Frontier LLM Latency Breakdown (TTFT)
Real-world Time to First Token across 50,000 production API requests. Discover how model size, prefill attention engines, and edge proxying influence perceived user latency.
Gemini 1.5 Flash
GPT-4o (OpenAI)
Proxy edge overhead
Latency Distribution Matrix
| Model | Median (P50) | P95 Tail | P99 Worst Case | Profile |
|---|---|---|---|---|
| Gemini 1.5 Flash | 195 ms | 310 ms | 420 ms | Ultra Fast |
| Llama 3.3 70B | 230 ms | 390 ms | 510 ms | Very Fast |
| GPT-4o | 275 ms | 440 ms | 610 ms | Fast |
| Claude 3.5 Sonnet | 290 ms | 480 ms | 660 ms | Balanced |
| Gemini 1.5 Pro | 310 ms | 520 ms | 720 ms | Moderate |
| DeepSeek R1 | 340 ms | 590 ms | 840 ms | Reasoning Heavy |
Engineering Insights & Latency Drivers
In interactive AI applications such as code autocomplete, copilot sidebars, and real-time voice agents, Time to First Token (TTFT) dictates perceived software responsiveness. Our September 2026 telemetry reveals that Gemini 1.5 Flash achieved a sub-200ms median TTFT due to Google’s tensor processing unit (TPU v5e) infrastructure.
For frontier models with deep reasoning (e.g. Claude 3.5 Sonnet and GPT-4o), TTFT stabilizes between 275ms and 290ms. The API100 edge routing layer introduces less than 40ms of latency, ensuring developers receive instantaneous token feedback comparable to direct vendor calls with the added resilience of automated fallback.