I Tested 6 Gemini Models for Voice AI Latency. The Results Will Change How You Build.
The author benchmarked 6 Gemini models across 20 scenarios to determine the best model for real-time voice AI applications, finding that the Gemini 2.5 Flash-Lite model is the fastest with a 381ms average time-to-first-token.
Why it matters
The findings in this article can help developers make more informed decisions when choosing the right Gemini model for their voice AI applications, prioritizing latency over other factors.
Key Points
- 1The Gemini 2.5 Flash-Lite model is the fastest, with a 381ms average time-to-first-token
- 2The 'thinking: minimal' configuration for Gemini 2.5 Flash reduces latency by 73% compared to the default settings
- 3Lite models are not necessarily worse, but can be faster than their non-Lite counterparts for voice AI
- 4Latency is the single most important metric for voice AI, as it impacts the user experience
Details
The author conducted a 600-call benchmark to test 6 Gemini models across 20 realistic scenarios, measuring time-to-first-token (TTFT) and total response time. The results showed that the Gemini 2.5 Flash-Lite model was the fastest, with an average TTFT of 381ms, significantly faster than the default Gemini 2.5 Flash model at 1879ms. The author also discovered that the 'thinking: minimal' configuration for the Gemini 2.5 Flash model can reduce latency by 73%. This highlights that Lite models are not necessarily worse, but can be optimized for real-time performance in voice AI applications where low latency is critical for a natural user experience.
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