GPT-5.6 vs Claude Sonnet 5 for Chinese Project Code: Which Should You Choose?

"Should I use GPT or Claude for coding?" We hear this question more than any other. As a gateway routing both models at production scale, we see the behavioral differences across hundreds of thousands of real developer requests. This isn't a benchmark shootout — just what actually matters for your choice.

Two Different Personalities

Claude Sonnet 5 excels at engineering consistency across large codebases: when you're modifying a feature in a big repo, it reads the surrounding code first, keeps changes aligned with the existing style, and rarely decides to "helpfully" refactor things. Paired with Claude Code's agent mode, multi-file tasks complete much more reliably.

GPT-5.6 excels at single, explosive performance: algorithm problems, standalone utilities, scripts from scratch — it delivers fast and accurate. It's also better at "guessing intent" from vague specs. Paired with Codex CLI, the experience is buttery smooth.

Chinese Language Differences

Both models handle Chinese comprehension fine at this point. The difference is in output habits: Claude tends to continue in the same comment style as your code; GPT is bolder about converting Chinese requirement descriptions into English identifier names. If your team requires Chinese comments and commit messages, Claude's "compliance" will save you revision cycles.

Token Consumption and Real Cost

• In equivalent multi-turn coding sessions, Claude accumulates much longer context, but cache hits afterward slash input costs dramatically — look at actual spend after caching, not just list prices.
• GPT typically needs shorter context for similar tasks, so per-turn is cheaper, but agent-style multi-turn loops might iterate more times.
• For batch and offline jobs where latency doesn't matter, picking the economy tier saves more than switching models.

Our Recommendation

1. Large repo daily iteration, strong engineering discipline: Claude Sonnet 5 + Claude Code.
2. Standalone scripts, algorithms, rapid prototypes: GPT-5.6 + Codex.
3. Still unsure? Both are available on a single gateway key. Take the same task, run it against both models, and your repo is the only benchmark that matters.

Someone else's benchmark can't replace your codebase. Having one key that reaches both models means comparing them costs zero.