GPT-6.1 Terra: US$1/US$5 per million tokens, on Bedrock and Copilot
OpenAI's new mid-tier model targets computer use and everyday agentic coding at half Sol's price; the benchmark numbers are OpenAI's own.
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OpenAI released GPT-6.1 Terra on 26 October 2026, a mid-tier model aimed at computer use, visual reasoning and everyday agentic coding, with API prices of US$1 per million input tokens and US$5 per million output tokens. Amazon Bedrock and GitHub Copilot made it generally available the same day. Most of the performance figures below come from OpenAI itself, as relayed by BleepingComputer; we could not open OpenAI's own announcement page from our systems, so we say "OpenAI says" wherever a number rests on it and have not checked those numbers independently.
Key points
- GPT-6.1 Terra is priced at US$1 per million input tokens and US$5 per million output tokens, and its API model name is
gpt-6.1-terra(BleepingComputer, quoting OpenAI). - OpenAI says it scored 15 percentage points higher than GPT-5.6 Terra on its Agents' Last Exam evaluation at medium reasoning effort, at about one-third of the cost, and completed tasks more than three times faster than GPT-6 Astra and GPT-6.1 Sol.
- It accepts text and image input with a context window of up to 1 million input tokens (AWS).
- It is available in ChatGPT Work and Codex for Plus, Pro, Enterprise and Edu users, in Amazon Bedrock, and in GitHub Copilot for Pro, Pro+, Max, Business and Enterprise users, with a gradual Copilot rollout.
- OpenAI still recommends GPT-6.1 Sol or GPT-6 Astra for the most complex coding tasks.
What happened
Three separate announcements landed on 26 October. AWS said GPT-6.1 Terra is generally available on Amazon Bedrock, describing it as expanding "the GPT-6 family with a balance of capability, speed, and cost efficiency for everyday workloads." GitHub's changelog said the model is generally available in Copilot and is "designed for everyday agentic coding and terminal work at a lower cost than GPT-6.1 Sol." BleepingComputer reported OpenAI's own launch post, which presents Terra as a fast, efficient model that pairs detailed visual reasoning with precise computer use and "clearer, more natural writing."
The tasks OpenAI highlights are the ones that fill a working day: drafting proposals, interpreting technical drawings, filling in forms and editing videos. AWS lists a similar set (drafting proposals, interpreting technical drawings, investigating codebases and building agents that operate across business systems).
On price, BleepingComputer reports OpenAI saying Terra's API pricing is 50% lower than GPT-5.6 Terra and GPT-6.1 Sol, at US$1 and US$5 per million input and output tokens. AWS phrases it as input and output prices "at least 50% lower" than those two models. Our own check of OpenAI's price page, published earlier today in our look at how LLM API prices fell since June, lists GPT-6.1 Sol at US$2 and US$10, which is consistent with Terra at half. We did not confirm GPT-5.6 Terra's price: third-party price trackers we saw disagree with each other on it, so we leave it out.
Background
OpenAI sells its models in tiers. Sol is the larger tier, Luna the small one, and Terra sits between them for balanced workloads. In the same price article we recorded GPT-6.1 Sol at US$2 and US$10 and GPT-6 Luna at US$0.10 and US$0.50 per million input and output tokens, after a run of cuts since June. Terra's US$1 / US$5 slots between them.
"Computer use" means a model that looks at a screen and operates it by clicking, typing and reading, rather than calling a clean API. It is slow and error-prone when the model is large and expensive, which is why speed and price matter for it more than for chat. A model that is cheaper per token and finishes in a third of the time changes what is worth automating.
The numbers
The figures below are OpenAI's, reported by BleepingComputer, except where marked. OpenAI describes the error-rate figure as coming from its internal benchmark and the coding figure as a Terminal-Bench 4.0 result at maximum reasoning effort.
- Agents' Last Exam: 15 percentage points above GPT-5.6 Terra at medium reasoning effort while costing about a third as much; OpenAI says it "performs on par with GPT-6 Astra at only one-tenth its price per token." AWS repeats the 15-point gain, the parity with GPT-6 Astra and the more-than-three-times speed.
- Error rate (internal benchmark): at low reasoning effort it fell from 53.6% with GPT-5.6 Terra to 16.7% with GPT-6.1 Terra; at maximum effort it fell by 18 percentage points to 4.3%, "nearly matching" GPT-6 Astra and GPT-6.1 Sol.
- Terminal-Bench 4.0 at maximum effort: 50.3% against GPT-5.6 Terra's 21.5%, more than double.
Using the output prices listed on the vendors' pages (read for our earlier article) plus Terra's launch price:
Note what the chart does not show: input prices, cached-input discounts and how many tokens each model needs for the same job. A cheaper model that needs more steps or more reasoning effort can cost more per finished task, so measure on your own workload.
What it means
For developers. A model that OpenAI says matches its flagship on an agent benchmark at a tenth of the per-token price is worth testing for any agent loop that currently runs on Sol or Astra. GitHub says Terra solved noticeably more terminal and C# coding tasks than GPT-5.6 Terra in early testing, and that it "works in focused, consolidated steps on well-scoped tasks." That wording is worth heeding: OpenAI itself still points to Sol or Astra for the hardest coding work, so treat Terra as the workhorse and not as a replacement for everything.
For teams on Copilot. Terra is billed at provider list pricing under usage-based billing, according to GitHub, and new models are enabled automatically for Business and Enterprise unless an administrator has switched off the global default or disabled this model. If your organisation controls model access, check the model policy before the rollout reaches your users.
For the market. Compared with Anthropic's list prices (Sonnet 5.5 at US$2 / US$10, Opus 5.5 at US$4 / US$20 as of our earlier check), Terra's US$1 / US$5 undercuts the mid-tier. We have no head-to-head benchmark between Terra and those models, and we will not infer one.
What to do
- API users: use the model name
gpt-6.1-terra, and test it against your current model on a sample of real tasks, comparing cost per completed task, not only per token. - Amazon Bedrock users: the model is generally available; AWS points to its documentation for supported Regions, endpoints, APIs, features and pricing, which vary.
- Copilot users: pick GPT-6.1 Terra in the model picker (VS Code, Visual Studio, Copilot CLI, the cloud agent, github.com, GitHub Mobile, JetBrains IDEs, Xcode and Eclipse are listed). If it is not there yet, wait: the rollout is gradual.
- Copilot administrators: review the model policy in Copilot settings if you do not want new models enabled by default.
What we don't know yet
- Independent results. Every performance number here comes from OpenAI or from partners repeating it; we have seen no third-party benchmark of Terra yet.
- How Terra's error rate and Agents' Last Exam score compare with non-OpenAI models, which OpenAI's figures do not cover.
- The full launch post. We could not open OpenAI's page, so details such as reasoning-effort settings, rate limits and safety notes are not covered here.
- Cached-input pricing and availability in regions on Bedrock.
What to watch next
Independent evaluations from groups that run their own agent and coding tests, any follow-up price moves from Anthropic, and whether OpenAI extends Terra to more products. We will add a dated update if the figures change or independent results appear.
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