OpenAI release October 26, 2026
OpenAI Ships GPT-6.1 Terra at Half Price, Aimed at High-Volume Enterprise Automation
OpenAI released GPT-6.1 Terra on October 26, a mid-tier model priced at $1 per million input tokens and $5 per million output tokens, with cached input at $0.05 per million: half the standard API rates of both GPT-5.6 Terra, the model it replaces, and GPT-6.1 Sol. It is available to Plus, Pro, Enterprise, and Edu users in ChatGPT Work and Codex, and to developers through the API under the model ID gpt-6.1-terra, with a context window of 1.05 million tokens and maximum output of 128,000 tokens. OpenAI positions it for high-volume work such as reading invoices into accounting software, drafting proposals, interpreting technical drawings, filling out forms, and editing video. The performance figures are OpenAI's own: on Terminal-Bench 4.0 it reports Terra solving 50.3 percent of tasks at maximum reasoning effort against 21.5 percent published for GPT-5.6 Terra, on its internal error-prompt benchmark the error rate at low effort fell from 53.6 percent to 16.7 percent and to 4.3 percent at maximum effort, and the company says Terra completes tasks more than three times faster than GPT-6 Astra and GPT-6.1 Sol in its own timing runs. AI Weekly notes those benchmark numbers rest on OpenAI's figures, with no independent replication cited in the verified reporting at publication.
Executives
A capable model at half the previous price changes the arithmetic on automation projects that were marginal at the old rate, especially high-volume document work. It does not change the governance arithmetic: a cheaper model doing more tasks touches more data, so the approval, logging, and vendor review you apply to AI tools should scale with the new volume, not with the invoice.
CIO / CISO
Route any Terra adoption through the same AI inventory and data classification path as prior models, and record the model ID change in your AI register, because outputs, retention behavior, and effective cost per task will shift under existing use cases. Note the benchmark position honestly in internal papers: the gains are vendor-reported so far, which under the NIST AI RMF Measure function argues for your own evaluation runs before production commitments.
Engineers
Migration is a one-line model ID change, followed by measurement: re-run your own evaluation suite on a test key, compare output tokens per task, because verbosity drives real cost, and check the pricing cliff on inputs over 272,000 tokens before sending long-context jobs. Codex CLI users need version 0.159.0 or later.
Everyone (non-IT)
The AI tool at work may quietly get faster and cheaper this week. That is a reason to use the approved tools for the document tasks they are good at, and no change at all to the rule about what sensitive information belongs in them.