GPT Realtime
- Strength
- Second-fastest response at 1.58s with 95.53% infrastructure-clean calls.
- What can be improved
- In a noisy-audio run, a long pause was followed by lost digits and a skipped tool action.
- gpt-realtime-2.1
LiveKit is ahead on repeatable reliability, task completion, infrastructure reliability and voice tone and clarity, and GPT Realtime on tool call accuracy, interruption handling and response time.
Last updated Methodology by Luis Ojeda
Pick GPT Realtime for accurate tool calls (0.12 higher), handling interruptions (0.01 higher) and fast replies (1.01s faster). Pick LiveKit for reliability across repeated runs (6.10 points higher), completing the caller's task (2.44 points higher), calls that connect and stay up (3.66 points higher) and how the agent sounds (0.11 higher).
Bars share one scale across all 8 platforms. The place next to each figure is its rank in the field.
Share of the 82 scenarios that passed on all three runs.
LiveKit, 6.10 points higher
Calls where the expected outcome was fully reached.
LiveKit, 2.44 points higher
Calls with no connection, audio or platform failure.
LiveKit, 3.66 points higher
Mean score for calling the right tool with the right arguments.
GPT Realtime, 0.12 higher
Mean score for how clear and natural the agent sounds.
LiveKit, 0.11 higher
Mean score for yielding and recovering when the caller cuts in.
GPT Realtime, 0.01 higher
Mean time for the agent to start replying after the caller stops.
GPT Realtime, 1.01s faster
246 calls per platform: 82 scenarios, 3 runs each.
LiveKit is more reliable, by 6.10 points. GPT Realtime passed 64.63% of the 82 scenarios on all 3 runs and LiveKit passed 70.73%. A scenario only counts when every run of it passed.
GPT Realtime starts replying sooner, 1.01s faster. Mean response time is 1.58s for GPT Realtime and 2.59s for LiveKit.
GPT Realtime reached the expected outcome on 92.68% of calls and LiveKit on 95.12%. LiveKit is ahead on task completion.
Both ran the same Appointment and Medicare agents through the same 82 caller scenarios, 3 times each, with the same evaluators and mock tools. Only the platform and its speech and model components changed.