GPT-5.6 Sol vs GPT-6.1 Sol: Everyday Prompts

Comparing GPT-5.6 Sol and GPT-6.1 Sol should start with work you actually repeat: rewriting an email, summarizing notes, organizing a day, or fixing a small function. This walkthrough provides a reproducible comparison, not fabricated test results. Follow the steps to discover which model produces usable answers with the least correction.

Step One: Separate Documented Capabilities From Everyday Results

OpenAI describes GPT-5.6 Sol as a flagship model for complex professional work. That establishes its intended role, but does not establish how well it will rewrite your particular email. Treat that question as something to test.

OpenAI positions GPT-6.1 Sol for complex coding, computer use, and professional work. Its launch evaluations cover demanding workflows, rather than a controlled comparison against GPT-5.6 Sol on the everyday prompts below. Do not turn those published results into an unsupported claim that it wins every routine task.

Step Two: Keep Both Model Requests Comparable

Use identical system instructions, source material, output limits, and tool access. Start each task in a fresh conversation. Record the requested model identifier, reasoning setting, elapsed time, and provider-reported usage. Keep the first pass free of browsing so that you are comparing answers to the same supplied information.

GPT-5.6 Sol supports reasoning settings including none, low, and medium. For this exercise, explicitly select medium rather than relying on an application default.

GPT-6.1 Sol also supports medium, but does not support none or minimal. Use medium for both models, while recognizing that matching setting names does not prove identical computational effort. For tool-enabled follow-ups with GPT-6.1 Sol, use the Responses API.

BestRelay, this article’s publisher, describes its service as a quality-first API relay that forwards requests to the official named model without substitution. Its stated coverage spans Claude, GPT, Gemini, DeepSeek, Kimi, GLM, Qwen, Grok, and more, with support for Claude Code, Codex, Gemini CLI, and any OpenAI SDK. Keep that routing commitment separate from your evaluation of answer quality.

Step Three: Compare Writing And Summarization Against Constraints

Run these two prompts through both models, preserving every word:

• Email: Rewrite this message in a warm, professional tone using at most 70 words. Preserve the deadline and do not invent a reason: Send the revised proposal by Thursday at noon. We cannot approve the current version.
• Summary: Summarize these notes in exactly three bullets, separating decisions from unresolved questions: Launch stays Monday. Priya owns testing. The budget is not approved. The team discussed Friday but rejected it.

Check the deadline, word limit, bullet count, ownership, and unresolved budget. Mark invented explanations as errors. Prefer the response you could send without repairing its facts, not simply the one with smoother phrasing.

Step Four: Test Planning And Code With Checkable Answers

Next, introduce constraints that make correctness easier to inspect:

• Planning: Schedule three 30-minute tasks between 9:00 and 11:00, leaving 9:30–10:00 unavailable. Include one 15-minute break. Show start and end times without overlaps.
• Code: Write a Python function that removes duplicate strings while preserving their first appearance. Treat uppercase and lowercase as different. Include tests for empty input and repeated values.

For planning, total the durations and inspect the blocked interval. For code, execute the tests in an isolated local environment and add your own cases. Then ask each model to shorten its answer without changing the solution. Score that revision separately to test whether follow-up instructions preserve correctness.

Step Five: Measure Cost Per Accepted Everyday Answer

Repeat each task three times and hide model names while reviewing. Assign zero to two points each for correctness, constraint compliance, clarity, and readiness to use. Record retries separately, and define an accepted answer as one needing no substantive correction.

At the checked standard API rates for short prompts, GPT-5.6 Sol lists $4 per million input tokens and $20 per million output tokens; GPT-6.1 Sol lists $2 and $10 respectively. Those rates alone do not establish total task cost.

BestRelay describes its billing as pay-as-you-go using provider-reported token counts, with cache reads and writes priced separately. Its referral commissions arise when invited users top up; exclude those incentives from quality scoring. If you want to reproduce this comparison there, create a BestRelay account.

Divide total measured spend, including failed attempts, by accepted answers. Choose the model that meets your quality threshold at an acceptable cost and latency, and retain separate winners for different tasks when your results justify it.