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OpenAI Introduces GPT-6.1 Terra, The Latest Model In The GPT-6 Series

OpenAI introduced GPT-6.1 Terra, the latest model in its GPT-6 series. Standard API pricing is $1 per million input tokens, $0.05 per million cached input tokens, and $5 per million output tokens. OpenAI is treating the model as critical in cybersecurity and high in biological and chemical capability under its Preparedness Framework.

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What happened

OpenAI introduced GPT-6.1 Terra, the latest model in its GPT-6 series. Standard API pricing is $1 per million input tokens, $0.05 per million cached input tokens, and $5 per million output tokens. OpenAI is treating the model as critical in cybersecurity and high in biological and chemical capability under its Preparedness Framework.

Confirmed

Global impact / market context

The pricing sets a concrete cost per unit of text processed, which matters for companies building products on the API: input tokens are the text sent in, output tokens the text returned. Cheap cached input, which means reused stored text, rewards repeatable workflows and could shift spending toward high-volume applications.

Analyst inference

The safety ratings signal OpenAI sees meaningful misuse risk, which could invite closer regulatory attention to AI model releases. Investors in AI-linked companies may weigh compliance costs and potential usage limits against the lower per-token prices that make automated text processing more affordable.

Analyst inference

What to watch

  1. OpenAI's stated pricing of $1 per million input tokens, $0.05 per million cached input tokens, and $5 per million output tokens is the baseline to compare against future model releases and competitor offerings. Confirmed
  2. Watch whether OpenAI publishes further details on the Preparedness Framework ratings for cybersecurity and biological and chemical capability, which would clarify what safeguards, usage limits, or access conditions apply to the new model. Proposed
  3. Track whether the cheap cached input price drives businesses to move repeatable text-processing workloads onto the API, lifting usage volumes and shifting revenue toward high-volume automated tasks. Analyst inference

Evidence

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