AICodeKing runs Sonnet 5.5 through OpenCode on eight Kingbench tasks, using fresh folders and high reasoning effortReasoning effort is the amount of internal computational work an AI model applies before producing an answer or action.. The review separates working interactions from appearance and treats the final grades as subjective comparisons, not a standardized independent benchmark.
The elevator simulation transports queued passengers successfully but has crowding and counter issues. A contact-lens case provides independently opening caps and recognizable detail, while a folding table animates reversibly but has minor geometric intersections. The SVG illustration is judged visually coherent, and the archery game's tested mechanics work apart from a cancellation shortcut issue.
Sonnet 5.5 solves the constrained counting problem and checks its answer with a program after correcting portability issues. It also builds a local Gemma fine-tuning workflowFine-tuning continues training a model on selected data so its behavior becomes better suited to a task, domain or operating environment. and a fact-generating web app, although some training facts need correction and the evaluation reuses training examples.
The interactive wristwatch supports two time zones and functioning controls. AICodeKing rates the full run below an earlier Opus 5.5 review while highlighting Sonnet 5.5's practical strengths. The recommendation is to test generated projects by exercising their important interactionsEvaluation measures how well an AI system performs against defined tasks, criteria and failure conditions using repeatable evidence. rather than judging screenshots alone.
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