High TTFT (~2s) with Gemini Flash vs ~150ms on Groq – Any optimization or throttling insights?

Google AI Studio
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Hello team,

We are building a real-time voice AI platform at Vachan AI and are currently evaluating latency characteristics of different LLM providers for production-grade voice interactions.

We’ve observed a consistent gap in Time To First Token (TTFT) between Gemini and other providers, and wanted to understand if this is expected or if there are optimisations we should consider.

Observations:

Below are sample logs from our production-like environment:

2026/03/31 19:28:08 INFO :stopwatch: [Gemini] TTFT (Time To First Token) ttft=2.181539791s model=gemini-3-flash-preview

2026/04/02 12:01:23 INFO :stopwatch: [Groq] TTFT (Time To First Token) ttft=156.328ms model=qwen/qwen3-32b
Gemini Flash TTFT: ~2.1 seconds
Groq (Qwen 32B): ~150 ms

This ~10–15x difference is consistently observed across requests.

Context:

Use case: Real-time voice AI agent (low latency critical)
Interaction pattern:
Short conversational prompts
Streaming responses enabled
Deployment: Using Google AI Studio APIs (paid account (#01CCC5-23F3A5-EFE266) with ~₹26K credits remaining)
Geography: India
Model: gemini-3-flash-preview

Questions:

We would appreciate guidance on the following:

  • Is ~2s TTFT expected for Gemini Flash in real-time scenarios? Or are there known optimizations to reduce this closer to sub-second?
  • Could regional routing or endpoint selection impact latency significantly?
  • Would switching to Vertex AI endpoints improve TTFT consistency?
  • Does Gemini apply any form of dynamic throttling or shared-capacity queuing that could impact latency even when well within quota limits?
  • Are there recommended best practices for low-latency voice applications?
  • Are preview models inherently slower vs stable GA models?
  • Any tips on Connection reuse / warm-up patterns and Streaming configurations?

We are aiming for:

<500ms TTFT for a seamless voice experience

Currently, Gemini’s latency makes it challenging to use as a primary model in the real-time path, despite its strong capabilities.

We’d love to:

Validate if this is expected behavior
Understand if there are architectural or configuration improvements possible
Learn best practices from the team for real-time AI systems

Happy to share more logs or test configurations if helpful.

Thanks in advance!

—
Vachan AI