OpenAI released GPT-6.1 Terra on October 26, the newest member of its GPT-6.1 series. The positioning is clear: a fast, efficient model that combines detailed visual reasoning with precise computer use, alongside clearer and more natural writing. OpenAI describes it as a significant step up from GPT-5.6 Terra, aimed at the tasks that fill the workday — drafting proposals, interpreting technical drawings, filling out forms, and editing videos.

Price-performance is the story

Terra's standard API prices are 50% lower than both GPT-5.6 Terra and GPT-6.1 Sol: $1 per million input tokens, $0.05 per million cached input tokens, and $5 per million output tokens. Several numbers from the official evaluations stand out:

  • On Agents' Last Exam, a benchmark of professional computer-use tasks, Terra at medium reasoning effort scores 15 percentage points higher than GPT-5.6 Terra at about one-third the cost, and performs on par with GPT-6 Astra at only one-tenth its price per token.
  • At maximum reasoning effort, Terra is over three times as token-efficient as Claude Opus 5.5, at one-fourth the price per token — roughly a 9x lower cost per task. It also completes tasks over three times faster than both GPT-6 Astra and GPT-6.1 Sol.
  • On MMMU-Pro, a visual reasoning benchmark, Terra outperforms GPT-5.6 Terra at every reasoning setting; on OSWorld 2.1, which tests desktop-app agents, it surpasses GPT-5.6 Terra's best score at a fraction of the cost per task.

Factuality and coding: doubled, with honest limits

OpenAI says it has invested heavily this year in reducing hallucinations. On its internal benchmark of error-inducing prompts, the error rate at low reasoning effort fell from 53.6% with GPT-5.6 Terra to 16.7% with GPT-6.1 Terra; at maximum effort it dropped 18 percentage points to 4.3%, nearly matching GPT-6 Astra and GPT-6.1 Sol.

On the coding side, Terra solves 50.3% of tasks on Terminal-Bench 4.0 at maximum reasoning effort — more than double GPT-5.6 Terra's published score of 21.5%. But OpenAI is explicit that for the most complex coding tasks, GPT-6.1 Sol and GPT-6 Astra remain the better choice — a boundary statement worth noting. On Terminal-Bench-Science 0.1, Terra surpasses GPT-6.1 Sol and comes within five points of Opus 5.5 at less than one-fifth the estimated cost.

On safety, Terra meets the Critical threshold in cybersecurity under OpenAI's Preparedness Framework, keeps the protections developed for GPT-6 Astra, and ships with a published system card. It is rolling out now to Plus, Pro, Enterprise, and Edu users in ChatGPT Work and Codex, and is available through the API as gpt-6.1-terra.

So what

Read the announcement end to end and Terra's real pitch is not "strongest model" but "most work per dollar". When computer-use capability matches the flagship at one-tenth the per-token price, high-volume, low-tolerance workflows — reading invoices into accounting systems, batch-filling forms, watching screens to run processes — finally have automation economics that add up. The center of gravity in model competition is shifting from leaderboard scores to unit economics, and for developers that is more practical than any title: how much your agents burn next month depends on whether you know how to switch models.

Original announcement: https://openai.com/index/introducing-gpt-6-1-terra/