OpenAI has launched GPT-6.1 Terra, a new reasoning model that it says delivers near-frontier performance on visual reasoning, professional task execution, and computer-use benchmarks without the premium price tag. Replacing GPT-5.6 Terra, the model is aimed at organizations seeking better accuracy and automation capabilities for large-scale operational workloads.
Claiming it to be a step up from GPT-5.6 Terra at 50% lower price, OpenAI said its new mid-tier model is designed to handle everyday professional work such as drafting proposals, interpreting technical drawings, filling out forms, and editing videos.
“We’re introducing GPT-6.1 Terra, a fast, efficient model that combines detailed visual reasoning and precise computer use alongside clearer, more natural writing,” the company noted.
GPT-6.1 Terra targets developers and enterprises looking to automate everyday knowledge work at scale without paying premium prices for frontier AI models, said Pareekh Jain, CEO at EIIRTrend & Pareekh Consulting. “Enterprises need AI models that balance intelligence, speed and cost. Frontier models are expensive for routine tasks, while smaller models may lack sufficient capabilities. Terra addresses this gap, competing with Anthropic’s Claude Sonnet and Google’s Gemini Flash models.”
Better performance at lower cost
The new reasoning model sits between OpenAI’s lower-cost GPT-6 Luna and its higher-capability GPT-6.1 Sol and GPT-6 Astra models.
As OpenAI has positioned computer use as a central capability of GPT-6.1 Terra, it is backing those claims with a series of benchmark scores. On Agents’ Last Exam, a benchmark of professional tasks, at max effort, GPT-6.1 Terra scored 60.0% at a cost of $1.01 per task, compared to 50.7% for GPT-5.6 Terra ($3.58), 58.6% for GPT-6.1 Sol ($2.00), and 59.3% for GPT-6 Astra ($7.24). In comparison, Claude Opus 5.5 scored 63.2% at max effort, costing $8.82 per task.
Visual reasoning results on the MMMU-Pro benchmark show GPT-6.1 Terra outperforming its predecessor, GPT-5.6 Terra, while nearly matching top-tier frontier models like GPT-6 Astra and GPT-6.1 Sol across every reasoning configuration, at a fraction of the cost. At max effort, GPT-6.1 Terra achieved an 85.5% score at $0.012 per task, compared to GPT-5.6 Terra’s 81.7% at $0.079. Within the current family lineup, GPT-6 Luna scored 79.9% ($0.0021 per task), GPT-6.1 Sol hit 86.3% ($0.013 per task), and flagship GPT-6 Astra led at 86.7% ($0.065 per task). OpenAI did not include competitor model comparisons for this specific benchmark.
On the OSWorld 2.1 benchmark, which evaluates how AI agents navigate desktop applications to perform complex multi-step tasks, GPT-6.1 Terra scored 74.2% at max effort, costing $1.19 per task. That marks a substantial upgrade over GPT-5.6 Terra’s 57.1% score at $4.76 per task. However, it still trails Anthropic’s Claude Sonnet 5.5, which led the category at 83.9% with a cost of $5.74 per task.
OpenAI also claims to have invested heavily in reducing hallucinations and factual errors produced, and as per the 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, and at maximum effort, the error rate fell by 18 percentage points to 4.3%, nearly matching the rates for GPT-6 Astra and GPT-6.1 Sol. On cybersecurity, GPT-6.1 Terra meets the Critical threshold under OpenAI’s Preparedness Framework and retains the safeguards developed for GPT-6 Astra.
“Terra is ideal for high-volume tasks such as invoice processing, document analysis, application automation and routine coding. It improves on GPT-5.6 Terra in speed and accuracy, while GPT-6.1 Sol is better suited for complex reasoning, research, and software engineering,” noted Jain.
However, Jain cautions that technical benchmarks alone are insufficient. Enterprises should test Terra on their own workloads, measuring task completion rates, accuracy, execution time, cost per workflow, human intervention, and compliance with security requirements.
Economics, not just pricing
GPT-6.1 Terra is available to all Plus, Pro, Enterprise, and Edu users in ChatGPT Work and Codex. Developers can also access it through the OpenAI API as gpt-6.1-terra.
OpenAI has priced GPT-6.1 Terra at $1 per million input tokens, $5 per million output tokens, and $0.05 per million cached input tokens, positioning it as a lower-cost alternative to its high-tier models. By comparison, entry-level GPT-6 Luna costs $0.10 for inputs, $0.50 for outputs, and $0.01 for cached inputs per million tokens. GPT-6.1 Sol sits at $2 for inputs, $10 for outputs, and $0.10 for cached inputs, while flagship GPT-6 Astra commands $10 for inputs, $50 for outputs, and $1 for cached inputs per million tokens.
Claude Sonnet 5.5, on the other hand, is priced at $2 per million input tokens and $10 per million output tokens, and cached costs $0.10 per million tokens.
“Cheaper inference improves one component of agent economics. Terra’s base rates are half Sol’s, but review and integration still require funding,” Sanchit Vir Gogia, chief analyst at Greyhound Research. “Token processing is fuel with a higher operating cost. A CFO relieved by cheaper fuel needs to establish what happens to acceptance and review demand. In the illustrative coding workload, 5.4 additional review seconds erase the inference saving. At scale, the relevant purchase is completed work that remains correct and authorized.”
The article originally appeared on InfoWorld.