OpenAI announced GPT-6.1 Terra, which it describes as a fast model combining visual reasoning, computer use, and writing, aimed at workday tasks such as drafting proposals, interpreting technical drawings, filling out forms, and editing videos. According to OpenAI, standard API prices are 50% lower than those of GPT-5.6 Terra and GPT-6.1 Sol, at $1 per million input tokens, $0.05 per million cached input tokens, and $5 per million output tokens. OpenAI reports gains on several benchmarks it cites: on Agents' Last Exam, it says Terra scores 15 percentage points higher than GPT-5.6 Terra at about one-third the cost. On its internal benchmark of error-inducing prompts, OpenAI states the error rate at low reasoning effort fell from 53.6% to 16.7%, and at maximum effort to 4.3%. On Terminal-Bench 4.0, OpenAI says Terra solves 50.3% of tasks at maximum reasoning effort. OpenAI notes that for the most complex coding tasks, GPT-6.1 Sol and GPT-6 Astra remain better choices. The company states the model meets the Critical threshold in cybersecurity under its Preparedness Framework. The model is available to Plus, Pro, Enterprise, and Edu users in ChatGPT Work and Codex, and via the API as gpt-6.1-terra. These figures are vendor-reported and not independently verified.
- Standard API pricing: $1/M input tokens, $0.05/M cached input, $5/M output — 50% below GPT-5.6 Terra and GPT-6.1 Sol (OpenAI)
- OpenAI reports internal error rate fell from 53.6% to 16.7% at low reasoning effort
- OpenAI says it meets the Critical cybersecurity threshold under its Preparedness Framework
- Available now in ChatGPT Work, Codex, and the API as gpt-6.1-terra
What it means for you
This is a cheaper, faster version of a model that handles visual tasks and 'computer use' — meaning it can click through apps and complete multi-step tasks like reading an invoice and typing its details into accounting software. The headline for operations is price: OpenAI says it costs about half of what the prior version did, which makes high-volume, repetitive data work cheaper to automate. All the performance claims here come from OpenAI's own benchmarks and haven't been independently checked, so treat them as a starting point, not proof.
Try this
Pick one high-volume, repetitive task your team does by hand — invoice entry, form-filling, moving data between systems — and map the actual steps before you touch any tool. If the process is messy or exception-ridden today, fix that first; automating a confusing process just automates the confusion.
Who should care
Teams already building on the OpenAI API, and whoever owns high-volume document or data-entry work (invoicing, forms processing, order entry) where cost per task matters.
Skip this if
You don't use OpenAI's API or ChatGPT Work/Codex, or you have no high-volume repetitive workflow where token cost is a real line item. A cheaper model doesn't change anything for you yet.
Sources: OpenAI — read the original