Devin Reimer

For the LLM component, Reimer prefers open-weight models, with controllability as the core reason; closed frontier models have structural problems such as speed possibly declining over their lifecycle and not necessarily catering to P99 latency.

End-to-end latency is defined as the time "from when the user finishes their last utterance to when they hear the returned audio"; about 1.5 seconds is the threshold at which users stop actively complaining about speed, and pushing it further to around 1.2 seconds makes conversation more natural, but below 1 second it may feel eerie.

Latency perception depends heavily on context, and there is no universal number of milliseconds; the engineering goal should target a comfortable range of about 1.5 seconds; consistency matters more than peak speed, and attention should be paid to P99 latency rather than peak speed.

Reimer's measured answer is: speech-to-speech models are typically 3 to 5 times more expensive than parallelized pipelines, which is a deal-breaker for cost-sensitive scenarios like games.

Running models locally faces four hurdles: compute, speed, quality and portability; hybrid approaches (run locally when possible, otherwise go to the cloud) are also not as cost-effective as imagined, because even small models cost only a few cents in the cloud.

AstroBeam founder and CEO Devin Reimer, drawing on the hands-on experience of two fully voice-driven games, "Stellar Cafe" and "Robo Detective," breaks down the trade-off logic of virtual voice Agents among latency, intelligence and cloud economics.

When selecting components, the metrics Reimer focuses on include: the audio framework's input and output latency, STT's confidence time, the LLM's time to first sentence (a combination of time to first token and tokens per second), and TTS's time to first audio (which must be measured up to the point where the user actually hears sound).

When using early LLaMA models, although users could just barely get through, the development process was extremely fragile, with the model frequently producing unreasonable replies, forcing the team to put substantial engineering effort into patching it.

Reimer's testing found that the more expressive the Agent, the more intelligent users perceive it to be; response speed is also directly associated by users with intelligence signals—fast replies sound smarter.

If it were a free-to-play game with average playtime of 10 hours, 100% voice-driven, and revenue of $3 per user, then each user would lose $10, and the economic model would not hold at all.

The only clear advantage of speech-to-speech models is understanding the user's tone (such as sarcasm), but Reimer believes this gap will narrow as STT capabilities evolve.

Reimer's judgment is: local models will very likely ultimately win for price-sensitive applications, but today they still mostly serve specific use cases (such as command-style interaction).