GPT-6.1 Sol reasoning levels determine how much work the same AI model puts into an answer. Think of them as choosing between a quick fix, a careful second opinion, and a full project team—not buying six different AIs. Here is a plain-English guide to Light, Medium, High, Extra High, Max, and Ultra, with six side-by-side examples and real websites you can open.
Updated October 19, 2026 · For ChatGPT Work, Codex, and developers · Examples below are guidance, not promised outputs.
The quick answer: which GPT-6.1 Sol level should you use?
Start with Medium. Use Light for simple, reversible edits; High for difficult debugging and multi-step tasks; Extra High for a carefully checked deliverable; Max when a single problem deserves the deepest analysis; and Ultra when a big task can actually be divided among multiple agents. More reasoning can cost more time and tokens, but it does not guarantee a better outcome.
GPT-6.1 Sol levels explained
Important naming detail: The OpenAI API exposes low, medium (default), high, xhigh, and max. Ultra is a Work/Codex option involving possible subagent delegation, not the API value ultra. Also, Ultrafast is a separate speed/service tier, not a deeper reasoning level. Availability varies with plan, client, and workspace settings. GPT-6.1 Sol is available in Work and Codex rather than regular Chat as of this update.
Six side-by-side examples: same task, different reasoning levels
The tables describe the kind of work you should request or expect the agent to attempt at each setting—not literal transcripts, standardized performance scores, or a guarantee that Ultra will start helpers. You still need to review and test results.
On phones: Swipe left within a comparison table to see all six levels. The whole page stays in place.
1. Update a WordPress landing page
Prompt: “Make our WordPress hosting page clearer and easier to use.”
2. Fix a broken checkout
Prompt: “Customers say discount codes sometimes double-apply.”
3. Turn data into a business answer
Prompt: “Why did hosting trial conversions fall this month?”
4. Create an SEO article
Prompt: “Write a beginner guide to website uptime monitoring.”
5. Build an interactive web app
Prompt: “Create a customer-facing DNS troubleshooting dashboard.”
6. Review a risky infrastructure change
Prompt: “Plan migration from one hosting platform to another.”
Pattern: Light and Medium are for getting something useful on the page. High and Extra High are for making it dependable. Max is for extremely difficult individual work. Ultra becomes useful when independent parts can run in parallel. This is a task-design recommendation, not a measured level-by-level output benchmark.
Real GPT-6.1 Sol websites you can try right now
These are real, publicly accessible builds, not mock screenshots. In an October 4, 2026 independent test, Spectrum AI Labs gave Codex the same website prompt at six settings, then published the resulting pages. The author says the pages were rebuilt to run on its own domain; they are experiments, not official OpenAI websites.
3D Colosseum: compare graphics and engineering
The same tester also published interactive Three.js Colosseum builds. Try Light, Extra High, Max, and Ultra. On a slower laptop, 3D pages may take a few seconds to load.
What the test actually found: Max ranked highest in one blind AI review of code quality for both tasks; Ultra did not start any helper agents in those runs. This is a limited experiment—two prompts, one run at each setting, and one AI reviewer—not proof that Max always wins. It is a useful reminder that an elaborate-looking site is not necessarily maintainable code.
Website and app showcases shared on X
Here are two relevant X posts collected by Promptslove’s September 2026 roundup. X restricts some automated access, so the descriptions below are attributed summaries from that report, not independently verified recreations of the posts.
From @viktoroddy: luxury car-rental website challenge
A side-by-side video reportedly compares GPT-6.1 Sol and another model building a cinematic car-rental website from the same prompt. View the original for the visuals, and judge the result yourself.
From @aniketjart: browser-based 3D game comparison
According to the same roundup, this post links to a playable fantasy/sci-fi Three.js project and compares results from an identical brief.
For developers: one model, multiple effort settings
Here is a basic Responses API example using the default-to-practical medium setting. For a more challenging single-agent task, change medium to high, xhigh, or max. Do not set API reasoning.effort to ultra.
const response = await client.responses.create({
model: "gpt-6.1-sol",
reasoning: { effort: "medium" },
input: "Audit this function and suggest a tested fix."
});At standard API pricing, OpenAI lists $2 per million input tokens, $0.10 per million cached input tokens, and $10 per million output tokens for GPT-6.1 Sol. These are token rates, not a promise that every request or effort level costs the same total. Extra reasoning, tool use, and longer runs can increase consumption. For standard ChatGPT chats, the model choice and thinking controls are different from Work and Codex.
A practical way to choose without wasting time
- Define success: Say what the result must do, what must not change, and how to verify it.
- Start at Medium: Try Light instead when the task is tiny, obvious, and easy to undo.
- Escalate for a reason: Use High for unclear failures; Extra High when verification or edge cases matter; Max for the hardest single-agent analysis.
- Use Ultra only if work splits: Parallel review, build, tests, and documentation are a good fit; one bug fix usually is not.
- Check the work: Run tests, review permissions, and keep human approval for production changes, security, legal, or financial decisions.
Frequently asked questions
Is GPT-6.1 Sol Ultra a different model?
No. It is a higher-level Work/Codex execution option for GPT-6.1 Sol that may delegate subtasks to agents. It is not another separately named base model.
Is Max or Ultra smarter?
Max focuses effort on a single task; Ultra can coordinate workstreams. Neither wins every test. In one published two-task comparison, Max scored highest in the author’s blind code review, while Ultra never spawned helpers.
What is the best GPT-6.1 Sol setting for coding?
Use Medium for routine changes, High for real debugging, Extra High for careful production review, and Max for the hardest isolated problems. Use Ultra for a codebase project that divides naturally among agents.
Does Light cost less than Max?
Often in total use, because shorter reasoning usually consumes fewer tokens and less time. The standard API price per token is not itself a separate rate card for each effort level.
Is Ultra the same as Ultrafast?
No. Ultra is a reasoning/orchestration setting in supported Work/Codex clients; Ultrafast is a separate speed tier with availability that depends on plan and product.
Can I select GPT-6.1 Sol in ordinary ChatGPT chat?
As of October 19, 2026, OpenAI lists it for ChatGPT Work and Codex, not ordinary Chat. Eligibility and controls vary by plan and account.
Bottom line
Use the lowest reasoning setting that can reliably finish and verify the job. Medium handles most everyday work; High and Extra High are a sensible upgrade for complexity; Max is for depth; Ultra is for genuine multi-track projects. The live sites above show why there is no single perfect choice: appearance, code maintainability, speed, and delegation are different measures of success.
For more context, see LogicWeb’s GPT-6.1 Sol vs. GPT-6 Astra comparison and its GPT-6 reasoning-level primer.
Sources and methodology
Research checked October 19, 2026. All six scenario grids are editorial illustrations of appropriate scope, not transcripts of actual runs. Real-world build claims are attributed to their creators and remain subject to later changes.
- OpenAI ChatGPT Learn: models, reasoning levels, Max, Ultra, and access
- OpenAI API: GPT-6.1 Sol model specifications and reasoning values
- OpenAI: introducing GPT-6.1 Sol, pricing and evaluations
- Spectrum AI Labs: 12 published builds and test methodology (Oct. 4, 2026)
- Promptslove: five app builds and a roundup of X posts (Sept. 2026)