# GPT-6.1 Terra vs Sol vs Luna: Which Fits Your Business Work?

*Comparison — 2026-11-09 — by Mahmoud Zalt*

Compare OpenAI models for business automation by task, quality, total cost and human review.

**TL;DR.** **Compare GPT-6.1 Terra, GPT-6.1 Sol and GPT-6 Luna against your real tasks, quality requirements and total cost.** Model prices are only one part of the cost of a finished business workflow.

Choosing a model for business automation is not a contest to find one winner. A routine classification, an invoice extraction and a difficult coding task place different demands on the system and carry different costs when they go wrong.

This guide explains how to evaluate three OpenAI models for practical work. Product availability and API prices change, so check OpenAI’s current documentation and the terms of the application you use.

## What are Terra, Sol and Luna?

GPT-6.1 Terra, GPT-6.1 Sol and GPT-6 Luna are different OpenAI models. They are not the same thing as an AI employee or a complete business workflow: a model generates and reasons over information, while the application around it determines what information it receives, which tools it can use and how a person reviews the result.

OpenAI positions Terra for tasks that combine visual understanding, computer use and writing. In its launch material, OpenAI also says Sol remains a better choice for the most complex coding tasks. Luna is a lower-cost option to evaluate for focused, high-volume jobs. These are starting points for a test, not guarantees about your own data.

## When to test GPT-6.1 Terra

Terra is a candidate when a workflow involves reading a screen or document, extracting information and drafting a result. Consider a supplier invoice process: the model might identify an invoice number, date and amount from an image, then prepare a record for review. The surrounding application still needs to check permissions, catch errors and decide whether to create or change a record.

OpenAI reported computer-use benchmark results for Terra in its launch announcement. Those tests are useful context, but they are vendor-reported results under specific conditions. They do not establish accuracy for your invoices, your interface or the way your application handles exceptions.

## When to test GPT-6.1 Sol

Sol deserves a comparison when the assignment requires deeper reasoning, difficult code or several constraints that are costly to get wrong. For example, a team might compare it with Terra on a complex spreadsheet transformation, asking both models to explain assumptions and identify missing information.

A higher-priced model is not automatically the best choice for every step. If a simpler task is already completed accurately and consistently by another model, extra capability may not repay its cost. Test the difficult and ordinary cases separately rather than judging the entire process from one demonstration.

## When to test GPT-6 Luna

Luna can be a candidate for narrower, repeated tasks where volume and price matter. Examples to evaluate include classifying incoming messages into a small set of categories or extracting a few well-defined fields from a consistent form.

A cheap classification is only useful if errors are caught and routed. Include ambiguous examples in the test and give the workflow an option to ask for human review. If a wrong label sends an urgent customer request to the wrong queue, the correction cost may exceed the model savings.

## Understand price per task, not only price per token

OpenAI publishes separate API rates for input and output, and other terms can apply to cached inputs, long requests or different service modes. OpenAI’s launch announcement says Terra’s standard API prices were lower than Sol’s. Prices can change, so use the current pricing page for a purchasing decision.

The amount you pay for a completed job depends on more than the listed rate. A task can require several model calls, retries, long documents or more reasoning. Include the cost of the application, connected tools and a person’s review. Compare the average cost of an acceptable completed result, not just the cheapest individual call.

For a simple estimate, multiply the amount of input and output used in a representative run by the relevant rates, then add retries and other charges. Repeat the calculation on several real examples. If you use a packaged AI application rather than the API directly, check that application’s own pricing and usage rules.

## How to compare the models on real work

Choose one business process and collect a small set of representative examples. Include a straightforward case, a messy or incomplete one, and a case that must be escalated. Remove information you do not need to share and ensure your organization permits the test.

Give each model the same job and a clear definition of a good result. For invoice extraction, that could include the correct supplier, invoice number, currency and amount, with missing values marked as unknown. For support triage, it might include the correct queue and an escalation for urgent or uncertain cases.

Have a reviewer score correctness, completeness, time to review and whether the model acknowledged uncertainty. Track actual cost and latency separately. A public benchmark can help you choose what to try, but your own examples reveal whether a model is suitable for the work you delegate.

## The model is only one part of an AI employee

A capable model still needs the right business context, connected tools and a defined assignment. It also needs boundaries: which information may it read, what can it change, and when must someone approve the result? Those decisions are about the whole workflow rather than a model name.

Sistava offers AI Employees for business roles including sales, marketing, support and operations, with shared context and connected tools. Begin with one clear assignment and review the outcome. Check the model choices and integrations available for your account instead of assuming a particular provider or model is included.

## FAQ

### Is GPT-6.1 Terra always cheaper than Sol?

OpenAI reported lower standard API rates at launch, but total cost per task depends on usage, retries and the service you use. Check current pricing and measure a representative workflow.

### Which model should a small business use?

Test the models on your own examples. Choose the option that meets your quality and review requirements at an acceptable total cost.

### Does a benchmark guarantee accuracy in my workflow?

No. Benchmarks test particular tasks and conditions. Your data, instructions, integrations and review process can produce different results.

### Is an AI model the same as an AI employee?

A model provides capabilities used by an application. An AI employee also needs a role, business context, connected tools and appropriate review.

**Tags:** GPT-6.1 Terra, GPT-6.1 Sol, GPT-6 Luna, business automation