We chose OpenAI because it consistently strikes the best balance between capability, reliability, and developer experience. The models are strong across reasoning, multimodality, and real-world tasks, but what really stands out is how quickly those advances become usable products.
Beyond model quality, the ecosystem matters: stable APIs, clear documentation, and a fast-moving community make it easier to go from prototype to production. Compared to alternatives, OpenAI feels less like a single model and more like a long-term platform we can confidently build on.
Flowtica Scribe
the listening-signal part is the interesting bit to me, not the interruption handling. most "full-duplex" demos look great one-on-one in a quiet room but the real test is a noisy kitchen or a car with the radio on, where the model has to decide what's actually speech directed at it vs background noise it should just ignore. curious if that discrimination holds up outside a controlled demo or if it still needs a wake word to reset context when it gets confused
the pause and interruption handling is what I've been most curious about — natural back-and-forth is still the hardest part of voice UX. building on the voice side too and the latency when delegating to a background reasoning model is the piece I'd love to stress-test. does it feel seamless in practice or do you notice a perceptible gap?
Natural interruptions and pause are something most voice assistants still struggle with. If GPT Live handles those well, it could make voice AI much more practical.
Portuguese of Portugal still a bit choppy but perfectly usable!
Honestly surprised how smooth the docs are compared to what I expected, made it easy to get a quick prototype running in under an hour.
Finally a more natural sounding voice model.