OpenAI released GPT-6.1 Terra on October 26, marketing it as a cheap, fast model for high-volume everyday work, priced at a tenth of its GPT-6 Astra flagship.

As the mid-priced model in its GPT-6 family, OpenAI lists Terra at $1 per million input tokens and $5 per million output tokens, a mere tenth of Astra’s $10 and $50. The AI giant also reports that Terra completes tasks more than three times faster than Astra in its Agents’ Last Exam benchmark. Those computer-use results are not directly comparable to the three tests below. 

Early benchmarks showed some limitations, alongside gains. Plotly reported that Terra caught three of five planted patterns in its analytics test and passed 40 of 43 questions overall. Artificial Analysis found that it guessed on 38% of questions it couldn’t answer instead of saying so, down from 88% for GPT-5.6 Terra in the same benchmark. DualEntry’s accounting benchmark put its accuracy at 78.2%. 

In this AI arms race, each launch needs to be marketed as the best; otherwise, it risks being ignored. This can create a confusing experience for users, especially when reality doesn’t quite match the promise. I compared Terra with Astra on three synthetic tasks to see how their speed, estimated token costs, and accuracy differed. This is a separate test of those trade-offs, not a replication of OpenAI’s computer-use benchmark.

I ran the final version of each test once per model through OpenAI’s API with reasoning effort set to high. I didn’t set an output-token cap, though the models still have built-in output limits.

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