GPT-5.6 Sol during a small repository refactor
A small repository refactor is a useful place to compare coding models: enough context to expose misunderstandings, but a narrow enough task to inspect every change. Consider moving request validation into a shared module while preserving public behavior. GPT-5.6 Sol and Claude Sonnet 5.5 are two candidates for that work. The useful comparison is not which produces the more confident explanation, but which leaves a correct, reviewable patch with less intervention.
The notes below describe a controlled comparison, not measured benchmark results. They separate model behavior from the coding tool around it and keep the acceptance criteria the same on both sides.
§ Repository orientation, side by side
GPT-5.6 Sol: Start with the repository instructions, relevant entry points, and the command used to run focused tests. Ask for the validation call sites and a short implementation plan before permitting edits. Check whether the plan accounts for shared types, error handling, and callers outside the obvious directory.
Claude Sonnet 5.5: Provide the same starting context and request the same deliverable. Judge its repository map against the same checklist rather than rewarding a longer explanation. A model that names more files has not necessarily found the important dependency.
For both, record unnecessary searches and requests for information already available in the repository. These are workflow costs even when the final plan is sound.
§ Producing the first patch
GPT-5.6 Sol: Request the smallest change that extracts validation without changing exported behavior. Explicitly exclude dependency upgrades, formatting sweeps, and unrelated cleanup. Review whether the resulting diff respects those boundaries.
Claude Sonnet 5.5: Use the identical scope restriction. Inspect the same risk areas: import cycles, changed error messages, altered defaults, and duplicated validation left behind. Do not assume that either model is inherently more conservative; establish that from the patch.
Compare complete diffs, not summaries. A concise response can conceal a broad edit, while a detailed response can accompany a small one. The relevant measure is how much code a maintainer must understand before approving the change.
§ Repairing a failing test
GPT-5.6 Sol: Give it the actual failing command and output, then ask for a diagnosis before another edit. Look for a connection between the failure and the changed execution path. Reject a repair that weakens an assertion merely to make the suite pass.
Claude Sonnet 5.5: Supply the same failure evidence and apply the same rule. Check whether it distinguishes a regression from an unrelated environment problem instead of treating every failure as permission to rewrite surrounding code.
Track repair attempts separately from first-pass success. One clean correction may be preferable to a superficially successful patch that lacks meaningful coverage. Preserve the original tests so that this distinction remains visible.
§ Keeping the comparison fair
Model choice is only one variable. Tool permissions, repository indexing, context truncation, and automatic retries can all change the outcome. Use the same harness where possible. If comparing Codex with Claude Code, label the result as a comparison of complete workflows, not an isolated model test.
Vana API provides one OpenAI-compatible endpoint and also supports Anthropic Messages and Gemini native protocols. It works with Codex, Claude Code, Gemini CLI, and OpenAI SDKs. That can simplify access, but protocol compatibility does not make tool behavior identical. Keep workspace state, instructions, permissions, and test commands consistent between runs.
§ Counting the whole session
Vana API bills per real token with no monthly fee. Compare the total billed usage of a completed task, including repeated context and repair turns, rather than inferring cost from the length of the final answer. Record elapsed time and human review time separately.
Create a Vana API account to run a small comparison on a repository you can safely test. Keep both branches, the prompts, test output, and usage records. Prefer GPT-5.6 Sol when those records support it for your work; choose the alternative when they do not. The durable result is a repeatable acceptance process, not a permanent model ranking.