GPT-5.4 Thinking now shows its reasoning plan before it commits to a final output. Users can interrupt the model mid-response and inject additional details, redirecting the work without starting a new conversation turn.
This matters because it attacks a core inefficiency in LLM workflows: wasted compute and time from misaligned outputs that only reveal themselves at the end. Catching a wrong assumption at the planning stage is cheaper than regenerating a full response.
The full video is worth watching for how the interruption mechanic actually behaves in practice: when you can intervene, what counts as a valid mid-course correction, and how the model reconciles new input with work already in progress.
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