I also suffer from finding the appropriate word I want as I've gotten older and slower, and this fast-voice-gpt just ends up frustrating me more than helping. I have to sit there and think out the whole sentence in my head before I say anything -- not very natural.
Suppose you have 100ms audio latency and no wait time. Then, natural pause will trigger response immediately but you won't notice it has started until after ~200ms (round-trip time). Twice as annoying.
If you meant there is a case where reducing the network latency at the same delivery reliability for a given audio stream is actually a negative then I'd love to hear more about it as I'm a network guy always in search of an excuse for latency :D.
I think the solution is to handle pauses more intelligently rather than having a higher latency protocol. With low latency you can interrupt and the bot can immediately stop rambling.
I often use it while I’m walking and tell it to not respond until I initiate a conversation.
Knowing when to respond requires semantic understanding, which probably only the model itself is capable enough.
Maybe it’s hard for them to train it to only respond once it seems appropriate to do so?
However things like 'Call center helpline' turn based actually seems better! You don't want to be interrupted when giving information back and forth (I think?)
I also think it spends most of its iq on sounding good rather than thinking about the problem. “Yeah absolutely I can see why you’d like to…” etc. This is likely because it’s on a timer and maybe voice is more expensive to process? Text responses spend more time on the task.
I don’t think it even has reasoning tokens, so it’s no surprise that it’s as most as smart as the “instant” models (i.e., not very).
I've tried to convey this to OpenAI through various available channels (dev forums, app feedback, etc.).
Grok solves this by having an optional push-to-talk mode, but this is not hands-free and thus more cumbersome than just having a user-configurable variable like seconds_delay_before_sending_voice_input.
The actual implementation is at fault. I had some luck with instructing the model to only respond with "Mhm" until I've explicitly finished my thought and asked it a question. Makes this much less of an issue.
But I've decided that their voice mode is completely unusable for a different reason: the model feels incredibly dumb to interact with, keeps repeating and re-phrasing what I said, ends every single answer with a "hook" making the entire interaction idiotically robotic, completely ignores instructions when you ask it to stop that, and - most importantly - doesn't feel helpful for brainstorming. I was completely surprised how bad it is in practice; this should be their killer app but the model feels incredibly badly tuned.
1-2s replies feel natural and like you pointed out pausing for 2-3s mid sentence is super normal.
Still, it’s worth to keep in mind that these are not frontier models, differently from when they were released.
(Please Sam, if you read this, release the new realtime audio models)
Surely the number refers to the total users of ChatGPT overall, and the fraction of those who use voice features is considerably smaller, is it not?
That’s the kind of thing that influences business decisions like knowing how much hardware and software optimization to throw at a problem.
Pipecat's smart turn model is really good for VAD - https://huggingface.co/pipecat-ai/smart-turn-v3
- openai is wrong. almost of the issues they described are issues with libwebrtc, not with webrtc, kubernetes, network architecture, etc. the clue was when they said "the conventional one-port-per-session WebRTC model."
- there are no alternatives worth trying. everything else open source in the ecosystem, like pion, coturn, stunner, are too immature.
- libwebrtc is the only game in town.
- they haven't discovered libwebrtc feature flags or how it works with candidates, which directly fix a bunch of latency issues they are discovering. a correct feature flag can instantly reduce latency for free, compared to pay for twilio network traversal style solutions
- 99% of low latency voice END USERS will be in a network situation that can eliminate relays, transceivers, etc. it is totally first class on kubernetes. but you have to know something :)
this is the first time i'm experiencing gell mann amnesia with openai! look those guys are brilliant, but there is hardly anyone in the world who is doing this stuff correctly.
Even for clients you have things like libpeer that libwebrtc can't hit.
i think the challenge is that pion is an excellent product today. it would benefit me if its innovations were subsumed into libwebrtc, because eventually those innovations will show up in the iOS stack, which is one of the customers that matter to me. it is subjective if it is the MOST important customer, that is my belief and it is probably true of openai, at least until they get their own device out the door.
there can be many, many use cases though! not everything has to be, try to make the thing for 1b people that has to interact with all the most powerful and meanest businesses on the planet.
Take any popular technology problem that has been around for a few years such as... wrangling Kubernetes with YAML config files. There's probably hundreds of thousands of discussions, source code samples from GitHub, official docs, blogs, bug reports, pull requests, etc... all discussing the nuances, pitfalls, pros/cons, etc. During pre-training the AIs internalise this and can utilise it later.
Now compare this with anything recent and (relatively) obscure, such as new .NET 10 features which were first officially publishing in November 2025, a month before GPT 5.5 cutoff.
As a human developer, these new language capabilities are on the same "level" for me in my day-to-day work as the features from .NET 9 or .NET 8. Similarly, my IDE has native refactoring and code cleanup support that can take C# code from the previous years and bring it up to the idiomatic style of $currentyear.
The AIs just can't do this, because one single Microsoft release note and one learn.microsoft.com page is nowhere near enough training data! The AI hasn't seen millions of lines of code written with .NET 10, taking advantage of .NET 10 improvements, and hasn't seen thousands of discussions about it. Not yet.
This is a fundamental issue with how LLMs are (currently) trained! Simply moving the cutoff date is not enough.
Human learning is second-order. If I see even the tiniest bit of updated information that invalidates a huge pile of older information, my memory marks everything old as outdated and from that second onwards I use only the new approach.
AI learning is first-order. It has to be given the discussions/blogs/posts that say "Stop using the legacy way, it's terrible! Start using the new hotness"! That, it can learn, but it'll be perpetually behind the rest of us by at least a few years.
Not to mention that thanks to AI forums like StackOverflow are dying, so... where is it going to get this kind of training data from in the future!?
AI training needs to switch to "second order", but AFAIK this is an unsolved problem at this time.
(though knowledge cutoffs in practice can be bit fuzzy)
I wonder if they run the STT model's output through the current model (that we're chatting with) as a final pass - since the text seem to be well aligned to the current conversation context.
For long prompts, I often speak to OAI web/app and copy-paste the text to Claude / Gemini :)
As someone use to podcast at 3x speech and sapi text to speech at much higher rate, listening to AI at human speech is a chore.
lol, definitely didn't need to know there's 900M weekly users for this post. I mean yeah, there's a lot of users and they serve globally, that's relevant. But this is just pulling out your biggest stat because you can. How many voice users you have would actually be relevant and interesting but, to baselessly speculate on motivation here, might be a number that doesn't add as much fuel to an upcoming IPO as reminded people that you're almost at a billion users does.
WebRTC + Kubernetes
Yet another reason to not consider anything else like that for low-latency networking. Golang (or even Rust and C++) is unmatched for this use-case.
Node.js's initial release was May 27, 2009
Golang 's initial release was November 10, 2009
They're different, yes, but it's not like
