Opus 4.7 vs GPT-5.4: How They Compare on Real Tasks

The Honest Comparison

This is not about who wins on a benchmark leaderboard. It is about what you will experience doing real development work with both models. The answer depends heavily on what you are working on.

Code Generation

Both models write solid code. The differences appear in edge cases: Opus 4.7 tends to produce code that handles error conditions better out of the box. GPT-5.4 tends to produce more concise, idiomatic code for common patterns. For straightforward tasks, they are roughly equivalent. For tricky edge cases, Opus 4.7 is more likely to get it right without you having to correct it.

Reasoning Tasks

On multi-step reasoning, both models are strong. Opus 4.7 shows better coherence over very long contexts — if you are debugging a system where the root cause is several layers deep and requires tracking state across many files, Opus 4.7 is more reliable. GPT-5.4 is faster and cheaper for the same reasoning capability on shorter tasks.

Context Window

Opus 4.7 has a 200K token context window. GPT-5.4 is in a similar range. At the high end of context utilization, both models degrade, but Opus 4.7 degrades more gracefully — it maintains coherence further into the context than GPT-5.4.

Cost and Speed

GPT-5.4 is faster and cheaper per token. For teams running high volumes of tasks, the cost difference is significant enough to default to GPT-5.4 for most work and reserve Opus 4.7 for the cases where it matters.