GPT-6.1 Terra vs GPT-6.1 Sol: Which Should You Use?
If you're choosing between GPT-6.1 Terra and GPT-6.1 Sol, I'd start with Terra for visual workflows and clearly defined tasks where cost matters. For complex, multi-file coding changes, I'd compare Sol before deciding. But a lower token price doesn't necessarily mean a cheaper finished task. The real question is which model gets the work through review with less wasted effort.
After looking through OpenAI's model documentation, published evaluations and community discussion, I found that the choice is less straightforward than the price table suggests. Let's look at what those sources mean for coding, visual tasks and the cost of getting work done. The prices and access details here reflect information checked on October 27, 2026; if you're about to sign up or buy, check the latest details first.
GPT-6.1 Terra vs GPT-6.1 Sol: Key Differences
| Question | GPT-6.1 Terra | GPT-6.1 Sol |
|---|---|---|
| Starting point | Visual reasoning, computer use and defined tasks | Complex coding, computer use and demanding professional work |
| Standard API input | $1 per million tokens | $2 per million tokens |
| Standard API cached input | $0.05 per million tokens | $0.10 per million tokens |
| Standard API output | $5 per million tokens | $10 per million tokens |
| Context window | 1,050,000 tokens | 1,050,000 tokens |
| Maximum output | 128,000 tokens | 128,000 tokens |
The prices and specifications come from the OpenAI Terra model documentation and OpenAI Sol model documentation. These are API rates, not subscription prices. Service tiers, longer inputs, cache writes and tool calls can affect the bill.
The table makes Terra look like the obvious bargain. I think that is a reasonable starting assumption for tasks with easy-to-check outputs, but not a final verdict. Sol can also handle visual and computer-use work, and Terra can write code. The difference is where I would begin testing, not a wall between their abilities. For more background, see our GPT-6.1 Terra pricing and access guide.
GPT-6.1 Terra vs Sol for Coding and Visual Work
Consider a form that accepts an empty email address. The task is narrow: identify the validation path, reject an empty field, add a test and leave unrelated behavior alone. Terra is a sensible first candidate because the result has clear acceptance criteria. If it passes the test but changes the wrong files, that still counts against it.
Now consider changing authentication across several services while preserving compatibility with existing clients. I would compare Sol here, particularly if the model has to follow dependencies across files and explain tradeoffs. OpenAI itself recommends Sol or Astra for the most complex coding work in its Terra announcement. That is a reason to include Sol in the comparison, not proof that it will win every repository task.
Visual work has a similar distinction. Give both models the same screenshot of a settings page and ask them to identify the field labels and the next action. Check whether the labels actually exist and whether the proposed action is reversible. A screenshot task is not automatically easier than a coding task, especially when the interface is crowded or changes between steps.
Codex is the tool through which you may run a coding workflow; Terra and Sol are model choices within supported environments. Our GPT Codex guide explains that distinction in more detail.
GPT-6.1 Terra vs Sol: API Price and Task Cost
Suppose each model processes exactly 10,000 uncached input tokens and produces 2,000 output tokens under standard API pricing:
| Model | Input cost | Output cost | Total |
|---|---|---|---|
| Terra | $0.01 | $0.01 | $0.02 |
| Sol | $0.02 | $0.02 | $0.04 |
That is an arithmetic example, not an observed bill. It excludes caching, cache writes, extra tool charges and retries. Actual token counts may differ, including reasoning tokens where applicable. At the same token counts, Terra's listed input and output rates are half Sol's; at the same task, the cost ratio can be different.
Imagine the cheaper run produces a patch that takes three further attempts to pass review. Even if those attempts are inexpensive, an engineer still has to inspect them. I would track cost per accepted result: add the model and tool charges for all attempts, including failed ones, then divide by the number of results that pass review. Record review and correction time separately, so it is clear what appeared on the API bill and what took human effort. If no result is accepted, report the total spent and the failure rather than dividing by zero.
What Do the Terra and Sol Benchmarks Tell Us?
The most interesting detail in the OpenAI launch evaluation is that the answer changes with the benchmark. OpenAI reports a strong Terra result on visual reasoning and computer-use tasks, while also positioning Sol for the hardest coding work. Its Agents' Last Exam chart plots task scores against cost at different settings. Each point belongs to that evaluation and configuration; it is not a price quote for your own job.
I would use those results to decide what to test, rather than treating one chart as a universal leaderboard. Match the task, reasoning setting, environment and measurement before comparing any figures. A model that reads a screen well may still need closer review when it edits a large codebase.
In a comment on a Reddit r/codex discussion about Terra usage limits, one user describes using Terra at max and Sol at medium and seeing different results and allowance consumption. The tasks and settings are not controlled, and one person's experience cannot establish typical limits. It does illustrate why comparing model names without recording the settings can be misleading.
How to Compare Terra and Sol in Your Own Workflow
I would run two small comparisons before changing a default: one coding task and one visual task. Start from the same repository snapshot or screenshot, use the same prompt and tools, and define what counts as success before looking at the answers.
- Pick a bounded code change, such as the empty-email validation fix, and a screenshot task with labels you can verify.
- Run each model at the same supported reasoning level. Keep the environment, instructions and test commands consistent. If you later compare different levels, label that as a separate comparison.
- Check the output yourself: tests passed, unwanted file changes, missing steps and errors in the visible interface.
- Record elapsed time, retries, actual token and tool charges if available, and review time. Repeat enough times for your decision; a single run can be an outlier.
| Task | Model and setting | Result and errors | Time | Retries and review | API/tool cost | Accepted? |
|---|---|---|---|---|---|---|
| Same task | Terra, record setting | Record after testing | Record | Record | Record if available | Yes or no |
| Same task | Sol, record setting | Record after testing | Record | Record | Record if available | Yes or no |
For a subscription workflow, record the displayed usage or credits instead of inventing an API cost. Keep any sensitive repository material within your organization's approved environment. If you are also choosing between coding agents, the separate Claude Code vs Codex workflow comparison looks at the tools rather than this particular model pair.
How Can You Access Terra and Sol?
OpenAI says Terra is available through Work, Codex and the API for the eligible plans described in its launch announcement. Sol is also offered through the OpenAI API; check the current model picker and plan documentation for availability in your own Work or Codex environment. A model's API listing does not mean the same option appears in every GPT interface.
A GPT subscription and API usage are billed separately. If paying directly for a personal GPT Plus subscription is difficult because of your payment method, FamilyPro offers a GPT Plus personal-account top-up. Check the service terms before purchasing. A top-up addresses payment; it does not guarantee access to either model or provide API credits.
GPT-6.1 Terra vs GPT-6.1 Sol: FAQ
Is Terra Better Than Sol for Coding?
Not for every task. Terra is worth testing for a bounded change with clear tests. For complex, multi-file work, compare Sol's output and review effort before choosing a default.
Is Terra Always Cheaper Than Sol?
No. Its standard API token rates are lower, but total task costs depend on the tokens generated, service tier, tools and retries. Subscription usage is a separate question.
Can I Use Both Models With a GPT Subscription?
Availability depends on the product, plan and current rollout. Check the model menu and OpenAI's current plan guidance for Work or Codex; a subscription does not automatically include API credits.
Conclusion: Which GPT-6.1 Model Should You Choose?
For GPT-6.1 Terra vs GPT-6.1 Sol, my starting rule is straightforward: try Terra on visual workflows and clearly scoped work you can check quickly. Compare Sol when the task is harder to verify or has costly downstream mistakes. If Terra's result is accepted with little review, its lower API rates are useful. If Sol saves enough rework, its higher rate can be worthwhile. Your own accepted results are the deciding evidence.