From extensive use, it’s clear that GPT‑4.0 and GPT‑5 exhibit fundamentally different reasoning behaviors — and the difference goes beyond tone or preference. It’s structural.
Here’s what stands out:
· GPT‑4.0 produces more layered, expansive reasoning. Its responses unfold across multiple inference steps, with strong internal scaffolding. It doesn’t just answer, it builds context recursively.
· GPT‑5 is faster, but this comes with a reduction in reasoning depth. Its outputs are flatter, more immediate and transactional, often addressing only the surface of the prompt.
· Instruction-following is significantly weaker in GPT‑5. When a task includes multiple constraints or directives, GPT‑5 will often follow some and drop others. GPT‑4.0 handles these tasks with much greater consistency. This alone has serious implications for prompt design, especially in production environments.
· Response length is longer in GPT‑4.0, not due to verbosity, but because it explores more implications and addresses edge cases more reliably.
· Vocabulary richness and semantic nuance are more present in GPT‑4.0. You can adjust tone in either model, but the underlying reasoning depth in 4.0 gives its language more weight.
No amount of system prompting or profile tuning seems able to bridge this gap. This is not a style issue, it’s architectural.
I can’t speak to the underlying mechanism, maybe it’s fewer synthetic “connectors” between reasoning modules, or a shallower inference stack. But the difference is perceptible and consistent. GPT‑5 feels structurally simplified, likely in the name of latency. And in that process, something essential was lost.
To be clear: I’m not saying GPT‑5 is useless. It may be slightly better at certain code-generation tasks. But in virtually every other area, (reasoning depth, instruction retention, coherence), GPT‑4.0 is the superior model.
Curious if others are seeing the same patterns.