GPT-6.1 Terra vs GPT-6.1 Sol: Price, Speed & Use Cases

Quick verdict: GPT-6.1 Terra is the lower-cost choice to test first for high-volume visual understanding and bounded computer-use workflows. GPT-6.1 Sol costs more per token but is positioned for more complex coding and professional work. Both can handle long context and computer-use tools; the right choice depends on task success, latency, tool costs, and review effort—not the model name alone.

Reviewed October 26, 2026. This comparison uses OpenAI’s published model documentation and pricing. Aiverio has not independently benchmarked either model, and provider-reported benchmark results should not be treated as independent test results.

GPT-6.1 Terra vs. GPT-6.1 Sol at a glance

Specifications and prices above are from the official GPT-6.1 Terra model reference and GPT-6.1 Sol model reference. Check those pages before deployment because availability, rates, and limits can change.

What is the practical difference?

Choose Terra first for cost-sensitive visual and computer-use tasks

OpenAI describes GPT-6.1 Terra as a fast, efficient model for visual reasoning, precise computer use, and clear writing. Examples include matching product photos to catalog entries, interpreting technical drawings, reading invoices, extracting fields, filling out forms, and operating application screens through supported tools. Its standard token rates are half the corresponding standard input and output rates of GPT-6.1 Sol.

That makes Terra a sensible first candidate when you need to process many similar tasks and can validate each result. It is not a guarantee that Terra will be cheaper for every workflow: retries, tool calls, output length, and human correction can outweigh token-price differences.

Choose Sol for more demanding coding and professional workflows

OpenAI positions GPT-6.1 Sol for complex coding, computer use, and professional work. It is worth testing when a task requires sustained reasoning across a codebase, several dependent tool calls, or a more involved workflow. The higher token rate may be justified if Sol completes more tasks correctly on the first attempt or requires less human intervention.

Do not assume Sol is automatically better at every task. OpenAI’s launch material reports that Terra performs strongly on selected computer-use and visual-reasoning benchmarks, including some results that approach its more expensive models. Those are provider-reported evaluations on particular test sets, not a substitute for testing your own workload.

How much can the API price difference matter?

At standard short-context rates, Terra costs half as much per input and output token as Sol. For a simple example, suppose one task uses 100,000 input tokens and 25,000 output tokens, with no cached input or separate tool charges:

This illustration stays below the long-context pricing threshold and excludes caching, tools, retries, hosting, taxes, and human review. It is not a quote for a real application. For 1,000 tasks with identical usage, the token-only estimate would be $225 for Terra versus $450 for Sol—but only if the tasks use the same token volume and both models meet the same acceptance criteria.

Important pricing details:

• Prompts with more than 272,000 input tokens use 2x input and cache rates and 1.5x output rates for the full request, according to the current model documentation.
• Cache writes are billed separately; the standard 30-minute cache-write rate is $1.25 per million tokens for Terra and $2.50 for Sol. Twelve-hour cache writes have different rates.
• Batch and Flex pricing can be lower than Standard; Fast mode costs more. Check the current pricing page for the applicable service tier and tool-specific charges.
• Use the actual billed token counts from a representative pilot. A lower price per token does not guarantee a lower cost per successful task.

For the complete rate card, see OpenAI API pricing.

API compatibility and tool use

Both models support tool calling through the Responses API. GPT-6.1 Sol also supports Chat Completions without tool calling; if your existing application relies on function calling or hosted tools, verify the endpoint and request format before switching models. Do not assume that a model swap is only a one-line configuration change.

Before rollout, test structured outputs, streaming, image inputs, function-call arguments, tool permissions, and error handling separately. Confirm which tools are enabled for your account and model, and keep irreversible actions behind approval steps.

Which model should you choose?

A fair evaluation checklist

1. Build a representative test set of real tasks, including ambiguous inputs, edge cases, and failures.
2. Use the same task instructions, files, tools, permissions, and success criteria for both models.
3. Record task completion rate, factual or extraction accuracy, latency, tool-call failures, retries, and human correction time.
4. Measure input and output tokens, cached tokens, cache writes, and separate tool charges.
5. Calculate cost per accepted task—not only cost per request—and include the cost of review or rework.
6. Start in a sandbox with least-privilege access. Require human approval for writes, payments, deployments, or external submissions.
7. Roll out gradually and monitor quality, errors, latency, and spend after the switch.

For related guidance, see Aiverio’s AI computer-use tools guide, Best AI Coding Assistants, and AI Agent Governance Checklist. For the individual profiles, see GPT-6.1 Terra and GPT-6.1 Sol. A direct comparison with a different provider is available in Gemini 4 Argon vs. GPT-6.1 Sol.

Frequently asked questions

Is GPT-6.1 Terra half the price of GPT-6.1 Sol?

Its standard input, cached-input, and output token rates are half the corresponding standard rates for Sol. Your total bill may differ because of long-context adjustments, caching, processing tier, tool calls, retries, and review time.

Do both models support computer use?

Yes. OpenAI documents computer-use support for both through the Responses API. Reliability depends on the application, task, permissions, and safeguards; test the exact workflow before production use.

Does Terra replace Sol for coding?

Not universally. Terra may be sufficient for routine coding or visually grounded tasks, while Sol is positioned for more complex coding and professional work. Compare both on representative tasks before choosing.

Are the published benchmark results independent?

No. The cited launch results are reported by OpenAI. Aiverio has not independently reproduced them. Use them as context, then run your own controlled evaluation.

Official sources

• OpenAI: Introducing GPT-6.1 Terra
• GPT-6.1 Terra API model reference
• OpenAI: Introducing GPT-6.1 Sol
• GPT-6.1 Sol API model reference
• OpenAI API pricing