Sonnet 5.5 vs GPT-6 Sol: choose by coding task and cost

Claude Sonnet 5.5 and GPT-6 Sol are sensible models to compare for everyday coding, but there is no evidence that one is cheaper for every repository task. Sonnet’s standard Claude API input/output rates are $2/$10 per million tokens. GPT-6 Sol’s short-context standard input/output rates match those figures; long-context pricing needs separate treatment. Equal rates do not mean equal token use or equal success rates.

This guide is for developers choosing a model for a bounded bug fix, a small feature or a review step. It uses official documentation and an independent launch evaluation, checked September 29, 2026. It is not an Ofox head-to-head coding benchmark. If you need a recommendation for your repository, the evaluation worksheet below gives you a way to make that decision without treating a leaderboard as a production guarantee.

Compare the operating conditions first

Sources: Sonnet model specification, GPT-6 Sol model documentation, and OpenAI pricing. The table intentionally does not turn different vendors’ effort labels into equivalent settings.

What the launch evaluation can tell you

Artificial Analysis describes Sonnet 5.5’s strong high-effort results alongside heavy output-token consumption. In its cost-versus-intelligence analysis, Sonnet’s high setting was close to a Sol configuration, while other settings had less attractive trade-offs. That is a useful reason to include both models in a trial rather than select one from the headline score.

The evaluator also reports that it tested a pre-release Sonnet deployment affected by a structured-output bug, with relevant evaluations to be rerun. Preserve that qualification. Vendor charts, different benchmark suites and a test against an older deployment cannot be pasted into one table as though the runs used the same tasks and conditions.

For a production team, the unresolved question is narrower: which configuration produces an acceptable patch on your tasks within your budget? A terminal benchmark says something about agentic execution. It does not measure your reviewers’ time, project-specific rules or the cost of an extra deployment rollback.

Run a bounded coding comparison

Choose a handful of representative tasks rather than one spectacular demo. Include a regression fix with a known failing test, a feature with explicit acceptance criteria, and a review task with a seeded issue. Prepare the expected outcome before seeing either model’s response, and keep the task inputs free of production secrets.

For every attempt:

1. Start from the same clean commit and tool permissions.
2. Give both runs the same issue description, repository instructions and relevant files.
3. Record the exact model, effort, API or subscription route, client version and date.
4. Run the same tests and inspect the final diff, including unrelated edits.
5. Save token usage, cache categories, retries, elapsed time and human review time.

A model that passes after three retries has not matched a first-attempt success simply because the final patch looks similar. Conversely, one failure is not enough to declare a model incapable. Report the number and nature of the tasks, not just a winner badge.

A cost example that exposes the trade-off

At an assumed $0.10 per attempt, ten accepted tasks out of ten attempts cost $0.10 per accepted task. If another configuration uses twenty attempts costing $0.07 each to complete the same ten tasks, its accepted-task cost is $0.14. These are synthetic numbers, not measurements of Sol or Sonnet.

The example shows why the lower invoice line per request can lose after retries. You should also preserve a separate failure count: repeatedly abandoning difficult tasks can make a cheap model look efficient if you quietly remove those failures from the denominator.

For interactive work, elapsed time matters alongside cost. Measure time to an acceptable patch rather than only output tokens per second. A quick first answer that creates another debugging cycle may be slower at the task level.

When to start with each option

If your application already has a well-tested Claude tool loop, trying Sonnet can require less client work than changing API families, although the 5.5 migration still needs checking. If your workflow already uses OpenAI tooling, Sol is the natural baseline to retain in the comparison. These are integration-cost judgments, not capability rankings.

Start with the model whose existing integration lets you run a controlled trial. Then switch only when the other model improves an outcome you measured: accepted-task cost, time, patch quality or a specific failure rate. Avoid turning a Sonnet-versus-Sol article into a catch-all ranking of every flagship.

Use the Sonnet cost worksheet to separate input, output and cache billing. For the choice within Claude, read Sonnet 5.5 versus Opus 5.5. The Sol/Luna/Astra task guide covers the broader OpenAI tier decision.