Research · October 28, 2026

OpenAI GPT-6.1 Terra: Half the Price, Three Times the Speed, Built for the Workday

OpenAI's newest model is not a bid for the hardest problem. It is a bid for the most common one. On October 26, 2026, OpenAI introduced GPT-6.1 Terra, a fast, efficient model built for detailed visual reasoning, precise computer use, and clearer, more natural writing — the communication and computer-based tasks that fill a workday, from drafting proposals and interpreting technical drawings to filling out forms and editing videos. It is a significant step up from GPT-5.6 Terra, and OpenAI is explicit about what it is not: for the most complex coding tasks, GPT-6.1 Sol and GPT-6 Astra remain the better choice.

The price is the strategy. Standard API pricing is $1 per million input tokens, $0.05 per million cached input tokens, and $5 per million output tokens — 50% lower than GPT-5.6 Terra and GPT-6.1 Sol. OpenAI's example of what that unlocks is deliberately mundane: reading incoming invoices and entering their details into accounting software. In ChatGPT Work and Codex, the same usage allowance covers more tasks than with GPT-5.6 Terra, and substantially more than with GPT-6.1 Sol or GPT-6 Astra. The model page adds the shape of the offer: a 1,050,000-token context window, up to 128,000 output tokens, a June 1, 2026 knowledge cutoff, and reasoning effort from none to max. Tool calling requires the Responses API, and fine-tuning is not supported. Long prompts carry a surcharge — over 272,000 input tokens, the whole request bills at twice the input and cache rates and one-and-a-half times the output rate — while Batch and Flex processing cut standard prices by half.

The benchmarks, all vendor-reported. On Agents' Last Exam, a test of professional software tasks such as turning architectural drawings into a 3D model, Terra at medium reasoning effort scores 15 percentage points higher than GPT-5.6 Terra at about one-third the cost, and performs on par with GPT-6 Astra at one-tenth its price per token. At maximum effort it is over three times as token-efficient as Claude Opus 5.5 at one-fourth the price per token, for roughly nine times lower cost per task — and it completes tasks over three times faster than both Astra and GPT-6.1 Sol. On MMMU-Pro visual reasoning it beats GPT-5.6 Terra at every reasoning setting and nearly matches Astra and Sol at much lower cost. On OSWorld 2.1 desktop-app tasks, medium-effort Terra surpasses GPT-5.6 Terra's best score at a fraction of the cost per task. On software engineering, Terminal-Bench 4.0 at maximum effort reaches 50.3%, more than double GPT-5.6 Terra's published 21.5%. On factuality, OpenAI's internal error-inducing-prompt benchmark falls from 53.6% to 16.7% at low reasoning effort, and to 4.3% at maximum effort, nearly matching Astra and Sol. Every one of these numbers is OpenAI's own measurement, on its own tests, under settings it chose. They establish the direction and the price of the bet, not the cost or success rate of any particular customer's workflow.

The guardrail that comes with the capability. GPT-6.1 Terra meets the Critical threshold in cybersecurity under OpenAI's Preparedness Framework, and maintains the protections developed for GPT-6 Astra. OpenAI also reports stronger scope adherence, more honest disclosure of limitations and blockers, and strong resistance to prompt-injection attempts designed to divert the model from its task. The blitzscaling read is the combination: a model cheap and fast enough to run continuously inside ordinary business software, released together with the admission that it is powerful enough to trip the company's own highest cybersecurity classification. Distribution at this price is the moat-building move; the safety classification is the tax on it.

What to check next. Availability is immediate — Plus, Pro, Enterprise, and Edu users in ChatGPT Work and Codex, plus developers through the API as gpt-6.1-terra — so the test that matters is not another launch chart. It is whether high-volume computer-use workloads actually migrate to the cheaper tier, and whether the 50% price cut holds as the reference price for the next generation of agent deployments, rather than being undercut again within a quarter. Watch adoption in the workday tasks OpenAI named — invoices, forms, drawings — because that is where a cheaper, faster model either becomes infrastructure or stays a demo.

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