{"id":3183,"date":"2026-11-02T15:00:34","date_gmt":"2026-11-02T07:00:34","guid":{"rendered":"https:\/\/www.benpay.com\/blog\/?p=3183"},"modified":"2026-11-02T15:00:36","modified_gmt":"2026-11-02T07:00:36","slug":"gpt-6-1-terra-api-pricing-setup","status":"publish","type":"post","link":"https:\/\/www.benpay.com\/blog\/index.php\/gpt-6-1-terra-api-pricing-setup\/","title":{"rendered":"GPT-6.1 Terra API on BenPay: Pricing and Setup"},"content":{"rendered":"\n

If you want to try out OpenAI’s latest mid-range model – GPT-6.1 Terra API – but also want to understand what tasks it is suitable for, how much it costs for long context requests, and how to integrate it? This article will clarify all these points.<\/strong><\/p>\n\n\n\n

BenPay AI API currently lists GPT-6.1 Terra<\/strong> with the public value\/<\/code> Model ID value\/gpt-6.1-terra<\/code>.<\/p>\n\n\n\n

Compared with OpenAI’s long-context rates, BenPay’s listed prices are about 20% of the official rates.<\/p>\n\n\n\n

These comparisons use long-context pricing<\/strong> and are per million tokens. Your actual cost depends on input, output, caching, and any features used in the request. This guide explains what GPT-6.1 Terra is, compares the prices, and covers setup and compatibility checks.<\/strong><\/p>\n\n\n\n

What Is GPT-6.1 Terra?<\/h2>\n\n\n\n

GPT-6.1 Terra is an OpenAI model. OpenAI describes it as a fast, efficient model for visual reasoning, computer use, and drafting or revising text. Example tasks include interpreting technical drawings, understanding what is shown on an application screen, and drafting or editing written content. Read OpenAI’s GPT-6.1 Terra announcement<\/a><\/p>\n\n\n\n

Results depend on the task, prompt, and settings. Test the model on a small set of real tasks instead of choosing based on the model name alone.<\/p>\n\n\n\n

GPT-6.1 Terra on BenPay AI API<\/h2>\n\n\n\n
Item<\/th>Information shown by BenPay<\/th><\/tr><\/thead>
Model<\/td>GPT-6.1 Terra<\/td><\/tr>
BenPay Model ID<\/td>value\/gpt-6.1-terra<\/td><\/tr>
API format<\/td>OpenAI-compatible<\/td><\/tr>
Listed interfaces<\/td>Chat Completions, Responses<\/td><\/tr>
Context window<\/td>1M tokens<\/td><\/tr>
Billing<\/td>Pay as you go<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

When calling the model through BenPay, use the full Model ID shown on its page: value\/gpt-6.1-terra<\/code>. Do not substitute OpenAI’s official Model ID, gpt-6.1-terra<\/code>.<\/p>\n\n\n\n

BenPay lists a 1M-token context window. OpenAI’s model documentation lists 1.05M tokens. The figures are not identical, so configure requests according to the service you are using and its current documentation.<\/p>\n\n\n

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\"GPT-6.1
GPT-6.1 Terra is Now on BenPay AI API<\/figcaption><\/figure>\n<\/div>\n\n\n

GPT-6.1 Terra API Pricing: Long-Context Rates<\/h2>\n\n\n\n

OpenAI’s standard rates are $1 for input, $5 for output, $0.05 for cached input, and $1.25 for cache writes per million tokens. For prompts with more than 272K input tokens, OpenAI applies long-context pricing: input and cache rates are doubled, while output is priced at 1.5 times the standard rate.<\/p>\n\n\n\n

The table compares those OpenAI long-context rates with BenPay’s value\/<\/code> rates. All prices are in USD per million tokens.<\/p>\n\n\n\n

Charge type<\/th>OpenAI long-context rate<\/th>BenPay value\/ rate<\/th>Comparison<\/th><\/tr><\/thead>
Input<\/td>$2.00<\/td>$0.40<\/td>About 20% of the official rate<\/td><\/tr>
Output<\/td>$7.50<\/td>$1.50<\/td>About 20% of the official rate<\/td><\/tr>
Cache read<\/td>$0.10<\/td>$0.02<\/td>About 20% of the official rate<\/td><\/tr>
Cache creation (30-minute)<\/td>$2.50<\/td>$1.00<\/td>About 40% of the official rate<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

So it would not be accurate to say that every charge is 20% of the official rate. Input, output, and cache reads are about 20% of OpenAI’s long-context rates; cache creation is about 40%. This comparison does not include any applicable tool-call charges, and it does not mean the two services have identical context limits or feature support.<\/p>\n\n\n\n

A Simple Long-Context Cost Example<\/h3>\n\n\n\n

Suppose a request uses 800,000 input tokens and 100,000 output tokens, with no caching and no additional tool-call charges:<\/p>\n\n\n\n