GPT-6.1 Terra
At a glance
- Price per 1M tokens (input)
- $1.00
- Price per 1M tokens (output)
- $5.00
- Context
- 922,000 tokens
- Free access
- yes (see below)
Refreshed daily; data verified October 28, 2026. Published October 28, 2026.
GPT-6.1 Terra by OpenAI is designed for business teams that need reliable multimodal processing and structured automation. It suits document analysis, image understanding, and workflows that require tool calling, which lets the model execute external functions such as database queries or API requests. Step-by-step reasoning supports complex tasks like policy review and multi-stage troubleshooting. OpenAI's approach emphasizes alignment research and staged safety testing before model release.
Specifications & pricing
| Input (per 1M tokens) | $1.00 |
|---|---|
| Output (per 1M tokens) | $5.00 |
| Cache read (per 1M tokens) | $0.05 |
| Cache write (per 1M tokens) | $1.25 |
| Context window | 922,000 tokens |
| Max output | 128,000 tokens |
| Capabilities | images, tool calling, reasoning |
LiteLLM community dataset (MIT), verified October 28, 2026. Official OpenAI pricing.
What GPT-6.1 Terra would cost on your workload — run it through the cost calculator →
Where to try GPT-6.1 Terra for free
- OpenAI offers a free chat — ChatGPT (free plan). A vendor's free chat may run a different model from the same family — the exact model is not guaranteed.
Frequently asked questions
What tasks is this model good for?+
It handles image understanding, document processing, and automation through tool calling. Step-by-step reasoning makes it useful for compliance checks, technical support, and multi-stage analysis where intermediate logic must be visible.
How do I get started?+
Access the model through OpenAI's platform, obtain an API key, and integrate it using the chat completions interface. Define your functions in the tool calling schema so the model can trigger external actions when needed.
How does it differ from sibling models in the line?+
This variant combines vision capabilities with tool calling and explicit reasoning steps. Other models in the family may focus on text only, omit tool execution, or use a different reasoning style without showing intermediate steps.
What are its limitations?+
Image understanding depends on image quality and may miss fine details. Tool calling requires correctly defined schemas and does not guarantee successful external execution. Reasoning traces can be long and may not cover every edge case in novel domains.
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