OpenAI launched GPT-6.1 Terra on October 26, and included two direct comparisons with Opus 5.5 in its marketing materials. It claimed Terra came within five percentage points of Opus 5.5, with fallbacks at less than one-fifth the estimated cost, on Terminal-Bench-Science 0.1, which has agents write code for research tasks such as tracking cell divisions.

At max effort, OpenAI describes Terra as more than three times as token-efficient as Opus and one-quarter the price for standard input and output tokens. Separately, it reports an estimated cost per task of about one-ninth. On Agents’ Last Exam, a benchmark of multistep computer-use tasks, Terra scored 60.0% at $1.01 per task, and Opus scored 63.2% at $8.82. That is a 3.2-percentage-point score gap and an 8.73-to-one cost ratio, not identical accuracy.

At standard uncached API rates, Terra costs $1 per million input tokens and $5 per million output tokens, compared with Opus 5.5’s $4 and $20. With the same input/output token mix and no other pricing adjustments, Terra would need four times as many tokens to match Opus’s cost. Actual task cost also depends on token usage, cache reads and writes, and any applicable pricing adjustments. 

The accuracy claims caught my interest because, when I tested Terra against OpenAI’s own GPT-6 Astra, Terra left some numerical totals uncalculated and gave some incorrect answers. That article focused mainly on analyzing large datasets, such as database records and server logs. 

Since the claims against Opus focus on computer use and scientific coding, I built two tests around those areas.

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