GPT 6.1 Terra's pricing sits below Soul and Astra, but is it actually cheaper per task? Here's the real cost breakdown across effort modes.
What does GPT 6.1 Terra cost compared to Soul and Astra? #
GPT 6.1 Terra is priced as OpenAI’s mid-tier model, positioned below the flagship Astra and the second-tier Soul. On paper it costs fractions of a cent less per token than Soul at similar effort levels, but the real story is cost per completed task, not sticker price. Across several published benchmarks, Terra comes in slightly cheaper than Soul on raw per-token pricing, yet because it often needs more effort or produces worse results per dollar on certain tasks, the actual savings are inconsistent. Where Terra clearly wins is in agentic and computer-use workloads, where it reportedly reaches most of Soul’s and even Claude Opus 5.5’s quality at a small fraction of the cost.
TL;DR #
- GPT 6.1 Terra sits in OpenAI’s model hierarchy below Astra (flagship) and Soul (second tier), with Luna below it as the smallest, cheapest option.
- Per-token pricing is only marginally lower than Soul on several benchmarks, meaning the headline “cheaper model” framing doesn’t always hold up once you compare at matched effort levels.
- Cost-per-task tells a different story than cost-per-token , with Terra reportedly averaging a lower total cost per completed task than Soul despite close per-token pricing, because it tends to finish faster and use fewer tokens.
- Independent testing found GPT 6.1 Terra finished coding tasks in under half the time of Claude Opus 5.5 , using roughly a third of the output tokens and costing somewhere around one-twelfth as much in total.
- On the Agents Last Exam benchmark, Terra’s highest effort mode scored close to Opus 5.5 while costing a small fraction of the price, a tradeoff many builders will find worth making.
- Terra performs best on computer-use and agentic tasks , with OpenAI’s own team pointing to it as a meaningful step forward for automating screen-based work.
- Terra is not a strict upgrade over Soul in raw intelligence , scoring lower on composite benchmarks like the one tracked by Artificial Analysis, so it’s a tradeoff model rather than a pure downgrade or upgrade.
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Where does Terra fit in OpenAI’s model lineup? #
OpenAI currently organizes its models into four tiers: Astra at the top as the flagship, state-of-the-art model, Soul one step below, Terra below that, and Luna as the smallest and cheapest. Mapped against Anthropic’s lineup, Astra plays the role Claude’s top model does, Soul lines up with something like Opus, Terra compares to a Sonnet-class model, and Luna compares to Haiku, the fast and cheap option meant for lightweight tasks.
That makes Terra the “good enough, much cheaper” option in theory. In practice, pricing and benchmark comparisons show the gap between Terra and Soul is narrower and messier than that framing suggests.
Is GPT 6.1 Terra actually cheaper than Soul, dollar for dollar? #
It depends entirely on which effort mode and which benchmark you look at. On MMLU Pro, running Soul at its highest (“max”) effort costs just over a cent and scores around 86.3%. Running Terra at the same max effort costs only a fraction of a cent less, but the intelligence score drops. At that price point, the discount is barely noticeable while the performance drop is clear.
On OSWorld 2.1, a benchmark that scores practical multi-step task completion, Soul at max effort costs roughly $1.15 for a score around 78%, while Terra at max effort actually costs slightly more (around $1.19) for a lower score near 74%. At medium effort, Soul costs about 65 cents for a 73.5% score, compared to Terra’s roughly 50 cents for a 66.7% score. In that case Soul’s medium effort beats Terra’s extra-high effort at essentially the same price. On Terminal-Bench 4.0, the pattern repeats: Soul scores meaningfully higher for a similar price to Terra.
The upshot is that comparing Terra and Soul purely on per-token or per-call pricing often favors Soul, not Terra, once you factor in the intelligence tradeoff. Terra’s pricing advantage only becomes clear when you look at cost per completed task rather than cost per unit of compute.
How does cost-per-task pricing change the picture? #
Aggregate benchmarking from Artificial Analysis, which scores models holistically and tracks average cost and time per task, shows Soul scoring around 55 on its composite intelligence index versus Terra’s roughly 51, confirming Terra is the less capable model overall. But on cost per task, Soul averages around $1.27 per task while Terra averages closer to $0.96. Terra is also marginally faster per task than both Soul and Astra, though not dramatically so.
That’s a real but modest advantage, not the order-of-magnitude discount some builders might expect from a “lower tier” model. The practical question becomes: if you want cheap and smart, does Terra’s modest savings justify the drop in quality compared to just using Soul? For many straightforward tasks, the answer from available benchmarks leans toward sticking with Soul unless cost sensitivity at scale is the deciding factor.
Where does Terra’s pricing actually make sense? #
made for the enterprise.
The clearest value case for Terra shows up in coding and agentic workloads rather than single-turn question answering. In head-to-head testing against Claude Opus 5.5, both models given the same prompts, same tools, and run on their native coding harnesses (Codex for Terra, Claude Code for Opus), Terra finished in under half the time, roughly 24 minutes versus nearly 44 minutes for Opus 5.5. Terra also used about a third of the output tokens Opus 5.5 needed. Combined, this reportedly put Terra’s total cost at around one-twelfth of what Opus 5.5 cost for comparable (if not identical) output quality.
That tradeoff doesn’t mean Terra matches Opus 5.5 on quality across the board. Testing showed Terra struggling with complex 3D design work and some game-design tasks where Opus 5.5 produced noticeably better results. But for simpler UI builds, landing pages, and data visualizations, Terra reportedly got close enough that the massive cost and speed advantage made it the more practical choice.
On the Agents Last Exam benchmark specifically, Terra’s highest effort setting scored close to Opus 5.5 (roughly 95 to 97 percent of Opus 5.5’s score) while costing around one-ninth to one-eleventh as much. That kind of efficiency, delivering most of the performance for a small slice of the price, is the strongest argument for choosing Terra over either Soul or Astra when cost per workflow matters more than squeezing out the last few points of accuracy.
Does effort mode change Terra’s pricing significantly? #
Terra ships with multiple effort or reasoning modes, commonly described as light, high, extra high, max, and ultra. One notable pricing detail from benchmark testing is that the price difference between Terra’s lowest and highest effort modes is smaller than you might expect. On the Agents Last Exam benchmark, moving from roughly 10 cents at low effort to around $1.25 at max effort pushed the score from about 50% up to around 60%. That’s a meaningful accuracy gain for a cost increase that, while real, isn’t as punishing as jumping effort tiers on some other models.
This matters for budgeting: builders don’t necessarily need to default to Terra’s cheapest mode to stay cost-effective. Running higher effort modes for better accuracy often remains affordable relative to Soul or Astra at comparable settings.
Is GPT 6.1 Terra worth using over Soul or Astra? #
Based on available pricing and benchmark data, Terra makes the most sense for computer-use automation, coding-heavy agentic workflows, and high-volume tasks where shaving time and tokens off each run compounds into real savings. It makes less sense as a general-purpose daily driver if raw intelligence per dollar is the priority, since Soul often matches or beats Terra on a straight cost-to-score basis for single-shot benchmarks. Astra remains the choice when top-tier output quality matters more than cost, and Luna remains the cheapest, fastest, least capable option for simple on-device style tasks.
Frequently Asked Questions #
What is GPT 6.1 Terra?
GPT 6.1 Terra is OpenAI’s mid-tier language model, positioned below the flagship Astra model and the second-tier Soul model in the company’s current four-tier lineup (Astra, Soul, Terra, Luna).
Is GPT 6.1 Terra cheaper than GPT 6.1 Soul?
On a per-token basis, Terra is only marginally cheaper than Soul, and in several benchmarks Soul actually delivers better cost-to-performance. Terra’s real pricing advantage shows up in cost per completed task, particularly in coding and agentic workflows, where it tends to finish faster and use fewer tokens.
How does GPT 6.1 Terra compare to Claude Opus 5.5 on price?
Nothing gets lost.
In head-to-head coding tests, Terra reportedly completed tasks in less than half the time of Opus 5.5 while using about a third of the output tokens, putting its total cost at roughly one-twelfth that of Opus 5.5 for broadly comparable results on simpler tasks.
What is Terra best used for given its pricing?
Terra’s pricing and speed make it best suited for computer-use automation, agentic coding tasks, and high-volume workflows where cost per task matters more than squeezing out maximum accuracy. For complex 3D design or top-tier output quality, Astra or Opus 5.5 remain stronger choices.
Does GPT 6.1 Terra’s effort mode affect its cost significantly?
Yes, but less than expected. Moving from Terra’s lowest to highest effort mode on the Agents Last Exam benchmark increased cost from around 10 cents to roughly $1.25, while raising the score meaningfully, making higher effort modes a reasonable option rather than a steep cost penalty.