GPT-6.1 Sol vs Sonnet 5.5: Coding Pilot

Summary

Choose a coding model by independently accepted changes, not a release chart. This plan uses representative tasks, fixed starting states, meaningful checks, and an explicit adoption decision. It is an evaluation design based on September 2026 announcements, with no claim that WhichAITools has tested either model.

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Priority tasks: Code generation tasks • Code review tasks • Debugging tasks

What the announcements establish

Pricing and published access information last verified: October 5, 2026. OpenAI’s launch announcement lists Work, Codex, and API access as gpt-6.1-sol; Chat was excluded at launch. Anthropic announced Sonnet 5.5 on September 28. Both providers list $2 per million uncached input tokens and $10 per million output tokens for standard API use. For GPT-6.1 Sol, those rates apply up to 272K input tokens; above that threshold, the full request costs twice the input and cache rates and 1.5 times the output rate. Mode, region, caching, and tool charges can also affect spending. These are published terms, not verified access in your account. The providers’ capability and efficiency gains are not our measurements. Confirm the exact model, effort, access, and billing surface before a pilot; subscriptions and API charges are different units.

Pick three tasks that expose different failure modes

Use a reproducible defect, a feature crossing a module boundary, and a review task with a recorded issue. Each must remain small enough for independent assessment. The defect reveals diagnosis and regression handling; the feature reveals coordination and convention adherence; the review reveals whether findings describe real problems. Avoid using only tasks already demonstrated to the model. Preserve expected outcomes privately until the run finishes, so the exercise tests problem solving rather than copying an answer. Keep failed review findings in the record as well as correct ones.

Freeze the task, then document the tool environment

Create separate working copies from the same commit with equivalent dependencies and task instructions. Record model identifier, product, effort, tools, and permissions. Effort labels across providers do not necessarily represent equal computation; describe settings without claiming perfect experimental control. Document differences such as repository search or browser access. A comparison of different tools can still be useful, but its conclusion applies to those configurations. Set the time budget and clarification policy before running; changing either after a weak result makes the winner depend on operator intervention.

Write the acceptance contract before starting

Define success through observable behavior: the original symptom, required output, verification command, and scope constraints. Ask the assistant which checks it ran, then run meaningful checks independently against the final files. Its tests can help, but they should not be the sole evidence when they merely encode its implementation. For review tasks, validate findings against the repository and reproduce consequential failures where practical. Apply the same threshold to both candidates. Capture enough evidence for another reviewer to reach the same decision.

Measure cost per accepted task

Retain costs from unsuccessful attempts and retries. Discarding them makes a model needing several attempts look deceptively cheap. Separate direct charges, subscription allocation, and reviewer time; combine them only with explicit assumptions. Save exposed usage information because caching and output volume affect the bill. If the product provides no per-task charge, record the gap rather than inventing tokens. Measure elapsed time to an accepted artifact, including validation. A rapid answer requiring extensive repair can lose to a slower, correct change. Also note setup time if it recurs on ordinary tasks.

Inspect failures before naming a winner

Classify failures as incorrect behavior, incomplete work, environment problems, scope drift, or unsupported completion claims. This tells you whether to change the model, repair the environment, or improve instructions. Inspect the diff and decisive tool outputs. Repeat conflicting outcomes on unfamiliar examples of the same workload. A tiny pilot filters candidates; it does not estimate general success rates reliably. If results are similar, prefer the configuration your team can operate and review consistently. Avoid manufacturing a leaderboard winner from differences smaller than the uncertainty in your sample.

Make a workload-specific adoption decision

In week one, identify workloads yielding accepted results with tolerable review effort. In week two, repeat them on fresh tasks and inspect new failure patterns. Write a scoped decision: use this configuration for bounded fixes, retain the incumbent elsewhere, and revisit after a defined number of tasks. Keep a rollback path to the prior model and instructions. This prevents release-driven adoption from spreading into untested work. Update the record when versions, access, tools, or effort change, since those changes can invalidate the original comparison.

Primary Sources

These links point to the official product and documentation pages reviewed for this guide.

• OpenAI: GPT-6.1 Sol capabilities, API pricing, and availability — September 2026
• Anthropic: Sonnet 5.5 capabilities, pricing, and effort settings — September 28, 2026
• OpenAI: GPT-6.1 Sol API model and pricing conditions — verified October 5, 2026

Frequently asked questions

Related Guides

• AI Coding Tools: Practical Selection Guide for Product Teams
• AI Release Brief: Sep 26–Oct 2, 2026

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