, while its own launch figures place the model below GPT-6.1 Sol and GPT-6 Astra on the hardest tasks. Released Monday, October 26, Terra costs half as much per token as GPT-5.6 Terra and GPT-6.1 Sol at standard API rates. OpenAI says it performs on par with Astra on one computer-use benchmark at medium reasoning effort, but identifies Sol and Astra as better choices for the most complex coding work.
Lower prices for screen-based work
Terra is designed for visual reasoning, computer use and writing, with examples including interpreting technical drawings, filling out forms, editing video and processing invoices in accounting software. OpenAI says the model can interpret a screen and act across applications, a capability aimed at multi-step office tasks rather than only question-and-answer chat.
For API customers, standard pricing is $1 per million input tokens, $0.05 per million cached input tokens and $5 per million output tokens. OpenAI says those standard prices are 50% below GPT-5.6 Terra and GPT-6.1 Sol. The company’s API documentation says computer-use tool calling requires the Responses API and that tool-specific models can carry a separate fee per tool call, so token rates do not necessarily represent the full cost of an automated workflow.
OpenAI’s benchmark case—and its limits
OpenAI compared Terra with other models on Agents’ Last Exam, a benchmark of professional tasks that includes turning architectural drawings into 3D models and reviewing animated film footage. At medium reasoning effort, the company reports that Terra scored 15 percentage points above GPT-5.6 Terra at about one-third the cost, and performed on par with GPT-6 Astra at one-tenth Astra’s price per token.
At maximum reasoning effort, OpenAI says Terra used tokens more than three times as efficiently as Claude Opus 5.5 and cost one-fourth as much per token, producing an estimated ninefold reduction in cost per task. The same announcement says Terra completed tasks more than three times faster than GPT-6 Astra and GPT-6.1 Sol. Those comparisons are OpenAI’s reported results; the launch post does not establish how the models perform across every customer’s applications or workloads.
Cheaper does not mean the strongest option
OpenAI’s coding results show where the model’s lower price comes with a clear boundary. At maximum reasoning effort, Terra solved 50.3% of Terminal-Bench 4.0 tasks, compared with GPT-5.6 Terra’s published 21.5% score. But the company says GPT-6.1 Sol and GPT-6 Astra remain the better choices for the most complex coding tasks.
OpenAI also reports that Terra’s error rate on its internal benchmark of difficult prompts fell to 16.7% at low reasoning effort, from 53.6% for GPT-5.6 Terra. At maximum effort, it reports a 4.3% error rate. The benchmark is internal, and those figures describe OpenAI’s evaluation rather than a broad, independently reported measure of real-world accuracy.
Available in ChatGPT Work, Codex and the API
OpenAI says GPT-6.1 Terra is available to Plus, Pro, Enterprise and Edu users in ChatGPT Work and Codex, and to developers through the API under the model name gpt-6.1-terra. The API model page lists computer use among its supported tools and offers reasoning settings from none through maximum.
The launch announcement also says Terra meets the “Critical” cybersecurity threshold in OpenAI’s Preparedness Framework and retains protections developed for GPT-6 Astra. For developers weighing a switch, the practical choice OpenAI describes is specific: use Terra for defined visual and computer tasks where cost and speed matter, and turn to Sol or Astra when the work demands more complex coding or reasoning.