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.

What equal prices do—and do not—buy

For a 50,000-token uncached input and 3,000 billable output tokens, both standard short-context calculations are $0.10 + $0.03 = $0.13. This fixes the token quantities to isolate the rate card. It does not predict that both models produce 3,000 tokens, take one turn or reach the same answer. A valid comparison therefore needs two views: a fixed-usage price calculation and an observed cost-per-accepted-task result.

Long inputs change the first view. At 300,000 input tokens and 5,000 output tokens, Sol’s documented greater-than-272K rule prices the whole request at $4/$15, giving $1.20 + $0.075 = $1.275. Applying Sonnet’s published standard $2/$10 rates gives $0.60 + $0.05 = $0.65. These are vendor-list calculations, excluding caching, separate tools and nonstandard service options. The example establishes a rate difference for these conditions, not a Sonnet quality advantage. It also does not imply you should send 300K tokens: reducing irrelevant context may improve both budget and reviewability.

A useful boundary test has two inputs around Sol’s threshold. Record the provider’s actual counted input, rather than estimating from file size. The surcharge applies to the full request once the documented threshold is exceeded, not merely to the last few tokens beyond it. A tiny change in included history can therefore matter more than a small change in answer length. Recheck the current pricing documentation before turning that boundary into a hard-coded budget rule.

Choose a starting model for a concrete coding job

The following recommendations are editorial starting points derived from integration cost and testability, not a ranking from an unpublished benchmark.

For a regression fix, give the model the failing test, observed output and intended behavior. A useful answer changes the implementation and preserves unrelated behavior. A patch that merely changes the test expectation has not fixed the bug. For a feature, define whether backward compatibility, accessibility or a data migration is part of acceptance before the model edits code. Otherwise the comparison rewards whichever model assumes the smaller scope.

For review, prepare a small set of confirmed defects and some clean changes. Count whether the model identifies the defect and supplies a verifiable explanation. Also count incorrect allegations against clean code. More review comments are not automatically more useful; five speculative warnings can consume more human time than one accurate finding. A model’s self-reported confidence is not independent confirmation of a bug.

Keep the same task while preserving each API’s semantics

A fair comparison does not require sending identical JSON to incompatible APIs. Keep the task, source files, available actions and acceptance criteria equal, then implement the native request shape correctly for each vendor. Translate tool declarations and results at the adapter boundary. Preserve identifiers linking a tool result to the corresponding call, and verify that the loop handles multiple calls, failures and a final text response.

Tool permissions are part of the experiment. If one run can execute tests and the other only reads files, label the comparison accordingly. If a tool performs an external side effect, use an isolated fixture or a no-op substitute for both. Never allow a benchmark attempt to send real emails, alter production records or publish a package merely to make the scenario realistic.

Response formatting and model quality also need separate diagnosis. First check that the adapter accepted the response, decoded the intended content and returned tool results properly. Only then judge the patch. An invalid client request is evidence about integration readiness, not that the model cannot solve the problem. Keep these failures in operational costs, but do not mix them into a capability score without labeling them.

A usable scorecard and a decision rule

Create one row per attempt with task ID, base commit, configuration, attempt cost, duration, test result, reviewer decision and failure category. Keep a separate task summary that includes every attempt. For a ten-task pilot, report “8/10 accepted” rather than just “80%”: the small denominator matters. Repeat the most variable cases before drawing a broad conclusion; a tiny local pilot is a deployment decision aid, not a statistically established universal ranking.

Here is a synthetic decision example. Configuration A finishes 9 of 10 tasks for $2.70 total; B finishes 8 of 10 for $2.00. Their costs per accepted task are $0.30 and $0.25. If the missing task is a release-blocking migration, B’s lower average does not settle the decision. Show the failed categories next to the mean and retain a separate completion threshold for critical tasks. Conversely, paying more for a configuration that produces longer explanations but no additional accepted work is difficult to justify.

Choose your decision rule before reading the outputs. One team might require every critical regression to pass, no unrelated edits, and total review time within its present workflow; cost decides only among configurations that meet those constraints. Another might prioritize short interactive latency. These are product requirements, not vendor facts. Publish the rule with any eventual result so readers can see whether the conclusion applies to their work.

An economically meaningful switch also includes maintenance. If the new adapter takes engineer time, amortize that cost over the volume you expect to run; do not invent an hourly rate or hide it inside the token bill. For low-volume use, preserving a reliable integration may matter more than a few cents per task. For a large workload, even a modest measured saving can justify the work. Neither conclusion can be reached from the headline input price alone.

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.