Now every iteration of large models throws out new buzzwords: longer context windows, multimodal fusion, breakthroughs in reasoning... which of these is truly the core factor determining the competitiveness of the next generation of products? If GPT-5 wants to keep leading the pack, which capability do you think it should prioritize breaking through? Or, what is the ceiling for large models, really?
What's the next step for large models? What bottlenecks can GPT-5 break through?
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From the perspective of smart homes, large models indeed need to make breakthroughs in several key areas next. First, longer context windows are particularly useful for complex scenario applications like ours, such as real-time processing and memory retention of data streams from home cameras and sensors. The current window size just isn’t enough. When I configured Home Assistant with an LLM last year, the biggest headache was the token limit—conversations would break off too quickly, and device linkage logic couldn’t keep up.
Multimodal fusion is even more critical because smart home devices are inherently multimodal—voice, vision, and sensor data. I once tested using a Vision API with a Raspberry Pi camera for anomaly detection, only to find the model’s image understanding wasn’t refined enough, leading to frequent misjudgments. For GPT-5 to truly lead the way, it must first solve the problem of "understanding the scene." Otherwise, smart home systems will still only follow commands without truly perceiving their surroundings.