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.The testsI 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. I graded each test against keys I built and verified, after developing three versions of each test. These are single-run results on synthetic tasks, not estimates of repeatability or performance across all workloads. Matching the effort setting does not impose the same reasoning-token budget on both models. Both models returned the same score on easy and medium-difficulty tests. To test the real soft spots, after the same results returned a third time, I ran hard tests against a weaker model, GPT-5 Mini, until Mini’s score was very low.Here is the final iteration of the tests. These are the tests detailed in the remainder of this article. In the usage figures below, “out” includes reasoning tokens; the parenthetical reasoning count is a subset, not an additional charge. Costs are estimates calculated from the reported tokens at standard uncached input and output rates, excluding any tool charges or billing adjustments. They are not verified invoice totals.SQL reasoning – I built a fake database of 500 software builds and asked each model 25 questions about it. Some builds were deleted, some were retries, costs are in three currencies, and a few questions depend on time zones. Two of the questions can’t be answered from the data.Data validation – I gave each model a spreadsheet of 4,950 fake customer records and 19 rules they’re supposed to follow. Some rules only make sense across the whole file, like no more than three customers at one postal code and no referral loops; forty records break a rule, and 10 look wrong but are actually fine.Incident log triage – I wrote about 7,000 lines of logs from a fake outage across more than 20 services. I planted errors like drifting clocks, error messages blaming the wrong service, and unrelated changes sending alerts before the one that actually caused the outage.SQL reasoningThis test showed the widest split between the two models. Astra scored 48/50 and Terra 26/50. Each of the 25 questions was worth two points, for a total of 50. An answer had to be exactly right to earn the two points, so a close answer earned zero. Unanswered questions also earned zero points. For the questions that can’t be answered from the data, the correct answer was to identify that. On every other question, a model that didn’t give an answer earned zero.Terra processed all 500 rows and handled soft deletes, retries, and three currencies. Its one miss was the total run time for 151 builds in a single region. Astra’s answer came in 406 seconds short of the correct 31,428. Its answer was 31,022 seconds, about 1.3% below the stated correct total. The final answer alone doesn’t show whether the error came from addition, a skipped row, or a misread value.Astra was more accurate in this run, but its incorrect total still needed checking. It generated 27,970 reasoning tokens, and the response took 319.7 seconds—more than five minutes.On the six questions that required adding up costs or durations across all 500 rows, Terra didn’t attempt an answer. It wrote a SQL query for me to run instead and marked each one as not calculated. Terra didn’t make up numbers, which is good. But it left the hardest part of the job, running the query and getting the totals, for you to do yourself. This is the first time I’ve seen AI hand work back.The bigger problem is that Terra answered six questions wrong. Four of them were counts that were off by just one or two. Terra gave those answers with the same confidence as its correct ones, so nothing signaled that they were wrong. If you used those numbers in a report, you wouldn’t know which ones to double-check.I’m including the numbers for both Terra and Astra here, but since the results were so different, the lower price and faster time aren’t wins.Terra 133.1 s, 21,060 in, 11,554 out (10,707 reasoning), $0.079Astra 319.7 s, 21,060 in, 28,806 out (27,970 reasoning), $1.65Data validationAstra scored a perfect 50, and Terra scored 45. 40 of the 50 points came from the 40 records that broke a rule. A model earned one point for each record it flagged, as long as it named the rule the record broke. The last 10 points came from records that looked wrong but followed every rule. A model lost a point for every correct record it accidentally flagged as incorrect.Before giving its answer, Terra warned that it couldn’t reliably check all 4,950 records against the rules and might miss some (as it did in the SQL test). The reported input fit within the model’s context window, and I did not set an output cap. Terra generated 1,841 reasoning tokens compared with Astra’s 12,831. The responses took 31.4 and 145.0 seconds, respectively; these elapsed times don’t isolate reasoning time.Terra correctly flagged 35 of the 40 invalid records and flagged none of the valid records: 87.5% recall and 100% precision in this run. Its 45/50 score is a test score, not an accuracy percentage across all 4,950 records. Three of the five it missed only break a rule when you compare them with other records in the file, such as a customer who reused another customer’s phone number. Terra flagged everything correctly, but it missed five invalid records. The results don’t explain why it missed them.Astra caught all 40, but it took more than four times as long and cost about 12 times as much. Terra used fewer reasoning tokens and missed records in this run; that association does not establish the cause of the misses.The numbers:Terra 31.4 s, 234,045 in, 2,336 out (1,841 reasoning), $0.25Astra 145.0 s, 234,045 in, 13,369 out (12,831 reasoning), $3.01Incident log triageThis one was a tie, with both models scoring 50. I graded this test using a 50-point list of what a correct answer needs to include. To earn the full 50 points, a model had to trace the outage to the settings change in the ledger service, find the three servers with wrong clocks, and avoid blaming the planted false leads. False leads included a fraud service update that was rolled back, a payments setting that changed seconds before the real cause, a database switchover, and an unrelated memory problem in the search service.Error messages blamed the fraud service, but both models traced the outage to a settings change in the ledger service and spotted the three servers with wrong clocks. Neither model took the error messages at face value. Both checked the evidence underneath, which is what you’d want from an engineer during a real outage.Astra cost about 11 times as much as Terra and took 2.6 times as long to complete the work. Most of Astra’s additional output tokens were reasoning tokens: 4,628 of the 6,650-token difference. Both models earned the same score here despite that difference. These runs don’t show whether additional reasoning led to better scores on the other tests.The numbers:Terra 94.3 s, 251,651 in, 7,508 out (5,114 reasoning), $0.29Astra 244.5 s, 251,651 in, 14,158 out (9,742 reasoning), $3.22ResultsTestTerra (out of 50)Astra (out of 50)GPT-5 Mini (out of 50)SQL reasoning26486Data validation455010Incident log triage505012Total (of 150)12114828Estimated token cost$0.61$7.88—Total time258.8 s709.2 s—I included GPT-5 Mini’s baseline results for reference. I rebuilt these tests several times and kept making them harder because both models kept returning identical scores. Because I selected these harder versions after seeing earlier results, they shouldn’t be treated as a representative sample of everyday work. A low Mini score is a comparison point, not an independent measure of test difficulty. What do I think?In these three final high-effort runs, Terra generated fewer reasoning tokens and finished sooner than Astra. It scored lower on SQL reasoning and data validation, and tied Astra on incident log triage. That is a trade-off in the results, but it doesn’t tell us whether Terra was overwhelmed or why it made specific errors. The measured totals were 258.8 seconds for Terra and 709.2 seconds for Astra, so Astra took about 2.74 times as long overall. At the stated standard uncached rates, the estimated token costs were $0.61 and $7.88; Terra’s cost was about 7.8% of Astra’s. Terra scored 121/150 and Astra 148/150 under these test rubrics. The runs show a cost, time, and score trade-off; they don’t show that OpenAI deliberately cut reasoning to achieve it. Terra matched Astra’s scores on my earlier easy and medium versions, but that does not establish its accuracy on every task at those difficulty levels. On the final tests, I would be cautious about using it for unchecked numerical aggregation or exhaustive data validation. It may be an acceptable lower-cost alternative for some workloads, including tasks like the incident triage tested here, as long as you verify the results. Astra’s incorrect SQL total also reminds you to check its work. The post GPT-6.1 Terra vs. GPT-6 Astra: One sacrifices accuracy for lower cost and higher speed at high effort appeared first on The New Stack.